We now have pretty good estimates with clear methodologies from neutral third parties on most chatbot models available right now. Specifically, EcoLogits are much more critical of AI’s environmental impact than I am, include the embodied impacts of AI hardware in their calculations, and make their methodology fully available.

I took their numbers for most current available chatbots, cited them all, and put them in an interactive tool here where you can enter exactly how many of each type of chatbot prompt you send and how long your responses are, and compare them to all the other ways you emit and consume water throughout the day. I cite every number. I’m doing this to give you a way of seeing all this directly without having to trust anything I’m saying, including the numbers I hadn’t written about much before for much longer prompts. Basically these are the most solid numbers we have right now, they don’t rely at all on my personal trustworthiness, and the AI estimates specifically come from a much more environmentally critical group. This is the most solid case I can make that AI chatbots do not add to your emissions in a meaningful way.

Because my name’s become somewhat toxic in the debate as more people accuse me of being a shill, and because I’d really rather just get this information out there, one thing you can do is just copy, either by hand or with an AI agent, all this info and these graphics over to your own website without attributing it to me at all. The state of the debate is just so goofy right now that I’d rather let a lot of other identical websites bloom without getting credit. I include a note at the bottom of the page for your coding agent that it can feel free to copy this all without crediting me.

If your friends are guilting you about the horrible environmental effects of your chatbot use, I also included a button to generate a report with citations for every claim on exactly how much your chatbot use is adding to your personal emissions and water use:

There are a lot of crazy questions in the debate about AI and the environment. Data centers are being planned that will individually eat whole percentage points of US power. But the question “Are chatbots adding to MY personal footprint?” has long been settled, and yet more and more people seem to think it’s not. I’m hoping this will help a bit in getting the word out that it’s time to move on to focusing exclusively on data centers more broadly, and hopefully from there to the idea that the main fight that matters is greening the grid.

To override this, a simple trick I use when I’m talking to people about data centers is to imagine a situation where not building the data center violates the status quo. I usually don’t use the word data center for this at all, and instead just frame it around lost tax revenue. I just ask “Would we spend this much in tax revenue to avoid the externalities of the data center?” This brings the effects of losing out on the tax revenue into the spotlight. If I could just have everyone in the debate communicate this way, I think the whole thing would be clarified.

An example

Loudoun County Virginia has come up with a new plan. They are going to spend $1.3 billion of their citizens’ tax money every single year1 to improve the county’s environment through a series of projects. This is about 38% of their general fund,2 about $2800 per resident per year.3 These projects will in total:

• Reduce the county’s CO2 emissions by about 80%.4

• Reduce the county’s water use by 10%.5

• Make 3% more of the county’s land available to be used by businesses or kept as nature.6

• Save the average household and business about $6 each in electricity bills each month, for a total savings of about $15-20 million per year, about 1/65th of the cost of the program.7

• Reduce regional NOx air pollution by 4%.8

The second they stop spending this money, all these impacts come back. Is it worth spending $2800 per person per year forever to maintain these gains?

I think it would obviously be a terrible idea to spend money this way. For each of these, there are way less expensive ways of getting the same results. Some of these might be wrong or not work, but I think the type of comparisons I’m doing here need to be how the debate is framed:

Carbon

Carbon emissions are the only truly difficult externality to deal with here. The main problem with climate change more broadly is that we as a society are consistently failing to price in the carbon cost of what we do and invest to reduce it, because doing this is expensive. Here we have 11 million metric tons of CO2 per year and $1.3 billion dollars, so about $118 per metric ton.

One way to do this is via the Regional Greenhouse Gas Initiative (RGGI) that Virginia will rejoin on July 1, where anyone, including a county government, can buy allowances that shrink the number of tons the region’s power plants are allowed to emit. Covering all 11 million metric tons takes about 12 million allowances, around $300 million a year at this spring’s clearing price, or $620 million at the higher prices traders expect by December. So we have one way of reducing emissions that would cost up to 50% of the full money available in the program.

But this probably isn’t the cheapest way to reduce emissions. Eleven million tons is the emissions of about 26 TWh of gas generation, and that can be replaced by building about 12 GW of solar at Virginia capacity factors, or 8 to 9 GW of wind. Building the solar itself actually costs close to nothing on net. Unsubsidized utility solar costs about $38 to $78 per MWh, and PJM’s (the Regional Transmission Organization serving a lot of the mid-Atlantic) wholesale market paid an average of about $51 last year, so the power earns back most or all of its cost.

The thing preventing us from just building out huge amounts of basically free solar is issues with the grid. Since 2020, PJM has signed interconnection agreements for 103 GW of new power generation, but only 23 GW have come online. New power projects are routinely dying when they’re billed for the grid upgrades, which cost about $240 per kilowatt on average. Loudoun can just pay those bills to help build the plants. Covering the upgrades for all 12 GW at that average is about $3 billion as a one-time cost. Financed at 4%, the interest is about $120 million a year, under a tenth of the program’s cost and about $11 per ton against the program’s $118.

Water

10% of the water the county consumes is roughly 4,900 acre-feet. An acre-foot is about 326,000 gallons, it’s literally an acre covered in a foot of water.

Loudoun could pay to convert ocean water about 70 miles away in the Chesapeake Bay to drinkable water. The Carlsbad plant near San Diego turns seawater into drinking water for about $3,400 per acre-foot.9 This is about the most expensive drinking water in America. Treating this much water at this rate would cost $17 million per year, only 1.3% as much as the program.

Surely it would cost way too much to get the water all the way to the county? You need to consider that $1.3 billion per year is just so, so much money that stuff like this becomes possible.

4,900 acre-feet a year is about 4.4 million gallons a day, which can be delivered by a single 24 inch pipe.10 This pipe would have to raise the water about 500 feet above sea level over its journey. Pumping water over let’s say 100 miles costs about 1,000–2,000 kWh per acre-foot11, about half a million a year. So the cost of pumping the water gets us from $17 to at most $18 million.

What about building the pipe? Pipes like this can cost about $2-3 million per mile12, so this would be a one-time cost of $200-300 million. If we run with the interest-only cost this gets us $8-12 million per year. So altogether this plan to supply 10% of Loudoun’s water with ocean water from 100 miles away would cost maybe $30 million per year, or 2% of the cost of the project. The project is just so so expensive!

Land

3% of Loudoun’s land is about 10,000 acres of 333,000. More than 85,000 acres in Loudoun County are permanently protected by conservation easements, where a landowner sells off the right to ever develop the land while continuing to own and farm it.13 The federal ACEP program pays 95% of fair market value for these, capped at $16,000 per acre in Loudoun. Ten thousand acres of permanent easements at $10,000-16,000 per acre is $100-160 million, and this is a one-time cost. If the county borrowed that money at 4% and only ever paid the interest, that’s $4-6.5 million a year. 0.5% of the cost of the program.

Electricity bills

The program saves the average household and business about $6 a month on electricity, $15-20 million a year county-wide. So already that’s just 1/65th of the cost of the program. One way to save this money would be utility efficiency programs, like paying for insulation, heat pumps, lighting upgrades, and smart thermostats. Across the country, these programs save electricity at an average cost of about 2.5 cents per kilowatt-hour, while Virginia residential customers pay around 14-15 cents for each one.14 To produce $15-20 million in annual bill savings, you need to save about 110-140 million kilowatt-hours, which costs about $3-4 million a year in program spending. So we could get the program’s entire bill benefit for about 0.3% of its cost.

NOx air pollution

We need to cut regional NOx pollution by 4%, about 3,000 tons per year. The federal government already runs the Diesel Emissions Reduction Act (DERA), which is basically a procurement market for NOx reductions. From 2008 through 2018, $801 million in DERA awards bought 491,000 lifetime tons of NOx reductions, which means they get about $1,630 per ton. This also removed 16,800 tons of PM2.5 and 5.3 million tons of CO2.15 Removing 3,000 tons a year at that price is about $5 million a year, about $1700 per ton. For a sanity check, power plants buy and sell NOx allowances under EPA’s Cross-State Air Pollution Rule, and those mostly trade between $3,000 and $6,500 per ton. Even if we assume a pessimistic $10,000 per ton, we’re at $30 million a year.

Adding them up

Altogether, these would cost maybe $150 million a year, a little over a tenth of the cost of “the program.” 9/10ths of the money spent on avoiding these externalities would just be wasted. That’s about 1.1 billion dollars wasted per year, $2500 for each individual citizen every single year for nothing at all.

Spending this much money is exactly the same as banning data centers in the county. The effects are exactly the same. You lose out on x amount of tax revenue for the sake of avoiding y and z externalities. But here, it’s obvious how ridiculous it is.

Maybe you think there are other externalities I haven’t considered, like heat or noise. Instead of saying “you’re leaving those out!” try to figure out how much is actually happening and what you think it would be reasonable for a county to pay to remove the externality.

Other ways this helps

We can also frame other talking points about data centers this way. Some people have been worried the “buildout is happening too fast” and that might be true in specific places, but consider how you’d react if this program were happening, and someone said it was being “scaled down too fast.” That could be true, but you’d want specific evidence for why scaling it down slower would be worth the trade-off of all that huge cost to the taxpayers.

This can also help us select which comparisons to data centers actually help us understand them. Saying “this data center consumes a million bottles of water per day” sounds compelling, but if you ask “should the county pay $50 million dollars to use one million bottles of water less per day” you notice how the comparison doesn’t give you much useful info. You’d want to know how much water the county has available or what other industries use similar amounts in order to figure out if this is a good trade, not how many bottles of water the data center uses in a vacuum.

A lot of people are worried about data center tax cuts, and sometimes imply that the tax cuts mean that data centers shouldn’t be built. It’s true that data center tax rates have been cut in specific places, but they mostly still pay huge amounts of taxes in total. Here’s how much a $1 billion data center would pay in taxes every year in some of the places that are building the most:

Data centers pay different types of taxes in different places. Notice that Santa Clara County is the only place on the chart with a yellow bar for M&E (machinery and equipment) sales tax on data centers. Every other place here exempts data center equipment from its sales tax, or in Hillsboro’s case has no sales tax at all. One of the most influential articles on how people think about data center taxes is CNBC’s “In race to attract data centers, states can forfeit hundreds of millions of dollars in tax revenue to tech companies.“ That article is mainly about the sales tax in that one yellow bar. CNBC counted 42 states that either give data centers full or partial sales tax exemptions or have no sales tax to begin with. California is one of just eight sales-tax states that offer no exemption. And it’s true that states are forgoing real money this way. Virginia’s exemption alone was estimated at more than $730 million for 2024. But Loudoun County is still doing very well despite taxing data centers at a much lower rate than Santa Clara. In the Tax Foundation’s model, the same $1 billion data center pays about $12 million a year in Loudoun taxes against $22 million in Santa Clara County taxes. Maybe it’s a problem that data centers aren’t also paying this additional revenue, maybe they could pay more, but that’s a separate question from whether completely blocking the data center would be a good decision. If I said “This Loudoun program for helping the environment will only cost $2800 per person instead of $5100” that gives us new information, but the big drop still isn’t close to enough to make the program worthwhile. Similarly, tax cuts tell us data centers could maybe pay much more, but that isn’t the final word on whether they should be banned, or whether it’s worth getting merely $2800 per person in new tax revenue instead of $5100, and we can be much more precise than simply saying “Data centers have all these externalities, and now they have all these tax cuts too. Who’s to say if they’re good?” We need to make the actual specific comparisons with the numbers we have.

Data centers provide so much tax revenue that in many places a fraction of the money could be used to completely offset their environmental externalities, but people seem to have trouble thinking this way, and see the externalities as brutally real whereas the tax revenue is either abstract or a lie. People bring up these long lists of the harms of data centers, and then at the end gesturing vaguely at their tax revenue. For Loudoun they might say “They use so much water and pollute the county and raise electric bills and use our land… oh and yeah they do provide tax revenue, but is it worth it?” I always read this as a form of learned helplessness. We’re adults. We can take the next step and just give the specific numbers for each, including the taxes, and then find ways to decide “Is this revenue worth the cost?” We don’t have to waffle in this weird “Oh they use so many resources, who can say if they’re good?” mode so many people go into. We don’t think this way about any other industry. We can come up with different ways of comparing the costs and benefits. My personal favorite is to just ask “What if we were spending this much tax money to avoid the externalities?” to break the status quo bias. When we do that, I think the debate is clarified.

Why aren’t places with data centers already completely offsetting their impact like this? The reason is that voters don’t actually rank these as the most important problems. If people could get better funded schools at the cost of additional percentage points of water use, they usually opt for the better schools over saving water. Local governments could spend way more data center revenue to offset the environmental cost specifically, but a part of the reason they don’t is the revealed preference of locals for spending on other things instead. They seem to think the trade-off is worth it.

Throughout all this, the only claims I’ve seen that actually stood up to scrutiny were:

• Data centers will meaningfully add to America’s CO2 emissions.

• Elon Musk’s data centers specifically added local air pollution to already heavily polluted residential areas. This is the clearest case of a data center I would’ve personally wanted to prevent from operating.

• Data centers built right up against people’s homes create noise pollution.

• Data centers can sometimes in specific circumstances raise electricity bills somewhat, though this hasn’t been the norm so far.

For everything else, whenever I look into where people are actually getting their ideas about the hundreds of other data centers being built, the source always leads back to some confused misreading of local reporting, a wild calculation error, a bad game of telephone, or a wildly misleading article. For anyone interested in receipts, this is my big collection of deep dives I’ve done. This is where the receipts are.

Almost everyone talking about data center water impacts shares the same story of the Georgia couple whose wells ran dry, and never mentions this was definitely due to some issue with construction and not the normal operation of a data center. People regularly claim everyday people’s water access has been harmed by the normal operation of data centers, but no one can ever point me to where that is. Most stories I read about droughts being “exacerbated by data centers” don’t add the portion of water data centers are actually using, and when I look into it, it’s almost always vanishingly small compared to the area in general. Talk about data centers “poisoning water” seems to have been invented out of thin air.

Most people commenting on data center impacts on electricity prices bring up a (I think bad) Bloomberg article that people easily mistake to read that data centers raise electricity prices as much as 267% near where they’re built, despite this reflecting a rate that residents don’t actually pay at all and only one of several big factors that determine electricity bills, this mechanism not actually having any causal effect on the residential rate increases the article goes on to describe, and the fact that the authors are starting from a baseline in April 2020 which was a unique low point for electricity prices as COVID lockdowns were in effect, and the nominal price of electricity rising a lot in general in the last 6 years, mostly due to inflation. In reality there’s a surprising negative correlation between the states with the most data center capacity and states where electricity prices have increased the most.

Data center heat exhaust harms seem entirely speculative. The only one that looks really dangerous so far is the very largest data center ever proposed, in the place most likely to trap heat (Utah desert valleys), and the only study done on the air effects of data center heat exhaust found it was comparable to the normal slight temperature increases of living near other dense human activity in a city.

The most popular video made about data centers this year was full of wild misrepresentations of every study it was citing. No claims from it held up.

People commenting on the air pollution from data centers in general almost never talk about how they compare to other things, and when they do they find that, say, the largest cluster of data centers in the whole world consuming as much energy as a large city together emits about as much harmful PM2.5 pollution into the county as a single gas plant. Their NOx emissions are much more, but those spread out much more before becoming dangerous, and in the clean air of the county likely fade into the background. Not nothing, but definitely not some unique environmental catastrophe. The main pollution harms of data centers are I suspect actually offsite coal plants far away they may draw from.

Data centers for the most part pay huge amounts of taxes almost everywhere they’re built, with the exception of maybe Michigan. More on that in a future post.

The so-called massive environmental costs of training cutting edge AI models seem to fade into the background of other things we create for hundreds of millions of people to use over months. My best guess is that the emissions are the same as a day of normal manufacturing of Coke cans.

The idea that America doesn’t “have enough land” for buildings optimized down to the literal atom that will collectively take up about a third of the land we currently use to grow Christmas trees seemed so obviously silly that I felt ridiculous posting my land use article, and yet that’s the one that’s drawn the most scorn and backlash by far.

After a year of basically feeling like I’m living in this weird dream reality where every single thread I follow leads me to a dead end instead of a reliable source, I need to basically beg my critics: I need a specific case for why data centers are going to be especially harmful along these axes compared to other regular industry, instead of just fading into the background of the normal externalities of other large light industries, because I just can’t find it at all. I need something, anything, that actually justifies this public outcry. I need some kind of basic overview of water harms that doesn’t just gesture at “millions of gallons of water” without context. I need some realistic estimates of air pollution when data centers with onsite backup generation are following normal EPA rules where they don’t just fade into the background of other normal sources of air pollution. I’m just not seeing it.

I’ve received one good account of why data centers can throw off the ways water rights work in specific areas, which I’ll publish and respond to soon. That specifically struck me as good pushback. But besides that, I want to send up a signal flare that I need someone to try to make a comprehensive grounded case to me beyond just saying “Everyone knows these things are a disaster.” Show me your thread of reasoning that leads back to something solid that I can grip onto and understand.

People will often cite “experts” but these experts are often random professors just echoing common wisdom that’s developed about this rather than actually giving specific solid numbers for why we should be especially worried here. I have yet to find a serious expert in the field giving a comprehensive reason why we should be especially worried about data centers. There are experts who have raised legitimate concerns about specific data centers, like climate experts and physicists concerned about the warming effects of Kevin O’Leary’s massive Utah data center, the largest ever proposed, but these never generalize into giving us much alarming info about the buildout more broadly.

A lot of people covering this often lean into nuance, and will say “Well this is complicated” and “the truth is probably somewhere in the middle.” It’s true that the truth about data centers is complicated, it’s not that they have no externalities at all, but I mostly just read this as evading being more specific about how this buildout compares to most of the other ways the environment’s harmed. We have good numbers on how many AI chips are being manufactured and approximately how much energy and water they’ll require. We can make some solid claims here, and even the more thoughtful people covering this often kind of retreat to simply say “Well everyone’s voices should be heard” or “People have a right to ask questions about such a large buildout.” Yes, they do, but we also have a responsibility to make sure the public debate isn’t evaporating into claims and intuitions that all fall apart when you poke at them yet each have so much moral urgency behind them that the pokers get punished. Simply leaving the gigantic range of possibilities open between “data centers are a catastrophe” and “data centers never cause any problems” is basically a highbrow way of ceding the debate to the people making the most hyperbolic memorable claims.

On the climate criticism specifically, I’m seeing two camps:

• People worried that data centers will add huge amounts of emissions despite AI being useless.

• People worried that data centers will add huge amounts of emissions because AI will be really economically useful and powerful and continue to grow.

The first view just doesn’t make sense to me. If AI is a fad, it will not rise to be whole percentage points of American emissions. At some point people will wake up before we spend that much energy on it, because that much energy is expensive.

I myself take pretty weird wild futures of AI seriously, because I think it’s clear that AI’s capable and likely to get much more capable relatively quickly. In those worlds, AI can potentially rise to be huge portions of our energy grid and emissions. But almost no one I talk to about climate seems to share my worries about this. Their objection is that emissions are wasted on AI because it’s useless rather than that we’re doing something potentially incredibly dangerous that could at best lead to explosive economic growth. The people I know worried about AI risk will often imply that everyday people protesting data centers are also secretly worried, yet I barely see this coming up in public discussion.

I haven’t had time to finish deep dives on every last issue with data centers. I need to write my big climate post, finish a series on pollution, write something on heat exhaust, and explore noise. But I do already know a lot about each, and for each I am just not seeing anything that justifies the level of public outcry we’re seeing. And yet when I venture into polite society and say this, I’m often treated as basically a conspiracy theorist or a shill for big AI promoting what amounts to climate denial. All these ideas that I know individually are overblown work together as a kind of shield. If I poke at any one, the person I’m talking leans back on all the others. They can’t possibly all be wrong, that would be ridiculous! And yet for any one I poke at, the person can’t lead me back to anything that seems solid at all.

This is a weird position for me to be in! I’ve always been a good predictable lib and don’t find myself on the opposite side of almost all media coverage often. It’s driven me a little crazy and does make me worry I’m missing something everyone else seems to see. But after a year of following this with no really satisfying answers, it’s become hard not to start to think I’m the one with my feet on the ground here.

After writing this, Shaolei Ren reached out to me with some clarifications for how the estimate was made. While I can’t share everything, I do now have access to the EcoLogits methodology where he got the number. A lot of the information here was my best guess but turned out to be incorrect. I’ll circle back on this post with an update on what actually happened once I’ve had time to work through the methodology. Importantly, Ren, the person who came up with the “ChatGPT uses a bottle of water per prompt” estimate, now agrees that while this was a reasonable estimate at the time, the actual water cost of GPT-4 was likely much lower, at around 15 mL per prompt, and only 5 mL in the data center itself, as opposed to a 500 mL bottle of water, and since then AI models have become much more energy and water efficient. So no one researching this, including the person who came up with the original statistic, actually believes that chatbots use a whole bottle of water per prompt anymore. This statistic needs to go away. The actual cost of a chatbot prompt is about ~200 times lower.

I’ll update this with the full story once I have time.

Intro

The most widespread misconception about AI and the environment is that each prompt uses a whole bottle of water. This came from this Washington Post article, which opened with this graphic.

The authors explained that they got these numbers from Shaolei Ren, an assistant professor of electrical and computer engineering at UC Riverside. Specifically:

Water and electricity costs were calculated by Ren for ChatGPT-4 at an average American data center.

Ren had previously done a study on GPT-3 where he and a team had estimated that about 10-50 prompts would add together to cost a whole bottle of water. Here, his estimate for GPT-4 was about 30 times larger. How did this happen?

Ren never explained how he got this number, but I might have figured out exactly how it happened. To understand, you first need to know a few things about his study “Making AI Less Thirsty” because he plugged a new energy value for GPT-4 into the same methodology for had used for GPT-3. Once you fully understand what happened, it becomes hard not to think:

• This was a napkin math estimate that didn’t account for pretty simple things we knew about what GPT-4 was probably like, or AI hardware. It shouldn’t have been allowed to have basically any influence over the discourse or get written up as an infographic in a major newspaper.

• It’s about 50-250 times too high, not even accounting for the fact that many people misunderstood to mean all this water is used in data centers themselves.

Making AI Less Thirsty

The goal of this paper was to estimate how much water AI uses, both for training and per prompt. To get the total water cost, the researchers included both the water consumed onsite in data centers and offsite in the power plants generating the electricity. This table has their findings:

Explaining the columns one at a time:

• “PUE” is the “power usage effectiveness” of a data center: a measurement of how much power is wasted by dividing total power going in by the power finally delivered to chips. If 5 units of power go into a data center, and 1 is lost to heat before getting to the chips, the PUE will be 5/4 = 1.25.

• Onsite WUE is the water usage effectiveness, how many liters of water are used to cool chips per kilowatt hour of energy used.

• Offsite EWIF is the “Energy-Water Intensity Factor” or how much water is consumed generating the electricity at the power plant offsite. So according to the table, the average kilowatt-hour of energy in the US consumes 3.142 liters of water to generate.

So the formula here is pretty simple. Take the energy cost of a prompt in a data center, and multiply it by [onsite water cost of cooling per unit energy + offsite water cost of generating the energy] and you get the full cost of a prompt. The original study was looking at GPT-3 and estimated that it used on average about 4 Wh per prompt.

Three key things to notice about this table:

• The offsite water cost used to generate electricity makes up the vast majority of the water used on AI prompts everywhere. Most of the cost here is not happening in the data center itself.

• A few states have absurdly high water costs, especially Washington. Why is that one so high? About 65% of Washington’s electricity comes from hydroelectric plants. All of the water measured to be “consumed” by those plants is actually water evaporated off the surface of lakes and rivers dammed by them. Importantly, this exact same amount of water would evaporate regardless of whether the data center drew water from the plant, and it’s vastly preferable that Washington use hydroelectric power instead of fossil fuels. This is a weird but common addition to the “water cost of an AI prompt.” Taking out this evaporated water would reduce the “cost of an AI prompt” in Washington State from 47.5 mL to about 5.5 mL (because so much water is evaporated off lakes compared to the water evaporated by other power plants). Altogether, roughly 50% of the US grid’s consumptive water cost per kWh is water evaporated by dammed lakes and rivers that would probably still be dammed regardless of whether data centers drew from them, so that’s about 50% of the offsite water cost per prompt. The authors make it clear that they’re including this water as part of the cost.

• The average water cost of a prompt in the US in the data center itself (onsite) is 2.2 mL. 1/227th of a 500 mL bottle of water.

So the full water cost of an AI prompt looks like this:

Here it is compared to a bottle of water:

How I think Ren got his estimate for the Washington Post article

Why did the water cost jump so much?

The Washington Post article was published a year and a half after the original preprint of the study, on September 18th 2024.

Unlike previous coverage of AI’s per prompt water cost, this article is focused on GPT-4 rather than 3. Previous articles had used Ren’s numbers from his study to say that 10-50 AI prompts use a bottle of water. This one claims that now with GPT-4, it takes just a single short prompt to use a whole bottle of water. Just generating 100 words.

The authors say they’re assuming a single GPT-4 query uses 140 Wh of energy. 35 times more than Ren’s estimate of 4 Wh for GPT-3.

So the reason the water cost grew so much from ChatGPT-3 to 4 is that Ren’s using the same methodology, but plugging in 140 Wh instead of 4 Wh. This massively increases the water cost per prompt, because both onsite water used to cool servers and offsite water used to generate electricity scale linearly with energy used. The water numbers the Washington Post gives are equal to the original numbers multiplied by about 30.

So the claim “AI uses a bottle of water per 100 word email” is based on the estimate that GPT-4 would require 140 Wh to write this email.

140 Wh per prompt is wildly, wildly unrealistic, and fails multiple basic sanity checks:

• The servers GPT-4 was running on were designed to handle lots of prompts at once, and drew 10 kW of power. 140 Wh implies that a single prompt would take up the entire server for 50 seconds. In reality, lots of prompts are handled by these servers and only take a few seconds each to answer.

• Ren’s estimate is orders of magnitude larger than any other estimate given for similar models. Compare it to EpochAI’s estimate from a few months later:

  

  Epoch’s absolute maximum input query is still over 3 times smaller than the number Ren estimated for GPT-4 handling a single 100 word email. Other estimates fell in a range, but never rose above 3 or 4 Wh.

So where did this number come from?

How I think Ren got 140 Wh

Ren never gave a methodology. But I think I figured out what happened.

Ren appeared on the podcast Policy for the Planet, and said this about GPT-4:

What about bigger model like GPT-4? Because although nobody really knows the exact size, but it’s commonly believed to be 1.8 trillion parameters. Based on some interpolation, because all we know is the energy consumption for some small open source models. So we have to do some interpolation for those bigger models. So based on this interpolation, the GPT-4, if you write two emails using GPT-4, the amount of energy could be enough to drive a Tesla Model 3 for one mile.

A Tesla Model 3 uses about 273 Wh per mile. Divided by two emails is about 137 Wh per email, which rounds exactly to WaPo’s 140 Wh. So we know that he interpolated from a rumored 1.8 trillion parameter count of GPT-4, using what he knew about small models.

Later in the interview he clarifies which small model specifically he’s interpolating from, and how long the emails are:

Just to give you a context, if you write two medium-length emails, let’s say 100 to 200 words emails, just two emails, the amount of energy required to write two emails using Llama-3-70B is enough to give you a full charge on your iPhone 16 Pro.

So he’s assuming each email is 100-200 words, and is thinking about this in terms of the model Llama-3-70B.

A few months after this Washington Post article, Ren and a team published a paper where they estimated that the same model (Llama‑3‑70B) used 17 Wh to write 500 words. If we take the middle of his 100-200 word email range, 150 words, we get the cost of writing an email using Llama-3-70B as 17 x 150/500 = 5.1 Wh. 2 x 5.1 is 10.2 Wh, close to the iPhone 16 Pro’s 13.86 Wh battery. If you assume we have a 200 word email and scale up energy linearly with words, this comes out to 13.6 Wh, almost exactly the full charge of the phone.

He says in the interview that he just interpolated from a smaller model’s parameter count and GPT-4’s reported parameter count to estimate how much energy it used. The “70B” in the Llama model name means it has 70 billion parameters. He said GPT-4 has 1.8 trillion parameters. So this means Ren believes GPT-4 is 25.7 times as large as the Llama model he looked at, and therefore uses 26 times as much energy to do the same thing. 5.1 Wh to write an email times 26 is 133 Wh, pretty close to the 137 Wh for driving the Tesla, and the 140 Wh the Washington Post used. I’m pretty sure this is how he got his estimate for how much energy a GPT-4 prompt used.

It seems like he might have given the same “100 to 200 word” range, and said something like “GPT-4 can use 140 Wh to write a 100-200 word email” and the Washington Post authors didn’t want to constantly say the full range and just rounded down.

Stacking errors

This was not a reasonable way to get the estimate, for a few reasons that stack together.

GPT-4 was likely a mixture of experts model, so the active parameters were way lower

While GPT-4’s exact architecture has never been confirmed by OpenAI, by the time this article came out, the best reporting on it we had (a July 2023 leak detailed by Dylan Patel at SemiAnalysis) said GPT-4 was a “sparse mixture of experts” model, which means that most of its parameters wouldn’t be active (and therefore not using energy) for a given token. GPT-4 was rumored to have 16 “expert blocks” that each had about 111 billion parameters, and only two would be active for every token generated. It also had 55 billion additional parameters on for attention, something it’d take too long to explain here. This is also where we got the 1.8 trillion parameter estimate in the first place, so Ren had access to the idea that GPT-4 was probably a MoE model. What this means is that when answering a prompt, GPT-4 would only have 280 billion parameters active at any one time, not 1.8 trillion. GPT-4’s energy cost should have therefore only been 4 times as big as Llama-3-70B (because it had 4 times the active parameters), not 26 times bigger as Ren implied.

Thus, Ren’s estimate was likely 6x as large as it should have been, presuming GPT-4 was in fact a MoE model.

The numbers Ren was using for Llama-3-70B were improperly scaled, and from a range of numbers way too high to begin with

The numbers Ren was using for Llama-3-70B were themselves inflated. He said in the paper that the numbers came from a Microsoft research project that was specifically testing how different organizations of AI chips change their energy use. The research tested Llama-3-70B across different arrangements of hardware to see which was most and least energy efficient.

The paper found that energy use ranged from 4.28 Wh per response in the most efficient setup to 6.45 Wh in the least efficient one. The takeaway was that the worst setups waste significant amounts of energy and data centers can save significant amounts of their energy by switching between arrangements as their workloads changed.

These measurements were for medium-length inputs (under 1024 tokens) paired with medium-length responses (under 350 tokens). A 500-word page is maybe 660 tokens, so Ren and his team needed to estimate how much the energy cost would grow for a longer response. They say:

When deployed on Nvidia H100 server clusters utilizing state-of-the-practice techniques, Llama-3-70B consumes about 0.008 kWh on GPUs for producing a long output with over 350 tokens given a medium-length prompt

If you take the energy cost of the most-efficient arrangement for generating 350 tokens and naively scale it linearly with token count, you get 4.28 Wh x 660/350 = 8.07 Wh.

Incorrect scale-up for more tokens

The paper’s own data on the similarly sized Llama-2-70B (note, not 3) model implies that the scale up from 350 to a “long output” would add about 25% more energy. The paper didn’t run Llama-3-70B across different output-lengths, so Llama-2-70B’s energy is the best evidence we have for how energy actually scales with output length on this hardware. But the two models share the same parameter count and architecture family, so the scaling behavior should be very similar.

It’s not clear what a “long output” is, but their “long inputs” are defined as 1024-8192 tokens, as much as 8x the medium input upper bound. The paper sets these thresholds using the 33rd, 66th, and 100th percentiles of lengths from a chatbot conversation, so “long” just means whatever was in the top third of actual conversational outputs. 660 tokens is likely below the average long response, so the energy increase here is probably even less than 25%.

So it seems very likely that the scale up from Llama-3-70B from 350 to 660 tokens should have only involved a 25% energy increase, not a linear scale up with tokens. This would mean the most efficient model going from 4.28 to 5.35 Wh, not 8 Wh.

So this mistake inflated the cost by 1.5x.

A correct scale-up for overhead

Ren’s team’s method for getting from 8 to 17 Wh for the final cost made more sense. They were adding 67% for non-GPU server energy and 17% for data center cooling overhead. These are both reasonable. 8 Wh x 1.67 x 1.17 = 15.63, close enough to 17 Wh for me to not worry about the specifics of how they calculated this.

There’s a possibility that the 17% for onsite cooling cost might have double counted the onsite water cost if Ren included it in his calculation for GPT-4’s energy, but his water cost model is so dominated by offsite water cost of generating the electricity that this one’s safe to ignore.

These numbers were massively overinflated in the first place

There was a much bigger problem with using these numbers though. The Microsoft study setup was way less efficient than how AI companies actually run models. A follow-up paper from Microsoft a year later specifically mentions that research estimates of the kind used in the original paper could overestimate real-world energy use by 4-20 times, because there are a bunch of different ways of saving energy used in cutting edge AI data centers that the research measurements didn’t include.

So that’s another 4x to 20x mistake.

The Washington Post article ran with the low end of Ren’s range of 100-200 words

It seems like Ren had given an estimate for the energy cost of about 150 words, called it a “100 to 200 word” cost, and the Washington Post team might have just ran with the lowest number. Using 150 words for what’s actually 100 words inflates the final cost by 50%, and for much bigger models with much more fixed overhead costs like GPT-4, it’s more reasonable to assume energy costs scale roughly linearly with token count. So the final estimate is probably 50% higher than it should be, so that’s another 1.5x. It’s reasonable to scale energy linearly with tokens here because for a model the size Ren assumed for GPT-4, the per-token cost dominates everything else.

Putting all the errors together

These errors aren’t all perfectly independent, but naively multiplying them together you get 6x1.5x(4x-20x)x1.5 = 54-270x.

Dividing 140 Wh by these gets a final energy cost of GPT-4 of 0.5-2.6 Wh. This is much more in line with other estimates made of short outputs on similar models. Plugging these into Ren’s study gets approximately 0.3-1.4 mL of water per prompt used onsite and 1.8-8.4 mL used offsite (including water evaporated off lakes dammed by hydroelectric plants) for a total of 2.1-9.8 mL per prompt.

So I think the “bottle of water per prompt” number is inflated by somewhere between 50-250 times, and this is before adding the fact that most of this water is offsite, and half of that offsite water is water evaporated off rivers and lakes dammed by hydroelectric plants that would exist with or without the data centers.

If I’m right, this is what’s actually in the “bottle of water”:

While the Washington Post article did make it clear that much of the water measurement was offsite, many viewers who just saw the graphic mistakenly thought the whole bottle of water was used in the data center itself. Correcting for the errors brings the onsite water cost surprisingly close to Google’s published estimate of Gemini onsite water costs per query at 0.26 mL.

This is the most consequential mistake in the history of writing on AI and the environment. To this day, everyday people still look at me like I’m crazy if I say that AI actually does not use a whole bottle of water, and only about a thousandth of that in the data center itself.

After this article was published, social media jumped all over this claim. Hundreds of the most popular short form videos and images ever made about this topic were about the bottle of water per email claim. This is one of the most popular TikToks on ChatGPT and the environment ever, and concludes with saying each prompt uses a whole bottle of water, which is why you shouldn’t use it. It spread like wildfire and cemented itself in everyday people’s understanding of AI.

It appeared in so many news articles about AI:

• Sept 19, 2024 -TechRepublic: “Sending One Email With ChatGPT is the Equivalent of Consuming One Bottle of Water” (Sept 19, 2024)

• Sept 19, 2024 - Tom’s Hardware: “Using GPT-4 to generate 100 words consumes up to 3 bottles of water — AI data centers also raise power and water bills for nearby residents”

• Sept 21, 2024 - Futurism: “The Environmental Toll of a Single ChatGPT Query Is Absolutely Wild” ()

• Sept 19, 2024 - Water Education Colorado: “Briefly: A bottle of water per email”

• Nov 29, 2024 - Engineering & Technology Magazine (IET): “ChatGPT consumes one 500ml bottle of water per 100-word request, according to research”

• Jan 16, 2025 - Snopes: “AI’s Impact on the Environment, Explained” ()

  • EVEN SNOPES GOT TRICKED!!

• Feb 12, 2025 - Fortune: “California wildfires raise alarm on water-guzzling AI like ChatGPT”

• Jan 22, 2026 - Business Energy UK: “ChatGPT Energy Consumption Visualized” ()

• Feb 27, 2026 - Katie Couric Media: “How Much Water Does ChatGPT Use? Calculate Your AI Footprint”

• Feb 11, 2026 - Newsweek: “Backlash Mounts Over AI Caricature Trend”

• EESI (Environmental and Energy Study Institute): “Data Centers and Water Consumption”

This seems to be where everyone’s idea that chatbots use crazy amounts of water per prompt came from. It’s crazy that this is basically all coming from a one-off napkin math estimate that naively scaled energy with parameter count using a baseline model in a completely different situation.

I don’t actually feel invested in the data center buildout itself, but I do feel very invested in opposing some but not all of the background beliefs guiding data center opponents, and I’ve been disturbed at how common these beliefs have become in educated circles. I’ve described these beliefs here, and if you want to know where I’m coming from that post is the best place to start. This series will be more political than usual, since I can’t help but bring up the political dynamics I see happening and how I react to them. I’m especially worried about the decline of institutional trust and political liberalism in popular thinking.

I’m exclusively writing about panic over data center impacts on local communities and the environment. I don’t mean panic about AI risk, which I see as very real. One person who won’t feature in this series is Bernie Sanders, because when he talks about data centers he’s mostly talking about them from an AI risk perspective. I think AI risk is real and serious, and because I don’t know what to do about it, I don’t jump in to criticize different people’s plans for how to stop it. I’m personally skeptical that local opposition to data centers will help with AI risk, but that’s an argument for another day. Here’s my list of recs of things to read on AI risk. You don’t have to agree with me on this at all to read this series.

Part 1: Pre-ChatGPT and the creation of common wisdom

There are a few key things that happened in the lead up to the data center panic that are each necessary for understanding it, that all happened before ChatGPT was released and AI moved from being a niche interest to a major public debate. First you need to understand the situation with the early environmental critics of AI. This will be the bulk of this post. At the end I’ll reflect a little on the political situation leading up to ChatGPT, the state of popular environmentalism at the time, and the loss of technical talent in journalism.

Early environmental critics of AI

In 2019, Karen Hao wrote “Training a single AI model can emit as much carbon as five cars in their lifetimes” for the MIT Tech Review. This was at the time a shocking statistic. The training of the AI model in question was very different from what we currently think of as training. Here, thousands of the same model were trained at once, and Google researchers selected the one that performed the best at the end. This looked like it carried a big energy and carbon cost, resulting in a just slightly better model for translating English to German that didn’t end up being used by anyone anyway. This seems pretty wasteful! Spending this much emissions on a slight improvement on a specific model was probably not worth it.

Right?

The original research Hao was citing was from this paper, one of the most cited of all time on AI and the environment, by a research team at UMass Amherst, who had made a few goofy assumptions that made the cost look 90 times larger than it actually was. They had made 3 key mistakes that a later researcher flagged and explained in this paper. In a nutshell, the mistakes were misunderstanding the algorithm, hardware, and background grid:

• The type of training Google did tests thousands of candidate models to find the best one. The Google team ran a very low energy screening test on each model candidate and only fully trained the ones that looked promising, but the UMass team assumed that all the thousands of models were fully trained.

• The UMass team assumed the training ran on standard graphics cards, but Google was using specialized TPU chips.

• The UMass team assumed Google was using an average commercial data center, which at the time could lose 60% of its electricity to cooling and overhead, but Google’s data centers lost closer to 10%.

Together these made the training process look like it used two orders of magnitude more energy than it actually did.

This statistic made it into the most cited AI ethics paper of the era, where it played a pretty central role. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” from 2021 has over 13,600 citations on Google Scholar. The environmental section opens with the UMass Amherst team's numbers and leans heavily on them, treating them as the typical cost of training language models. That paper was widely read, and so the conventional wisdom that training an AI used preposterous amounts of energy and emissions was in the water by the time ChatGPT came out a year and a half later.

This number was repeated in lots of other foundational articles that went on to influence the current discourse. The Nature article “The Carbon Impact of Artificial Intelligence” has over 100,000 reads and over 400 citations. It uses the UMass study as its central example, and says “This is of the order of 125 round-trip flights between New York and Beijing, a quantification that laypersons can visualize.” This is one of the first instances of one of the more slippery comparisons made in the space. “125 round-trip flights” doesn’t tell you if this is per-passenger or the whole plane, and if you do the math it’s the per-passenger cost. Correcting for the 90x error, saying “training an AI model emits as much as two of the AI researchers flying from LA to Paris and back” doesn’t sound quite as shocking. Every year, 20-30,000 AI researchers fly to the NeurIPS conference alone, so Google doing this research had about as much impact on the environment as maybe 1/10,000th of the conference. This does not make it look like training emits unreasonable amounts.

You might pause here and ask how serious of an issue this is. Getting this wrong at the time might have been bad, but they were “directionally correct” because models did in fact grow to use huge amounts of energy. I’d argue that what matters throughout is how many users each AI model will end up getting, for the same reason that you can't assess the total environmental cost of producing large amounts of cans of Coke without knowing how many people are buying them.

The resulting model was going to be used by relatively few people to slightly improve one specific translation function, and was mainly a piece of research. I think it’s okay for 2 AI researchers to sometimes take long flights to meetings in other places if they think the result might yield useful research results. I also think it’s okay for hundreds of millions of people to each purchase a CD to install a computer program, and the per-person carbon emissions of manufacturing CDs are about the same as the per-person emissions of training frontier models. See my full argument on training AI models here, which boils down to the fact that if we compare the emissions cost of creating new AI models to other products used by similar numbers of people (hundreds of millions) they don’t stand out as being especially wasteful:

So both before and after ChatGPT, the idea that training was especially environmentally wasteful was, I think, wrong, and only looked plausible if we accepted the 90x inflated number that got shared everywhere. The common wisdom invented here was, I think, completely incorrect.

Coverage of this study also gave us the earliest instance of one of the worst comparisons in the space: the cost of building an AI model used by hundreds of millions of people vs your personal consumption costs.

This might have made more sense if you assumed the user base was going to be really small, but now graphs like this make as much sense as saying you shouldn’t buy a can of Coke because the combined emissions of global Coke production are much larger than the emissions of your personal car. This talking point has always been goofy, and was invented by a study that was super clunky with numbers and amplified by both the media and people researching AI’s environmental impacts at the time.

There's a big split between how much attention the correction got from researchers and how much it got from the media. The UMass study has 2730 citations compared to the correction’s 1613. But the correction itself was barely covered in the media. The original statistic was promoted by:

• MIT Tech Review

• Cory Doctorow in Boing Boing

• The Columbia Climate School

• The Guardian

• Earth.Org, which just published another article using the same bad statistic last November

Many articles covering the Stochastic Parrots paper also mentioned the environmental costs the paper brought up.

Jeff Dean at Google had publicly asked the original lead UMass author to stop citing the debunked statistic after seeing it in a more recent paper of hers, and argued the original figure had actually been inflated by more than 3000x. The author didn’t update the paper, and Stochastic Parrots has also not been updated.

A lot of the early bad comparisons between AI’s costs and other things came from the bad UMass number. The comparison of training to things individual people do, the “flights” comparison without clarifying if it’s passengers or the whole plane, the failure to contextualize models with how many users they’ll get, all came from discourse about this single misunderstood statistic, and became part of the common wisdom before ChatGPT launched. This also started the pattern of lopsided media coverage, where alarming statistics are understood to get attention but corrections and clarifications mostly don’t. I don’t know of other statistics on AI’s energy and water use at the time that got similar amounts of attention or pushed the idea that AI was uniquely bad for the environment. The whole idea seems to flow from this single error. Other prominent researchers on AI’s environmental cost, like Shaolei Ren, were mostly making sober comparisons between different data centers and chips and algorithms and writing about how their energy and water cost could be gradually reduced. The environmental critique outside of this number seemed completely level-headed and similar to normal environmental science research on any other topic.

If you look for scientific studies on AI and the environment pre-ChatGPT, Parrots is the most cited of all time, and the UMass study is the second. The Nature paper is #15. Besides those, all the rest seem pretty great and stand up to scrutiny. The story of bad early thinking on AI and the environment is I think the story of specific echo chambers hyping up the UMass study claim and inventing lots of new misleading comparisons that would explode in popularity when ChatGPT later entered the scene. Here are the top 20 papers by citations on Google Scholar. I’ve bolded the ones that use the bad numbers from UMass:

1. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?

2. Energy and Policy Considerations for Deep Learning in NLP ← the UMass study itself

3. The role of artificial intelligence in achieving the Sustainable Development Goals

4. Green AI

5. Tackling Climate Change with Machine Learning

6. Quantifying the Carbon Emissions of Machine Learning

7. Sustainable AI: Environmental Implications, Challenges and Opportunities

8. Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning

9. Carbon Emissions and Large Neural Network Training

10. Estimation of energy consumption in machine learning

11. The AI gambit: leveraging artificial intelligence to combat climate change, opportunities, challenges, and recommendations

12. Green Algorithms: Quantifying the Carbon Footprint of Computation

13. The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

14. Aligning artificial intelligence with climate change mitigation

15. The carbon impact of artificial intelligence

16. Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model

17. Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models

18. Carbon Aware Computing for Datacenters

19. Anatomy of an AI System

20. Measuring the Carbon Intensity of AI in Cloud Instances

So the field itself was producing a lot of great work, but a few specific mistakes that went unnoticed for years in the very most cited papers, more media attention on the alarming numbers, and weird rhetorical moves together seeded common wisdom that AI was uniquely bad for the environment, which would later be picked up once ChatGPT took off. I’ll cover that more in Part 2.

Stochastic Parrots ends its section on the environment like this:

These models are being developed at a time when unprecedented environmental changes are being witnessed around the world. From monsoons caused by changes in rainfall patterns due to climate change affecting more than 8 million people in India, to the worst fire season on record in Australia killing or displacing nearly three billion animals and at least 400 people, the effect of climate change continues to set new records every year. It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental impact and inequitable access to resources — both of which disproportionately affect people who are already in marginalized positions.

I don’t think this conclusion would have made as much sense describing two individual people taking a round trip for an AI research conference. It only really makes sense assuming the inflated number. Writing about AI’s unique environmental harm became common in AI ethics papers as a result.

And that’s how it became common wisdom to talk about the unique environmental harm of AI.

Deep history

In 1999, computers were using more and more energy.

This alarmed some people, who saw it as ridiculous that digital products could have such an effect on the physical environment. Forbes published the article “Dig More Coal — The PCs are Coming” and opened with “Southern California Edison, meet Amazon.com. Somewhere in America, a lump of coal is burned every time a book is ordered on-line” The contrast between physical infrastructure and something you do online was meant to shock.

I’d really recommend reading through the whole Forbes article. It’s full of the exact moves I write about a lot, more than anything giving contextless large numbers over and over. I’d be especially interested for strong environmental critics of data centers to read this article and tell me what it’s getting wrong that modern criticism isn’t. There’s also this great debunking of the article and others on the environmental “harms” of online activities, focused on contextless large numbers. More articles on this topic include the goofy “Climate Crisis: The Unsustainable Use of Online Video” from 2019, or this fun one about the time the UK government considered cracking down on one-word emails for the sake of the climate due to a misreported inflated statistic.

An underrated aspect of the story of AI and the environment is that it has always been offensive to readers to find out that ephemeral digital goods have a physical real-world cost. I think this is one reason the water objection to AI is so popular compared to the others. Water is precious and physical, and AI is ephemeral and flashes on your screen quickly and then you forget about it. The contrast offends people.

This time is different. AI will in fact on net use more energy than all of these. But is this on its own bad? I think an underrated part of the story of the data center panic is that computers are now rising to truly industrial levels of energy usage and emissions, and people think only physical goods should be allowed to do that. I personally think that if computing is proving to be so valuable that people want to use it this much, it should be allowed to rise to meet the demand.

The left and right become more anti-tech

The rise of anti-tech sentiment on the left and right is a key part of the story of the data center backlash. There are a few complaints about tech the left and right share, like the general idea that smartphones have mostly made the social world worse. Another is that technology in the last ten years has only really gotten good at giving us quick dopamine hits, little treats that don’t actually make our lives better. On this I feel like I’m living in a parallel reality. The internet especially has been a utopian technology for me personally, but it’s become very unpopular to say that out loud.

On the left, tech became much more associated with right wing politics. A lot of liberals and leftists came to see social media as being a big part of why Trump won in 2016 and 2024, and reacted to the rise of alternative media promoting anything from anti-vax conspiracy theories to the manosphere. They also began to associate tech leaders with right wing politics, and obviously weren’t entire wrong. Tech leaders cozying up to Trump after 2024, and the general rise of the tech right, made it clear that the left had powerful new enemies who had been made rich by technology that often seemed either neutral or positive when it was first rolled out.

Elon descending into posting almost daily on Twitter about insane racist conspiracy theories deserves a special mention here. The richest person in the world who made his money in tech regularly retweeting and publicly agreeing with vocal white supremacists has given the left completely legitimate reasons to think there’s a huge danger in ignoring the values and beliefs of the people selling us new gadgets. Here I’m in complete agreement.

The place I strongly disagree with many of my fellows on the left-of-center is that they’ve coupled this justified suspicion of the current Silicon Valley elite with a distrust of the value and abilities of the products themselves. It’s easy for me to say that Elon is an evil person who’s made extremely useful products. I would very happily give up all those products in exchange for the richest person in the world not being a white supremacist. But this is very different from saying that Starlink is useless. It’s become frowned on in populist left-wing spaces to imply that Silicon Valley is capable of producing anything new and useful at all, and saying so is seen as coming from a secret desire to give status to evil people. In low trust populist politics, the only real question is who you’re giving status to with your words and actions, not the complex boring questions of what we actually want out of our lives and how to get them together, so any question of the usefulness of a new technology becomes a question of whether you like the people who made it, and it’s seen as naive to think otherwise. To even imply that AI can be useful enough to make it worthy of any carbon cost at all is seen as completely missing the big picture. It makes sense to me that people would want to boycott AI to not empower tech leaders, but the idea that AI itself must be a scam that has no use value at all is very common in conversations about AI and the environment, and I suspect a lot of that comes from this background sense that Silicon Valley can’t produce anything useful.

I’m less familiar with right wing anti-tech sentiment because most of my time is spent with people left-of-center, and the right wing people I do talk to regularly are all high institutional trust and generally pro tech. Specific factions on the right have obviously always had an uneasy to negative relationship with technology. MAGA contains warring factions of various shades of pro-tech and pro-market capitalists or anti-tech and anti-market localists, and I can’t really tell who’s ascendent right now. The right is also much more wary of corporations on average than they were when I was growing up.

One last note on politics is that I get the sense that both the right and the left have become more low trust in general, less interested in preserving pluralism, and think more often in terms of good guys and bad guys. It seems less likely that the average educated adult, when asked “is it okay if a corporation buys some land, power, and water in your town and uses it to do something that benefits people far away instead of you and makes a lot of money doing it?” will answer yes. But I don’t have the data to back that up.

The rise of pop environmentalism, and the good feelings of discovering a villain hiding in everyday life

This section is almost entirely pure armchair speculation, but it’s also about the area I’m most familiar with, so I feel I’ve earned some right to pontificate.

When I became a physics teacher I was motivated in large part by wanting to improve the general state of science education to get students to a higher level of understanding of the climate situation. Over my time following the issue I came to think that the public was veering off course from what actually helps the climate, and developed an armchair psychology reason for why they veered.

The most important thing we need to do for the climate is to change the energy grid. This can be accomplished with good policy, like carbon taxes, or the government funding renewable energy projects or allowing high energy transmission lines. The grid also needs to be massively scaled up as society separately electrifies formerly fossil-fuel dependent processes. Making all cars electric will require huge amounts of new power plants and transmission.

This means that by far, the way you can impact climate the most isn’t actually by changing your lifestyle. It’s by helping to move this process along, either by local political advocacy or national communication or donating to the right places. I made this tool to compare different climate interventions you can make, mainly to show that changes to the grid completely dwarf everything else you can do even if you divide by the number of people you’re working with to make them happen.

If you are going to make a change to your lifestyle, most people who think seriously about helping the climate agree that tiny individual cuts you make to your lifestyle don’t help at all, because they basically just buy you moral license or literal saved cash to spend on other things that emit just as much. There are a few big cuts you can make that do make a difference, going vegan or living in a big city and walking or biking or taking public transit a lot instead of driving. I’m already doing this, and it’s nice to know that I have a meaningfully lower carbon footprint than average, but I know these cuts are still completely dwarfed by tiny ways I could contribute to changing the grid around me.

I already knew this when I was in college in the early 2010s. If I could go back in time and tell my past self that in 2026, people are worried about the emissions of their personal use of a computer program, past me would feel like the basic message of climate had failed to reach average people. But this is weird, because people are consuming a ton of content about climate now, way more than when I was in college. The issue I think (and here’s where I get really armchair) is coming in large part via short form video.

I’m not a short form video hater by default. I know Jonathan Haidt’s work mostly doesn’t replicate, yada yada. But I do think the nature of short-form makes it a uniquely bad place to communicate about climate well, and there’s a specific dynamic that incentivizes hyping people up about the very least effectual things they could do.

When you watch short-form video, you’re often getting a quick parasocial hit of companionship with the person talking back to you. Something that feels really, really good is finding a hit of companionship that makes you feel like you know a secret about the world the people around you don’t, you’ve ascended, and there’s something that’s polluting them that you’re free of now via your newfound companionship. These evil invisible parts of everyday life do in fact exist, but the tendency to go looking for them can veer you off course, because it’s so good to feel like you’ve discovered them regardless of whether they’re real.

I’m vegan, and when I’m out at a restaurant and see someone eating meat, I have to admit that my brain sends me the general vibe “There’s this big global struggle that I’m a part of, and the civilian over there doesn’t even know about. I’m not just some guy in a restaurant eating the same mapo tofu I had a few days ago, I’m part of a global struggle against evil.” That feels good!

A lot of conversations about climate on short form video take the form of someone speaking into the camera to you about how something small in your daily life is actually evil. I think stuff like this is basically perfectly fit to make us feel the same thing I feel when I see someone eating meat: by making this cut, you will ascend into a big global struggle against the forces of evil. The smaller the cut, the easier it is to ascend. You’ll be on the same team as this person speaking to you. It’s like you’re a secret agent getting assigned a mission. We should expect this style of advice to be pretty popular, and for people to be pretty excited about videos that advise tiny cuts to individual lifestyles (the least effectual thing you can do to help with climate) over contextualizing and helping with the situation with the grid (the best thing you can do).

And in my personal experience, this has been a huge part of the climate conversations I’ve had over the last ten years. A lot of people will talk as if small individual things other people do are actually carrying some deep, secret evil that needs to be purged. This precedes social media, but I suspect people are getting a lot more hits of this style of thinking now that short form video’s more popular.

I suspect the basic tendency I’m describing here was a big underrated contributor to the chatbot moral panic. I go into more armchair theorizing on that here. The panic about chatbots is a necessary step to the panic about data center impacts on local communities more broadly, which I’ll get into more in the next post.

A result of more people’s understanding of climate coming through social media influencers also meant other fake common wisdom was invented. A very common one is that because we’re in a climate crisis, we can’t allow “new emissions” from a new industry. When you think about it, the category of new emissions is weird, because every time we emit at all, the emissions are new. This talking point maybe makes sense for talking about additional total emissions, but often gets warped to say it is unacceptable for a new industry to appear during the climate crisis.

The decline of science journalism

US newsroom employment fell by 26% between 2008 and 2020, from 114,000 to 85,000 jobs. Newspapers were hit the hardest and lost 47% of their staff, going from 71,000 to 38,000. Digital news outlets grew, by only added about 6,000 new jobs.

Some journalists are more expensive than others. Beat reporters who cover one specific area for a decade and get a feel for what quality research looks like are expensive. They get less out in the same amount of time and prioritize depth. Scientific specialists were more likely to be cut because they were more expensive. When the Canadian Science Writers’ Association was founded in 1971, there were at least 30 full-time newspaper science reporters in Canada; by the late 2010s that had dropped to about six. American newspapers once had dedicated science, health, and environment desks, but most of those are now gone. A 2024 Nieman Lab piece titled “Science journalism becomes plain old journalism” notes that every reporter, regardless of their beat, now gets deputized as a science reporter when the story calls for one. There’s just way less technical understanding in media than there was 20 years ago.

This left journalists much more eager on average to defer to credentialed experts on topics, without contextualizing a given problem with the other sources of harm to health or the environment. Journalism also maybe became more self-referential, as authors pressed for time would mainly just defer to other trusted publications, which left the field more susceptible to echo chambers.

The business model has also changed. The rise of the pageview economy in the 2010s forced journalists to prioritize clicks and reads and shares. Shocking titles often win out over in-depth writing, and much of the worst sources of data center misinformation have come from shocking and misleading titles rather than the contents of the article itself.

For example, the most influential piece on data center water use is this from the New York Times:

Almost every time I say anything about AI and water use online, this story gets shared, or this variant of the same story from the BBC:

Almost no one sharing the articles seems aware that the water issues seem to have happened due to construction, either sediment runoff or issues with blasting nearby, and don’t have anything to do with the normal water draw of the data center, even though the content of the articles makes that clear. What’s happening here is that the alarming headline is a tool to get the article shared widely in the hopes that 1 in 10 of the people sharing it actually read and digest it. This is why the initial “Training AI emits as much as 125” flights talking point made headlines, but the correction didn’t. No one’s going to click on a story about how a process you’ve never heard of before doesn’t use much energy.

(Except for you, dear readers. Thank you for your support)

Journalists in general were feeling more precarious and burnt out before ChatGPT, and were probably not excited about a new major source of competition.

Conclusion

The political situation leading up to ChatGPT had primed both the right and left to distrust big tech, and to think their products weren’t worth any additional environmental cost at all. Politics had maybe become lower trust, and people were more interested in identifying good guys and bad guys than passively allowing lots of different types of businesses to operate out of sight. The popular understanding of environmentalism had been diluted by excitement about tiny personal cuts that could make you feel more with it than the people around you. Journalists had lost technical talent and were more incentivized to write shocking headlines to generate clicks and shares. And the two most cited papers ever published on AI and the environment contained a statistic off by two orders of magnitude that had inaugurated a series of bad comparisons and metaphors for thinking about AI and the environment. These together I think were the seeds of the data center panic we’re living through now.

The paper has other shocking estimates, perhaps most is that emergency backup diesel generators around Northern Virginia data centers alone create hundreds of millions of dollars in public health costs, with damages large enough to register as far away as Long Island.

The shading on the map shows each county’s rank by modeled annual health cost from the Virginia generators. Each of Long Island’s counties are very dark, which the middle graph says corresponds to maybe $100,000 to $1 million in expected annual health damages each, so altogether Long Island’s 4 counties (I’m including NYC) may be losing as much as $4 million in public health harm. These diesel generators are modeled to create $220-$300 million in public health costs.

This seems crazy. Northern Virginia’s two biggest data center counties generate about $1.2 billion in local tax revenue each year1, and the study implies at least a fifth of that is cancelled out by the harm of their backup power alone, with most of the damage falling on people far from where the sources are built and regulated. It’s as if Northern Virginia were stealing $4 million a year from the health of Long Island. This seems wildly unjust, counter-intuitive, and a candidate for a data center problem that dwarfs the overblown concerns about water or land.

However, while local air pollution problems with data centers are very real, this specific paper is easy to misread and come away from thinking the national-level harm is much worse than it actually is. To see the difference, you first need a ton of grounding in the situation with air pollution in the US.

Going into this, based on what I understood about air pollution, I was under the impression that this was a serious problem with data centers at the national level. However, I’ve come away thinking that while data centers can definitely create serious local problems for air pollution and need to be carefully and transparently governed (and there are clear places where that’s already failed), I no longer believe that on net they will stand out as a significant national problem for air pollution in the near future.

Air pollution is complicated, but not too complicated. You need a lot of individually simple facts that together paint a complex picture before understanding how data centers fit into it. This first post on air pollution will go over these facts. I think that I’ve been able to finally get my footing, and this post is basically all my notes on what I think is most important to understand to get your footing as well. This is far and away the longest of the four, but I think also very fun and interesting once you get beyond the necessary technical details at the beginning.

The ethics of air pollution are wild. Air pollution is both complex and urgent enough that it forces us to break through simple vapid generalities and actually wrestle with how the core values we’re all running on to keep society together conflict with each other, and to think in a much more meta way about how to trade those values off. Even if you don’t care about data centers at all, I think the ethics of air pollution is on its own such an interesting intersection of so many different questions that it can be a blast to read about. Part 2 will focus on this.

Part 3 is on why I don’t expect data centers to become a significant national air quality problem in America. I also unpack the paper above and contextualize its predictions against other industries.

Part 4 covers data centers at the local level, and where they have and can turn into significant problems for air pollution, especially when they take advantage of lax permitting rules. The most obvious case is xAI’s Colossus in South Memphis, where xAI used dozens of gas turbines near already heavily polluted residential areas. I’ll cover that in detail in Part 4.

One important note here is that I’m excluding the climate effects of air pollution. Effects on climate are so different from other harms of air pollution that they deserve a separate post. All death and DALY figures I use for air pollution harms are taken from the IHME’s Global Burden of Disease project.

Contents

• The three main ways long-term exposure to air pollution harms us

• Particles that make PM2.5 and ozone

• Harms from short-term exposure

• Anthropogenic vs natural air pollution

• American air is way cleaner than it was decades ago, and way cleaner than the world’s average

• A lot of US pollution harms occur in either average US air quality, or California

• How air pollution compares in deaths and DALYs to other problems

• When does an air pollution source have a very local impact vs a very broad impact?

• Comparisons between different pollution sources are often counter-intuitive

• Climate change has many pathways to create more air pollution, so a source’s main effect on air pollution might often be via its effects on climate

• The main sources of outdoor air pollution in the US, and their decline

• Quantifying the public health cost of air pollution

• How air pollution is governed in the US

• Inequity in exposure to air pollution, and failures of governance

• What does the future look like?

• Takeaways

The three main ways long-term exposure to air pollution harms us

There are many forms of air pollution, but the main way it currently harms us is via one of three pathways: PM2.5 (fine particles), ozone (a gas), and direct carcinogens. Their types and levels of harm are very different. As of writing, no safe threshold of air pollution has been identified, it appears to be harmful at all concentrations we encounter, and the more we’re exposed the more harm happens, both in terms of concentration and time. There’s no threshold where air pollution starts being harmful, it always is, just different levels of harm.2 The vast majority of ways air pollution harms us is due to long-term exposure, mostly via the buildup of risk that we develop specific diseases.

PM2.5

PM2.5 is fine particulate matter, particles 2.5 micrometers or smaller. It causes the large majority of the harms of air pollution. Roughly 70,000 of the 83,000 annual American deaths caused by outdoor air pollution are due to PM2.5.3 It can either come out directly from a pollution source (this is “primary PM2.5”) or be created when other types of air pollution interact with the atmosphere (“secondary PM2.5”).

Half of all premature deaths from PM2.5 are cardiovascular rather than respiratory. You’d expect something that harms you when you inhale it to mainly be bad for your lungs, but one of the reasons PM2.5 particles are so dangerous is that some of them are small enough to slip through your lungs’ membrane into the bloodstream, and get distributed everywhere blood flows. PM2.5 is associated with many different causes of death, here’s the 2021 global breakdown:

Something really weird about PM2.5 is that the long-term harm of additional units of it decrease the more there already is in the air. Here’s a rough chart of how these harms compare:

This is a simplified approximation, more serious estimates say the curve may look more like this:

This means that if we graph how much additional harm is created by each additional unit of PM2.5, we get a graph that looks like this:

When you get to the concentration at the EPA’s daily limit, a decent amount of the potential harm is already being done.

We don’t actually know if this pattern holds for all the ways PM2.5 harms us. For example, very high indoor exposure to PM2.5 in pregnant women is associated with lower birthweight, and the trend there appears to be roughly linear.

The relationship between harm and time exposed is probably linear for long-term exposure, though short-term exposure has its own separate bad effects.

Some types of PM2.5 are more dangerous. One study found that PM2.5 created by sulfur dioxide from coal plants is about twice as harmful as average.

PM2.5 can last a long time in the air, sometimes as much as 4-7 days.4 This means it can be carried hundreds or even thousands of miles downwind before harming someone. PM2.5 from a wood fireplace in Vermont can end up being breathed by someone in Boston.

PM2.5 concentrations in US air have declined steadily since 1990, but recently stalled out due to among other things an increase in forest fires exacerbated by climate change.

Ozone

Ozone causes 13,000 American deaths a year.5 Unlike PM2.5, the vast majority of ozone’s long-term harm to our health is via just one pathway: COPD (Chronic Obstructive Pulmonary Disease), which is a progressive, irreversible lung disease that makes it difficult to breathe. Ozone seems to be responsible for about 10% of COPD deaths, smoking causes about 80%6. It can have other respiratory effects as well.

Ground-level ambient ozone generally isn’t emitted directly by ordinary outdoor pollution sources. It forms in the air when NOx (explained below) and volatile organic compounds react in sunlight.

Unlike PM2.5, the harm of ozone seems to scale basically linearly with how much concentration there is in the air. Each additional unit adds about the same level of harm. Ozone concentration has had a more gradual drop than PM2.5, but as explained below, this is in part due to more ozone being outside of our control.

Direct carcinogens

Some forms of air pollution are carcinogens that increase your risk of cancer when you inhale them. These cause a much smaller part of total US deaths from air pollution, because there’s so little in the air, each unit adds only a tiny amount of cancer risk, and cancer is both rarer and slower to develop than cardiovascular disease.

Direct carcinogens also aren’t emitted in especially large quantities by the power generation sources data centers draw from. The study that found that data centers could cause $20 billion in health damages doesn’t even consider them as an avenue of harm, though they are a risk when onsite gas turbines are built close to homes, which will be the main source of carcinogen exposure that I'll focus on in Part 4.

How does the harm of PM2.5 compare to ozone?

Per unit, PM2.5 is far and away the worst. A 10 microgram per cubic meter (µg/m³) increase in long-term PM2.5 exposure raises all-cause mortality by about 5-8% while the same increase in long-term ozone raises mortality by something more like 0.5-1%. So PM2.5 is roughly an order of magnitude more harmful per microgram than ozone, though this comparison is super fuzzy because the two are very different in how they’re delivered and how many people are exposed to them.

Here are the rough numbers on how each type of air pollution harm compares. HAP here is Hazardous Air Pollutants, another name for direct carcinogens:

This graph can give you some idea of how harmful each is, but there’s no single final answer because the harm depends a lot on the specifics of where emissions happen.

Long-term exposure is where the vast majority of air pollution harm comes from

The large majority of the modeled health costs of air pollution come from lost years of life from longterm exposure.7 Most of the diseases that air pollution causes develop over years or decades. Things like heart disease or stroke or lung cancer or COPD. Ozone damage to lung tissue also accumulates over long periods of time. Acute spikes in air pollution can trigger heart attacks or asthma attacks in people who are already vulnerable to either, but they are a small fraction of the total health costs of air pollution.

A long-term 10 μg/m³ increase in PM2.5 raises annual all-cause mortality by about 5-8%.8 A one week 10 μg/m³ spike raises mortality that week by something like 0.2 to 1%, and then the effect fades.9

When looking at sources of pollution like data centers, the main question for total health cost is how much they increase long-term annual averages of air pollution. Short-term spikes do matter a lot too, and have their own bad short-term health effects, but a big part of their harm to us comes from the increases in total average ambient air pollution they add to our net exposure.

Particles that make PM2.5 and ozone

NOx

NOx (pronounced as a word rather than spelled out, when reading it aloud say “knocks”) is the name of the group of two molecules: nitric oxide (NO) and nitrogen dioxide (NO₂).

A lot of NOx’s long-term harm comes from the fact that when it enters the atmosphere, it can react to other chemicals and create ozone or PM2.5. NO₂ can also cause short-term harm, and this shows up where data centers have built onsite generation too close to people’s homes, but its total health harm mostly comes from the ozone and PM2.5 it can create.

NOx creates way more ozone than PM2.5, it produces way more by mass, but since PM2.5 is roughly 10x more harmful, it can still make up a significant part of NOx’s total harm to people.10

There are six key things to know about NOx.

First, the types of dangerous pollution it creates depends on the air conditions it’s released into. To turn into ozone, it needs sunlight and volatile organic compounds (VOCs), so ozone from NOx is highest in the summer and mostly doesn’t get produced on cold cloudy days. PM2.5 formation needs ammonia (mostly from agriculture) and happens more in cold humid conditions. Trees are a huge source of VOCs, so rural areas tend to have lots of VOCs but not much NOx, and cities have lots of NOx and not much VOCs, and when excess city NOx blows downwind into rural areas, a lot of ozone can be created. A lot of the harm of NOx emitted in cities happens nearby in more rural areas.

Something weird here is that NOx itself also reacts with ozone and turns it into oxygen. So releasing a little NOx can create ozone, but if an area releases enough the NOx can start to cancel out the ozone the previous NOx created. In a city with a lot of NOx already, adding more NOx can actually reduce local ozone, but this also leads to more total NOx in the air, which can spread downwind and cause more ozone miles away where there wasn’t much NOx before. So there are situations where applying pollution controls in already polluted areas actually increases local ozone but decreases total regional ozone.

Second, NOx can travel far before having the reactions that turn it into dangerous pollution. It last about a day in the air, which is long enough to be carried for tens to sometimes hundreds of miles by wind before being converted to something else.11 The secondary PM2.5 it can create lasts even longer, so PM2.5 effects from NOx can show up hundreds of miles away from the source. This is how you get harm from data centers in Virginia showing up on Long Island.

Third, how physically high up NOx is emitted matters a lot. Emissions close to the ground concentrate NOx locally, because it’s more difficult for it to blow away. Tall stacks at power plants let it more easily disperse far away as it’s carried away by wind.

Fourth, different sources of air pollution emit different amounts of NOx. For data centers, we really only care about power generation. We’ll get into the specifics more in Part 4.

Fifth, NO₂ itself has specific short-term health harms, among other things asthma. There’s some evidence that consistent exposure to NO₂ also causes long-term harm, but it’s too uncertain to say either way because it’s often so bundled with other harmful pollutants.

Sixth, US NOx emissions have declined a moderate amount over the last two decades.

SO2

SO2 (sulfur dioxide) is the other thing that makes secondary PM2.5 relevant to data centers. Like NOx, its main long-term harm comes from what it makes in the atmosphere rather than direct effects of breathing it. It can also irritate airways and trigger asthma symptoms, similar to NO₂, but these are dwarfed by the harms of the PM2.5 it creates.

The vast majority of SO2 produced by data center power draw comes from coal power plants specifically. Coal is the only major US electrical power source that emits significant levels of sulfur.12

Backup diesel generators can also create SO2. These rarely run, but will still be an important part of the story with data centers.

Like NOx, SO2 lasts for a day or two in the air, meaning it can be carried hundreds of miles downwind and cross state lines, and the PM2.5 it creates can itself be carried much farther, and harm people far away from the coal plant.

US SO2 emissions have collapsed since 1990, driven by the Clean Air Act Acid Rain Program’s cap-and-trade system, scrubber installations, and coal plant retirements. Total US SO2 is down roughly 95% from its peak.13 SO2 reduction is a part of why US air pollution mortality has dramatically fallen, yet it’s so harmful that it’s still a key part of the story of how data centers can harm us via air pollution.

Harms from short-term exposure

PM2.5, ozone, and NO₂ can each trigger asthma attacks (especially in kids), reduce lung function and exercise tolerance, irritate the eyes/throat/nose, and increase ER visits for respiratory problems.

In the US, roughly 25 million people have asthma (about 8% of the population), driving around 1 million asthma ED visits and ~95,000 hospitalizations a year at a total economic cost near $80 billion annually. It might cause one in six new childhood cases of asthma.14

PM2.5 spikes can also trigger heart attacks and strokes in people with existing heart disease, and are linked to worse cognitive performance. Daily PM2.5 spikes are followed within a few days by small bumps in mortality.15 Ozone’s short-term effects are mostly respiratory.

NO₂ (one of the two types of NOx) is a short-term direct lung irritant, and can cause inflammation that makes people more vulnerable to respiratory infections. Significant increases in NO₂, which can come from nearby onsite gas turbines data centers use, are associated with increases in more children having asthma attacks that send them to the ER. One study estimated that traffic-related NO₂ was responsible for about 134,000 new pediatric asthma cases per year in the US, about 17.6% of all childhood asthma incidence.

NO₂ either reacts with the atmosphere or disperses quickly, so concentrations drop off within a few hundred meters of where it’s emitted, usually under a mile.16 Only people very close to the source (like a gas turbine or a highway) experience the short-term spikes.

There has been a big general reduction in absolute ambient NO₂ concentrations in US air, though large relative disparities remain. Both the annual mean exposure to NO₂ and the 98th percentile 1-hour daily max NO₂ exposure are well below the corresponding EPA National Air Quality Standards (NAAQS) for them.17

Short-term exposure can significantly add to your cumulative long-term exposure

One last harm of short-term air pollution is when you get such a big hit of it that it increases your total average exposure for the year. Wood has so much PM2.5 that standing around a wood fire for an hour can raise your annual total PM2.5 exposure by 0.3%.18 One study found that people who didn’t smoke but used wood stoves for at least 30 days per year were twice as likely to get lung cancer as non-smokers who didn’t use the stoves. This was caused by their average exposure going up as a result of individual quick hits of way higher PM2.5.

Anthropogenic vs natural air pollution

There are natural and anthropogenic (human) sources of air pollution. Most analysis mainly focuses on anthropogenic air pollution, because that’s what policy can actually reduce.

About 15-20% of the PM2.5 the average American breathes comes from natural sources19, mainly dust, sea salt, and the fine particles that form when trees and other plants emit gases that react in the atmosphere. Something that’s on the border between natural and manmade, and the biggest single source of primary PM2.5 emissions in the US, is wildfire smoke.20

In general, burning wood just emits crazy, crazy amounts of PM2.5 compared to most other things. Whenever you can smell burning wood, whether it’s a Canadian wildfire a thousand miles away or a quaint New England wood stove, you should assume you’re getting a much higher dose of PM2.5 than almost anything else you encounter outdoors.

A much bigger share of US ozone is natural, or otherwise beyond our control. Somewhere between 40% and 70%21 of the ozone at ground level in the US comes from things like the upper atmosphere drifting down, trees and plants releasing gases, lightning, soil, pollution blowing in from abroad, and methane emitted around the world. This changes a lot based on where you are.

Most estimates of the harms of air pollution, like EPA’s benefit-per-ton estimates, measure in terms of the harms of the increase in the amount of air pollution rather than the total amount, because a big fraction of the total is outside our control.

American air is way cleaner than it was decades ago, and way cleaner than the world’s average

American air is not perfectly clean, but it is way cleaner than it used to be decades ago, and way cleaner than the global average.

Whether you look at average PM2.5, ozone, or NO₂ exposure, or NOx or SO2 emissions, the story of American air over the last 20 years has been significant and impressive improvement, with progress still left to go, and sometimes stalling. Meanwhile the economy continued to grow as normal. We’re getting way more value per unit of pollution emitted than we used to.

These averages can also mask significant disparities in local exposure, which will be discussed below.

In general, metro areas are more polluted than rural areas. Here’s every US cities’ PM2.5 concentration compared to all global cities:

The US has it somewhat easy in managing PM2.5 exposure partly because our population density is so low. The average US citizen just isn’t living super close to huge numbers of other people all producing pollution. There are lots of other reasons, like our energy mix, wealth, and pollution controls. India and China top the global lists for PM2.5 exposure, Europe is also higher than ours on average, and if you just look at a population heat map of the regions, it becomes more obvious why they have a harder time keeping pollution low compared to us:

Other reasons India and China have more pollution than the US include heavier coal use, less emissions-control, less governance and enforcement capacity on air pollution, and different industries.

Because the global average is so much higher than the US, globally PM2.5 makes up a much larger percentage of deaths and DALYs lost than it does in the US. Globally, PM2.5 is responsible for about 7-8% of all deaths, whereas in the US it’s 1.7-2%. It’s responsible for about 5% of all global DALYs lost, whereas in the US it’s between 1.5-2%.22

US air has in general become significantly cleaner.

A lot of US pollution harms occur in either average US air quality, or California

Here’s a graph of every county in America’s exposure to PM2.5 in 2019, the width of each bar is the population of the county:

I chose 2019 because 2020 is the last year we have good data, and 2020 was a weird year for air pollution in the US, both because of COVID and because of the wildfires that year.

If you’d like to see where your county shows up here, I’ve turned this into an interactive visual on my website where you can scroll through every county in the US.

The outliers on the left are mostly in California, I’ve highlighted every California county in red.

Why is California such an outlier?

The worst air quality in California is concentrated in two bowls: the Central Valley, and the LA Basin. Both have this crazy combo where air is trapped by surrounding mountains and prevented from circulating, persistent temperature inversions keep air pollution close to the ground where people breathe, and these places get very little rain to wash the PM2.5 out of the air.

The emissions are pretty bad in both places too. The LA area has the world’s busiest port, which means a constant huge input of diesel exhaust. The Central Valley is one of the most productive agricultural regions in the world, and agriculture is a huge contributor to PM2.5 because it creates lots of ammonia that reacts with NOx. There’s nowhere else in the country that has this much ammonia, NOx from vehicles, too little rain to cancel out the PM2.5, and is inside a valley where air doesn’t circulate out.

This isn’t even including wildfire exposure. If you look at how each county changed in 2020 relative to 2019, the California counties spike even higher because it was a bad wildfire year.

If you bracket off California’s unique air pollution situation, the vast majority of PM2.5 exposure and modeled death is happening below 10 μg/m³. This about the average air quality of Germany or the UK.

This pattern has been indicated by more scientific methods. One study estimated that 99% of the mortality burden of PM2.5 in 2017 happened at exposure levels below the EPA’s at the time 12 μg/m³ standard.

The EPA’s old 12 μg/m³ standard is about average to somewhat dirty air in comparison to other countries with comparable GDP per capita. Here are the top countries by GDP with a line drawn showing where 12 μg/m³ is, excluding countries in the Middle East because desert dust and heavy oil and gas production skew them way higher in a way I don’t think is helpful for the comparison:

Many people mistakenly believe that air pollution deaths only really happen in extremely polluted areas. They assume air pollution is only dangerous beyond some threshold. In reality, American air pollution deaths are for the most part clustered in the normal air we breathe, which is very clean by global standards, but because it’s not perfectly clean, and because air pollution carries some level of harm at any level, and PM2.5 in particular has a declining curve of harm where a lot of its total harm can happen before the air gets especially dirty,

and because so many people live in clean but still somewhat polluted air, the roughly 70,000 deaths PM2.5 causes in the US each year largely happen because hundreds of millions of people are each getting tiny increases in the odds that we die from the air we breathe, which in aggregate can add to huge amounts of total harm. Individual communities’ risk can go way up when they’re exposed to really high levels of air pollution, but most of the people being harmed by air pollution nationally are breathing pretty normal American air, which is relatively clean by global standards.

How air pollution compares in deaths and DALYs to other problems

One thing we can measure is how many deaths it causes, or how many years of healthy life it robs from people. These paint different pictures.

Air pollution in America kills more people than car accidents, but most of the people who die due to long-term exposure to air pollution are much older, and often lose a few years of expected life. In comparison, people who die in car crashes are usually way younger, and so each death causes the person to miss out on decades of life. The common measurement of life years lost is “disability-adjusted life years,” or DALYs, measuring both years of life lost to death and years spent in poorer health or wellbeing. If you die 10 years early, you lost 10 DALYs but only 1 life. If air pollution gives you a permanent illness that your life qualitatively worse for the next 20 years, you didn't lose any years of life, but we still want to try to quantify what you did lose to poorer health. The goal of DALYs is to try to capture that, using a disability weight: a number from 0 (perfect health) to 1 (equivalent to death) assigned to a given health state. Mild asthma might get a weight around 0.02, severe COPD around 0.4, terminal cancer closer to 0.5–0.6. The full DALY cost is the weight multiplied by the years spent in that state and you get years lived with disability, plus years of life lost to premature death.

If we compare deaths from air pollution and car crashes, air pollution looks much worse, but this flips if we compare DALYs, because so many car crash victims are young.

If we break up air pollution into PM2.5 vs ozone, PM2.5 becomes a slightly larger fraction of the total burden of pollution.

Air pollution compares differently to other problems based on whether we look at deaths or disability-adjusted life years. Either way, almost all of the harm is in PM2.5. PM2.5 costs as many life-years in America as lead exposure and interpersonal violence, whereas ambient ozone costs fewer life-years each year than choking, and just 22% as many as secondhand smoke.

The typical American killed by long-term exposure to air pollution is over 70 and has an existing heart or lung disease. The average person whose death is attributable to PM2.5 dies about 3-4 years earlier than they would.23 Air pollution basically slowly chips away at key parts of our health, and this mostly doesn’t show up until some of us die a few years early in old age as a result.

If I live in Washington DC my whole life instead of the countryside, my life expectancy drops by about a month.24 This doesn't actually mean that I and everyone I know here will die a month early. It means that chronic PM2.5 exposure slightly raises the lifetime odds that any one of us dies somewhat earlier than we otherwise would, and across the whole population that averages out to about a month per person.

While some people die from long-term air pollution exposure younger than 65, the vast majority of deaths happen after.

When does an air pollution source have a very local impact vs a very broad impact?

When I talk about “national” vs. “regional” vs. “local” impacts of data center air pollution, you might think I’m using an accounting trick. Isn’t the national number just the sum of all the local harms? This will just look arbitrarily low because not everywhere has data centers, so they bring the average way down, right? What does this actually tell us?

This is actually wrong. Air pollution can travel for hundreds or even thousands of miles before harming people, so it makes sense to separately talk about national air quality and how it’s affected by different industries. Think back to the wildfires in Canada a few years ago and how they affected US air quality. These were broadly a national problem created by far-away PM2.5 sources rather than just a sum of local problems created by local air pollution. We can make general statements about US air quality overall that aren’t just the sum of the harms of very local air pollution sources.

The quality of the air you breathe is basically the stacked effects of the national-level, regional-level, and very local level of air quality. Different types of air pollution can affect each very differently.

The main type of air pollution data centers create, NOx, can travel tens of miles or more before producing ozone or PM2.5, and the PM2.5 can travel much farther. Many studies that focus on data center impacts on air pollution focus on how they affect national air or large regional air quality, because most of their pollutants are carried far away.

Some sources of pollution mainly harm the people immediately around them. Others mainly harm people hundreds of miles away. This matters because among other things it can mean air pollution harms are more evenly spread rather than being unfairly bunched in specific places. What determines where air pollution goes?

Four factors determine how locally or broadly a data center’s pollution harms are spread.

The first is whether the pollutants are primary (emitted straight out of the source) or secondary (the result of chemical reactions with emissions and the atmosphere). Primary PM2.5 is dangerous the moment it’s emitted, and concentrations drop off quickly with distance from the source. Secondary PM2.5 forms over hours when NOx or SO2 react with the atmosphere. Secondary PM2.5 can appear tens or hundreds of miles downwind. Ozone is almost entirely secondary. It isn’t meaningfully emitted from combustion sources and forms when NOx reacts to specific compounds in the atmosphere. NO₂ is emitted directly from sources and its short-term respiratory harms are largely local and concentrated in the plume around the source, though because it’s NOx it can go on to contribute to ozone and PM2.5 downwind.

Somewhat related, the atmospheric lifetime of a pollutant determines how long it can survive in the air before becoming something else. NO₂ often reacts within minutes to hours, NOx more broadly can last about a day, so it can travel farther. PM2.5 typically lasts about a week, and longer in some conditions, which is why wildfire smoke can cross continents and do harm thousands of miles away. Man, PM2.5 is crazy!

The height of the thing doing the emitting matters a lot too. Pollution emitted near the ground often stays near the ground, so people are more at risk of breathing it or its secondary effects. If a stack is hundreds of feet in the air it lets the pollution out into an area where wind is stronger and it can mix more with the air higher up. Basically, the higher the stack emitting the pollution, the broader the range it can travel is, so its effects are way more dispersed. Sources close to the ground can harm very local air much more.

Finally, a lot just depends on local conditions. Valleys can more easily trap pollution locally and concentrate it. Background chemistry in the air determines whether NOx creates or destroys ozone. Secondary PM2.5 forms much more easily when there’s ammonia in the air from agriculture.

This can create problems for thinking about the ethics of air pollution, which I’ll write about more in Part 2. Two air pollution sources can do the same amount of very general harm, but if one spreads that harm by increasing the risk of a huge population in a big area by a tiny, tiny amount, it seems ethically different than if it concentrated all that harm on a very local area. This is a place where general harm reduction on its own doesn’t tell us much and we need to think about egalitarianism and past injustice as well.

So the question of how data centers impact your air quality actually depends on three overlapping questions: How do all national data centers harm national-level air quality? Regional-level air quality? And very local air quality?

The full effect of data centers on the air you breathe is the stack of all three.

Comparisons between different pollution sources are often counter-intuitive

I think almost all comparisons between different sources of air pollution are so counter-intuitive that you can basically never rely on them to give you a good sense of how harmful something is.

Take cars. Cars today emit shockingly little PM2.5. Their PM2.5 emissions are so low that they “emit” about as much via their tires making contact with the ground as from burning their gas, sending little bits of tire and dust into the air. They emit NOx, which mostly creates ozone, and some secondary PM2.5. But even their NOx emissions are much lower than they used to be. Old cars used to be way way worse, but catalytic converters are just really good now and combust most of the air pollution before it’s emitted. A new truck emits roughly 99% less hydrocarbons per mile than a 1970 pickup truck.25

We still think of cars as causing huge amounts of dangerous air pollution, so if you read that something is emitting as much air pollution as “thousands of cars” it sounds like a disaster, even if it actually fades into the background of what’s actually causing harmful air pollution.

Let’s say you’re given two options for what will happen near your home:

• Someone using a gas-powered two-stroke leaf blower nearby for 1 hour.

• Someone driving a truck in circles around your home over and over for 300 miles.

Which would be worse for your health?

Turns out it’s the leaf blower. A modern truck is the result of 50 years of advancements in pollution controls and tightening emissions rules. A two-stroke leaf blower has minimal emission controls, it’s basically a small, dirty engine with a fan attached.

The pollution that does the most damage when you’re right next to the source is primary PM2.5. One hour of the leaf blower puts about 15 grams of PM2.5 into the air around your home. The truck over 300 miles emits roughly 4 grams, even after including tailpipe soot, brake dust, and tire wear. The leaf blower wins by ~4×, even though it’s running for one-fifth as long.26

When you think about it this makes intuitive sense. Imagine what your city would smell like if everyone you saw driving were using a leaf blower instead.

This leads to a lot of confusing comparisons for air pollution. Take this quote from Newsweek on the study I mentioned above:

The researchers estimated how much air pollution might arise from the computing work that goes into training some AI large language models. They found that training a model the size of the Llama 3.1, launched this summer by Facebook parent company Meta, could produce the same amount of pollution as 10,000 cars driving round-trip between Los Angeles and New York.

How much air pollution is 10,000 car trips between LA and New York? Sounds like a lot! But cars are a terrible way of doing air pollution comparisons, because they now emit so little, but we still think of them as emitting a lot.

This study is only looking at the PM2.5 produced by cars, not the ozone. 10,000 cars driving round-trip between LA and NYC emit about 500 kilograms of PM2.5 in total. That’s roughly equal to about one hour of NYC’s commercial cooking emissions, two or three days at a mid-sized cement plant, a single large cruise ship idling in a harbor for a day, or the winter heating emissions from about 10 American homes that burn wood for heat.27 Not nothing! But these paint a very different picture than “10,000 cars driving thousands of miles.”

Climate change has many pathways to create more air pollution, so a source’s main effect on air pollution is often via its effects on climate

There are a few ways climate change puts us at risk of more local harmful air pollution:

• Increasing chances of wildfires, which are just so uniquely bad for PM2.5 exposure.

• Higher temperatures accelerate the types of reactions in the atmosphere that produce ground-level ozone from NOx.

• Drought and soil aridification increases windblown dust PM2.5.

• Longer growing seasons which worsen exposure to pollen, which can interact with PM2.5 to create worse respiratory harms.

• Consistent heat can trap pollutants near the surface for longer stretches.

Combining these all implies that, because American air pollution sources have radically cut their PM2.5 and NOx emissions compared to a few decades ago, but are still emitting lots of CO2, for many already-clean sources the CO2 channel plausibly does as much or more damage to US air as their direct PM2.5 and NOx, even before we count effects outside the US.

A back of the envelope calculation for how these compare for a Toyota Corolla and a grid-scale gas plant suggests that the Corolla creates 2-8x as much modeled health harm from PM2.5 in America via the effects on climate of its CO2 emissions compared to the PM2.5 it actually creates directly in the atmosphere28. For a modern gas plant it’s 3-10x29. This is mainly a story of us getting very good at removing PM2.5 from pollution sources, but not good at reducing CO2 emissions.

Most climate-driven wildfire and ozone mortality currently lands in South America, Australia, India, and Eurasia, not the US,30 so the harms are many times greater there. This implies that the main thing to worry about for net air pollution harms from an emitter at the national level is actually its CO2 emissions, not the direct harmful-to-breathe pollutants it creates in the atmosphere.

So I’d expect a really huge part of data center effects on air pollution to actually be via their effects on climate. Climate’s complicated enough that I’ll save the full deep dive on this for another post.

The main sources of outdoor air pollution in the US, and their decline

PM2.5

The most recent study of deaths from PM2.5 (both primary, emitted at the source, and secondary, created by other emitted pollutants like NOx) by sector was in 2019. I’ve made a chart comparing the deaths in 2019 to 2002 to show how much safer different sectors have become, all from this study.

PM2.5 deaths have dropped in America. There were twice as many in 2002, and coal-electricity deaths specifically have fallen 89%. This mainly came from a cleanup of the electricity and transportation sectors.

The drop in electricity sector pollution deaths isn’t attributable to using less electricity, because we were using 8% more in 2019 than in 2002:

The authors find that natural gas, oil, and biomass collectively killed about 1,000 people a year through PM2.5 in 2019. Coal killed about 6,000. Comparing these numbers to how much electricity each generated in 2019, coal kills about 6.3 people per TWh, and gas, oil, and biomass collectively kill about 0.6 people per TWh. This implies that if in 2019 we had replaced all coal with gas, oil, and biomass, something like 5,400 people’s lives would have been saved from air pollution, bringing the total deaths from PM2.5 from electricity generation down from about 7,000 to about 1,600 per year. Remember that coal also produces SO2, which creates PM2.5 about twice as dangerous as average, so the gains from displacing coal are bigger than the deaths-per-TWh ratio alone suggests. A grid made up entirely of natural gas over coal would involve way fewer pollution deaths.

Putting pollution controls on coal plants, and swapping out coal for natural gas, have together been one of the biggest positive wins for air pollution. The rise of renewables have also played a role here, as have better pollution controls on coal plants. This is the year-by-year estimate of deaths from electricity-sector PM2.5 compared to the grid makeup over the same time:

You might notice that the chance in mortality drops off fast around 2008, despite coal not budging much. This is because around this time the Clean Air Interstate Rule from 2005 had forced a big retrofit of coal plants with much more effective pollution controls.

How has the American grid changed since 2019? As of 2024, coal generated 33% less energy, so modeled PM2.5 deaths from electricity generation were likely lower than in 2019, and dramatically lower than in the early 2000s.31

Obviously your takeaway here absolutely should not be “gas is always good.” We eventually need to move from gas to renewables, and gas is especially bad if turbines are built very close to people’s homes. The data center buildout more broadly is seeing a huge uptick in natural gas demand that wouldn’t have otherwise happened, and this is a big loss for air quality and climate. None of this is lessened at all by good vibes gas has built up by replacing coal. But I do think your takeaway should be “coal being replaced with gas specifically is so so so preferable.” Thousands of additional people don’t die every year because of the rise of natural gas replacing coal. We can continue that trend by over time replacing gas with cleaner sources, but natural gas has been a vital stepping stone in reducing huge amounts of death and sickness along the way.

Ozone

Remember that ozone is responsible for a much smaller portion of US deaths than PM2.5, maybe 14,000 to PM2.5’s 70,000. We don’t have any direct way of tracking which industries are most responsible for ozone, because the way it forms depends on atmospheric conditions and the availability of volatile organic compounds. The best we can do is track NOx emissions (which produce ozone) by industries over time. These have significantly fallen over the past 20 years, and the decline came almost exclusively from cleaner vehicles with better catalytic converters, better controls on coal plant emissions, and the general decline of coal.

Total ozone declines have been significantly less dramatic than NOx declines:

But this is largely because there’s a lot more uncontrollable background ozone we can’t cut by reducing emissions.

Quantifying the public health cost of air pollution

When people say air pollution creates “$x billion in public health costs” they don’t usually mean that actual people are paying this amount in hospital bills or other costs in their daily lives. Instead, this cost comes from someone putting a dollar value on human life in general, and multiplying it by how much human life will be lost in expectation to air pollution.

This dollar amount is the “value of a statistical life” or VSL, which is modeled off what people are collectively willing to pay for small reductions in their mortality risk. The VSL EPA uses is $7.4 million in 2006 dollars, updated for inflation to something around $12 million today.32

One popular tool for estimating harm from pollution is EPA’s COBRA, short for CO-Benefits Risk Assessment. It estimates how much PM2.5 and ozone a pollution source creates, applies that added risk to each county’s death rates by age, counts the resulting total expected number of deaths, and multiplies them by the value of a statistical life.

Here’s an example to get it across, though it’s simpler than the actual math COBRA uses: if you released an extremely toxic form of air pollution around 5 people, one of them is old and has a 50% chance of dying from it, and the others are young and each have a 10% chance of dying, this model adds up the total odds of death among the 5 people, (0.5 x 1) + (0.1 x 4) = 0.9, and considers this to mean that in expectation 0.9 people will die. It then multiplies by the value of a life (around $12 million) to get a final cost of 0.9 x $12 million = $10.8 million. So COBRA would say releasing this extremely toxic form of air pollution around the 5 people had a health cost of $10.8 million. Notice that this isn’t the cost of taking care of the people, it’s saying a human life is worth $12 million, and multiplying that out.

COBRA isn’t measuring actual deaths here, it’s measuring statistical deaths. If 150,000 people each face an added mortality risk of 0.001 percent, the model counts that as 150,000 x 0.001% = 1.5 statistical deaths. It’s a measure of expected value, because we can’t actually trace who died of air pollution. Expected value calculations are very good and should be the basis of way more decision-making.

COBRA also attempts to put a dollar value on non-fatal health conditions that make life worse and are caused by air pollution, like non-fatal heart attacks, respiratory and cardiovascular-related hospital and ER admissions, and asthma. However, EPA’s recent analysis finds 85-95% of the monetized harm of polluted air comes from premature deaths, not other health conditions.33

COBRA does not consider how many life years were lost due to air pollution deaths. It values the death of an 89 year old and a 30 year old equally, at the same $12 million. I think this is weird, because we obviously don’t think this way about our own lives. Suppose you were going to be forced to play Russian roulette, where you had a 1/6 chance of dying. Would you rather that happen when you were 30, or 89? If you could pay money to make it happen at 89 instead of 30, how much would you pay? If you’d pay more than zero, it shows you put a dollar value on those extra years instead of just wanting to pay to avoid death in general.

The EPA knows this is a tension, and there are different reasons for why they use the model they do, but one of them is political. In 2003, EPA tried using an age-adjusted value in a Clear Skies analysis where it ranked the lives of people over 70 as having a slightly lower dollar value. Critics in government and the media called it a “senior death discount,” and EPA dropped it.34

Different organizations have attempted to price the full health cost of US air pollution using models similar to COBRA. One study put PM2.5 damages at $886 billion and 107,000 premature deaths in 2011. Another estimated $790 billion in 2014, roughly 5% of GDP, falling by more than 20% from 2008 to 2014. A third landed at about $1 trillion. And one COBRA study found that zeroing out emissions from the six main energy-related sectors alone would yield $608 billion in annual health benefits and avoid 53,200 premature deaths, without even including agriculture or wildfires. The models converge, roughly, on a full anthropogenic price tag somewhere around $1 trillion adjusting for inflation. This is about 1/30th of US GDP.

But we’d probably not choose to actually spend 1/30th of US GDP to reduce air pollution as it exists right now, and so it’s hard to compare these numbers to other costs, and spending this money elsewhere would save way more lives. We also don’t usually behave like pollution harms measured this way should actually be paid for by the people who create them. A few hours of a typical fire in a fireplace imposes something like $15–17 in damages on the surrounding community, because wood puts out so much PM2.5. We don’t actually expect people to be taxed $20 per use of their fireplace.35

The study that I quoted at the beginning that mentions ‘$20 billion dollars in health harms from data centers’ is called “The Unpaid Toll” which sort of implies data centers ought to actually pay for this toll or something similar. But this isn’t a standard we hold any other industry to. We can and do tax externalities, but often not at the level these health cost models imply we should.

Surface area and population

Models like COBRA measure harm in dollar amounts by multiplying the number of people exposed to pollution by the additional risk the pollution adds. This means that if air pollution hits a city, it’s measured as way way worse than in a rural area, because way more people are exposed to it. This obviously makes sense, but a weird result is that because air pollution spreads so far, maps of air pollution harm largely look like population density maps.

Here’s the map of data center air pollution harms from the study I mentioned in the beginning:

I made a population heat map by percentile using a similar color scheme:

So the first is mostly also just a population heat map. The reason data center pollution shows up across so much of the country is that NOx travels far before forming secondary PM2.5 and ozone downwind, and these are the main harms of data centers. Basically, if a fossil power plant within 100 miles of you is generating any extra electricity because a data center is on the grid, that plant’s air pollution is going to show up as an extremely tiny but real increase in your daily risk. Almost every American lives within 100 miles of one of the thousands of data centers and the plants serving it, so a map of who’s being harmed by data center air pollution ends up looking almost exactly like a normal population map.

These maps can be useful at times, but don’t give you a sense of the scale of this problem compared to most other sources of air pollution. If an incredibly tiny cost is spread across all of America, then in total it will look large, and a heat map of where it’s having the most impact mostly just tells us where people live.

All air pollution harms expressed in dollar amounts at the national level always look bad in isolation

The singular sin of bad environmental writing is giving people contextless large numbers, and any model that quantifies harm in dollars over huge aggregates is very good at generating them. It’s very common to see people talk about “billions of dollars in harms” without actually clarifying how this compares to other things we do. I’m not saying here that you should assume billions of dollars in harm is small, just that you should try to contextualize it with other issues we’re facing.

Suppose that an industry causes $1 billion in public health costs to America overall each year. Should we ban it? Well, any small increase in relative harm multiplied over lots of people and then multiplied by the full money amount we value being alive is going to produce a gigantic number regardless of what we look at. How does this compare to other stuff?

I’ll try to put some general number values on other harms using DALYs and HHS’s cost of a life year, about $550,000.36

Secondhand smoke costs around 990,000 DALYs in the US every year. Multiplying by the life year cost gets us to $544 billion. So this new industry is harming America 1/540th as much as secondhand smoke.

How would you expect secondhand smoke to compare to all harm from the pollution created by literally all power plants in the US to every single person it affects? Well, all US electricity-sector air pollution costs between 85,000 and 190,000 DALYs each year, so even at the maximum it costs $105 billion, only a 5th of secondhand smoke.37

So this new industry is about 1/100th as bad as the fossil fuel electricity sector, and 1/500th as bad as secondhand smoke. I’m from New England, where a lot of people still use wood as primary or supplemental heating, and because burning wood is just so bad, a back of the envelope calculation implies that it costs around $3–4 billion in health costs. So this industry is about 1/3 as harmful as all New England wood heating.38

How air pollution is governed in the US

American air pollution law is designed to be agnostic about the social value of what an emitter produces. The law just regulates specific pollutants and the things that create them. A ton of NOx is regulated the same whether it powers AI training or a hospital.

Because any amount of air pollution carries some harm, American air pollution law allows large numbers of people to be exposed in aggregate to industrial and individual emissions. The legal structure has two main layers:

• Something to control general ambient air quality.

• Something to control what individual emitters do regardless of the ambient air quality.

Basically all industries, commercial buildings, and recreational buildings in America impose some harm on the surrounding community via air pollution, and sometimes on more national American air in general. This on its own isn’t a reason to shut them down. It’s perfectly legal to impose some level of air pollution harm on other people, and if it weren’t society would grind to a halt, because everything we do creates some. The main question is whether an industry is (along with the others in the region) pushing air quality above some specific legal or ethical threshold of harm.

The main federal air pollution law is the Clean Air Act, which tells the EPA to set National Ambient Air Quality Standards (NAAQS), for six “criteria” pollutants: carbon monoxide, lead, nitrogen dioxide, ozone, particulate matter, and sulfur dioxide. These measure the air quality in general.

In 2024, EPA tightened the PM2.5 standard to 9.0 µg/m³, but plenty of harm still happens below this line. EPA standards don’t represent some magic point where pollution stops harming us. You might think their decision to set this level has something to do with balancing health against the economic costs of restricting air pollution, but it is actually illegal for them to consider economic costs when setting the clean air threshold, according to a unanimous Supreme Court case in 2001. Cost-benefit questions can come up at lower levels when state governments decide how to implement the standards, but the standards themselves are decided purely by considering human health.

States meet the clean air standards with “State Implementation Plans” or SIPs. Areas that meet a standard are in “attainment.” Areas that don’t are in “nonattainment,” and have to use stricter rules for the pollutant they are failing to reduce down to required general levels.

One problem with this general setup is that states may keep their own air clean but pollute air far away, because air pollution can travel far. The Clean Air Act addresses this through what EPA calls the “Good Neighbor” provision, which requires every state’s SIP to prohibit emissions that significantly contribute to another state’s nonattainment, or interfere with another state’s ability to maintain the standards. If a state fails to submit an adequate Good Neighbor SIP, or EPA disapproves it, EPA can impose a federal plan instead.

The current state of Good Neighbor enforcement is up in the air. The Supreme Court stayed EPA’s 2023 federal Good Neighbor Plan in June 2024, and EPA has since been reconsidering. It isn’t currently operating as a tightly enforced rule across the country.39

For data centers, the most important thing to understand is emissions permitting. Before building a new source of air pollution or making a major change, data centers may need a New Source Review (NSR) permit.

If the area is in attainment (meeting the requirements of the Clean Air Act), the permit they’d seek as a major source of pollution is called Prevention of Significant Deterioration, or PSD. PSD is basically the “don’t let clean places get too much dirtier” program. The data center has to use required pollution controls and show that it will not push air over the EPA limits or consume more than the allowed PSD increment.

If the area is in nonattainment for a pollutant, they’d get a permit called a Nonattainment NSR, which is stricter. It requires the lowest achievable emission rate, emission offsets, and public involvement.

Data centers aren’t just regulated by how much they actually emit, they’re also judged by their “potential to emit” or PTE. If a data center has hundreds of backup diesel generators that it rarely uses, those are also considered and regulated. The EPA now has a data-center-specific Clean Air Act page with PTE guidance for emergency backup generators.

There are other tools available to regulate air quality besides air permits. Regulators can determine what gets built where through zoning. State review and public comment can affect the design of a new data center.

Inequity in exposure to air pollution, and failures of governance

Racial inequity in exposure to air pollution harms in America is pretty stark. White Americans on average are exposed to about 17% less PM2.5 than they cause through consumption, while Black Americans are exposed to about 56% more and Hispanic Americans about 63% more than they cause. The disparity between groups corresponds to roughly a 1% increase in average all-cause mortality risk from PM2.5 alone, and this isn’t even considering disparities in extremes of exposure.40 Total exposure for every group has fallen significantly since 2000, but the relative gap has been stubborn.41

Big inequalities also exist in NO₂ exposure, and this is going to matter a lot for the story of data centers and local air pollution harms. The reason is that, as mentioned above, NO₂ spikes happen very close to where they’re emitted, and dissipate farther away. NO₂ can contribute to regional and national PM2.5 and ozone formation, but this is a very tiny increase over a very broad population. Here the harms are felt more equally, and are governed well for the most part. In comparison, breathing large amounts of NO₂ from a nearby source directly is harmful separate from how much PM2.5 and ozone it might eventually make, so there are much more disparate impacts from very nearby NO₂ sources determined in large part by background patterns in city design, and there are lots of holes in US governance of very local air quality compared to regional and national-level air.

Total exposure to NO₂ has been significantly reduced in absolute terms, but the relative differences between groups remains large. The top tenth of census tracts for NO₂ attributable mortality in America were 29% Black, even though Black Americans make up only 12% of the total population. In Chicago and Detroit, roughly 60% of the population living in the highest NO₂ mortality tracts was Black, even though Black residents were less than about 21% of the populations. Predominantly Black tracts had NO₂ attributable mortality rates about 47% above the national average, because they had both higher NO₂ exposure and higher underlying mortality risk.

How much is very local pollution to blame for inequity in pollution exposure, compared to general patterns in where different groups live? California for example has a much higher Hispanic population than average, and also way more polluted air on average, so maybe this explains the gulf in Hispanic exposure?

The Spatial Decomposition of Air Pollution Concentrations Highlights Historical Causes for Current Exposure Disparities in the United States breaks this problem down. It splits the disparities into 3 categories:

• Between-state: How much of the disparity is determined by which states people live in.

• Within-urban: How much is determined by which parts of a city you live in (closer or farther away from major emitters, things like industries or roads and highways).

• Other (urban-rural and between-urban): This is a catch-all for two differences: whether a population is more likely to live in cities or in rural areas in the same state, and whether they’re more likely to live in the more or less polluted cities within the same state. LA is much more polluted than Sacramento etc.

The study found that disparities exist for different reasons in each group analyzed:

So within-urban disparities are big parts of the inequity in air pollution exposure Black and Hispanic Americans face, and are a real but lesser part of the story for Asian Americans, though their total NO₂ exposure is the largest of any group.

Within-urban disparities often reflect the legacies of redlining. Redlining maps from the 1930s remain strong predictors of pollution exposure today. The same dynamics that make inequity in within-urban air pollution exposure persistent can matter for data centers too, especially in cases where developers propose onsite gas generation near residential neighborhoods.

Why are old redlining maps such strong predictors of current exposure to air pollution? There are lots of compounding reasons:

• Highways were deliberately built through them.

• Redlining made it difficult for residents to build up equity, and without accumulated wealth, neighborhoods had less political power to resist new polluting industries, fund lawsuits, or extract concessions from developers.

• Many cities used “expulsive zoning” to permit heavy industrial uses in Black residential neighborhoods.

• It was easier to build new polluting industry in areas already polluted, because permitting reviews evaluate each new polluter’s marginal contribution to local pollution rather than the cumulative burden on the neighborhood.

• Redlined areas have less tree canopy on average, and trees absorb some pollutants directly and cool the air down, which somewhat suppresses ozone formation.

While the Fair Housing Act and later reforms formally outlawed redlining and related housing discrimination, the highways and industries were still where they were built before it. One recent study found that historically redlined districts still have on average 56% higher NO₂ levels than the parts of the same cities that were designated the most desirable for federally backed mortgage.

A huge issue of any new large industries that have onsite pollution sources, like large data centers, is that they can reinforce the dynamic that keeps formerly redlined neighborhoods way more polluted than average. This is one of the clearest real potential dangers of the data center buildout. Data centers are on the market for things that could correlate with historically disinvested areas, like cheap land and an easy permitting environment.

A key question is how much we can rely on local government policy to keep communities safe from data centers pushing air pollution in very local communities over the line of harmful NO₂, and broader regions over the line of ozone and PM2.5. In places where government can’t be relied on to keep total air pollution below accepted thresholds, communities should say no to data centers with significant onsite primary power, and society more broadly should name and shame specific companies imposing excessive pollution on already overburdened communities.

Gaps in exposure persist despite the Clean Air Act’s general success, because the act mainly targets regional and national air quality, not so much very local sources of very local pollution. Exposure to local pollution often has more to do with what’s allowed to be built very close to you. So even as national and regional air quality improves, very local sources of primary PM2.5 and NO₂ can still harm the same neighborhoods they did 50 years ago.

The main hole to worry about in government air pollution control is very local air quality around big emitters, because that isn’t regulated as well. To fully consider the question of how and when we can and cannot rely on governments to make good decisions about air quality and data centers, I need to give a much more detailed overview of how data centers specifically contribute to air pollution, which will need to wait for Part 4. For now, your takeaway should be that data centers, as large industries that need to generate huge amounts of power, threaten to continue and reinforce these unjust patterns in who gets exposed to air pollution if they are built with huge amounts of onsite gas power close to residential areas.

What does the future look like?

Before the year 2000, there was a lot of low hanging fruit for reducing air pollution, because everything we did emitted crazy amounts. To put this in perspective, if you naively assume that data centers cause the average levels of pollution from the grids they draw from per unit of energy, the grid in the 2000s was so dirty that the first big data center buildout at its peak was adding over twice as much PM2.5 to the air (both primary and especially secondary) as data centers will by 2030. Notice that even as coal declines, it’s still a huge part of total PM2.5 from energy.

This is because the PM2.5 per unit energy of the American grid has been dropping off rapidly, partly because there are way more pollution controls on sulfur from coal plants (the CAIR Act in 2005 forced a retrofit of old coal plants with new pollution controls that you can see start to really take effect in 2007), and because coal plants were replaced with natural gas over time. Here’s a rough guess at the PM2.5 cost of a unit of energy:

The story of NOx and ozone is more mixed, because gas still emits a lot, so data centers in 2030 could be emitting more than ever.

Though NOx intensity has also declined a lot.

The grid being cleaner means that the remaining harms of air pollution will be trickier to reduce here.

The most promising future cuts for air pollution are usually going to overlap with what’s emitting the most, though not always. We can go line by line on the sectors emitting PM2.5 and NOx to think about what the future will look like. Note that these “anthropogenic” sources don’t include forest fires, which will be discussed more in the climate change section below.

PM2.5

Agriculture

Agriculture causes about 12% of total US anthropogenic PM2.5 mortality, and the large majority of that is from livestock. Maybe it’s time for you to join me in veganism ❤️🐖🐔🐟❤️.

There are a lot of relatively cheap ways to reduce this that could plausibly save thousands of lives a year together, maybe somewhere around 6,000, depending on the what’s actually done, which is about as much as all remaining deaths from electricity-sector PM2.5 emissions.42

Simple air pollution regulations could be imposed on farmers to make sure that they cut emissions using these tools, but as discussed before, farmers are politically untouchable in America and while these regulations have been considered by different agencies, politicians haven’t actually implemented them. I’m pretty pessimistic about this route to lowering PM2.5, even though it’d be pretty cheap and easy. Question farmers.

Industrial

Industrial sectors that emit a lot of PM2.5 include cement, iron and steel, chemicals, and petroleum refining. Iron and steel alone cause between 900 and 2,000 deaths per year, concentrated in places like Gary, East Chicago, the Houston Ship Channel, and the lower Mississippi industrial corridor.43

Transportation

Transportation is a huge air pollution success story, thanks to better catalytic converters and the slow death of diesel. Most of the remaining PM2.5 from transportation come from things that use diesel, like long-haul trucks, trains, and ships.

Cars are surprisingly not promising for reducing PM2.5 much more, because their tires and brakes now produce about as much as their exhaust.

Residential

98% of PM2.5 emissions from residential fuel combustion sources are from wood-burning, but wood-burning only provides 2.2% of residential energy in the US. Come on!!! This is responsible for something like 10-17% of all US PM2.5 mortality. What are we doing?!?!?!44

You might think this is okay, because people burning wood in a fireplace are the ones harmed by breathing it, and so this is an optional risk they’re taking on. But these numbers are only for outdoor air pollution. This is getting added to the air around the home burning the wood.

All pollution from US electricity generation in 2026 kills fewer people than this. Seems like we should stop using wood as heating in homes. The occasional campfire is fun, but relying on wood more consistently is just so bad for air quality.

Electricity

The remaining large sources of PM2.5 are still largely coal, but that’s expected to keep falling as gas, wind, and solar are now cheaper and more competitive. It seems like a lot of the low hanging fruit in making coal more clean has been picked.

Gas plants are much cleaner than coal for PM2.5 and SO2, but they do emit significant NOx which still forms secondary PM2.5, though much less than SO2. New gas plants with SCR controls are roughly one to two orders of magnitude less harmful per MWh than the coal units they’re replacing. Renewables emit basically nothing.

The main question for electricity is how we can retire coal plants faster, then how we can replace gas with clean energy. Data centers are delaying both, and causing a huge uptick in demand for gas. We’ll look into this more in Part 3.

NOx

Highway and off-highway vehicles

On-road NOx is a huge success story in reducing air pollution. Catalytic converters are much better, and EVs don’t emit NOx at all. The remaining transportation NOx is mostly from diesel, and mainly from long-haul trucks, buses, locomotives, ships, and off-road equipment like construction machinery and tractors.

Stationary fuel combustion

Gas-fired power plants are now the largest stationary NOx source in America, and this is being driven way up by the data center buildout.

New gas plants with “selective catalytic reduction” (SCR) cut 80-90% of NOx and emit small amounts per MWh. Old gas plants emit a lot more, sometimes an order of magnitude.

Industrial processes

Cement and steel again show up here. The very high temperatures form thermal NOx. Cement kilns can install some pollution controls, but retrofits of these are expensive. Refining and petrochemicals contribute meaningful NOx from heaters. A lot of this is technically controllable, and Texas, Louisiana, and California have all gotten major reductions out of refineries over the past 20 years, but the lower-hanging fruit has been picked and the remaining cuts are more expensive per ton.

Other

Synthetic nitrogen fertilizer used in agriculture gets partially converted to NOx by soil microbes, and could be significantly cut with available tech.45 This isn’t really regulated at all… wonder why…

Climate change

Climate change is going to make dealing with air pollution in America more difficult. Tiny cuts in PM2.5 can be easily swamped by massive new forest fires, among many many other problems. We’re at a low enough level of PM2.5 and ozone emissions that I’d personally bet the most effectual way to preserve national-level air quality will be focusing on cutting CO2 over cutting additional PM2.5 or NOx. We can do both with green energy, but projections for cutting emissions to zero anytime soon are bleak.

Takeaways

• American air has, along most axes, gotten significantly cleaner in the last 20 years. While American air is way cleaner than it was decades ago and much cleaner than the global average, air pollution at any level is harmful, and so multiplied by our huge population still results in roughly 80-90 thousand deaths per year, almost all of them older and losing about 3-4 years each. Air pollution also causes short-term harm, among many other things it’s a big contributor to childhood asthma cases and ER visits.

• If you were to chart where this harm is coming from, most of it comes from pretty normal American air. Most people in America live in relatively clean air, but the harms are not zero here and still accumulate over huge numbers of people. The big exception is California, which has a ton of compounding ways that air pollution is way worse where lots of people live.

• Different types of air pollution have wildly different levels of harm. PM2.5 is the worst, followed by ozone as a distant second. NOx and SO2 are mainly harmful because they create PM2.5 and ozone when they react with specific particles in the atmosphere. NO₂ has important short-term harms, among other things it’s a major contributor to childhood asthma ER visits, and maybe long-term harms too.

• Comparisons between different things that pollute are often wildly counter-intuitive. Cars especially emit very little NOx or PM2.5 now and are a bad reference point for major industries.

• Maps of national pollution harm are often just population density maps.

• The decline of coal and its replacement with gas has been a huge part of the general decline in American deaths from air pollution. Data centers threaten to delay or reverse the decline of coal, and are encouraging a huge separate buildout of new gas capacity that isn’t offsetting old coal plants. Data center companies are also the main private funders of new clean energy as well, but on net they will make American air more polluted in the short-term.

• The effects of climate change on air pollution are large enough that, for many sources that have been cleaned to not emit much local air pollution, the CO2 they emit may “release” as much or more PM2.5 and ozone in expectation as the PM2.5 and ozone they emit directly.

• Because everything that we do creates air pollution either directly or through electricity generation, we cannot just ban all air pollution, and instead need to set thresholds of general air quality to aim for and modify the behavior of industries around those. This is how American law approaches it. American air quality is governed very well at the national and regional level, but the local level governance has holes and is less reliable at preventing exposure to things like direct PM2.5 or NO₂.

• Our intuitions about the harm of air pollution can be warped in a lot of ways:

  • We can underrate the harm of, by global standards, very clean American air. It’s still killing about 90 thousand people a year.

  • We can miss inequities by only looking at average levels of pollution.

  • We can be too optimistic about the future of air pollution reductions based on how the past has gone.

  • We can overrate the harms of tiny levels of pollution increase across very large populations or risks that we wouldn’t personally pay to remove.

  • We can overrate the harm of any new industry’s pollution if we don’t contextualize it with other industries or the general state of American air quality.

• The fact that air pollution is harmful at any level makes two opposite sides mad at you: people will get mad at you when you say it’s okay for an industry to emit some air pollution, because that comes with real human harms, and others will get mad at you when you say we need to limit air pollution, because all air pollution’s harmful anyway and why are you getting in the way of industry? Both of these are wild oversimplifications that would lead to terrible policy.

• When thinking about a new industry, the question should always be “Will this, along with all activity in the area, collectively reduce the general local, regional, and/or national air quality down to a level that we’ve separately agreed is too low?” If it does, we should rigorously regulate or just oppose it, and if not, we should allow it to emit reasonable levels of pollution like we do other industries. The key is drawing that arbitrary line at what level of air quality we think is too bad for all activity in an area to collectively degrade the air below. That line will always be wildly contentious, but we need to draw it and stick to it. Our national legal arbitrary line is the EPA National Ambient Air Quality Standards (NAAQS).

• The dollar amount measuring air pollution health harms comes from applying a dollar value to human life and multiplying it by the odds that pollution kills people times the number of people exposed. This is important to understand but doesn’t translate well to other normal costs we consider in society.

• The measured cost of air pollution is a useful tool but doesn’t tell us much in isolation. It’s important to know how the costs compare to other industries and whether a cost is being unjustly concentrated on already overburdened communities or spread equally across a wide area, very marginally increasing the average person’s risk.

• People with less economic power are in general less able to resist industries pushing their communities over the line we set for clean air, so large disparities still exist in exposure to very local air pollution, and those are the places we need to be hawkish on it and cannot rely on good local governance alone.

• We should expect fully electrified industries at this point to only produce huge amounts of new local air pollution at the extremes, because the electric sector is so much cleaner than it was 20 years ago (thanks almost exclusively to the decline of coal and its replacement with gas, plus pollution controls), but the extremes are in fact happening with the largest data centers.

• The promising cuts to make left in air pollution are among other things the continued closure of coal plants, transitioning from gas to renewables, regulating agriculture, and maybe not burning wood anymore. These decreases are likely to be offset a lot by climate change increasing air pollution, so the main way emissions create additional PM2.5 and ozone going forward might be via their CO2.

Surely this would create some environmental problems?

Surprisingly, data centers expelling even this massive amount of heat will probably not have a meaningful impact on the local environment, and definitely not on global air temperatures. To understand why, it’s helpful to think about how different the effects of heat are when you concentrate it compared to when you spread it out over a day.

Consider the effects of concentrating sunlight in a single place with a magnifying glass.

This can do a ton of damage or start a fire. But all this heat energy was already there before, it was just impacting a surface area the size of a magnifying glass, instead of a tiny point. If you concentrate a normal amount of heat energy in a very tiny place, it becomes dangerous. And therefore, if you expand a dangerous amount of heat from a hyper concentrated point to a larger area, it can fade into the background of the normal levels of heat we’re exposed to.

A data center spreads its heat exhaust very evenly across the day. Data centers run at basically constant loads 24/7. There aren’t really peaks or low points. In comparison, the Trinity Test released heat in less than a second and then it all dissipated. The Trinity Test concentrated the energy of a gigawatt data center from a full day to a second, it was at least 86,000 times as concentrated as the data center. Here’s 86,000 dots:

If each of these dots is a second of a gigawatt data center’s heat exhaust throughout the day, all of them together at once is the heat released in the one second the Trinity Test actually happened.

I hope this makes it a little more clear why comparing gigawatt data centers to nuclear bombs is a little goofy.

Because data center exhaust is so flat and consistent, it is basically the very least concentrated way of releasing a given amount of heat energy over time, and a nuclear bomb is the very most concentrated way of releasing the same amount of heat.

In comparison, a magnifying glass concentrates heat about 10,000 times. The physical area the light hits is around 1/10,000th of the original area it would’ve been. The relationship between sunlight concentrated by a magnifying glass and what it would normally do is similar to the relationship between heat concentrated by the Trinity Test and the long slow release of a data center.

I’ve recently seen news stories reporting heat exhaust in units of nuclear bombs, and I expect this to become more common as more gigawatt data centers are proposed.

This unit gives readers basically as much information as reporting how much sunlight will hit you in a day in units of hyper magnified beams of burning rays. Not only is this the most alarming comparison possible, and is comparing the most hyper-concentrated release of heat possible with the absolute least concentrated release of the same heat, it’s also bad because nuclear bomb heat energy can be tens of thousands of times as large as other nuclear bombs, and the specific nuclear bomb people are using for this comparison is among the smallest ever made.

In the stories I’ve seen, the authors are assuming a 16 GW total thermal load will be added by this data center, 9 GW for the data center itself and an additional 7 GW expelled as waste heat by the gas turbines it uses to generate its electricity. This is a reasonable estimate for the very largest this data center could ever be. 16 GW x 24 hours = 384 GWh, divided by 23 atom bombs gives 16.7 GWh per bomb. A GWh is about 0.86 kilotons of TNT, so this is about 14.3 kilotons of TNT. This is about the same as the Little Boy bomb dropped on Hiroshima. While horrible, this is also one of the smallest nuclear weapons ever developed. Using one of my all time favorite data viz tools, NukeMap, here’s what it would look like dropped on where I live:

Most nuclear bombs are way, way, way larger than this. Here’s what the average bomb in the US nuclear arsenal looks like in comparison:

And here’s the largest nuclear bomb ever tested:

Here’s the thermal radiation radius of each compared to each other:

The largest bomb ever tested was ~50,000 kilotons, 150 times as much heat energy released in a second as this largest ever data center will emit in a day. I could use this number and say that the Utah data center will emit the heat of just 1/150th of a single nuclear bomb, spread over the course of a whole day, and that would also be equally correct, but I think also deceptive. We need a better way of understanding how this heat energy compares to other normal things.

As a former physics teacher, it’s been a mild form of torture hearing people start to say in an alarmed way that all energy data centers use gets turned into heat, as if that’s crazy or wasteful. This is true for everything we do.

All energy we use is ultimately dissipated as heat in the atmosphere. The gas you fill your car with is full of stored chemical energy. When you drive, about 80% of that chemical energy is converted to heat energy in your engine, and either sent out through your exhaust pipe or radiated away from your engine block and radiator. The other 20% is converted to kinetic energy in your car to make it move (and a tiny amount of that is itself converted to electricity to later be light and sound energy from your car’s display and speakers, or to start your car). When your car impacts air particles, the friction between your car and the air converts some of the kinetic energy from the gas to heat on your car and the air. Whenever your car slows as you drive without applying the gas, what’s actually happening is that the kinetic energy is being taken from the car and turned into thermal energy on the car’s surface and the surrounding air. Applying the brakes converts a large amount of kinetic energy quickly into thermal energy on the ground and in your tires. By the time you stop your car, all the energy that was originally stored in the gas has been dissipated as heat, either in the initial explosion of the gas, the friction between your car and the air, or the friction between your wheels and the ground that you use to stop.

Here’s a rough Sankey diagram of how energy moves through your car to eventually become heat:

This means that driving your car adds exactly as much heat energy to the environment you drive it in as slowly burning the gas without a car over the time it takes you to use the full amount.

Whenever you see energy you’re using disappear, it’s almost always becoming heat. The lamps in your home constantly beam out light energy when on, but the room doesn’t get brighter and brighter over time. The light energy doesn’t build up, which means it must be going somewhere else. What's happening is that the light is being absorbed by the physical surfaces in your home. When a photon is absorbed, its energy is transferred into the random jiggling of molecules in what it hits, and that random jiggling is heat energy! So all energy that leaves your lights also becomes heat energy in the space around it. The conversion is from electric energy to light energy to heat energy. Visible light just carries so little energy that we don’t notice.

All energy we use, whether it’s the gas in our cars or the things we do with the electricity we buy, ultimately gets converted to heat energy in our environment. I live in Washington DC, which uses about 28 gigawatt-hours of electricity every day and roughly 37 gigawatt-hours equivalent of primary energy every day in our cars and elsewhere, for a total of approximately 65 gigawatt-hours of energy every day. All of that is being turned into heat in the area, so human energy usage in DC is equivalent to “dropping about 3.7 nuclear bombs on the city every day.”

Does that tell us much? The sun delivers about 750 GWh of heat to DC’s surfaces daily, 43 nukes’ worth and 12x all human activity. About 85% of this leaves the ground and heats the air. About 23% of incoming sunlight is absorbed by air before hitting the ground, adding another 370 GWh dumped into the atmosphere above the city. In total, the sun will deliver about 1000 GWh/day, or 57 nukes.

The water cycle moves enormous amounts of heat too. When water vapor condenses into cloud droplets or rain, it releases the latent heat that was absorbed when the water evaporated. Over DC’s land area, condensing just 1 millimeter of water releases about 108 GWh of heat, or about 6 nuclear bombs. One inch of rain over DC corresponds to about 2,740 GWh of latent heat release, or about 157 nuclear bombs. If I told you that an oncoming rain storm was going to unleash the heat of 157 nuclear bombs, how worried would you be? Would I be representing the situation well? I’d be technically correct, at least.

Wind moves even larger amounts of heat. A single heatwave can bring more heat energy into DC air in a day than all human activity adds in a year.

So now if I tell you that solar energy delivers 56 nuclear bombs to DC every day, human energy usage adds about 3.7 nuclear bombs, and rain can add 150, what does this tell you about life in DC?

I’ve given you no way of understanding how bad this actually is for the DC environment. This is because reporting raw huge amounts of energy like this, with meaningless alarming comparisons like nuclear bombs, gives you zero real footing in the issue at all. Whenever you read something about data centers, always ask “Is the person writing this actually trying to give me real footing in the specific problems this will cause, or just throwing out alarming huge numbers without giving me the context to understand what they mean?” If it’s the second one, maybe just stop reading.

Here’s a comparison that makes more sense to me: this data center is like building a bustling city in the Utah desert. The property it’s on will be about the size of DC, eventually with lots of gas power plants and data centers. The heat emitted by all of these together will be about 6 times the heat emitted by the cars, businesses, and households in DC. It’s like they’re building a city 6 times as dense as DC in the middle of nowhere.

How bad will this be for the environment? There’s a lot to say about air pollution and climate effects, but what about the effects of the heat exhaust? Well, what are the actual effects of the heat dissipated by the energy used by DC businesses and cars and homes? Most of the ways cities increase the temperature compared to rural areas is the urban heat island effect, which has two main causes: surface changes (asphalt, dark roofs, and lost vegetation absorbing sunlight that would otherwise reflect or evaporate) and direct waste heat from human energy use.

In most US cities, the surface effect dominates. A recent estimate puts the local air temperature increase at roughly 0.025 °C per W/m² of human energy use. This varies with city size, wind, and how high the heat can rise.

Using DC’s energy consumption and size, this implies DC’s human energy usage adds about 0.4 °C to the city’s temperature. Naively carrying this forward to the largest data center ever proposed, which could eventually use 9 GW of energy (2% of the energy draw of the entire country!) and dissipate 16 GW of heat, this implies it might raise the very local temperature on its property by 2.4 °C, about 4.3 degrees Fahrenheit hotter. This warming is extremely local to the specific space around the data center and would drop to zero close by as wind dissipates the energy. This is not nothing! It’s a pretty significant change to the very local land. But when someone tells you “This thing is like dropping 23 nuclear bombs per day” do you immediately say “Oh yeah, like an extra 4 degrees!”? If not, the unit is bad.

I’ve already covered water use, water poisoning, and infrasounds. There’s one fake issue left to address: land use.

Like biographies use their subject as a prism for the world around them, I use data centers as a prism for much larger but invisible environmental problems, hidden by our tendencies toward populism and localism that data centers offend. This post focuses on the ridiculous ways we use land in America, which (like most of our water issues) is downstream of farmers being shielded from popular criticism by political alliances and folk intuitions about the honest toil of growing food. By the end, I hope you’ll be much more skeptical of headlines like this:

and of the idea that data centers purchasing farmland is a unique evil.

I’m not claiming data centers should be built anywhere and everywhere. There are lots of places where they’ve been built too close to people’s homes. This post is about whether data centers waste the land they’re on relative to what they compete with, and my claim is that they mostly don’t compete with things we need more of (housing) and mostly compete with things we need less of (farmland).

Contents

• Data centers are the absolute most optimized use of land physically possible

• America isn’t lacking in land

• Data centers mostly don’t compete with housing

• The fact that data centers don’t create many jobs makes their land use way smaller

• What about the land use of new power and transmission capacity data centers will require?

• Data centers free up lots of land elsewhere

• It is not a crime against locals to raise their land value

• We have way too much farmland

  • Question farmers

  • About 1.5% of the contiguous United States is used for a farmer handout: ethanol

  • A lot of people are really bad at thinking about farms in a marginal rather than an absolute way

  • What about the effects of climate change on farmable land?

  • More evidence we have too much farmland

  • More examples of the goofy ways our politics and land use is warped by farmers

  • Conclusion

• Some bad recent articles on data center land use

  • “Farmer Hailed as Hero for Rejecting Huge Payment to Turn His Land Into a Giant Data Center”

  • A data center using as much land as 51 Walmarts

  • “This Land is Their Land”

Data centers are the most optimized use of land physically possible

No other large buildings are crammed this full of objects optimized down to the atom for maximum performance. The most advanced supply chains in the world work together to push the servers inside data centers as close to the physical limits of manufacturing as anything we make.

Northern Virginia has 13% of all global data center capacity, and Loudoun County has by far the largest concentration. But data centers only take up 3% of Loudoun’s land, and they generate 38% of all county general fund revenue. Using county tax info, I roughly estimated combined real- and personal-property tax revenue per unit land area (including land around the buildings data center companies own) for data centers vs. other major Loudoun revenue sources. Comparisons like this get a little ridiculous.

America isn’t lacking in land

By 2028, all data centers in America will occupy roughly 1,400 square miles. This is about 0.3% as much as America’s prime farmland. The vast majority of this will be land around the data center, the buildings themselves will collectively take up about 25 square miles, 0.005% of America’s prime farmland.1

Here’s a chart of how America currently uses land:

1,400 square miles is about 3.5 squares here, 3.5x as much as the land we currently dedicate to Christmas trees:

With the buildings themselves taking up an incredibly small part of that:

By 2030, data center buildings themselves will take up 1/15th of the land area we use to grow Christmas trees.

Data centers are uneconomical to build way far out in completely undeveloped land, since they need access to reliable large power grids. But they also don’t need to be built in or near dense residential areas. The vast majority of the contiguous US is within reach of high power lines, and the places data centers are built are mostly farmland or low-value scrub.

Data centers mostly don’t compete with housing

Some people worry that data centers take up land that could be used for housing instead, but data center companies want fundamentally different land than where housing shortages exist. Housing demand is overwhelmingly concentrated in and near existing urban cores, like California, the Boston-to-DC corridor where I live, Seattle, Denver, Austin, or Nashville. These are expensive because demand concentrates in specific neighborhoods already built out. The blockers on housing in those places are zoning, parking minimums, height limits, environmental review, neighborhood opposition, and the general legal apparatus that makes it illegal to build apartment buildings on most residential land in America. It’s rarely access to raw land.

Data centers want flat, cheap, large amounts of land with access to high-voltage transmission, fiber, and water for cooling, in places that permit a windowless industrial building. Anywhere with significant housing demand has land too expensive for large data centers. Data centers are mostly only built in exurban and rural land on the fringes of metro areas.

The fact that data centers don’t create many jobs makes their land use way smaller

If a new business that requires lots of workers moves into a town, lots of new residents have to move there too. This creates demand for massive land use changes. There will be more homes, commercial buildings, public services, and roads. I don’t think this is bad, but for people worried about land use changes in a specific area, a data center has a much lighter impact per unit of revenue because it needs so few employees.

A town allowing a large data center to be built is like buying a very large computer that hums in the background generating tax revenue with minimal maintenance. It doesn’t require reconfiguring the town the way a new large industry or commercial center would. Data centers are ideal for small towns that need lots of additional tax revenue but don’t want to have to deal with the infrastructure costs of having a lot of new people move there at once.

What about the land use of new power and transmission capacity data centers will require?

By 2028, US data centers are projected to use around 450 TWh of energy per year (the middle of Lawrence Berkeley National Laboratory’s 325–580 TWh range, with most other major forecasts clustering nearby). Data centers run with high load factors (meaning load is essentially flat and high 24/7) so 450 TWh annually works out to maybe 55 GW once you include grid losses. If served by firm power sources you’d need somewhere around 65–80 GW, or more if it’s coming from intermittent renewables.

The land footprint depends heavily on what gets built. Just getting to 450 TWh with solar would require ~1,500–2,000 square miles of panels2, and delivering that as reliable 24/7 power would require 1.5-3x as much, plus storage and batteries. So if data centers were all served by solar, they’d require maybe 2,500-5,000 additional square miles.

Wind requires way less land (unless you’re worried about infrasound…) but it seems unlikely to be a big part of data center power.

Natural gas plants are very land-efficient. One large new plant generates about 1 MW per 0.05 acres, and this drops to ~0.01 acres per MW if you just look at the buildings themselves. 80 GW of new natural gas would need between 12-40 square miles of plant sites depending on how you measure. I’d almost always prefer using more land to emitting more, so I’m not excited about gas and discouraged by the scale of new gas demand data centers are adding.

Nuclear is similarly compact, at about an acre per MW for the plant. I’d be much more excited about nuclear power serving data centers, but that seems unlikely in the short term.

A reasonable range for the additional power-generation land footprint is 50 to 1,000 square miles. This is significant but doesn’t dramatically change the picture. Adding it to the ~1,400 square miles of data center land for 2028 brings us to a maximum of 2,400 square miles. Definitely not nothing! But this means that all data centers, the land around them, and the power plants serving them would together come to at most 5% of the land used for ethanol, and roughly 0.08% of the contiguous US.

Transmission is harder to estimate, and it has the one land-use problem I think is real: utilities using eminent domain to route new lines through people’s property. I expect serious conflicts here. But transmission is also where data centers are most plausibly positive-sum. The green energy transition already requires roughly doubling US high-voltage transmission capacity by 2050. Data center demand mostly accelerates a buildout that had to happen anyway, and a line built to serve a data center cluster also moves clean power to everyone else on it. The eminent domain cost is real and serious, but it probably has to happen anyway. Farmers in other countries are far more likely to be seriously harmed by climate change, the green transition is necessary to fight it, and we need to pull a few eminent domain levers to enable it. The net positive effects of data centers forcing way more high-voltage transmission will outweigh the negatives, even though here the negatives are real.

Data centers free up lots of land elsewhere

The US has about 12 billion square feet (430 square miles) of retail real estate, and double that if you include the parking lots. Mall vacancies hit record highs in the 2010s, because people buy online instead. Amazon now operates about 11 square miles total of warehouse space, handling a large portion of all American retail sales. Hundreds of square miles of physical retail floor space and parking have been replaced by a few dozen square miles of warehouses, and a small fraction of that in data centers running the website, payments, and logistics.

There were about 100,000 bank branches at the 2009 peak. Today there are around 70,000 and falling because most banking moved into apps. Other things like travel agencies, photo labs, video rental, music stores, bookstore chains, paper-map publishers, have all either disappeared or sharply contracted, along with their land use. On net, data centers have freed up way more land than they use. Data centers have also enabled way more people to work from home, causing office demand to crater.

Is this all good? I’m not sure. Maybe it’s preferable to have malls over Amazon warehouses, or physical video stores over streaming. But regardless, the main effect of data centers on US land use has been to free up incredible amounts of land relative to the ~25 square miles they sit on. Most conversations about data centers only focus on the physical footprint of the buildings themselves without considering just how many other buildings they’ve made unnecessary.

It is not a crime against locals to raise their land value

A surprisingly common complaint in news stories about data centers buying land is that they raise the price of land around them.

In reading about bad environmental effects of industry, I’m used to reading stories of ugly industries moving in and lowering the value of land nearby. This is a problem because Americans have so much of their savings tied up in the value of their land and homes. The land value of farms specifically is a huge percentage of farmers’ total wealth. I’m not used to reading stories complaining that a new industry moved in and made nearby land way too valuable.

Data center effects on nearby land is really understudied. The few studies that have been done seem inconclusive, and have only focused on the effects of home prices specifically. The most thorough study looked at home sales in Fairfax, Loudoun, and Prince William counties near data center alley in 2023. They found that homes closer to data centers sold for higher prices, and their model (controlling for 13 factors that usually push up home prices) explained about 87% of price variance. The authors guessed that maybe this was because data centers cluster near the same infrastructure that makes housing valuable, and that a lot of data centers are tucked into places residents don’t even notice. The authors warned against generalizing too much from this study, especially in looser housing markets.

The potential way data centers make rural land around them more valuable is pretty straightforward: they’re willing to pay way more for land, pushing up the potential price for nearby land that has the same qualities data centers might be interested in in the future. If a data center causes a location’s grid and fiber to be built out, everything around it also becomes more valuable to future data centers.

Raising land prices benefits land owners and harms people who want to buy land. Lowering land prices harms land owners and benefits people who want to buy land. Which one is “right”? It depends on who you’re considering. In general, the locals who own land in the area where a data center is built benefit when their land goes up in value. Because Americans (especially rural farmers) have so much wealth tied up in their land, this often increases the wealth of people in the community where the data center is built.

The two complaints that usually pop up are that this raises local’s property taxes and that it makes it harder to buy land.

The property tax issue is real, but the same thing happens whenever any high value industry sets up in a rural area. Many states solve this with tax policy that taxes agricultural land based on its use-value, or other things like creating homestead exemptions or assessment caps. In states like Indiana that do this, when data centers are built, farmers nearby get immediately wealthier without having to pay more in taxes.

New buyers being priced out is also a real problem, but it’s a problem with making any place in America more desirable to live. Is the solution to keep rural land cheap by making sure nothing economically valuable ever happens there?

Building anything new in an area at all have some effect on local land values. Would you rather an industry raise or lower the value of the land around it? If you saw a headline that said “Data center built, lowers the value of everyone’s property around it” would you be mad? If so, it’s weird to also be upset that data centers are raising the value of people’s property. I think what’s happening here is that both involve trade-offs, and winners and losers (alternatively, the land owners or the people who want to buy land), and in both it’s very easy to focus exclusively on the loser while ignoring the winner. This leads to people being mad when a data center lowers property values, but also when it increases them. Loss aversion is often inherently change aversion.

A clear example of how strangely this issue is talked about in the media is this section of a More Perfect Union video (this one, at 2:00). They interview a local farmer, who complains that the land around him has become so expensive that he can’t buy more farmland to “feed more people.” They then talk to a guy explaining how new businesses can’t buy land there because the land’s become so valuable.

More Perfect Union frames this as “increasing costs,” but it’s increasing costs by making the locals way richer. The farmer’s complaint is that the data centers made his neighbors so much richer that they don’t want to make a deal anymore, and it’d be easier to buy their land if they were poorer and were more willing to sell it off. This is a bizarre complaint. In no other circumstance would we hear someone say “I would like to own more of my community’s land, but this evil company moved in and made all my neighbors so much more wealthy that they don’t want to sell to me anymore” and be sympathetic. But as we’ll explore in detail in the next section, farming has a weird halo effect where it warps our usual thinking about what’s fair.

We have way too much farmland

Farmers have the same combination of virtues and vices as the rest of us. But I think farmers (like any profession with a big positive halo) benefit so much from popular narratives that they get away with more environmental and economic harm, and their halo makes them harder to regulate and criticize. By the end of this section, I’d like you to share my intuition that it’s pretty normal and fine for farmers to sell their land to data centers (or solar power plants) if they want to. These are two private actors making an economic decision that might make sense for both of them. There isn’t some deep evil happening when farmer land is converted to a data center. Farming should be treated as one industry among many, that we can criticize or praise like any other industry, and that shouldn’t get special treatment or handouts because of the inherent virtue of toiling in the soil.

Question farmers

Once you start writing about any environmental issue, especially water, you quickly discover that farmers occupy a special place in American discourse where both right and left refuse to criticize them, because “they grow our food.” People have a simplistic idea of the inherent virtue of farming compared to other professions. This is how you get viral clips of local farmers “bravely” saying no to a data center company politely offering to buy their land for 10x what it’s worth, and reactions like this when you politely point out this is a little overblown:

Cultural conditioning has trained us to see the good rooted people standing up to the far-off unrooted elites as always in the right. You can sometimes sneak in criticisms of farmers by referring to them as “big ag,” but this term ignores small farmers, who are not without fault.

I was happy to see Tomi Lahren come out strong against the horrific “Save Our Bacon” act. It’s nice to see animal welfare becoming depolarized. But even here she danced around the main issue, and imply that it’s not the small farmers who are at fault, it’s the Chinese:

In reality, pig welfare on small American-owned farms is often just as bad as on large ones.

Once you see that people often find it offensive to criticize farmers at all, you start seeing it everywhere. Take this story from last year:

As I’ve talked about before, in Morrow County Oregon, an Amazon data center bought water from a source heavily polluted with nitrates, evaporated it, and sent the rest back to a wastewater treatment plant. Since nitrates don’t evaporate, the water sent to the treatment plant had a higher concentration. This likely raised nitrate concentration in the wastewater facility by less than 1%. Amazon was using a small fraction of the region’s water, but Futurism chose to superimpose cancer cells on data center server racks. They should have superimposed them on the main industry adding the nitrates:

Farmers!

For 35 years Oregon’s government has known nitrate pollution was worsening in the Lower Umatilla Basin, the only drinking-water aquifer for Morrow and Umatilla counties, and has known the main source was irrigated agriculture. For most of that time, the government response was just suggesting “voluntary best management practices” and “stakeholder engagement.” Meaningful enforcement was limited to the Port of Morrow wastewater facility. By 2024, hundreds of domestic wells had tested above the federal nitrate limit, with some exceeding it 7x. Residents reported clusters of miscarriages, kidney failure, thyroid disease, and rare cancers. In 2022, Morrow County declared the first groundwater state of emergency in Oregon history.

Why were farmers allowed to build up nitrate pollution in the water here for 35 years?

In early 2025, state senator Khanh Pham introduced SB 747, which would have required farms over 200 acres (the largest 10%) to fill out a form telling the Oregon Department of Ag how much fertilizer they used, the kind, and the crop it was used for. The bill imposed no limits on fertilizer use. It did authorize civil penalties up to $10,000, but only for failing to file the form, or for being judged by ODA, after the fact, to be over-applying based on the reported data. Pham told the committee that “Senate Bill 747 does not impose restrictions. It simply collects data so agencies can provide better technical support, improve efficiency and prevent fertilizer waste.”

About 75% of the testimony on the bill was opposed. The Farm Bureau called it a “fishing expedition” that turned family farmers into “boogeymen.” One rep told the committee that 96% of Oregon’s farms are “family-owned and struggling.” “Family-owned” is basically meaningless: it means that any family, no matter how rich, owns the farm together. Walmart is a “family-owned” business. Farming families have slightly higher median incomes and way higher median wealth than Americans overall.

Another complained the bill “arbitrarily targeted” large farms while ignoring “municipalities and urban homeowners,” even though in the Lower Umatilla Basin specifically, agriculture and CAFOs together caused over 80% of the nitrate pollution. The farm coalition’s position was that being asked to share this simple information on dangerous pollutants would be a ruinous burden.

Rep. Bobby Levy was the most prominent voice opposing the bill. Her family’s farming business, Windy River, is inside Morrow County and draws from the polluted aquifer the bill was meant to protect. She told the Senate committee that “making the suggestion that over application of fertilizer is widespread is both inaccurate and unfair.” The committee declined to schedule a work session before the deadline, and the bill quietly died without a vote.

A month after Levy’s testimony, the Oregon Department of Environmental Quality issued a citation identifying the Levy family farm as having over-applied fertilizer on its own corn fields throughout 2023, polluting the same aquifer the bill was meant to protect.

This all happened under a Democratic trifecta and while the Morrow water crisis was on the front pages of Oregon newspapers. It happened because you basically can’t say ‘small farmers are part of the problem’ in American politics and expect to keep your job. At best, you can blame “big ag,” or maybe China, but the politics of farming is so crazy that farmers are a permanent third rail politicians won’t touch, even while they poison a local community. For any other industry, this would obviously be crazy, and every other industry has regulations on how much it can pollute water. But farmers are organized, numerous, and have silly folk theories about the inherent virtue of growing food on their side.

Finally, in March 2026, the Oregon Department of Agriculture adopted the first ever mandatory nitrate management rules for the area. Even this only applies, to start, to farms over 1,000 acres, who don’t have to submit plans until May 2027. Farms between 500 and 1,000 acres will be phased in by May 2028, and smaller farms aren’t required to submit their plans to ODA, only to draft them and keep records. ODA only has one staffer to oversee the whole basin and will audit “as agency capacity allows.” The data center here served as a useful if unintentional scapegoat to distract from the people directly adding pollutants to the water.

This fits a national pattern. The 1972 Clean Water act explicitly exempts “agricultural stormwater discharges and return flows from irrigated agriculture” (meaning nitrates, phosphorus, manure, and herbicides washed into a river or lake from a farm by rain) and requires no permit, monitoring, reporting, or upper limit. Farmers are not obligated to tell anyone how much nitrogen they use. This is regularly upheld by federal courts, most recently in 2025.

Agriculture is by a wide margin the main source of nitrate pollution in the US in US water. 12% of domestic water wells in agricultural areas exceed the federal nitrate MCL of 10 mg/L, versus 1% of public supply wells in non-agricultural areas. Nitrate concentrations in agricultural streams run about 6× higher than in undeveloped watersheds. When a rural well or a small-town water utility tests over the legal limit, the source, with rare exceptions, is corn, soy, or manure upstream.

One peer reviewed estimate found that nitrates in drinking water cause between 2,300 and 12,500 cancer cases per year, four-fifths colorectal plus 3000 cases of very low birth weight, and 1700 very preterm births. They estimated that the total medical cost is between $250 million and $1.5 billion per year, plus $1.3 to 6.5 billion in lost productivity.

A few obvious policies in the US could fix most of the problems with nitrates:

• Mandatory reporting, where every farm above some size submits annual plans for how much and where they’re using nitrates.

• Specific number limits on how much nitrates farmers can apply.

• Treating large farms as point sources for water pollution in the way the Clean Water Act treats other industries.

• Forcing farm polluters to pay the cost of water treatment.

• Stop subsidizing ethanol, which alone is responsible for roughly a tenth to a fifth of all nitrates in US water.3

Denmark, the Netherlands, and Germany all have major agricultural sectors that operate well under way stricter rules than anything on this list. The only reason we don’t have these rules is that farmers are more politically untouchable here.

About 1.5% of the contiguous United States is used for a farmer handout: ethanol

Every year, the federal government forces oil refiners to blend 15 billion gallons of corn ethanol into America’s gasoline supply. This uses about 40% of all American corn. This is mandated by the Renewable Fuel Standard (RFS), and was sold as a policy to help address climate change.

However, every argument for mandating corn ethanol is terrible, and the only reason it survives is that politicians cannot question farmers.

The ethanol mandate was originally presented in 2007 as a path to energy independence and lower greenhouse gas emissions. Both arguments kind of sort of made sense then. Oil prices were high and the US was dependent on Saudi Arabia for petroleum. Not good! Ethanol’s lifecycle emissions looked like they might fall below gasoline if ethanol replaced part of the supply. Other arguments at the time held that ethanol would cut NOx emissions or sequester carbon.

But none of this is true now. Since the shale revolution, the US is now a net petroleum exporter. Studies now find that corn ethanol’s carbon intensity is at best roughly comparable to gasoline, and probably about 24% higher once you account for land-use change, nitrous oxide from fertilizer, and the conversion of grassland back into corn fields.

Because ethanol’s made of corn, it also has a high water cost. Corn used for ethanol is responsible for around 1–1.5 trillion gallons of withdrawals per year from aquifers and lakes, this is ignoring any rainwater it might use. That’s about 3% of all water withdrawn for US irrigated agriculture, for a stupid bad mandated product. I can’t help but add that this is roughly 40 times as much water as all the thousands of American data centers are forecast to consume onsite in 2028. Ethanol is generally more expensive than gasoline per unit of combustion energy. So on top of everything ethanol also increases gas prices.

But what’s most shocking to me about ethanol is its land use. About 1.5% of the contiguous United States is used to grow corn for ethanol.

Corn farms for ethanol specifically take up half as much space as all urban housing and commercial land.

So why do we have this mandate? The only people who benefit are corn farmers, ethanol producers, and fertilizer and equipment providers.

Like most bad laws, beneficiaries are concentrated and organized while those harmed are scattered and unorganized. Every American pays a tiny bit more for gas, the Gulf of Mexico absorbs more fertilizer runoff than it otherwise would, the climate as a whole has a little more CO2, aquifers are drained faster, but these harms are spread so thin across so many people that we don’t notice or get organized. In comparison, farmers notice what’s happening. They organize to keep the mandate, and no one bothers to oppose them, because it’s so politically toxic to criticize farmers.

Compounding this, rural Midwestern communities are amplified in American politics by the electoral college and presidential primaries, and the Senate’s overweighting of low-population states. There’s a strong bipartisan voting bloc in midwestern politics that treats the ethanol mandate as a third rail that politicians cannot touch.

So we’re stuck forcing oil companies to pay to support 1.5% of the contiguous United States being used for a stupid product no one wants.

When you see people talking about how any business, be it a data center or a solar farm or anything else, is “taking away our farmland” and that “soon we won’t have any left,’ consider this. Farmers currently have an area the size of New York State, as large as half of all American urban areas (suburbs included), specially dedicated to growing a product oil companies are forced by the government to buy, that makes our gas more expensive and dirtier, with zero benefit to anyone outside the farmers themselves. In an ideal world, farmers would lose the surface area of New York State to companies who would make better use of it, or even shut down and be replaced with nothing so that we stopped wasting 4% of our agricultural water on a product no one wants that makes our lives and the climate worse.

By 2028, all American data centers, including the land around them, will use about this much land compared to ethanol:

Converting all the land used by ethanol in the US to utility-scale solar farms would generate roughly 11,000 terawatt-hours of electricity per year, about about 2.5–3x current US electricity use, or roughly 35% of total world electricity generation.4 Consider this the next time you see news stories of corn farmers getting mad that their land is being bought by solar plants.

Solar plants and corn ethanol both have the same end-goal: generating usable energy. If you measure how much electricity solar panels generate vs how much energy is contained in the final corn ethanol product, and include all the energy costs of growing the corn, solar panels generate over 100x as much energy per unit area as corn farms used for ethanol over the same time period. This makes sense, because corn is in some sense just an extremely inefficient solar panel, slowly storing up energy from the sun to eventually release when it’s mixed in with gasoline. Fields of corn used for ethanol are kind of like very old-fashioned solar panels that are 60x less efficient than our current method.5

As a vegan, I do also have to highlight one other major land user:

We use more land to grow food for the animals we eat than to grow plants we eat directly. Everyone going vegan wouldn’t free up all this land, because we’d need more land to grow fully nutritionally complete meals. The best estimates we have right now is that this would free up roughly 141,000 to 289,000 square miles of American cropland, depending on if we also cut animal feed exports. It would free up around 1,250,000 square miles altogether, because most of the land footprint of current American diets is pasture.

We have too much land dedicated to farming.

Addressing a few other defenses of ethanol

Some argue ethanol is actually good despite its negatives because it reduces NOx emissions, a harmful air pollutant. But this seems to no longer be true. Every US gas car built after 2017 has a catalytic converter that removes NOx from the exhaust to very low levels no matter what fuel you burn, so any small NOx benefit ethanol gives during combustion gets erased. A 2022 California study on 20 cars found no meaningful NOx difference between regular E10 gas and higher-ethanol gas, and a 2025 UC Riverside study found the same.

It might be worse. The EPA now predicts ethanol blending raises NOx, smog-forming VOCs, and fine particulates compared to ethanol-free gas, because ethanol makes gasoline evaporate more easily. And for small engines that don’t have catalytic converters, like mowers, leaf blowers, or generators, the Department of Energy found ethanol blends raise NOx by 50–75%.

Some people bring up that fossil fuels also use water to extract. That’s true, but ethanol uses way more water per unit.

A lot of people are really bad at thinking about farms in a marginal rather than an absolute way

When a data center or solar plant replaces a farm, the public conversation often turns to which product is more valuable in total. Would we rather have food, or silly computers? People will say things like “We can’t eat AI.” This is obviously ridiculous, because the question is never “Should we replace all farms with data centers?” It’s always “Should we replace one specific farm with a data center?” Because we farm way more than we need, and data centers are so efficient with their land use and in so much demand, the answer is often yes. Because we have way too much farmland, I would support replacing many corn or alfalfa (water-hungry and used mainly for animal feed) farms with nothing, never mind another building.

People sometimes bring up soil quality and “prime farmland” as if it were sacred. Prime farmland in the USDA sense is a soil classification for land that could grow crops well 23% of the non-federal open land in the continental United States qualified as prime farmland. We don’t need every last piece of prime farmland actually farmed. We are not going to run out, and shouldn’t turn all of it into farms right now.

It’s true that we can’t eat data centers, but we also can’t eat the corn grown on the New York State-sized chunk of land used for ethanol. The relevant question is always “Is this specific piece of land better used as a farm or a data center?” rather than “Should all farms be replaced with data centers?” The absolute all-or-nothing way of thinking about farms would justify tearing down all our cities and public infrastructure, because we don’t need anything else as much as food.

A very common talking point when anyone suggests that we could use less farmland is “City boys think that food just comes from the store.” To use an outdated analogy, this feels equivalent to the government mandating that way too many copies of Grand Theft Auto get produced, and any time this gets criticized the main response being “Oh do you think games just magically appear at GameStop?” This talking point always assumes that saying the marginal farm shouldn’t exist is identical to saying all farms shouldn’t exist.

What about the effects of climate change on farmable land?

Maybe this is all true now, but we may need to worry a lot more about farmland as climate change worsens, right?

Under most mainstream forecasts, the US ends 2100 with roughly the same or modestly expanded total climatically suitable cropland area, but with the productive zone shifted 100 to 300 miles north of where it sits today.6 This is still going to be wildly disruptive, because this will reshuffle what can be grown where, but on net the US will have the same or more total farmable land. The issue is going to be what can be grown where, not total access to farmland.

There’s a viral talking point that the corn belt in the upper midwest will become unsuitable for corn by 2100. We’re not sure if this is true, because corn varieties have been continuously rebred for changing conditions for the last century. The corn grown in Iowa today is not the corn grown in Iowa in 1925, and that wasn’t the same corn grown in 1850. By 2100, after 74 more years of adaptation, it seems unlikely Iowa farmers won’t be able to grow corn. The Corn Belt-collapse literature mostly assumes static management.7

Climate change will create lots of other problems for US crops. Higher levels of CO2 in the air reduce the nutritional value of grain crops, so we’ll need to grow more food to get the same nutritional value. Water might become a more serious issue, and if the median climate forecasts are slightly off, a lot of formerly irrigated farmland won’t be useable because the water will be gone. This is a serious concern, but it’s mainly about water rather than raw access to land.

I think the most serious response you could give here is that we’ll need more farmable land to feed the global population stuck with way less farmable land. That’s fair, but the clearest way to free up land for this is to join me and others in our quest to end industrial animal agriculture. Buildings that will collectively take up under 100 square miles in the US won’t ruin farmable land.

More evidence we have too much farmland

The federal government literally pays farmers not to farm

For the Conservation Reserve Program, the federal government pays $1.85 billion a year to over 302,000 landowners to keep their collective 40,000 square miles of land idle. This is about the size of Kentucky, and 28x the projected 2028 footprint of all data center land in the US.

CRP was created in 1985 partly because farm groups were alarmed at how much food American agriculture was producing. Prices were low because too much was being grown, so the government started paying farmers to grow nothing. Since then the justification has shifted more toward environmental issues, but the program still runs mainly because the country produces more food than it can sell at prices farmers want. The program has been reauthorized in every farm bill for 40 years. The main intra-industry fight over CRP is how big it should be and which land it should target. 67% of producers support expanding CRP acres while only 10% disagree, and demand regularly exceeds the cap. So farmers themselves largely support spending even more than the billions we currently do to convert even more farms into unused land.

America is a major food exporter

About 20% of US food production by value is exported. The US is the world’s largest exporter of food, we shipped $176 billion in agricultural exports in 2024. Foreign trade is good, but we’re not at the edge of some failure in country-level self sufficiency in food.

We throw out a third of the food we grow

30 to 40% of the US food supply is never eaten. One estimate suggests that if all America’s wasted food were grown on one farm, the farm would be 125,000 square miles, or 89 times the size of all land owned by data center companies by 2028. This means that a 1% reduction in food waste would free up enough land for all data center building over the next 3 years if literally all of it were built on farms.

This wasted farmland generates as much greenhouse gas emissions as 42 coal-fired power plants, 3% of all US emissions. If you add in the emissions from ethanol, 5% of all US emissions come from unnecessary farming.

We’ve been shrinking our farmland for decades and growing more food on it the whole time

Total US farmland peaked decades ago and has fallen since. From 2000 to 2024, it shrank from 945 million to 876 million acres, an loss of 108,000 square miles, about the size of Colorado. This is 77x the projected footprint of all data center land in 2028, and not many people seem to know it happened.

Why did farmland shrink this much? It’s purely that yields per acre grew. American corn yields in the 1930s averaged 24 bushels per acre. In 2024 they averaged 180, a 7.5x increase. Total corn production is now more than 6 times what it was in the 1930s, but uses less land than it did in the 1930s. The same is true for soy, wheat, and most major crops.

The trend keeps on going. Corn yields have been rising at about 1.9 bushels per acre per year for 70 consecutive years with no signs of stopping.

If you follow this line, average corn yield in 2050 will be around 230 bushels per acre. We’ll need less land to grow the same food 25 years from now than we do today.

We’ve already abandoned tens of millions of acres of farmland and it was great

In 1850, much of New England was farmland. Vermont was about 70% pasture and cropland. Then mechanized farming opened up the Midwest’s deeper soils, the Industrial Revolution pulled rural labor into cities, and New England farms became uneconomic. The land turned back into forests. Today, about 70–80% of New England is forested. Where I grew up in Massachusetts, you can regularly find old stone walls from farmers hidden deep in the woods. The region has more forest cover now than in 1820. Vermont went from a stripped agricultural landscape to one of the most heavily forested states in the country.

Across the eastern US, hundreds of millions of acres followed this pattern between 1850 and 1950. The country was fine. The abandoned land was the marginal stuff barely worth farming, and the land that kept being farmed was the productive Corn Belt and Central Valley.

If a few thousand acres of mediocre Loudoun County farmland end up under Amazon and Microsoft and Google data centers, I suspect that nothing bad will happen, for the same reason that it’s not a disaster that most of New England was changed from farms into forests. The marginal hay field on the exurban edge is producing way less value to anyone than a hyperscale data center on the same land. A farmer who sells to a data center company for 10x what their land is worth is doing something economically rational, environmentally reasonable, and morally fine. They’re doing exactly what their grandparents in Vermont did when they sold their farms and let the trees come back.

More examples of the goofy ways our politics and land use is warped by farmers

There are too many other ways farmers’ disproportionate political power makes land use and policy goofy to keep writing about here. For more, you should look into ag-gag laws, the decline of the Ogallala Aquifer, use-it-or-lose-it water rights, and the “family-farmer” myth (discussed a bit above) which wealthy farming families use to garner sympathy. And on top of this, the unimaginably huge moral catastrophe of industrial animal agriculture. For a deep dive on the weird world of farm economics I’d recommend this podcast series from the Agricultural Economics Institute.

Conclusion

American farmers grow more food than the country eats, export 20% of the surplus, and we the buyers throw out another third of what’s left. Farmers collect $1.85 billion a year from the federal government to keep a Kentucky-sized 40,000 square miles of farmland intentionally idle, an area 28 times the full projected footprint of all land on American data center property in 2028 (with data centers themselves occupying just 2% of that land, just 25 square miles). The federal government forces oil refiners to buy 15 billion gallons of corn ethanol every year, occupying roughly the surface area of New York State, draining aquifers at 40 times the rate of every data center in the country combined, and producing a fuel that, once you account for land-use change and nitrous oxide, is 24% dirtier than the gasoline it replaces. The food we waste, if grown on a single farm, would cover an area 89 times the size of all land owned by data center companies by 2028, and emits as much CO2 as 42 coal plants, 3% of all US emissions. Between 2000 and 2024, farmers sold in total a Colorado-sized chunk of land all on their own, 77 times all land on data center property in 2028, and grew more food than ever on what was left. None of this caused any problems for US food access.

And then, in the middle of all this, a farmer in Loudoun County sells a few acres of mediocre hay field to a hyperscaler for ten times its agricultural value, and the response is that we’re running out of farmland.

The marginal Virginia hay field is worth more as a data center than as hay. The marginal Iowa cornfield going to ethanol would be much better if it were nothing at all. When a farmer in Loudoun County wants to sell to Amazon for ten times the land’s agricultural value, the correct response is to wish them well. We need way less farmland than we currently use, and it’s fine if data centers buy some.

Some bad recent articles on data center land use

“Farmer Hailed as Hero for Rejecting Huge Payment to Turn His Land Into a Giant Data Center”

From Futurism, the same magazine that published the story on Oregon nitrate poisoning that let farmers off the hook:

The immense hype surrounding AI has caused enormous data centers to crop up across the country, triggering significant opposition. It’s not just the loss of land: enormous power needs are pushing the grid into meltdown and driving up local electricity prices, catching the attention of politicians and their irate constituents.

idk I think it’s more than hype.

One 86-year-old farmer in Cumberland County, Pennsylvania, has heard enough. As local Fox affiliate WPMT reports, Mervin Raudabaugh, who has farmed the surrounding land for more than 60 years, turned down more than $15 million from data center developers in a package deal that involved three neighboring property owners as well.

The farmer was offered $60,000 per acre to build a data center on his property. But giving up his family legacy wasn’t in the cards for him.

“I was not interested in destroying my farms,” he told WPMT. “That was the bottom line. It really wasn’t so much the economic end of it. I just didn’t want to see these two farms destroyed.”

Instead, he sold the development rights in December for just under $2 million to a conservation trust, taking a significant loss but guaranteeing that it would stay farmland in perpetuity.

This guy turned down $58,000,000 to ensure that America continues to have way more farmland than we need.

Users on social media called him a “legend,” and argued he had “more integrity than the whole government.”

“Now that is a real hero in these gutless times!” another user tweeted.

“$15M is huge, but clean water, quiet land, and legacy don’t have a price tag,” another user argued.

Data centers don’t really pollute water anywhere.

The sheer amount of land being earmarked to construct enormous energy and water-sucking data centers is remarkable. A data center in Mount Pleasant, Wisconsin, is set to take up 600 acres, which could cost local residents their land, as ABC News reported this week. Another octogenarian farmer, the 83-year-old Tom Uttech, who has lived on his 52-acre Wisconsin property for almost 40 years, told the broadcaster that he “couldn’t believe” that a local utility company was looking to build “power lines that are 300 or something feet tall, taller than apparently the Statue of Liberty,” through his land to power the data center.

“Cost residents their land” meaning “Could mean residents are paid way more than what their land is worth if they choose to accept the deal.” 600 acres is about a square mile. Each of these is a square mile:

A private company purchasing a single square mile farm in a rural area wouldn’t make headlines. But data centers are seen as weird and untraditional, so they get way more scrutiny. We would need to build 50,000 data centers at this size to use as much land as ethanol corn.

“It breaks my heart to think of what’s going to take place here, because only the land that’s preserved here is going to be here,” Raudabaugh told WPMT. “The rest of every square inch is going to get built on. The American farm family is definitely in trouble.”

This is an insane hyperbolic thing to say. We have so so so much farmland, way more than we need. Every square inch is not going to get built on, and the average American farm family is incredibly well-off and coasting on powerful political allegiances and the confused goodwill of populists.

This same story was reported on in Fortune and Newsweek.

A data center using as much land as 51 Walmarts

From the Washington Post:

The town didn’t “plan” the data centers, they approved a private company to purchase land. This is a common weird framing in the data center discourse. People often talk as if data centers are a collective decision we're all making. In reality, private companies buy land, power, and water from towns and utilities like any other company.

This town is 17 square miles. The article says the data centers would together take up 14% of the town’s land, so ~2.4 square miles.

This is the town of Archibald PA that the article’s about:

It’s in a pretty rural area:

These are the sites where the data centers have been proposed:

If you found out that a small town like this were getting a college or farm, how would you react? Would this be an unacceptable imposition on the locals? The college would radically change the general culture and infrastructure needs of the town, whereas the data centers hum in the background.

There are definitely serious issues the locals bring up in the article (electricity costs, air pollution, noise) but the land use framing makes no sense to me. Archbald is in a pretty rural area with tons of wide open land. Purchasing 14% of a small town’s land on its own doesn’t seem newsworthy.

“This Land is Their Land”

The title image is already promising:

The subtitle is “Farmers are fighting AI companies offering fortunes to build data centers on their land. Can they withstand the pressure — and live to farm another day?”

Farmers are always framed as the good guys. Question this.

“Life-changing money.” That’s how Wendy Reigel describes the windfall developers are offering farmers for their land, potential sites for hyperscale data centers to meet AI’s massive processing needs. Reigel is a grassroots anti-data-center activist who successfully fought the building of a center just 300 feet from her house in Chesterton, Indiana; she now helps other communities mobilize against data center incursions. She can only make an educated guess as to how much money is on offer — almost everything about these facilities is shrouded in secrecy.

“$20,000 an acre would be low-balling it,” Reigel said. “$40,000 would be maybe a starting point. And I’ve heard as high as $90,000.”

This doesn’t exactly sound nefarious. They’re offering crazy amounts of money for people’s land. Would it be better if they offered less?

And with this wind beneath their sails, AI-giddy companies like Amazon, Google, Meta, Microsoft, and Oracle have embarked on a national land grab.

The new land grab here being in total about 2 times as much land as we use to grow Christmas trees by 2030.

Some farmers are loath to condemn fellow farmers who sell their land for this purpose. They don’t begrudge life-changing money to anyone who’s contended with their industry’s often brutal fiscal realities, although a farmer in Michigan who sold his land for an “astronomical” sum — in the interest of what he called progress — says his neighbors don’t wave at him anymore. With or without the animus, farmers who opt not to sell are left to contend with the potential destruction of their operations and their lives. “It ruins all our little farms around here that we worked all our lives on,” a couple in Coweta, Oklahoma, told the local news.

I think all farmers should be loathe to condemn other people for making private financial decisions that work out well for them if they don’t harm other people. The question is will the data centers harm the nearby farmers?

There are data centers popping up almost everywhere, from Oregon and Nevada to Georgia and Virginia. (Loudon County is our country’s “data center alley.”) But Indiana has become an especial target for hyperscale facilities, of which there are an estimated 1,130 globally; since Indiana produces a lot of corn, soy, and hogs, it’s also illustrative of the challenges that farmers in particular are up against.

43% of Indiana's corn crop went into ethanol production in 2024, and ~95% of Indiana hogs are in factory farm conditions. Neither inspires sympathy from me.

“That hurts us, because they’re paying a higher price than market value for the land; that drives property values up, then farmers are struggling to pay their taxes,” Blalock said. That price is also “completely out of reach” for any farmers starting out or looking to expand their operations. When data centers are willing to quadruple or quintuple an already-inflated price, affordability for farmers goes up in smoke.

Okay, well we have one answer to how data centers are “harming” farmers. They’re making the farmers’ land so valuable that property taxes go up.

For any farmer who owns the land they farm (which most do), rising land values are a massively good outcome. Land is by far the largest asset on most farms’ balance sheets (the article will go on to note this). They can borrow against it, sell and retire on it, or pass it to their kids. The article is asking us to feel sorry for farmers because someone is making their land massively more valuable. I need anyone reading this to ask: if it’s bad for the data center to raise farmer land values, doesn’t that mean it would be good for it to lower their land values? But if a data center moved in and made everyone’s farmland way less valuable, I think we’d also hear lots of negative stories about how it harmed a community, and there I’d take it more seriously.

The weirdest thing about all of this is that Indiana’s agricultural land tax is not supposed to be based simply on speculative industrial value. The state says agricultural land value is based on productive capacity, regardless of the land’s potential or highest and best use, and the 2026 agricultural base rate is listed at $2,120 per acre. So I’m not sure if rising land value will even raise taxes here.

Morgan Butler is a senior attorney at the Southern Environmental Law Center, who helped the tobacco- and dairy-producing community of Pittsylvania County, Virginia, fight off a 1,000-acre AI facility. He said developers are drawn to farmland because “They see a huge area. In their eyes there’s nothing on it, nothing particularly valuable, there aren’t that many residents so hopefully they won’t kick up a firestorm of public opposition.”

What needs opposing here? A data center drops in and offers so much money that it inadvertently makes all the land value around it go way up and all the farmers who didn’t interact with it got richer? What?

That rural “nothingness,” though, is precisely what community members opposed to data centers hope to preserve. One farmer in Kentucky turned down an $8 million offer for his land, citing his family’s sentimental ties to it and the community’s fondness for the landscape as it is. However, one bitter truth for farmers is that much of their net worth can be tied up in their land; retirement might necessitate selling property because they have few or no other assets, making a generous offer for acreage hard to refuse.

Okay, so far this article’s been implying that it’s a massive crime that the data centers are raising farmer land values so much, and now it adds that much of an average farmer’s net worth is tied up in their land. This implies that the data centers are doing farmers a massive favor by increasing so much of their total net worth? But the article never acknowledges this contradiction. In fact, a pull quote appears right below that makes this contrast more obvious:

A hyperscale data center can use upwards of 8 million gallons of water per year, mostly for cooling its servers. “We don’t have the water supply” to meet those needs, said farmer Bart Snyder. Snyder lives in Wolcott, an ag-centric town 135 miles northwest of Henry County, where Amazon is scouting a 330-acre facility 1,000 feet from one of his farm properties. He’s suing his town’s commissioner and redevelopment committee members (all of whom signed NDAs) for approving a farm-to-industrial land rezoning on behalf of that facility. “To consume that much water would absolutely devastate our row crops” and potentially create a deficit for Snyder’s 30 beef calves, which each drink 15 gallons of water daily. “People are literally scared to death,” he said.

Blalock is concerned that if a data center is approved in her county, it could deplete the local aquifer, causing neighbors’ wells to run dry and preventing her family from ever drilling one of their own for their cattle. She said that would put them out of business and leave their land valueless. “No one has answers for us about, what’s our backup plan? What if people lose water? You can take your family to go stay in a hotel but it’s not like doggy daycare; you can’t show up with 50 head of cattle.” And she worries: “Are we going to be the generation that loses the farm?”

The data center seems unlikely to do this.

Wolcott is in White County, Indiana. Irrigation on corn typically runs 6–10 inches per season. 8,695 irrigated acres at ~8 inches of supplemental irrigation consume roughly 1.9 billion gallons per year. The 8 million-gallon data center is 0.4% of that. This data center is using as much water as a local 35 acre irrigated farm. If one additional 35 acre farm appeared, I don’t think the farmers would be talking this way.

The rest of the article has legit worries about pollution and electricity prices. It also has an interesting concern about infrasound I hadn’t addressed last time: its effect on animals. I think that if animals can detect infrasound because they evolved to hear different frequencies than us, it makes complete sense that infrasound could be harmful to them. I will circle back on this once I know more about infrasound and animals.

After failing to respond to any of the ways I had pointed out in my last post that he was misrepresenting every study he flashed on the screen, Benn Jordan shared a blog post someone else had written called Andy Masley doesn’t understand how sound works. The conclusion of the post is that I don’t actually understand the science of sound, my criticism falls apart, and I’ve become ideologically motivated to defend data centers over seeking the truth.

However, the criticisms are all wrong, confused, and seem to be coming from someone who’s lurching to assume I have bad motives. And Jordan obviously knows that it’s all mistaken, because he works on and thinks about sound way more than me, and I can run circles around it. He’s once again tricking his audience. I’m going to use this post to respond to them individually.

I wasn’t originally going to respond to the blog post, because it’s pretty bad throughout, but since Jordan shared it I do feel compelled to explain why it’s wrong.

Criticism 1: Decibels: intensity vs pressure

The first section is called “He does not seem to know what a decibel is measuring”

Masley claims that “Every 10dB increase represents a 10x increase in sound intensity.” This is technically true, but it is the wrong quantity for the argument he is making. Intensity describes the power per unit area carried by the sound wave through the air. What matters for the biological mechanisms Jordan is describing (pressure on the eardrum, stimulation of vestibular structures, outer hair cell response) is not intensity but pressure. And while a 10dB increase represents a 10× increase in intensity, it represents only a ~3.16× increase in pressure. Reporting the intensity ratio rather than the pressure ratio inflates the apparent safety margin by roughly a factor of 300. By the quantity that actually maps to the effects Jordan is proposing, the gap between residential data center levels and occupational exposure limits is one to two orders of magnitude in pressure, not the five orders of magnitude that Masley’s framing implies. It matters significantly less what the power of sound is at the source here than the pressure change at the point of “impact” for the listener.

You can translate a sound’s decibel level into an intensity or a pressure ratio. They both describe the same thing. If residential data center infrasound is 50 dB below the limits where it would need to be to harm you, that gap is both a ~100,000× intensity ratio and a ~316× pressure ratio. Which one we should look at depends on the situation we’re trying to understand.

It’s true that sound pressure describes the potential for physical harm once the sound reaches your ear. But to understand the amount of energy you need to create that dangerous pressure in the first place, you need to know the intensity.

Intensity matters here because it tells you roughly how much more energy the data center would need to contribute to produce enough sound to be harmful to us. The intensity being 100,000x smaller than it needs to be implies that the data center would need to convert 100,000x as much of the energy flowing through it into sound as it currently does for it to harm us. This is obviously ridiculous!

This is a useful comparison to make to understand how reasonable the situation is. The critic just lurches to assume this doesn’t matter at all:

It matters significantly less what the power of sound is at the source here than the pressure change at the point of “impact” for the listener.

I think for understanding how realistic the chance of a data center harming us with infrasound is, it does in fact help to know that the data center would need to be 100,000x as large to produce adequate levels of infrasound to harm us. The critic assumes that this means I’m acting in bad faith. I disagree! The whole point of my post is to understand whether the source of the sound can produce enough sound to harm us.

Masley conveniently only cited the broad occupational guidance for 8-hour workdays while omitting the exposure limit for workplaces requiring maintained mental concentration, which is a much lower 86dB. OSHA also identifies 85dBA as the occupational limit for sound exposure over an 8-hour period. Using this number for comparison, data centers are only ~4x quieter than occupational standards allow for certain roles.

These figures are for eight hours of exposure. The dose makes the poison. It’s quite absurd to say “this is under the threshold for eight hours of exposure, so it’s surely under the threshold for 24/7 exposure!”

The study notes that the impacts of infrasound may pose greater risk to “pregnant women and adolescents,” possibly justifying an even lower rating of dBG.

I will concede a point here that occupational limits are far above residential limits. However, both remain far above what a data center can produce. The reason I hadn’t gone into detail about this is that as noted in the post itself, these occupational levels (including the advise for pregnant women and adolescents) are from old government advice that traces back to papers that don’t replicate, and that infrasound experts have commented on as not being reliable. I’ve already addressed this criticism, but the author is writing this as if I’m trying to hide it. Again, it’s hard not to read this as a bad faith response hoping that the reader doesn’t actually go through my original post.

Masley handwaves away this last point, but studies have shown that there is a potential link between chronic sound exposure during pregnancy and negative health outcomes. The CDC even tells pregnant women:

Avoid low frequency sounds (noises you feel as a rumble or vibration). Low-frequency noise travels through your body more easily than high-frequency noise. Low-frequency noise can cause changes that could affect your developing baby.

This page never mentions infrasound and only lists volumes of low frequency sound that I’ve agreed could be harmful at infrasonic frequencies. And the harms listed are fetal hearing development, not any of the symptoms Jordan lists. It’s also precautionary and doesn’t cite any evidence. Again, I’m not ignoring anything here.

Criticism 2: Perception thresholds/does infrasound become audible?

The second section is called “He does not understand what a perception threshold is.”

Masley’s central rhetorical move is structurally simple: the perception threshold at 10 Hz is 97 dB; residential infrasound levels are below that; therefore, there is no effect.

This is a non-sequitur.

A perception threshold is the level at which a conscious, reportable sensation occurs. It is the floor below which you cannot perceive something. It is not the level below which biological effects cease. These are different things, and conflating them is not a caution any serious researcher in this field would permit themselves.

The author is correct that these are different. I made that clear in the post itself.

I never once said that “Because infrasound isn’t audible, it can’t affect you.” I’m basing all this on what looks to me like a pretty solid separate scientific consensus that inaudible infrasound doesn’t seem to harm us, so therefore it only becomes harmful when it behaves like normal noise pollution (when we can hear it) or when it’s extremely extremely loud and physically harms us.

My argument isn’t a logical deduction from my blind belief that “you can’t hear it” to “it can’t affect you.” It’s an empirical summary of what the literature on infrasound health effects has found. For about sixty years now, researchers have gone looking for physiological and clinical effects of infrasound at sub-perception levels. Properly blinded exposure studies don’t find them. The studies that claim to find them are generally unblinded, badly controlled, or fail to replicate. The nocebo studies reliably produces the symptoms people attribute to infrasound just by telling subjects that infrasound is present, with no actual infrasound required. That’s the pattern I’m drawing on. “Sub-perception stimuli can in principle have effects” is true in the abstract. When you go looking in this specific place though, you don’t find the effects being claimed.

The author is describing my argument as a logical syllogism and then saying this is a non-sequitur. But this isn’t a syllogism at all, it’s a summary of all the good scientific literature on the problem. If the author (and Jordan) think the literature is wrong, they need to engage with it directly.

In fact, Jordan makes this point, comparing infrasound potentially to ultraviolet light: despite being imperceptible, he argues, these both can cause harm in humans. Masley dismisses this out of whole cloth simply because “this blurs two opposite ends of the audible spectrum.” Because one is infra- and the other is ultra-, it’s apparently not even worth considering the broader point that imperceptible waves can be harmful in some scenarios despite their imperceptibility.

No that’s not what I’m saying. I’m saying that Jordan is suspiciously lurching to the extremely high energy end of the light spectrum when we know that the low energy end (comparable to infrasound) doesn’t have negative impacts on us if we can’t detect its presence.

Maybe all comparisons to light are bad. I was mainly suspicious that Jordan lurched from an invisible harm we obviously know is real (UV damaging us) to infrasound. Maybe this was unfair? Idk.

Here is the extraordinary part. Masley himself cites this directly. He writes: “research suggests that while inner hair cells don’t respond to infrasound, outer hair cells of the cochlea can respond to very low-frequency sound at levels below what we consciously detect, and some studies have reported cochlear or brain responses under near-threshold or below-threshold conditions.” This implies that sub-threshold sound can have an impact on humans.

He then, in the very next sentence, dismisses the implication entirely: “But no one has shown this mechanism produces the long list of real-world symptoms people often blame on environmental infrasound.” He is now using his belief as argument in support of the same belief.

The author has a pattern of accusing me of circular reasoning when we actually just disagree about empirical claims.

• Claim 1: there exists a measurable physiological response to sub-threshold infrasound somewhere in the body.

  • This is true. Outer hair cells respond, as I acknowledge in the post.

• Claim 2: this response produces the specific symptoms Jordan is attributing to data center infrasound. Things like vertigo, heart palpitations, cortisol dysregulation, insomnia, cognitive impairment, and the rest.

  • This is not established at all, and all studies trying to find these symptoms from infrasound have come up with nothing.

Therefore, I conclude that because 2 seems to be wrong, our best bet right now is to assume the outer hair cells that do respond to infrasound don’t have an effect. If they did, why can’t we find it anywhere?

My argument isn’t “ignore the mechanism research.” It’s “the mechanism research does not license the leap from ‘outer hair cells respond’ to ‘residential data center infrasound is making people sick.’ That’s a separate claim that needs its own evidence, and the studies built to test it keep coming up empty.”

I’m not using my conclusion to support my conclusion.

Sub-threshold stimuli produce measurable physiological effects in virtually every sensory system we have studied. This is not controversial.

This sentence is doing a lot of work it hasn’t earned.

Yes, you can find detectable physiological responses to sub-threshold stimuli in lots of sensory systems. The magnitudes of these effects are typically tiny, measured in milliseconds of reaction time or microvolts of neural activity, and detectable only under tightly controlled lab conditions.

The author is using this to imply that sub-threshold infrasound could plausibly cause the symptoms Jordan’s describing. But given the state of the evidence, this is a huge unsupported leap. Making this leap is how you get Wind Turbine Syndrome.

There is a deeper problem here, too. Jordan’s entire argument is about sub-threshold effects. His claim is not “people can hear the data center” or “people can consciously detect the data center.” His claim is “the pressure waves affect biology below the level of conscious perception.” Masley’s rebuttal is “the signal is below the threshold for conscious perception.” Yes, obviously, that is the point.

Masley has spent hours attacking Jordan for claims Jordan never made, because Masley apparently believes “sub-audible” might as well mean “nonexistent.”

Jordan’s first video is literally called What You Can’t Hear Can Hurt You. The whole post is a response to that argument.

My actual claim is that when researchers design experiments to test whether sub-perception infrasound causes the health effects Jordan attributes to it, it doesn’t. That’s a direct response to Jordan. The critic has read “the studies testing Jordan’s claim consistently find no effect” and somehow heard “Jordan isn’t making that claim.” I don’t know what to do with that.

Criticism 3: Confusing “perceptible” with “audible”

The threshold error described above is quantitative: Masley treats the audibility threshold as the floor below which all biological response stops. What I want to flag now is a separate, conceptual error that runs beneath it – one he commits so consistently that I am not sure he realizes he is committing it.

Audibility and perceptibility are not the same thing.

Not only do I realize that, I make it clear in the post!

Throughout Masley’s piece, “inaudible” does the work of “undetectable,” “imperceptible,” and “biologically inert” – three claims that are not equivalent and that require separate evidence. He never makes this distinction. He appears not to know it exists despite citing research which clearly states:

Pure tones become gradually less continuous, the tonal sensation ceases around 20 Hz, and below 10 Hz it is possible to perceive the single cycles of the sound. A sensation of pressure at the eardrums also occurs. The dynamic range of the auditory system decreases with decreasing frequency.

I do make this distinction, I just say they seem to overlap despite being distinct. In my opening part on the science of infrasound, I explain that our experiments seem to find that 1) inaudible infrasound doesn’t have an effect, so 2) the only effects happen when it becomes audible. I also clarify that we can sometimes perceive infrasound as physical pressure on our ears and chest before hearing it, and at that point it becomes dangerous to us.

Consider the vestibular system. The semicircular canals of the inner ear respond to pressure changes and acceleration. They do not produce conscious auditory perception – they produce balance, orientation, and spatial awareness. Low-frequency pressure waves can stimulate vestibular structures at levels far below what the cochlea registers as sound. The sensation, if any, is not heard. It is felt as dizziness, unsteadiness, or nausea. This is not speculative. It is the operating basis of clinical tests like vestibular-evoked myogenic potentials, a standard tool used by ear, nose, and throat doctors.

The study is on very loud stimuli (in the 95-105 dB SPL range) which reliably evoke responses, delivered directly into the ear canal via insert earphones. Below about 75-85 dB SPL you stop getting reliable results, which is why the test uses the levels it does.

Studies have shown a signal can be inaudible and still make you dizzy above the occupationally safe decibel range.

This review doesn’t say what the author is claiming.

The sentence the author seems to be referencing describes "isolated reports" of vestibular symptoms following exposure to wind turbines and rocket engines, phrasing that, in a clinical review, means "case reports exist, no established syndrome." The same paper explicitly states that the phenomenon "is not fully understood" and that "ongoing research continues to study" whether low-level infrasound produces vestibular effects. And the research has since been done, and I cite it throughout my post!

The most telling moment in the piece is one we have already visited. Masley cites Salt & Hullar – a study specifically about a mechanism by which the inner ear detects infrasound without rendering it as conscious sound. The outer hair cells respond; the signal is processed; no auditory percept is generated. This is, precisely, detection without audibility. Masley cites it. Then he just handwaves away the implication because there hasn’t been enough follow-on research on the subject. This would be fine if the headline of his article was “I’m not entirely convinced that infrasound is a problem.” But no, the idea of infrasound being potentially problematic is fake, pseudoscientific, conspiracy theory.

I hand wave it away because it identifies a mechanism that could create an effect, but we’ve tested for that effect over and over and haven’t found it. Anyone claiming that science implies the effect is actually there is in fact doing pseudoscience.

The cash-out is this. Jordan’s entire claim is about perceptible but inaudible effects – pressure waves that the body registers through pathways other than conscious hearing. Masley’s entire rebuttal is that the signal is inaudible. These two writers are not disagreeing about evidence. They are using different definitions of what counts as the phenomenon being studied. And Masley does not appear to realize this, because he appears to believe that “you cannot hear it” and “nothing is happening” are the same sentence despite repeatedly citing studies which state the contrary.

This is so bizarre that I don’t even know how to respond to it. Reading it, I kind of just feel deeply tired. How can you come away from my post and honestly write this?

Criticism 4: Nocebos are unfalsifiable

Masley relies heavily on the work of Crichton & Petrie, a body of research on the nocebo effect: show one group a video emphasizing harm from wind turbine infrasound, show another group a neutral video, expose both to real and sham infrasound. The high-expectancy group reports more symptoms, including during sham exposure. This is a real finding. It belongs in any honest treatment of this research area.

But here is what the Crichton & Petrie studies actually show: high-expectancy groups report more symptoms than low-expectancy groups. They do not show that low-expectancy groups report zero symptoms. They do not show that infrasound has no physical effect at all. They show that expectation modulates symptom reporting.

A study showing that expectation amplifies a signal is not the same as a study showing there is no signal. Masley treats “nocebo effects exist” as synonymous with “nocebo effects mean your research is bunk.” This is not how that works.

This isn’t how I treat the nocebo effect in the post. I very clearly separate studies that try to test for infrasound harms from studies that test for a nocebo effect. I first try to show that studies testing for harms of infrasound below perceptible levels consistently don’t find any, and then talk about how studies show that you can get a pretty decent nocebo effect on infrasound and generate similar symptoms. I don’t say anywhere that the nocebo studies alone prove that infrasound is from the nocebo effect. The nocebo effect would be clearly falsified if people exposed to infrasound who weren’t primed to feel bad had any measurable negative reactions at all. Best I can tell from every serious inquiry that’s looked into this, they don’t. I use the nocebo effect as “this seems very likely to be what’s causing the symptoms once we know they’re not caused by infrasound” rather than “this proves that the symptoms are caused by the nocebo effect.” Again I think this is just a bad faith reading of my post.

Consider what this means for Jordan’s double-blind experiment. Jordan ran a study with a “haunted painting” cover story, specifically to equalize expectation across groups. Masley’s response is to dismiss the entire design by leading with the nocebo literature – in effect assuming a nocebo explanation regardless of what the blinding protocol was. This is unfalsifiable reasoning. If nocebo explains symptoms when the design is bad and nocebo explains symptoms when the design is good, then crying “nocebo effect” is more rhetorical shield than scientific analysis.

I addressed six separate problems with Jordan’s study, only one of them was that he was actively priming each group to feel bad (which does in fact seem relevant!). Again this just flatly misrepresents my post that anyone can read.

Furthermore, Crichton & Petrie show that expectation modulates symptom reporting. They do not show that infrasound has no effect. This is not the “exact opposite” of what Jordan claims. It is, at most, an important qualification to it.

I didn’t use Crichton & Petrie to argue that infrasound has no effect, I used them to say that the nocebo effect seems real, and since I had already used other studies to argue that infrasound has no effect, this looks like the next best explanation.

Criticism 5: The evolutionary argument

Natural infrasound – from thunderstorms, ocean microbaroms, atmospheric gravity waves, wind over terrain – is intermittent, spectrally broad, and non-stationary. It varies by season, geography, weather. It is not a constant feature of any individual’s sensory environment.

Industrial infrasound from a data center HVAC plant is continuous, tonally pure at specific frequencies, and temporally consistent.

It’s correct that natural infrasound is intermittent and broadband while modern industrial infrasound is continuous and concentrated at specific frequencies. But the point about evolution I’m making doesn’t depend on the sounds being identical, just whether human bodies have any reason to have evolved a generalized harm response to inaudible low frequency pressure waves. The biology just doesn’t seem to be there. If we evolved this way, people would mostly not live near the ocean and would be regularly sick if they did. We don’t actually observe any evidence that humans or other animals living near the ocean were evolutionarily selected against. And again, we’re just not getting any evidence that we evolved this way from any of the many good studies done on infrasound.

Arguing that humans must be adapted to industrial continuous-tone infrasound because we evolved alongside transient broadband natural infrasound is like arguing that humans must be adapted to staring at a 500-nit monitor six inches from our faces for twelve hours a day because the sun has always existed.

The sun has always been bright. This does not make a welder’s arc safe to look at without goggles.

The difference is that with these is that we evolved to be able to see the light a monitor gives off, and we evolved to see the light a welder’s arc gives off. We did not evolve to see the infrared light either gives off, but we did evolve to physically feel that infrared light when it gets to be so much that it’s dangerous to us. Similarly, we can detect infrasound when it’s starting to become dangerous to us. Thanks evolution! Have you ever worried about the infrared light your monitor gives off? That seems like the comparable case here, not visible light.

Refrigerators: typically ranging 40-50 dB at around 400-500 Hz (not infrasound!) Washing machines: ... the sound they emit is between 100-1000Hz (again, not infrasound!) Trains: ... these emit sound all over the audible range.

Refrigerators, HVAC systems, washing machines, and most rotating mechanical equipment do produce infrasound alongside their audible sound?

We’re surrounded by mechanical sources producing low-frequency and infrasonic energy alongside audible sound, and the population is not experiencing the symptom cluster being attributed to data center infrasound. This is worth talking about and bringing up.

Criticism 6: Am I just a stooge for the data centers?

An honest skeptic could say many things about Jordan’s video. They could say his Finnish citation is misleading. They could say the heart-contraction paper he relies on doesn’t replicate. They could say his haunted painting experiment has design problems. They could say the audible noise pollution story is a well-established mechanism for the symptoms residents report, and that Jordan hasn’t demonstrated the effects of infrasound rise above this level. All of these would be defensible.

What an honest skeptic could not say is that Jordan’s sources, as a body, say the “exact opposite” of what Jordan claims. That is a specific claim about the literature. It is also false. The sources Jordan cites are, at worst, ambiguous or overreached-from. At best, they say roughly what Jordan says they say. They do not, as Masley has repeatedly asserted, diametrically contradict the video they are supposedly citations for.

I’m not convinced by any of the author’s prior points here, so I maintain that Jordan’s sources either say the opposite of what he claims (that infrasound doesn’t cause harm) or are completely unrelated to what he’s saying (that audible noise pollution causes harm, with no mention of infrasound). I would on Twitter sometimes use some hyperbole and blur these together into “all his sources say the opposite of what he claims” so I guess the critic’s right that when I do that, I’m wrong.

Masley has staked out a position that effectively equates to “data centers are an unmitigated good.” He writes in his response to Jordan:

✅ Data centers don’t waste water.

✅ AI artwork is without victims.

✅ Using ChatGPT is not bad for the environment.

His first statement is pretty clearly correct at this point. He’s generally right on item 3, although it is still difficult to find adequate clean power for data centers. But Masley has painted himself into such an ideological corner that he can’t even recognize the ethical issues posed by AI art and the artists whose work on which it is trained without compensation.

The iPhone caused a massive drop in the number of bank teller jobs in the US. I think it’s both true that this was an unfortunate consequence of the iPhone, but also that the bank tellers weren’t “victims of the iPhone.” Maybe this is a terminological difference, I don’t know, but I think it’s reasonable enough to not think it means I’ve been blinded by ideology. Similarly, as I say in the art piece, I think the correct opinion of AI art is that it’s as if someone went to a museum, looked at a lot of other people’s paintings, and then went and made paintings inspired by them but distinct enough that they don’t breach copyright. When I was young I used to draw a lot of Tolkien-like maps. They were different enough that if I sold them, I wouldn’t be violating copyright, even though I learned them basically exclusively from looking at maps at the beginning of Lord of the Rings books. I don’t think it’s a sign that I’ve painted myself into an ideological corner to say that I wasn’t harming the Tolkien estate here by doing this, and that AI doesn’t steal from artists by doing similar things.

Have I staked out a position that data centers are an unmitigated good? Also no. In the piece, I say xAI Colossus was bad and shouldn’t have been built, that the crypto mine data center in Texas was clearly really bad, and that noise pollution issues with data centers are very real. Leading up to this I was also posting a lot about how pollution and climate change are the other real issues I’m worried about. Again, this author doesn’t seem interested in what I actually wrote, he’s just lurching toward these big claims to denounce me.

Why did Benn Jordan share this?

I normally wouldn’t reply to criticism this bad, but I felt obligated to after Jordan elevated it in the discourse and his followers seemed to agree that it showed I was wrong. Again, he’s tricking his followers. Why, if this is so easy for me to dunk on despite not thinking about sound much day to day, did Jordan share it as if it’s all correct, despite working on sound all the time? I think it’s because he’s again being dishonest here and knows who’s right, and is just looking for another way to dodge the obvious fact that none of his sources said what he claimed.

Flashing my credentials

I don’t normally like to do this, but I’ll point out that of the three of us, I’m the one with the physics degree. The author seems to have a degree in architectural technology and design. I assume Jordan knows a lot more than me about audio engineering, but I’d kindly request that if you see a title that says Andy Masley doesn’t understand how sound works you treat it with some skepticism. I have a degree in this stuff and taught it for 7 years! If you look up “IB Physics” in YouTube, one of the first things you’ll see is a picture of my face. I claim that I do in fact understand how sound works.

A music rec

My favorite piece of low frequency music is Monoliths and Dimensions by Sunn O))). A top 10 album for me. I ended up listening to it a lot writing this and the last post. Check it out.

• Intro

• What is infrasound?

• What the science says about infrasound harms

  • The history of infrasound science

  • The consensus

• Benn Jordan’s infrasound videos are a complete disaster

  • First video - Infrasound: What You Can’t Hear CAN Hurt You

  • Second video - Datacenters Behaving Like Acoustic Weapons

• Responses to this piece from the authors of the studies Jordan cites

• Jordan’s response to this article was pretty goofy and reaffirmed that he can’t defend any of his misleading citations

  • Bluesky back-and-forth

  • Jordan’s blog post rebuttal of this post

  • Response to “Andy Masley doesn’t understand how sound works”

• There’s a lot of social permission to treat data centers as boogeymen right now

Intro

There is a fast-spreading new idea that data centers cause a unique harm: “infrasound.” Environmentalists may remember this as a pseudoscientific idea promoted by some fossil fuel interests to scare people about wind turbines in the 2010s. Infrasound is now the topic of what looks like the single most popular piece of media made about data centers in 2026 so far: the video Datacenters Behaving Like Accoustic Weapons by the popular YouTuber Benn Jordan. Jordan’s previous video on infrasound is one of the first things Gemini recommends when you search it. These two videos are each a complete moment-to-moment disaster, and have received no meaningful pushback, so I will be the first. Even if you have no interest in either data centers or infrasound, I think this is an amazing sociological example of how highbrow misinformation is developed and rapidly spread. I found the experience of unpacking it jaw-dropping. After writing this Jordan accused me of being a paid shill, but you don’t actually have to trust anything I’m saying. All you have to do is trust the authors of the studies Jordan himself is citing everywhere.

This threatens to become yet another bizarre folk theory about data centers that educated people believe and give you funny looks if you say it’s fake, like that they heat the areas around them by huge amounts, they use a whole bottle of water per prompt, they seriously harm water supplies, or they have an outsized effect on national electricity prices.

I’ll argue here that all our best current evidence about the harms of inaudible infrasound implies that they’re fake, including recent popular claims about data centers. To be clear, data center noise pollution issues are very real. But “infrasound” is something very different, and is being smuggled into the real noise issues by pseudoscience enthusiasts to attribute almost mystical powers to data centers: inaudible sounds they give off that you can’t even hear can physically harm you and make you sick and mentally unwell.

Jordan’s video is a perfect case study of highbrow misinformation. The host is cool. He opens with the nuance that his video is hosted in a data center, and then immediately jumps into a pretty extreme claim:

1:28 - To put it simply, regardless of where your ethical, environmental, political, or economic interests are, if a data center is being built nearby your home, you’re generally kind of f*****.

Some wild claims have already been smuggled in, but by adding some points about how he’s also using a data center to host the video, they also seem like well-considered takes by a guy who’s willing to acknowledge trade-offs and nuance.

The video looks rigorous and technical, and is presented with the professionalism and chill vibes of the kind of explainer you might see from Hank Green and other trustworthy sources. He surrounds himself with equipment and images that look scientific. He talks to people who have been harmed by regular noise pollution, and shares their stories. A lot of legit-seeming studies are flashed on the screen. A big powerful bad guy is being taken down by a chill local dude. Why think about the individual claims too much?

Despite all this, the video is actually promoting a completely unfounded conspiracy theory, and every single study he uses to justify his conclusions either explicitly says the opposite of what he claims or has such terrible methodology that it’s useless. This is effectively a high-status Alex Jones video, it’s really uniquely terrible, but it’s also flying under the radar of most educated people because it looks and feels reasonable.

I'll show thoroughly and conclusively that the video’s wrong. Before I do that, I need to explain what infrasound is and the state of the science.

What is infrasound?

Sound is vibrating air particles. A sound’s frequency is how frequently the air particles move back and forth. A frequency of 3 Hertz (Hz) means the air particles move back and forth 3 times per second. The particles in this gif complete 1/2 of a full back-and-forth oscillation per second, so their frequency is 0.5 Hz.

The higher the frequency of a sound wave, the shorter its wavelength. We hear higher frequency sounds as higher-pitched. For more on the basic physics of waves you can watch my old physics videos.

Frequency alone doesn’t tell you how much energy a sound wave carries. The energy of sound in air, which we experience as volume, depends mainly on the pressure amplitude of the sound wave. Pressure amplitude is like the physical height of a wave in the ocean. If you were standing in the ocean, waves hitting you very frequently could cause you to tip over, but they would have less impact if each were small, and way more if they were really large, so their height matters a lot too. Sound works similarly: frequency and amplitude are different variables, and the volume we hear is determined by the combination of the two.

Infrasound is sound at very low frequency, below about 20 Hz. Human hearing sensitivity drops sharply in that range, so very low-frequency sounds usually have to be very loud before we can hear or otherwise perceive them. Infrasound is not all sound that isn’t loud enough to regularly hear. There are also sounds with frequencies too high for us to hear: ultrasounds. The prefix infra means “below” (“infrastructure” is structure metaphorically or literally below society) and ultra means “beyond” or “above.” This is loosely like the light spectrum: infrared light is below the lowest frequencies we can see (red), and ultraviolet light is above the highest (violet).

Ultraviolet light is dangerous because it carries more energy than visible light, high enough for electrons in atoms in our DNA to absorb and get knocked loose, which is why UV from the sun can cause cancer.

Infrared light carries less energy per photon than visible light, not enough to eject electrons from our DNA the way UV can. But infrared light can still transfer heat. The warmth you feel standing near a fire is from infrared light, and too much can burn you.

Low-frequency infrasound generally diffracts around or passes through obstacles more easily than higher-frequency sound, which can make it harder to block completely. It can travel much farther and through much more material than normal sound.

At high enough intensity, we may detect infrasound in ways other than hearing it. It can create pressure, fullness in the ears, or a pulsing or rumbling sensation. Infrared at high intensity can create warmth. There isn’t a crisp moment where it suddenly becomes detectable. The boundaries are fuzzy. The lower the frequency, the louder the infrasound needs to be before we can detect it.

For ordinary environmental infrasound, current evidence does not support major direct health effects below where we can hear and perceive it.

Sound pressure levels are measured in decibels, or dB, which is a logarithmic scale. Every 10 dB increase represents a 10x increase in sound intensity, every 20 dB increase is a 100x increase, a 60 dB increase is a million, and so on. The gap between 50 dB and 60 dB is a factor of ten in intensity. The gap between 50 dB and 100 dB is a factor of a hundred thousand.

Sound is measured this way because the range of real-world sound intensities is so huge that a linear scale wouldn’t work. A jet engine at takeoff is a trillion times more intense than the quietest sound a human can detect. To avoid having to write huge numbers out every time we want to measure sound, we just use a log scale. This is important for understanding infrasound because differences in dB can seem small, but actually represent huge orders of magnitudes in difference. The difference between a loud data center and enough infrasound to physical hear (maybe 60 dB vs 110 dB) may not seem like that much. On the scale it’s just a doubling. But in reality every 10 additional dB multiplies the power required to produce the sound by 10. So a difference of 50 multiplies the power required by 10x10x10x10x10, = 100,000 times as much power. People sometimes try to imply that infrasound could be dangerous if it just got a little higher on the decibel scale, ignoring that this would require it to get at least thousands of times as much power from its source.

Infrasound at 20 Hz needs to be at roughly 79 dB for us to detect it, about the level of a vacuum cleaner running a few feet away. A frequency of 10 Hz needs to be at about 97 dB, like a motorcycle at close range or a subway passing on the platform. A frequency of 5 Hz needs to be at about 107 dB, like a chainsaw at arm’s length.

Given the logarithmic scale, these thresholds are nearly three orders of magnitude apart. 5 Hz sound needs about 600 times as much power as 20 Hz sound to become audible to us.

It’s important to not confuse the effects of infrasound with ultrasound. Sometimes people talk as if the harms of all inaudible sound are interchangeable, but this blurs two opposite ends of the audible spectrum. It’s as confused as mixing up infrared light with ultraviolet light. The first can heat you up near a fire, the second can knock out atoms from DNA and give you cancer.

What are the ways infrasound could plausibly harm us? There are at least three:

1. Direct mechanical damage at very high intensities. Extremely intense low-frequency sound can cause discomfort, and injury at high enough levels. A sense of pressure in the middle ear starts showing up at roughly 127 dB of infrasound, like a jackhammer at arm’s length. Chest wall vibration, a gagging sensation, and visual field vibration occur at 145 dB, approaching the sound of a shotgun fired a few feet from your head. Abdominal wall vibration happens at 150 dB, which is the sound of a jet taking off at 25 meters away. This is loud enough to rupture an eardrum. These are not levels that occur in any normal residential environment. And infrasound is not “invisible” at these extremes. In any study, participants can tell when infrasound at this volume is turned on and off. They can either hear it, or feel these physical effects in the pressure of the air around them and know that something’s wrong.

2. Low-frequency rumbles that we can physically feel causing annoyance and sleep disruption. When infrasound is loud enough to detect, or when the thing producing it is also producing audible sound, it can affect us in the same way ordinary noise pollution affects us. It can mess with our sleep and stress response. In this case, infrasound is not really “invisible” anymore.

3. Vestibular or chochlear detection The inner ear contains structures involved in hearing, motion, and orientation. Research suggests that while inner hair cells don’t respond to infrasound, outer hair cells of the cochlea can respond to very low-frequency sound at levels below what we consciously detect, and some studies have reported cochlear or brain responses under near-threshold or below-threshold conditions. But no one has shown this mechanism produces the long list of real-world symptoms people often blame on environmental infrasound.

One last thing to note is that many things in our lives generate infrasound, and infrasound can travel so far that our environment contains sound created by things far away. Refrigerators, HVAC systems, distant highway traffic, wind blowing past a building, ocean waves, thunderstorms, washing machines, trains, airplanes, and even the swaying of tall buildings can generate them. Cities are constantly full of low-frequency sound energy. But the levels vary enormously by source, distance, frequency, and how you measure them. Nature contains a lot of infrasound as well. It would be a little strange if we evolved to feel bad when infrasound is present, because the Earth has always had a lot of it, in the same way Earth has a lot of sources of infrared light.

What the science says about infrasound harms

The history of infrasound science

People have been interested in infrasound for a very long time, but the first really rigorous work by modern scientific standards came out of NASA and the Air Force in the 60s. They were trying to simulate rocket launch conditions, and exposed volunteers to very loud infrasound at around 140 dB. At this extreme volume, the participants experienced ear pain, headaches, and nausea.

Geoff Leventhall is one of the most-cited researchers in the field. He’s spent a lot of time correcting public misunderstandings. A big complaint of his is people often read the NASA-style high-exposure studies as if they applied to ordinary environmental levels of infrasound, but in reality the bad symptoms from this study only show up at extreme volumes around 140 dB. The level of infrasound in people’s homes, even in pretty loud areas, is about a million times less powerful than the rocket launch levels these studies measured.

There’s a huge literature of good studies and reviews on infrasound since then, continuing into the present.

Marshall et al. 2023 is one of the most important. They took 37 noise-sensitive adults and had them spend three 72-hour periods in a sleep lab set up like a studio apartment. They were exposed to simulated wind turbine infrasound, sham infrasound, or traffic noise. Researchers monitored sleep physiology like brain activity, breathing, heart rate, muscle movement, as well as a lot of psychological, cognitive, cardiovascular, and self-report measures. Infrasound did not change the physiological or psychological outcomes tested, while traffic noise in comparison disrupted sleep.

The role of expectation itself, the “nocebo effect,” has also been tested directly. The most famous work is by Fiona Crichton and Keith Petrie at the University of Auckland in the early 2010s. They showed one group a video emphasizing harm from wind-turbine infrasound and showed another group a neutral or reassuring video. Then they exposed participants to both real and sham infrasound. The high-expectancy group reported larger symptom increases, including during sham exposure. The lower-expectancy group reported fewer changes. Most studies on this seem to imply that infrasound effects are nocebos.

Lots of public health bodies have done their own reviews, including the Australian NHMRC, Health Canada, the Massachusetts Department of Public Health, the Netherlands’ RIVM, and others. They’ve all concluded there’s no convincing evidence for a distinct health problem caused by ordinary environmental infrasound near or below the perception threshold.

There was also one huge Health Canada study of 1,238 households living near roughly 400 wind turbines in southern Ontario and Prince Edward Island, which found no relationship between wind turbine noise exposure (including infrasound) and hypertension, migraines, tinnitus, sleep disorders, or chronic disease. People who could physically hear the turbines at audible levels were annoyed.

One influential mechanistic paper often cited by people who think infrasound causes harm is “Responses of the ear to low frequency sounds, infrasound and wind turbines.” The cochlea is the organ in your inner ear that detects sound. It has two types of hair cells: inner and outer. Inner hair cells detect most of what we hear, and they turn out to be pretty insensitive to infrasound. Outer hair cells have a more complicated job, mainly amplifying certain sounds. A review by Salt and Hullar argued that these outer cells can respond to infrasound at levels below where a person would consciously register the sound. So it looks like there’s a possible pathway for infrasound to influence us, but no one’s produced any convincing evidence that it actually causes physiological or psychological effects outside the nocebo effect.

One other key fact: a lot of the good scientific studies of infrasound were specifically designed to look at wind turbines. In the 2010s there was a panic about “Wind Turbine Syndrome,” a long list of symptoms purportedly caused by wind turbine noise, pushed by an unholy alliance of international cranks, local NIMBYs, and some fossil-fuel interests. The whole thing originated in a single incredibly stupid book, written and published by an anti-wind couple who among other things compared wind turbines to Jim Crow. A lot of scientists knew this was pseudoscience and wanted to formally debunk it. Simon Chapman is the great public debunker of Wind Turbine Syndrome, so I refer you to his free book if you’d like the full history.

The consensus

Contrary to Benn Jordan’s video, where he claims infrasound is “grossly understudied,” infrasound and low-frequency noise have been studied for decades by a ton of reputable scientists and major health organizations. Controlled studies and public health reviews haven’t found a real, distinct health condition caused by ordinary environmental infrasound at the levels we actually experience, even in noise polluted areas. When low-frequency sound becomes strong enough to be heard or otherwise felt, it can cause annoyance, discomfort, and sleep disruption like any other normal noise pollution. At extremely high levels, it can physically harm us in the way other sound can. We can always detect it when it’s at this level.

The reported symptoms of inaudible, undetectable infrasound appear to be a nocebo. If you tell people infrasound is going to harm them but don’t actually expose them to any, they develop symptoms. If you don’t tell them, and play infrasound without telling them, they don’t develop any measurable symptoms.

Benn Jordan’s infrasound videos are a complete disaster

Benn Jordan has made 3 videos on infrasound:

The top one is his most viewed video of all time. I’m going to skip this one, because its topic is a little different: audible low-frequency sounds (sometimes called “the hum”) that some people can hear and find disturbing in the way other unpleasant sounds are disturbing. That falls under traditional noise pollution. And while I seriously doubt these sounds cause “psychosis” or “death,” the effects of traditional noise pollution are outside the scope of this post.

I’ll first unpack his original video on infrasound that’s inaudible to everyone, and then talk about the data center video. I’ll use quotes and timestamps for both.

First video - Infrasound: What You Can’t Hear CAN Hurt You

I would recommend watching a bit of this video before we dive in. It’s very easy to watch without noticing anything wrong. It’s very well-produced, and I think if you have the experience of watching it before reading what follows, this post will be much more jarring in a fun way.

0:00-1:40 - Intro

The intro to this video is already silly. He records infrasound in his home and then compresses and speeds it up until he can hear it.

0:24 - Whoa. I’ve done a whole lot of investigating and I still have absolutely no idea what’s causing that sound and it’s still going on right now as I’m recording this.

Lots of things in our lives make infrasound. It’s easy to record any of it, compress it, speed it up until it’s audible, and it always sounds alien and weird when you do. You can do this with infrasound from a fridge or HVAC or distant traffic. If you compress a whole day of it into something that’s audible, you’re going to get eerie sounds from far away. This tells you nothing on its own.

There’s a lot of infrared light everywhere too. I could aim an infrared camera at a dog, and then say “Whoa… something’s wrong with my dog. He looks Evil”

and this would tell us about as much as making infrasound audible.

0:32 - And if you’re in a city or suburb, chances are that similar sounds are emanating just below your hearing range.

Yes, most things we do and use create infrasound. This is like starting a video with a scary infrared image and then saying “And if you’re in a city or suburb, chances are that similar light is emanating just below the range you can see it.”

0:39 - And I have some bad news for you. A meta-analysis suggests that it can actually be pretty harmful to your health.

He flashes three papers on the screen here. He doesn’t link any of them in the video description. None of them support what he’s saying at all, they all go completely against it. None of these are a meta-analysis showing infrasound is harmful.

The first is an occupational measurement paper that implies infrasound is safe at levels hundreds of thousands of times more intense than anything people regularly experience in their homes, even in loud places. The second is part of a 3-part research project where the final report is called “Infrasound Does Not Explain Symptoms Related to Wind Turbines.” The third is a NIOSH workplace report that explicitly concludes the sound levels it measured are not known to cause negative health effects.

The first paper paper is “Exposure to infrasonic noise in agriculture.”

It’s an occupational exposure assessment for Polish farm workers. The author took a sound meter to 118 pieces of farm machinery in Poland and measured the infrasound levels they produce. And that’s all.

It’s not a meta-analysis. The abstract has some speculative language about infrasound having “possible ergonomic and health consequences,” and this is probably what Jordan is pointing at when he flashes the cover. The paper also asserts that “a very important harmful factor is infrasound exposure for pregnant women and adolescents at workplaces in agriculture.” That claim cites older Eastern European occupational health literature that’s never been replicated in modern studies. That older literature comes up over and over in infrasound harm claims. It’s almost all small studies with bad controls, often decades old, that don’t replicate. Most of the serious modern work on infrasound, and the major public health reviews from the last fifteen years, doesn’t find its evidence compelling.

Geoff Leventhall has flagged this pattern in his reviews of the infrasound literature. Governments set a regulatory threshold for infrasound exposure based on cautious extrapolation from very limited data, and then new papers cite that threshold as if it were evidence of harm at that level. Later papers cite those papers. The chain of legit-seeming papers starts with original evidence that either doesn’t exist or doesn’t replicate. This paper is part of that pattern.

The paper measures infrasound an operator would receive directly on a tractor or combine, with the engine and mechanical systems inches away. The measured infrasound levels here are really high. This doesn’t tell us anything about what everyday people experience in their homes. No source of infrasound (just like no source of regular sound) is nearly as loud as sitting on a tractor.

Worse for Jordan’s later video, the paper compares its measurements to the existing Polish occupational exposure limit of 102 dBG for an 8-hour workday. Data center and wind turbine infrasound at residential distances is typically in the 50 to 75 dBG range. That might sound like a small gap, but remember that decibels are logarithmic. Every 10 dB increase represents a 10x increase in sound intensity, and every 20 dB increase represents a 100x increase.

So this is telling us that even the loudest data centers near people’s homes are several hundred to over a hundred thousand times quieter than what the occupational standard the Bilski paper is using considers safe for a full 8-hour workday of direct exposure. The paper Jordan is flashing as evidence of infrasound harm is, if you actually read it, implying that our regular daily infrasound exposure could be multiplied by 1000 and still be totally safe.

The second paper is “Symptoms intuitively associated with wind turbine infrasound” published in Environmental Research.

The paper concludes that infrasound had no detectable effect.

The paper is one of the main outputs of a single research project commissioned by the Finnish Government and conducted by the Technical Research Centre of Finland (VTT), the Finnish Institute of Occupational Health, the University of Helsinki, and the Finnish Institute for Health and Welfare from 2018 to 2020. Its goal was to figure out if wind turbine infrasound actually causes the symptoms some residents attribute to it.

The three outputs of the project are:

1. The paper that Jordan shows. This is only the questionnaire study. Within 2.5 km of a wind turbine, 15% of Finnish respondents said they associated various symptoms with wind turbine infrasound, and the symptoms spanned multiple organ systems. The paper documents what people report and what they believe is causing harm, not whether infrasound actually harmed people.

2. “Annoyance, perception, and physiological effects of wind turbine infrasound.” Here participants were divided into two groups: people who reported the types of symptoms they attributed to wind turbine infrasound, and people who didn’t. Both groups were exposed to real wind turbine sound recordings, with and without the infrasonic components. Neither group could reliably detect when the infrasound was present. It had no effect on reported annoyance, or measurable effect on people’s nervous system responses. There were no differences between the symptomatic and non-symptomatic groups.

3. The full final report of the project, published by the Finnish Government under the title “Infrasound Does Not Explain Symptoms Related to Wind Turbines.” Jordan conveniently does not mention this title.

The project found exactly what every other serious investigation of this question has found. Some residents report symptoms, they intuitively attribute those symptoms to infrasound. When you actually expose them to infrasound in controlled conditions, they can’t detect it, it doesn’t produce the symptoms, and it doesn’t produce any measurable physiological response. Looks like a nocebo.

Jordan only shows the first paper where people claim that infrasound caused their symptoms, and never clarifies that the conclusion of this study was that infrasound wasn’t to blame for whatever was happening.

The third paper is “Evaluation of Low-Frequency Noise, Infrasound, and Health Symptoms at an Administrative Building and Men’s Shelter: A Case Study.”

In 2019, a nonprofit serving homeless people had two incidents of loud noise and strong vibrations coming from an adjacent landfill. Employees reported feeling unwell during and after. The buildings were evacuated. NIOSH was called in to conduct a Health Hazard Evaluation. This is the paper documenting that investigation.

The paper is clear that HHEs are workplace investigations to help employers recognize and control hazards. They are not research studies. This one is a descriptive report published after the fact. Its findings don’t support what Jordan is implying at all.

NIOSH arrived four months after the incidents and took sound measurements during normal flare operation. The overall measured sound levels across frequencies up to 100 Hz were 64 to 73 dB. The American Conference of Governmental Industrial Hygienists threshold limit values for occupational exposure to infrasound and low-frequency noise are 145 dB at specific frequencies and 150 dB overall. The measured levels were roughly 77 to 86 dB below the threshold at which harm is known to occur, at least tens of millions of times less intense, and possibly hundreds of millions of times less intense at the quieter end.

The paper explicitly says “The frequency-specific or overall sound levels we measured are not known to cause adverse health effects.”

The paper proposes explanations for the symptoms employees reported, and none of them involves infrasound damaging the body:

1. People audibly heard and felt two specific bad events and felt bad.

2. The routine sound at the site had an unbalanced frequency profile: more energy at low frequencies than at higher ones. This kind of imbalance is known to cause annoyance complaints even when the overall sound level is low. The paper cites a 2021 review by van Kamp and van den Berg that found, where health effects are observed near low-frequency audible (not infrasound) noise sources, those effects track with how annoyed people are, not with direct exposure to the low-frequency sound itself.

3. A lot of the symptoms people reported (headaches, fatigue, trouble sleeping) are common and nonspecific. When NIOSH looked at the employees’ medical records, a meaningful fraction had plausible alternative explanations for their symptoms that had nothing to do with the flare.

4. Employees felt the organization and the county had mishandled the incidents. The paper frames this as a breach of “psychological contract,” the sense that your workplace has not kept faith with you. That breakdown, the paper argues, shaped both how employees experienced their symptoms and their unwillingness to return to the building.

The paper’s Limitations section acknowledges that no definitive conclusions about the cause of the symptoms can be drawn.

And in a final twist, the main wind turbine study this paper cites is Michaud et al. 2016, the Health Canada study, and summarizes its conclusion: the study authors did not find a relationship between wind turbine noise and reported health effects such as headaches, dizziness, sleep problems, and stress.

So just to wrap, Jordan’s third piece of evidence here is a paper that:

• Measured sound levels well below any known harm threshold.

• Explicitly states those levels are not known to cause adverse health effects.

• Attributes the reported symptoms to annoyance and a breach of psychological contract rather than direct infrasound harm.

• Explicitly refuses to draw causal conclusions.

• Cites Health Canada to say wind turbine noise doesn’t cause the symptoms people attribute to it.

The actual meta-analyses and review papers on infrasound are overwhelmingly consistent: there are no health effects unless the sound is loud enough to be perceived, and at that point it behaves like regular noise pollution. There’s no serious meta-analysis that reaches the conclusion Jordan’s implying.

Why did he just flash three papers that obviously don’t conclude what he says? It’s such a bizarre move that my trust in him as a narrator cratered.

0:46 - Have you ever been to a particular place or maybe walked into a building and immediately felt like something was wrong? Like a rather noticeable and intense feeling of discomfort or maybe even fear despite there not being anything notably wrong with the environment. And then the moment you leave that place, you start feeling better.

Your bullshit detectors should be going off. This is a very common part of the human experience. A lot of places just look ominous, and a lot of people have anxiety.

Look at the images he chooses as examples here:

It’s obvious that these are just generally ominous places! The second is a famous image from /r/liminalspaces, which collects pictures of places that feel impermanent, and where you feel transient and possibly uneasy.

0:59 - Well, it turns out that there’s a chance that it wasn’t all in your imagination or anything paranormal. It could have just been infrasound.

All the thorough research that’s been done strongly implies that infrasound doesn’t cause this at all.

1:25 - We’re going to take a close look at the infrasonic research and see how how some types of these sounds can make people enjoy music less or feel incredibly ill.

He flashes this on the screen as he says this:

This is Chapter 11 of the WHO’s “Compendium of WHO and other UN guidance on health and environment” with the WHO, UN Environment, and UNICEF logos displayed. Flashing this implies WHO-endorsed evidence that infrasound makes people enjoy music less or get incredibly ill.

The document says nothing like this.

This is a policy guidance document compiling recommendations across environmental health topics. Chapter 11 is on guidance for environmental noise, listing recommended levels for road, rail, aircraft, wind-turbine, and leisure noise. It doesn’t discuss infrasound as a distinct health harm.

There is no overlap at all between what Jordan is narrating and what the document on the screen actually contains. He’s just showing a completely unrelated government document with health agencies his viewers trust, while saying completely different false stuff about what the infrasound research implies.

1:31 - And according to one famous study, even see ghostly figures near them who aren’t actually there.

He’s referring to Vic Tandy’s “The Ghost in the Machine” article, which he’ll discuss more later. He shows images from various press articles summarizing Tandy’s work, which tend to describe it as more legitimate than it was.

I’ll save the full overview of this article for when he goes into more detail, but for now just be aware that this “famous study” is not a study at all. It’s a personal anecdote from a single guy who felt uneasy after being told the place he was working was haunted, took a single measurement, decided infrasound was the cause, and published the story in a parapsychology journal founded to investigate ghosts. The study never replicated, and was specifically tested and failed when a proper experiment was finally run. Jordan has once again misrepresented his source.

1:40–3:48 - Philosophy of time

This section has nothing to do with infrasound. It’s about Greek concepts of time, water clocks, and objective versus subjective perception.

I normally give YouTube essayists slack here. They’re making videos for people with short attention spans, and tangents are fine. But this video’s already been so deceptive that I can’t help thinking this section exists to prop up the others. It makes Jordan look worldly and well-rounded, and lends credibility to everything else he’s saying. Showing himself making a DIY sun clock is neat, but I’m worried this is a move to look scientific, like a more hip internet-intellectual way of wearing a lab coat in the video.

There’s another really bad move he’s making here. He’s implying our capitalist western culture has trained us to focus on easy-to-measure effects and to ignore our felt subjective sense of what’s going on. He seems to be using this to open the door to wild claims about things that are invisible to our daily experience but can still harm us. The things he’s about to talk about aren’t actually invisible to the measurement tools of science, just to our senses. His next move is to use this setup for a wild connection between two different invisible things: UV light and infrasound.

3:48–4:34 - UV and infrasound

4:02 - Only when the wavelength of light enters the human visible spectrum do we call it a color. We can’t observe higher wavelengths like ultraviolet, but we can observe the effects of it when it burns our skin or causes cancer. And if you keep cranking up the frequency of invisible light to X-ray and gamma, even lower levels of light can eject electrons from atoms... And for you or I, that means death. A lot of death. It goes without saying that when waves of pressure happen between 20 and 20,000 times per second and become audible to us, do we call it a sound. And while pressure waves above our range of hearing can give us headaches and hearing damage, it fortunately doesn’t do anything as dramatic as rearranging atoms. But medical research is starting to trickle out data about the sounds that are happening below our range of hearing. And things are looking quite a bit more sinister.

As a former physics teacher, this section was torture.

He’s setting up this framing about how things that are invisible to us can still harm us, and then very quickly moves from ‘ultraviolet light can obviously kill us’ to ‘and there’s new disturbing evidence that infrasound can harm us’ without acknowledging the obvious problem that infrared light does not really harm us at all unless it’s so powerful that it heats up our bodies in the way other normal heat sources do. This is the correct analogy to infrasound, not UV light. Just like infrared light can only harm us if it’s powerful enough that we physically feel it in the way we do other heat sources, infrasound can only harm us if it’s so powerful that it causes us to physically feel its presence in the way we do other very loud sources of sound. He’s not so subtly reaching way across this spectrum to say that because something that’s so high energy that we can’t see it can cause damage, maybe something that’s so low energy that we can’t hear it can cause damage too.

In your everyday life you probably don’t spend much time thinking about prices measured in fractions of a cent, or in tens of millions of dollars. Both amounts are “invisible” to your daily experience, but for opposite reasons. What Jordan’s doing is kind of like saying “Because you never think about tens of millions of dollars, other money amounts you don’t think about might also damage your wallet if you spend them, and there’s new evidence that fractions of a cent can harm your wallet too.”

“Things are looking quite a bit more sinister” is doing a lot of work. The medical research is not “trickling out” and is not at all sinister. As covered above, we’ve had decades of solid research, and the conclusion of all serious scientific work on this is incredibly consistent.

4:50–5:21 - The symptom list

4:50 - For example, research has strongly suggested that infrasound can cause headaches, fatigue, loss of concentration, mood changes, depression, sleeping disorders, panic disorders, nausea, dizziness.

The research he cites doesn’t say this at all, but first I need to note that a long list of basically anything and everything bad you might experience in your everyday life is a common signature of a nocebo. Think about how infrasound compares to any other exposure to something bad for us. Carbon monoxide or lead poisoning produces very specific symptoms. Even normal noise pollution has very specific symptoms like hearing loss and cardiovascular effects of chronic sleep disruption. They don’t have these incredibly general long lists of symptoms.

What are the studies he’s flashing on the screen here to support this?

Well, first he puts this map of infrasound science up on the screen:

This looks authoritative and implies a century-long scientific history of studying the harms he’s discussing.

The image is from the 2020 paper “Low-Frequency Noise and Its Main Effects on Human Health — A Review of the Literature between 2016 and 2019,” published in Applied Sciences by four researchers at the University of Minho in Portugal. The timeline is a history of the general low-frequency-noise research field.

But he’s tricked his viewers again. The paper is about low-frequency noise, not infrasound. Those are not the same thing here. Low-frequency noise typically refers to the audible range roughly between 20 and 200 Hz, infrasound is below about 20 Hz and is generally inaudible. The paper uses “low-frequency noise” throughout and only occasionally touches infrasound specifically. So the timeline isn’t even a history of the field Jordan is claiming support from.

The paper doesn’t conclude what he’s implying at all. It’s mainly analyzing audible noise, not infrasound. He’s mixing up audible low-frequency noise (roughly 20–200 Hz, which you can hear) with inaudible infrasound (below about 20 Hz, which you generally can’t). The paper finds plenty of health effects from regular audible noise pollution, like annoyance and sleep disorders, mostly from things like road traffic, railways, the audible part of wind turbine noise, oil and gas drilling, and compressor stations. The paper explicitly excludes inaudible infrasound from its in-depth analysis: in the methodology, “low-frequency sound/infrasound” is listed as one of nine categories in the 142-paper database, but it’s deliberately not among the three categories selected for the 39-paper in-depth review. The paper doesn’t comment on whether infrasound is harmful or not.

The next section is even worse. He flashes and says a ton of individual symptoms described by the paper:

Not one of them is attributed to infrasound by the paper he’s citing, they are literally all caused by audible noise:

• Headaches and nausea come from Blair’s study of oil and gas well construction and drilling in a residential area. The paper is explicit that this was from continuous noise measured at 51.5 to 80.0 dBC, with headaches and nausea reported when levels exceeded 60 dBC. The “C” in dBC refers to C-weighting, a measurement scale for audible sound that captures low-frequency components. 60 dBC is roughly the loudness of normal conversation. The study wasn’t measuring inaudible infrasound, it was measuring the audible din of heavy industrial drilling next to people’s homes.

• Fatigue, lack of concentration, negative mood, and dizziness all come from the same sentence in the Pohl, Gabriel, and Hübner wind turbine study. And here Jordan is doing something more deceptive. These aren’t the results of a study, they’re options from a survey checklist, the list of 12 symptoms the researchers asked residents about. Quoting the paper: “ascertaining the physical and psychological symptoms referenced by residents that participated in the study (general mental indisposition, performance and reduced work capacity, lack of concentration, fatigue, tension, nervousness, negative mood, dizziness, irritability, indisposition, reduced sleep quality, and annoyance).” This is just the questionnaire the surveyors handed out.
  And what did the study actually find when residents were asked? The exact opposite of the picture Jordan is painting. Pohl concluded “the annoyance experienced was very low.” Only 8.5% of residents in 2012 reported any feelings of pressure related to the wind farm, dropping to 6.8% by 2014. Only 6.1% reported felt vibrations in their body, dropping to 3.8%. Symptoms of dizziness, one of the words Jordan highlighted, “were not observed in this study.”

• Anxiety and depression come from Abbasi’s study of wind turbine maintenance workers, who stand on and close to them. The study’s exposure levels were 60, 66, and 83 dBA. This is clearly audible, occupational noise. The paper’s own summary of the findings emphasizes that “the worst health status is due to working conditions and chronic exposure to occupational risk factors, such as noise” and that the harmful effects were concentrated in the maintenance team who were “in the vicinity of wind turbines, due to the reception of very intense noise.” In other words, the study’s own framing is that these effects track with exposure to loud audible noise, not infrasound.

• Sleep disturbance is discussed across several studies the paper reviews, and again the attribution is to audible noise. The paper says directly: “Long-term exposure to low-frequency noise from wind energy is a major factor in sleep disturbances in residents who live near wind farms.” Note the framing: low-frequency noise, not infrasound.

When the study did look at infrasound specifically, it noted that “the noise level of the wind turbine measured in the lower frequency range is below the human sensory threshold” and the paper reports this without treating it as evidence of an inaudible-harm mechanism. The paper does not argue that inaudible infrasound causes the sleep disturbances. It consistently attributes them to audible low-frequency components.

Jordan has now completely misrepresented all 11 studies he’s mentioned in the first five minutes of the video. 7 imply the exact opposite of what he’s claiming they do, 3 are completely unrelated to what he’s saying, and the only one that agrees with him isn’t a study at all, it’s a one-off anecdote written in a publication that mainly studies ghosts. From what I can tell his follow-up to this video is the most popular new piece of media made about data centers so far this year.

5:04 - I am very aware of the placebo effect and of course disclude my personal experiences with generated infrasound here in my lab when making this video. But I can tell you subjectively that it very much sucked. And even after turning the infrasound off, it took a while for things like intense nausea and dizziness to subside.

He’s confusing the placebo with nocebo effect here, but either way this is silly. He put himself in a position to experience the nocebo effect, and then he experienced it, and said “but it very much sucked.” I’d like a little acknowledgement that literally all good science points to this being a nocebo.

5:21–5:51 - The heart-contraction study

5:21 - A few years ago, a large published and peer-reviewed medical study showed that 100 dB of infrasound, primarily around 10 hertz, caused a very negative effect on the heart’s ability to contract with a 9% decrease in contraction force for every 10 dB above that. Your heart, an organ that some people would argue is necessary for survival, has to work quite a bit harder when you’re exposed to high levels of infrasound. And this is on top of quite a bit of animal research suggesting negative effects on heart, liver, nervous system, and lungs.

Jordan flashes “Negative Effect of High-Level Infrasound on Human Myocardial Contractility: In-Vitro Controlled Experiment” as he’s saying this.

You might be surprised to learn the full context of this paper implies something different.

Researchers at the University Hospital of Mainz took small samples of heart muscle tissue from patients having cardiac surgery, mounted them on tweezers in a laboratory apparatus, stimulated them electrically to contract, and exposed them to infrasound at 100 to 120 dB at 16 Hz for an hour. They measured a decrease in contraction force. That is the entire study.

Jordan gets the decibels slightly wrong. He says “100 dB.” The actual experiment tested three levels, 100, 110, and 120 dB, and most of the dramatic contraction-force decrease happens at 120 dB. Because decibels are logarithmic, that 20 dB gap between what Jordan said and what the experiment actually tested is a factor of 100 in sound intensity. But that’s a tiny problem compared to his weirder move here. In the same journal, two peer-reviewed critical responses of this paper were published and linked directly from the top of the PubMed Central page he’s screenshotting. They completely demolish the original paper. He either didn’t click them or hoped the viewer wouldn’t.

Here are the links:

• Comments on the Article “Negative Effect of High-Level Infrasound on Human Myocardial Contractility: In-Vitro Controlled Experiment”

• Can Infrasound from Wind Turbines Affect Myocardial Contractility? A critical Review

Three big fatal problems with this paper:

1. The critical reviewers found that Chaban’s apparatus produced excessive air movement, which caused the tips of the tweezers holding the muscle samples to vibrate. The muscle samples weren’t behaving differently due to the infrasound. They were being mechanically shaken by vibrating tweezer tips in the near field of the sound source. The critical review concluded: “The results of the paper of Chaban et al. are invalidated by a physically inappropriate setup that produces excessive air movement, not present in a regular sound wave of the same sound pressure in the far field.” Their overall conclusion: “Chaban et al. identified an artificial problem that doesn’t exist in reality.”

2. The reviewers pointed out that the total acoustic dose, the amount of pressure energy delivered over time, that caused an 18% decrease in contraction force in Chaban’s experiment (one hour at 120 dB) is matched by a single cycle of a swimmer doing the breaststroke. Every time a swimmer pushes their arms forward and back, the pressure their chest experiences from pushing through water equals or exceeds the total acoustic energy Chaban’s lab spent an hour blasting at dead tissue samples. If Chaban’s finding mapped onto anything biologically real, competitive swimming would be incredibly dangerous.

3. Infrasound never reaches anywhere near Chaban’s experimental volume in people’s homes. Wind turbine infrasound at typical residential distance from large modern turbines is about 65 dB, measured in high wind conditions at 200 m distance. Data center infrasound at residential distances is in a similar range. That’s 55 to 65 dB below what Chaban tested, a factor of hundreds of thousands in acoustic intensity.

I’m starting to think Jordan isn’t the most reliable narrator.

He pairs this paper with a vague appeal to “quite a bit of animal research suggesting negative effects on heart, liver, nervous system, and lungs.” He does not name the research. What he is gesturing at is the body of work around “vibroacoustic disease” or VAD, a hypothetical condition developed in Portugal starting in the late 1980s by a surgical pathologist named Nuno Castelo Branco and an engineer named Mariana Alves-Pereira.

Their central claim is that long-term exposure to low-frequency noise causes a “whole-body systemic pathology.” Symptoms include thickening of heart tissue, respiratory damage, cognitive decline, epilepsy, increased rates of tumors, depression, and eventually death. It’s a lot of responsibility for a single environmental mechanism! And this is the basic source of every claim about animal research and infrasound damaging organs.

VAD has never been accepted by any mainstream medical body. It has never been independently replicated in nearly forty years since it was proposed. The UK Health Protection Agency reviewed the evidence in 2010 and concluded that the disease “has not gained clinical recognition” and that there is no evidence infrasound at levels normally encountered in the environment causes it. A 2013 analysis of the VAD literature in the journal Noise & Health found that Castelo Branco’s self-citation rate across the body of VAD papers is 69%, and Alves-Pereira’s is 36%, meaning the “field” is mainly two authors citing themselves to build the appearance of a body of evidence. Leventhall, reviewing the literature, quoted Henning von Gierke, former chief of the USAF biodynamics laboratory, calling VAD “an unproven theory belonging to a small group of authors.”

Their animal studies use exposure levels way above anything found in residential environments, the kind of occupational exposure you’d get next to a jet engine. None of those studies have been replicated by independent researchers outside the Portuguese group.

When Jordan says “animal research shows effects on heart, liver, nervous system, and lungs” this is what he means. One research group, publishing on itself, at exposure levels nobody and no animals ever encounter, with a disease category no medical authority recognizes, that has failed to replicate for four decades.

5:51–6:37 - ‘It’s hard to study’

5:51 - Another pretty scary thing is that this list is likely very incomplete due to the difficulty and lack of research on infrasound. It turns out that not many people want to volunteer to feel super uncomfortable while their organs may get damaged for some reason.

Infrasound is one of the most thoroughly studied topics in noise research. We have decades of papers, dozens of controlled trials, multiple large epidemiological studies, and national health body reviews from multiple continents. The Marshall et al. 2023 study alone had 37 participants doing 72-hour laboratory stays with tons of equipment measuring basically all possible physiological effects. The Health Canada study covered 1,238 households. People absolutely do volunteer for these studies, they’ve been done.

“We just don’t have enough research” is a standard move in pseudoscience. It lets you dismiss the actual negative findings in the literature by asserting the literature is thin. But the literature is not thin, and its findings are not ambiguous. The reason he can’t cite studies showing what he wants them to show is not that the studies haven’t been done. It’s that the studies have been done and they’ve implied he’s wrong.

6:07 - If you look into this, a lot of the research that goes into the health effects of infrasound is overwhelmingly concentrated on infrasound caused by wind turbines. And yes, wind farms do generate infrasonic noise pollution. And it turns out that a lot of this research is either partially or completely funded by the fossil fuel industry to try and find something nefarious or wrong with wind energy.

This is an incredibly weird way to frame the actual research. Most of the infrasound research has been done in response to fossil fuel industry claims that wind farms cause harm, and the good studies have all shown they don’t. What Jordan is not-so-subtly implying is that the infrasound research is invalid because fossil fuel money touched it. In reality, basically every good study on infrasound cuts against the fossil fuel industry’s claims.

He does to his credit at least flash the Wind Turbine Syndrome Wikipedia page, which is a good debunking.

6:31 - And this situation is a major problem in science and research. I did a video covering some of this here

The phrasing implies that Jordan has a separate video laying out how the fossil fuel industry has distorted infrasound or wind turbine research, and that the curious viewer can go watch it. The viewer is meant to register “okay, there’s a deeper argument here and he’s made it elsewhere” and move on.

I watched the video he flashed on the screen. It’s where he’s growing wheatgrass and chia seeds in his home. He plays a 4,000 Hz tone at 72 dB at his seedlings for twelve days, observes that the sound-exposed plants grew about 28% more biomass, and speculates about the mechanism. The video is threaded with reflections on Gödel’s incompleteness theorems, the replication crisis, and his own decision not to go to college.

In this video Jordan doesn’t just misunderstand the replication crisis, he inverts it. He says:

The replication crisis is more about researchers not being funded to reproduce their own or other studies and some of that is probably because of capitalism.

This is not what the replication crisis is. The replication crisis is the finding that when researchers do try to reproduce published studies, a huge fraction of them fail. The Open Science Collaboration’s 2015 project tried to replicate 100 psychology studies and got statistically significant results in only 36% of the replications. The Reproducibility Project: Cancer Biology tried to replicate 193 experiments and found that positive findings replicated only 40% of the time. Similar patterns have shown up across economics and other fields. The crisis is about the validity of the underlying literature, not the funding pipeline for replication.

Jordan has the direction reversed. He’s framing the crisis as “we haven’t run enough replications yet,” when the actual crisis is “we ran replications and a lot of published findings didn’t survive.” The first framing implies scientific consensus should be trusted pending more work. The second implies scientific consensus has already been partially invalidated, and we need to figure out which findings still hold.

This matters because the whole video is a manifesto arguing that institutional science is credentialist, capitalist-corrupted, and not to be trusted, while independent researchers with their own setups are the real source of knowledge. Jordan explicitly tells viewers “there’s a major war on truth and objectivity happening right now” (I definitely agree with him that in the exact moment I’m watching this video, a major war on truth and objectivity is occurring) and that the replication crisis has been weaponized “to discredit the effectiveness of research and scientific method.” He then dismisses the reputation of credentialed scientists as “the battlecry of a really poor or weak scientific argument” describes institutional gatekeeping as “probably a much larger threat to science than the replication crisis,” and presents his own n=1 unreplicated wheatgrass result as meaningful.

This is what Jordan tells viewers he’s “covered some of” in that link at 6:37. It’s not an argument that the fossil fuel industry distorted infrasound research. It’s a much broader argument that mainstream scientific consensus should be treated as corrupt and that viewers should trust independent YouTubers running personal experiments instead. Once you understand that, the infrasound video makes more sense. He’s not trying to engage with the decades of controlled studies, epidemiology, and public health reviews that all point the other way. He’s pre-emptively dismissing them. The Marshall studies, the Health Canada survey, the Finnish project, the NHMRC review, these aren’t findings to be grappled with. They’re products of the institutional capitalist machine, and the institutional machine has been compromised. What you should trust instead is a guy with a hydroponic chamber and a sine wave generator.

His own n=1 wheatgrass experiment is exactly the kind of unreplicated finding the replication crisis is about. If the crisis teaches us anything, it’s that results like this should be held lightly until independent labs try to reproduce them.

6:37 - So, yes, wind farms do cause infrasonic noise pollution, while fracking in Texas, Oklahoma, British Columbia is literally causing record-breaking earthquakes. And how does the infrasound from all this fracking affect people? We don’t really know ‘cause we haven’t studied it nearly as much as wind farms.

The fracking point is wrong. The infrasound research on ambient industrial sources is directly applicable to fracking infrasound. There doesn’t need to be a fracking-specific study for the same reason there doesn’t need to be a study on the effects of infrared light from each different very specific industry. We know the general effects of infrared light, and the general effects of infrasound. The framing that every new industrial source needs its own full-scale research paper before we can conclude anything is a way of creating permanent scientific uncertainty and dismissing all the actual research. He’s also trying to separate himself from the main way infrasound pseudoscience has been badly used: by attacking wind turbines. I don’t think he gets to have it both ways here. If infrasound is actually harmful, wind farms at close range to homes do also create meaningful amounts, and he’d have to at least acknowledge the trade-off that infrasound harms are real but worth it for building out wind power. Instead he wants to have it both ways.

Also, on the earthquake point, USGS says felt earthquakes directly caused by hydraulic fracturing are extremely rare, and the big rise in places like Oklahoma is primarily tied to wastewater disposal. Fracking-induced felt events have occurred in some regions.

6:52–8:10 - The Vic Tandy ghost story

6:52 - In the late 1990s, Vic Tandy, a respected lecturer and engineer, was working in a laboratory in Warwick, England that had a long reputation of being haunted. He felt a cold sweat and an intense feeling of depression. And out of the corner of his eye, he saw a blurry gray figure.

Yes, in the late 1990s, Vic Tandy was a lecturer working in a medical equipment lab. The lab had a local reputation for being haunted. People working there had reported feeling uneasy, seeing things out of the corners of their eyes, etc. Tandy himself had an experience like this. He suddenly felt cold and anxious and saw a blurry gray figure in his peripheral vision that vanished when he turned to look at it.

The next day, Tandy was clamping a fencing foil in a vice when he noticed the blade vibrating on its own. He realized a large fan in the lab was producing low-frequency infrasound around 18.9 Hz, and he hypothesized that this infrasound might be what was making people in the lab feel uneasy and see peripheral figures. He published this idea as a short article titled “The Ghost in the Machine” in the Journal of the Society for Psychical Research, a parapsychology journal, in 1998. This wasn’t “a study” the way Jordan describes it. It was a short article speculating about a possible mechanism. Just one guy’s anecdote, no experiment, no controls, nothing.

When someone eventually did try to test Tandy’s hypothesis rigorously (the Goldsmiths Haunt Project, which will be discussed more below when Jordan brings it up) the infrasound had no effect on reported experiences. Participants’ prior suggestibility was the sole thing that predicted whether they felt uneasy. This paper reinforced the idea that infrasound effects are a nocebo.

In both cases, Tandy visited places with strong local reputations for being haunted, where the people he interviewed had already been primed by each other to feel uneasy. It’s very likely this was a nocebo.

7:33 - A large fan was causing infrasonic vibrations around 18.9 hertz. You know what else resonates around 18 hertz? The human eye.

The eyeball resonance thing sounds scientific but isn’t at all. The original NASA work it traces back to was about mechanical vibration transmitted through the body at very high energy, not infrasound at normal environmental levels. It’s like seeing the frequency you need to push a kid on a swing to make them swing higher and higher, and concluding that very lightly poking their back at the same frequency would also work. Nobody standing next to a fan in a British laboratory was experiencing the kind of vibration those studies used that would actually harm your eye.

7:49 - This would later be published in a rather famous paper titled The Ghost in the Machine.

It’s famous in pop-science and paranormal-adjacent writing. It is not famous in the acoustics or medical literature in the sense of being a foundational finding. Serious infrasound researchers treat it as an interesting anecdote that has never been replicated under controlled conditions.

7:54 - Soon after this Vic visited another famously haunted place, a cellar in Coventry.

A few years later, Tandy wrote a follow-up article called “Something in the Cellar,” also in the same parapsychology journal. He visited a 14th-century cellar in Coventry that visitors had reported feeling uneasy in, put a microphone in the room for 20 seconds, found a 19 Hz peak in the ambient sound, and declared this confirmed his earlier hypothesis. The measured sound level was 38 dB at 19 Hz, which as Tandy himself acknowledges is 50 dB below the human perception level for that frequency. His explanation for why inaudible infrasound at 38 dB might still cause uneasiness was to speculate about “hypersensitive” individuals and to cite a New Scientist article about hypothetical military infrasound weapons. Again, this wasn’t a “study,” it was a guy going around with anecdotes and random hypotheses.

The methodology is not an experiment. Tandy did not expose anyone to the infrasound. He did not remove the infrasound and see if the apparitions stopped. He did not compare the cellar to other cellars. He did not blind anyone, recruit a control group, or do anything that would count as a test of the hypothesis. He collected stories from tour guides about visitors who had felt weird in a cellar that was already advertised as haunted, then measured the ambient sound in the room afterwards. This is not science.

The anecdotes themselves are also pretty bad evidence. Tandy notes in his own paper that the cellar has “a growing reputation” meaning visitors were arriving primed. Two of the “witnesses” Tandy relies on were self-identified witches who visited the cellar specifically to commune with the spirit world. A third witch was “frightened to death” and left. One key informant is the tour guide herself, who, on her own account, “found herself talking to” the presence during tours. These are self-selected paranormal enthusiasts reporting paranormal experiences, and their testimony is being used to support the causal claim that inaudible infrasound at 38 dB produces supernatural perception.

Another obvious problem is low-frequency resonances are a feature of almost every partially enclosed space. A cellar’s dimensions and an entry corridor will produce standing waves in the infrasound range by basic acoustics.

So the “Cellar in Coventry” paper is a ghost-hunter-reviewed anecdote-plus-ambient-sound-measurement, at sound levels the author admits are below any established threshold for perception, reported in a parapsychology journal, using informants who included self-described witches, in a cellar already known as haunted, and detected normal infrasound levels for cellars. And when it was tested rigorously later, it failed. This is the second piece of Tandy’s case, and it is, if anything, weaker than the first.

8:00 - It’s not the most conclusive study in the world, but pretty damn interesting

This is definitely the pattern yes.

8:10–10:09: Backyard recording

This section’s framing, that he can’t identify the infrasound source and therefore it’s mysterious and maybe significant, is silly. Any outdoor microphone capable of picking up infrasound is going to record a continuous soup of low-frequency energy from a ton of sources at once. Distant traffic on highways miles away. HVAC systems in nearby buildings. Wind pressure on the microphone itself, even with a windscreen. Ocean waves if you’re within a hundred miles of a coast. Thunderstorms hundreds of miles away. Trains. Aircraft. He does at least clarify at the end of this section that infrasound can travel far.

The fact that he can’t identify specific sources for specific rumbles is because infrasound propagates extremely well over long distances (which he acknowledges later) and that there are innumerable sources creating it constantly.

When he says “Wow, I am not jealous of the people who live nearby” he’s blurring the line between commenting on noise pollution vs. infrasound harms. The first one’s real, second one’s fake.

He throws in a wind turbine and makes what I think is a pretty obvious point that the wind turbine isn’t as loud as a train starting up at close range or the world’s busiest airport. He’s doing this in part to distance himself from the anti-wind activists on his side of the infrasound debate. I don’t think he should get to have it both ways here. If he thinks infrasound is a problem, he needs to acknowledge that wind farms create it, and that living near a wind farm exposes you to new levels of infrasound. He can say “that’s bad, but worth it to have the wind farm” or agree with me instead and say “that’s fake, build the wind farm, don’t listen to the cranks” but he shouldn’t get to imply that wind farm infrasound is fine because it’s not as bad as a train at close range. Throughout the rest of the video he’s implied over and over that similar levels of infrasound as wind turbines create are bad for you.

10:25–12:00 - The South Dakota detour

10:25 - But I really wanted to hear clear infrasound far away from any form of human infrastructure. And to do that, we had to drive over 1,500 miles to a particular spot south of Badlands National Park in South Dakota on the Oglala reservation.

10:55 - From where I’m recording, the nearest paved road and infrastructure of any type is about 11 miles away, and it looks like this. So not exactly a teeming metropolis. The sound up here is remarkably quiet.

He drives 1,500 miles across the country, sets up on a remote spot on the Oglala reservation, records almost nothing, and concludes that rural places don’t have much infrasound while cities and suburbs do. He’s implying infrastructure is polluting the world with invisible infrasound, and only in the most remote places can you escape it.

But he’s actually done nothing to legitimately establish that infrasound in cities is causing the symptoms he’s been implying throughout. The contrast he’s trying to draw, rural silence versus urban infrasound poisoning, is not supported either by the scientific consensus or even the consensus of the articles he himself has flashed on the screen so far in this video.

11:38 - But this does not mean that nature is infrasonically quiet. The earth can produce infrasound that humans can’t even begin to compete with. This distant thunderstorm, for example, made a symphony of various rumbles.

This is an important admission that he glides past. Large-scale natural phenomena like thunderstorms, earthquakes, volcanoes, ocean waves, and wind blowing over terrain, produce infrasound at levels that dwarf anything humans have built. The Earth is and has always been an infrasound-noisy place. Humans evolved and thrived in this environment. It doesn’t make sense from an evolutionary perspective that these sounds would be harmful to us. If ambient infrasound at ordinary levels caused the broad symptom list Jordan describes, every coastal population on Earth would be chronically ill.

12:00 - And there’s some early research and testing on the accuracy of infrasound to detect and predict tornadoes.

This part is actually real, phew! Infrasound can be used to detect and predict tornadoes.

12:08–14:32 - The Yellowstone segment

He then drives to Yellowstone and records infrasound from geysers, hot springs, and the volcanic caldera. This section is pretty, and there’s nothing wrong with it scientifically. These are real sources of real infrasound, and recording them is neat.

14:24–14:49 - The inescapability frame

14:24 - But aside from these extreme places or unusual events, the vast majority of infrasound that the average person is exposed to is caused by infrastructure. And unless you want to live here, there’s little chance that you’ll ever be able to escape it or even significantly reduce it.

This is the rhetorical goal of the road trip. He’s pushing that he went to the most remote place he could find, and only there was it quiet. He’s trying to imply that infrastructure is saturating the world with infrasound you can’t escape, and that’s harming you.

The claim that “the vast majority of infrasound the average person is exposed to is caused by infrastructure” is also just wrong if you include natural sources honestly. Wind, weather, ocean activity (detectable far inland as microbaroms), atmospheric turbulence, and thunderstorms produce enormous amounts of infrasound that propagates long distances. A person in a quiet rural area in the Midwest is constantly bathed in infrasound from distant weather systems, microbaroms from the oceans, and atmospheric gravity waves. The infrastructure-produced component is not clearly dominant in most settings, and it’s a strange claim to make unsupported.

Jordan has already walked viewers through fake papers, a nocebo symptom list, and the Tandy ghost story, so “infrasound is harmful” feels established, and now he can compare the infrasonic silence of nature to big evil loud society. He’s sticking the landing for the basic idea that viewers are being poisoned by something invisible and inescapable. But if we got all the authors of the studies Jordan has cited, they would all agree (except for Vic) that infrasound doesn’t seem to be a problem.

14:49–18:00 - The physics detour and SpaceX launch

Here Jordan gives a physics tutorial on why low-frequency sound propagates farther than high-frequency sound, followed by a demonstration where he uses open seismograph data to track a SpaceX satellite launch across southern California. He shows the launch detected at 3 miles, 6 miles, 27 miles, 60 miles, and eventually over 140 miles away.

The physics is correct, low-frequency sound does travel way farther than high-frequency sound, which is why you can hear distant thunder as a low rumble even when the higher-frequency crack is gone. It’s similar to electromagnetic radio waves, which have a way lower frequency than visible light, and can travel way farther. And a SpaceX rocket launch is, unsurprisingly, a loud infrasound event that can be detected far away.

But it’s doing the same rhetorical work as the tornado and Yellowstone segments. Here is a dramatic infrasound event propagating very long distances, done by Elon, an obvious bad guy (who to be clear I agree is bad). The implication is that this tells us something about how dangerous infrasound propagates through your neighborhood. But SpaceX infrasound doesn’t harm people for the same reason ordinary traffic, weather, and HVAC infrasound doesn’t harm people: even very loud sources, by the time they reach residential distances, are well below any level scientifically shown to cause health effects, and Jordan has produced literally zero evidence to contradict this.

17:18–18:00 - Helmholtz resonance

17:18 - it’s worth pointing out that in many cases, infrasound is actually louder indoors than outdoors, even if the source is coming from outdoors. And this is due to a phenomenon called Helmholtz resonance.

Helmholtz resonance is real, but jumping from “open car windows can throb” to “your house amplifies residential infrasound into something harmful” is a big leap. The phenomenon exists. The idea that Helmholtz resonance in residential buildings makes infrasound loud enough to be harmful is, once again, not supported by any evidence.

18:00–18:56 - The Liverpool Cathedral study

18:00 - In 2003, a group of scientists from the UK National Physical Laboratory held a live musical performance at the Liverpool Metropolitan Cathedral and then took a survey from the audience on how they felt during the music. In some of those music performances, they had a gigantic 23 ft infrasonic cannon tuned to 17.5 hertz, a frequency that didn’t resonate the building in a way where it would shake things or create audible overtones. and they played that tone at 90 dB while some of the music was played to people who were completely oblivious to what was actually going on. It turns out that people were quite a bit more likely to have felt uncomfortable during the performance when the infrasonic tone was playing. And 22% of the audience said that they felt some type of strange or unexplainable effect such as sadness, chills, or even feelings of fear and anxiety.

This is the “Soundless Music” or “Infrasonic” experiment, a 2003 concert at London’s Purcell Room (not Liverpool Metropolitan Cathedral, Jordan has the wrong venue). It drew about 700 attendees and collected 522 questionnaires. This study is often misunderstood as implying that the infrasound caused 22% of people to experience something strange.

The core problems with this result have been covered by infrasound researchers repeatedly. First, no peer-reviewed paper with the underlying data was ever published and there was no control group. Second, the 22% figure reflects the baseline rate of people reporting something strange during evocative music in a dramatic venue. People at concerts report feeling moved, weird, uncomfortable, or spiritually affected all the time, regardless of whether there’s infrasound present. The “22% reported unexplainable effects” statistic reads as impressive out of context but less so when you realize it’s approximately the rate at which people report unusual feelings during evocative music without any infrasound at all. Third, the Goldsmiths Haunt Project (which will be covered next) tested the same hypothesis properly under controlled conditions and found that infrasound exposure did not predict reported experiences, suggestibility did.

18:56–20:16 - The Goldsmiths Haunt Project

18:56 - In yet another study from Goldsmith’s College, four researchers attempted to make a haunted room by filling it with various infrasonic frequencies and EMF or electromagnetic frequencies.

The researchers built a “haunted room” at Goldsmiths College and ran 79 participants through it. Each participant sat alone in the room for 50 minutes. The room had two manipulations they could turn on and off independently: infrasound at 18.9 Hz (the Vic Tandy frequency), and complex electromagnetic fields of the kind that have been proposed as a cause of anomalous experiences. Participants were randomly assigned to one of four conditions: infrasound only, EMF only, both, or neither. Everyone was told the room might produce unusual sensations.

After the session, participants filled out a questionnaire about what they’d experienced: feelings of presence, unusual body sensations, dizziness, emotion, anything out of the ordinary. They also completed a standard psychological measure of suggestibility beforehand.

Jordan narrates the study setup accurately, but then he misrepresents what it found.

19:23 - Out of these groups, an average of 3.7 participants in the control group, those sitting in the empty room with nothing unusual happening, had some type of haunted experience. When infrasound was present, this number grew to an average of 5.2 people having a haunted experience.

What Jordan leaves out is the actual finding of the paper. The number of reported experiences was not statistically different across conditions. The authors explicitly concluded that the number of reports was unrelated to experimental condition, and that suggestibility was the most parsimonious explanation for the experiences participants reported. Jordan cites the 5.2 vs 3.7 numbers as if they supported an infrasound effect, but the paper they come from concluded that the effect was not present and that the reports reflected participants’ prior suggestibility, not the physical conditions they were exposed to.

19:48 - Now, in my opinion, the study is fun, but not very well organized and definitely not conclusive.

He hedges on the study’s conclusiveness only after having cited its numbers in a way that implies the opposite of what it found. The hedge is positioned so the viewer walks away with the 5.2 vs 3.7 number in their head and a vague sense that “the science isn’t settled but it’s suggestive” when the actual published conclusion was that infrasound did not explain the reports.

This is yet another massive misrepresentation of a scientific paper in the video. The paper’s authors specifically tested Jordan’s hypothesis, found it was not supported, concluded suggestibility was the explanation, and published this. Jordan presents the paper as if it found the opposite.

20:16–End - The gear section

The final third of the video is a discussion of his microphone setups, the Raspberry Shake seismograph, accelerometers, and a mention of his “hum” video. This section doesn’t make any big new scientific claims about infrasound harming humans. I’m mainly worried this is yet another way to look more scientific and legitimate.

Conclusion

In a single 25-minute video about how infrasound is bad, Benn Jordan cited as evidence:

• A survey of farm equipment that noted infrasound is safe at levels 100,000x what we experience even in noise polluted areas.

• A WHO document about noise pollution that doesn’t mention infrasound.

• A Finnish government project whose final report is titled Infrasound Does Not Explain Symptoms Related to Wind Turbines.

• A NIOSH workplace report that explicitly says the sound levels it measured aren’t known to cause harm and attributes the workers’ symptoms to annoyance and a psychological contract breach.

• A literature review exclusively about the bad effects of audible low-frequency noise.

• A survey checklist of symptoms as if it were the symptoms subjects actually reported having.

• A study of wind turbine maintenance workers at 60 to 83 dBA of audible occupational noise.

• A study of people living near oil and gas drilling getting mad at the 80 dBC audible noise.

• A heart-muscle paper demolished by two peer-reviewed responses linked from the top of the page he screenshotted, which found the results were caused by vibrating tweezers, that the acoustic dose was matched by a single breaststroke, and that the intensity used was hundreds of thousands of times above anything in a home.

• “Animal research” that turns out to be vibroacoustic disease, a hypothetical condition published almost entirely by two authors citing each other for forty years and rejected by every mainstream medical body.

• A parapsychology essay written as a personal anecdote by a guy primed by ghost stories, and its sequel, which mainly interviewed self-described witches in a cellar already advertised as haunted at sound levels 50 dB below the perception threshold, and found that they felt somewhat uneasy.

• An experiment that found no effect, but Jordan presents it as having an effect.

• A study that found 22% of people at a concert with evocative music had strange experiences.

• NASA work on eyeball resonance frequencies without mentioning that these harms only appear when you are literally inside a rocket.

The running theme across every study that actually addresses it is that the symptoms people report are real but not caused by infrasound. They’re driven by expectation, annoyance, context, and suggestibility, which is the signature of a nocebo.

He implicitly analogized infrasound to UV light instead of the obviously more comparable infrared light. He used Helmholtz resonance to imply buildings amplify residential infrasound until its harmful without giving any actual evidence of harm. He called infrasound “grossly understudied” when it’s one of the most thoroughly studied topics in noise research. He implied the fossil fuel industry suppressed infrasound research when the fossil fuel industry is in fact the main funder of the pseudoscience he’s promoting. He claimed fracking infrasound needs its own dedicated research before we can conclude anything, despite fracking causing the same levels of infrasound as a ton of other sources.

He told viewers he had a separate video explaining the politics of all this. In that video he inverts the replication crisis, dismisses relying on credentialed scientists as “a really poor or weak scientific argument,” calls institutional gatekeeping a bigger threat to science than replication failures, and presents his own n=1 wheatgrass result as meaningful.

He drove 1,500 miles, emitting the CO₂ equivalent of half a million ChatGPT queries, to record silence in rural South Dakota and make a point he could have established with a Wikipedia search. He acknowledged on camera that the Earth produces infrasound that dwarfs anything humans have built, and then concluded that human infrastructure is saturating the world with harmful infrasound we cannot escape.

And this is, from what I can tell, the lead-in to what I think is the most popular piece of media made about data centers in 2026 so far.

Which brings us to the second video.

Second video - Datacenters Behaving Like Acoustic Weapons

Well now we can finally talk about data centers I guess.

I’m going to work through this in roughly the order of the video, but it’ll be useful to separate three things that Jordan deliberately runs together:

1. Audible noise pollution from data centers, which is real and harmful.

2. Ambient infrasound from data centers, which his own data shows is unremarkable. This is a fake problem.

3. The symptoms residents are reporting, which are real, but whose cause is the audible noise plus stress, not the infrasound.

The video’s central move is to associate 3 with 2, whereas in fact it’s all caused by 1

0:00–1:29 - Intro

0:00 - If you’re in the demographic of people who generally get recommended my videos, then chances are that you are not a fan of data centers.

As I said at the start of this post, he’s using this nuance as a credibility shield. It works on viewers because it reads as a signal that he’s willing to acknowledge nuance, but it’s really a way of pre-buying permission to make crazy uncharitable claims about an industry the audience already dislikes.

1:04 - Spoiler alert, the results are terrifying.

Spoiler alert, they reduce to random statistical noise in a terribly designed study by Jordan

1:36 - regardless of where your ethical, environmental, political, or economic interests are, if a data center is being built nearby your home, you’re generally kind of f*****.

Obviously I disagree. Data centers (with a few exceptions, like xAI, which comes up later in this video) mostly don’t seem to have harmed people. I don’t think the citizens of Loudoun County, one of the top 5 wealthiest counties in the country and home to the most data center capacity in the world, are “generally kind of fucked.”

1:56 - Last year I started hearing about some mysterious and troubling symptoms from people who live nearby newly constructed data centers

Jordan seems somewhat addicted to flashing completely unrelated studies on the screen.

This time he flashes a Dutch survey of 190 people who self-identify as experiencing a perceptible low-frequency hum in their environment, compared to 371 people who don’t. The researchers didn’t measure any sound in anyone’s home. They didn’t establish that the complainants were even actually being exposed to what they thought they were perceiving. They didn’t test causation.

Most importantly, they didn’t mention data centers once.

What the paper does document is that the complainants are disproportionately older and introverted, which is the exact demographic signature of other “environmental sensitivity” syndromes where the attributed cause has repeatedly failed controlled testing. The paper’s own authors explicitly acknowledge that “non-acoustic factors including sociodemographic and individual characteristics or personality constitute substantial predictors for reactions to noise.” This is likely a nocebo, and Jordan once again takes a paper that documents self-reports and presents it as a paper that proves causation.

He then flashes the symptoms list on the screen as he says this. You would think that if someone is saying “symptoms from people who live nearby newly constructed data centers” and is flashing a list of symptoms on the screen, those people would live near data centers, or the study would focus on this in some way. But you’d be wrong!

2:07 - I started looking into seismograph readings from around the country, and there was enough correlation for me to dig pretty deep into an area of research I already had some experience in

Consider what that first video was like, this is the “experience” he’s referring to.

It’s true that data centers register on seismographs. So do rail lines, highways, hospitals, college campuses, shopping malls, factories, power stations, and large intersections. I suspect he’s going to draw some bad inferences.

2:24–3:22 - The symptom list, round two

2:24 - Being in the presence of elevated levels of infrasound has been shown to show spikes in cortisol levels... It can cause vestibular issues leading to loss of balance, vertigo and nausea. Then there’s something called vibroacoustic disease which suggests that infrasound can cause abnormal growth of extracellular matrices... It’s been shown to cause high frequency hearing loss, shortness of breath, anxiety, depression, and it even makes your heart have to work harder to pump blood.

He’s flashing the same study from last time that only measured audible low-frequency sound, not infrasound. Great! He’s doing the same trick of flashing a bunch of highlighted words without making it clear these are all about audible sounds. He seems more confident than last time. He also references the disproved heart study.

“Has been shown” is carrying most of the weight. Every one of these claims is either true only at audible levels orders of magnitude above ambient, drawn from the VAD literature that no medical body accepts, or flat-out contradicted by the controlled studies he himself flashed on screen in the first video. Literally everything he cited last time either contradicted these claims, didn’t address them at all, or was published in a journal about ghosts.

In the last video Jordan didn’t explicitly mention VAD pseudoscience, he just vaguely referenced it as animal studies, so he’s escalated in mentioning it directly here. Again, VAD is the pet theory of a small Portuguese research group whose work has not been replicated and which the peer-reviewed acoustics literature has specifically assessed as unproven.

2:57 - So, it’s not like this anxiety-based or obsessive compulsive thing where an unpleasant feeling or an unusual situation makes you manifest symptoms. We know that infrasound can tangibly harm your health in a very real and measurable way.

As we saw in the last section, we don’t know this at all. Basically all the literature Jordan cited in the last video said the opposite of what he claims here. He just flashed all the same sources again that all show completely unrelated results to what he’s implying or aren’t respected in the broader literature at all and rely on circular citations.

Here he’s more directly rejecting the nocebo explanation, whereas in the last video he at least passively mentioned it. He’s now implying this is established science. He’s telling the viewer, in advance, that anyone who raises nocebo is essentially accusing people who were harmed of making things up. This is what you’d expect from someone who knows the nocebo explanation is the most supported one: shield the claim behind real victims.

4:06–9:10 - xAI’s Colossus

xAI’s Colossus is a massive outlier among data centers, and there are a lot of real, widely-covered problems with it. The biggest is that it ran up to 35 unpermitted methane gas turbines for roughly a year as primary power generation, behaving like an unregulated mid-sized gas power plant in an already heavily polluted poor neighborhood. The methane turbines, the local air quality impacts on Boxtown, the nitrogen oxide emissions, and the permitting problems are all real, well-documented problems.

As a quick aside he also cites xAI using a lot of water, but doesn’t give a frame of reference for how much it is and just zooms up on a depleted water source. In reality, Memphis has some of the lowest water bills in the world, their water bills haven’t risen as a result of xAI, and Memphis isn’t considered a water stressed-area.

But none of the problems with xAI are about infrasound, and lies about infrasound cheapen the real harm people experienced.

7:46 – The lowest of rumbles is peeking out at like -28. And then if we go down to infrasound, the infrasound that you can’t hear is like 10 dB louder than that.

He’s reporting levels in what looks like digital audio reference (dBFS) rather than sound pressure level in the environment. Without a way to understand how he’s measuring sound, you cannot convert arbitrary recording levels into absolute sound levels, and he isn’t showing his method anywhere. When he says “infrasound is 10 dB louder than [audible rumble],” we don’t know what absolute level that represents in the environment. The entire infrasound literature is about absolute exposure levels. Relative ones aren’t useful here, and 10 dB above an uncalibrated reference tells you nothing about whether the infrasound is near the threshold of where we can hear it.

Of course infrasound is louder than the audible component at distance. Low frequencies propagate better than high frequencies. The lower the frequency, the less it diminishes over distance and the better it passes through walls. The ratio of low-to-high energy always increases as you move away from any industrial source. He made this clear himself in the brief wave physics section of his last vide.

8:02 – This in particular is a great example of how higher frequency sounds are dampened by distance much more than lower frequency or infrasonic ones.

Yes, correct. But notice what he does next: uses this correct physics observation to imply that infrasound is doing something special and harmful at distance, when all the physics actually shows is that low frequencies dominate at distance. What matters for our health is exclusively audible sound. He has yet to actually demonstrate any harms from infrasounds, but this entire video will now just assume infrasounds are harmful, resting on the terrible edifice he constructed in the last video.

8:26 – This rumbling is no good and would make your life utterly miserable. And then this rumble down here is happening at an amplitude that’s loud enough to shake the frame of your house.

If infrasound were actually shaking the frame of a house, that would be an extraordinary claim and easily falsifiable. It would also be perceived as a rumbling vibration by residents. He provides no measurements to support the “shake the frame” claim, and none of the residents interviewed mention their homes shaking. There has never been any accusation at all that Colossus shakes nearby houses.

8:44 – Okay. So, I’m going to delete everything above 20 hertz here. So, all audible frequencies. And now we’re only left with the infrasound. Now, I’m going to interpret the sample rate four times faster. And I’m going to make it even four times faster than that.

Same trick as the beginning of the first video. If you speed up infrasound and make it audible, it sound ominous. Just like if you look at the infrared light coming from your dog, your dog looks evil. It’s not evidence that the specific source he’s recording near is producing unusual infrasound. It’s evidence that infrasound exists, which no one disputes.

The xAI section is him listing a bunch of real harms, water, and then doing a bunch of fancy tricks with fancy but inaccessible sound terminology and equipment to support his unrelated baseless conspiracy theory.

9:10–15:18 - MARA / Granbury

Noise pollution from the Marathon Digital / MARA Bitcoin mine in Hood County has caused well-documented harm to nearby residents. Cheryl Shadden’s experience is real. The family whose daughter had seizures and whose symptoms resolved after they moved is real, and I’m not going to second-guess what they went through.

What I am going to push back on is Jordan’s causal story. He’s going to take these people’s real terrible experience with noise pollution and imply it’s about his crazy conspiracy theory instead.

10:15 – While the noise pollution is described as a 70 to 90 decibel jet engine-like sound, a large number of reports site symptoms ranging from vertigo to pulmonary embolisms to heart palpitations.

70–90 dB sustained is awful. Chronic exposure at those levels sits above the threshold at which WHO guidelines associate environmental noise with cardiovascular disease, sleep disruption, and a range of stress-related outcomes. These effects are well-documented in the science of audible noise pollution and do not require any infrasound to explain. A family living across the street from a 70–90 dB jet-engine-like sound will absolutely experience vertigo, heart palpitations, anxiety, sleep disruption, and cascading health problems. The MARA case is an audible noise pollution disaster.

10:39 – Sustained audible noise pollution can be a blight on any living thing nearby. But a lot of these symptoms don’t line up with the symptoms of just noise pollution exposure.

This is just completely wrong. The symptoms he lists, like vertigo, heart palpitations, anxiety, sleep disruption, and nausea, are exactly the canonical symptoms of chronic environmental noise exposure. There is a massive literature on this going back to the 1970s. Every major environmental health textbook describe this symptom cluster as the expected health consequence of chronic exposure to 70–90 dB environmental noise. He’s just lying here.

At 11:39 he starts playing more scary clips of sped up infrasound, with no evidence this is harming anyone, after leading in about how the residents told him about the real effects of noise pollution. What a dumb way to hide a baseless conspiracy theory behind people’s real suffering.

The residents of Granbury are suffering from real terrible noise pollution. Infrasound adds nothing to the explanation, and Jordan’s last video gave us not a single reason to believe it’s real in the 25 minutes it runs.

13:08 – Like, what physical symptoms have you felt just from your property being directly across the street from the data center? Motion sickness, dizziness, nausea, GI side effects. My ears ring and buzz. I’ve got permanent conduction hearing loss now. Um, nervous, anxious, worried.

Every symptom Cheryl describes is well-documented as a consequence of chronic high-level environmental noise exposure. The conduction hearing loss in particular is a direct audible-noise injury. There’s no good evidence that infrasound causes hearing loss. Jordan presents these symptoms as mysterious that infrasound can explain, but they’re not mysterious at all.

13:24 - Cheryl thought that I was just doing a story on the noise pollution as I was keeping my infrasound research close to my sleeve.

Okay, so this guy nods along to this person who was harmed by very real noise pollution, and lists classic symptoms of real noise pollution, and then adds “Oh by the way, I didn’t tell her that I’m actually just interviewing her to promote my bullshit unrelated conspiracy theory that I’ve never found a single good source to justify, and I’m just nodding along and am going to lie about her symptoms actually being caused by this other thing.” This is terrible! He lied to this real victim of noise pollution.

13:32 - Yet, every single one of these symptoms is commonly associated with excessive infrasound exposure.

Here the sleight of hand becomes explicit. He’s asserting that because the symptoms overlap with the infrasound horoscope-list, they must be caused by infrasound. But the symptoms also overlap, far more completely and with actual mechanistic support, with chronic audible noise exposure, which is unambiguously what’s happening at Cheryl’s property. The correct inference from “Cheryl has symptoms X, Y, Z while living across from a 70–90 dB jet-engine-sound source” is “chronic audible noise exposure produces those symptoms,” not “there must be a hidden inaudible cause.”

14:03 – One family, their daughter was 6 years old. She started having seizures in the summer of 24... they basically walked away from their home here, took out a second mortgage, moved to Somerville County... If you know, has her symptoms kind of gotten better since she’s left? They went away.

The family’s experience is real and terrible. But did infrasound cause the seizures? Chronic sleep disruption from audible noise in a young child can trigger a wide range of neurological symptoms, including lowered seizure thresholds in predisposed children. Severe family stress from a noise-blighted home can too. Moving to a quiet environment resolves the audible noise exposure, the sleep disruption, the household stress, and any potential infrasound exposure all simultaneously. The case is entirely consistent with audible noise and stress being the cause. It provides no evidence for infrasound specifically. Jordan’s never produced any actually good evidence that infrasound causes seizures.

Presenting this family’s story as evidence of an infrasound mechanism is both scientifically unjustified and strategically unhelpful to families in their situation, because it directs the legal and regulatory conversation toward a fake mechanism rather than the obvious provable one: MARA is producing 70–90 dB of environmental noise pollution at residential property lines, and that alone is enough to make people sick.

I really don’t like that he flashed a quick out of context clip of this woman at the very beginning of the video to imply that infrasound caused her problems.

15:18–17:58 - The Permian Basin

This section accidentally undercuts the video’s central claim.

17:32 - But while the infrasound levels were nowhere close to ideal, they still weren’t as bad as they were near the data centers in Memphis or Granbury.

A region with thousands of earthquakes per year, massive industrial fracking operations, and oil and gas infrastructure as far as the eye can see produces less infrasound than two specific data centers? Come on.

Fracking, large pipelines, oil rig drilling, and induced seismicity all produce vastly more low-frequency energy than cooling fans and generators. If Jordan’s measurements show otherwise, the most likely explanations are measurement artifacts from uncalibrated or differently-positioned microphones across sites, wind loading differences on the recording equipment, or distance differences between measurement locations he isn’t controlling for. Ridiculous.

17:39 - The infrasound levels at these two data centers were so much higher than the ambient values that it doesn’t even make practical sense to use a linear scale like 25,000% higher.

“25,000% higher” is a red flag. In physical acoustics, a 25,000% increase in pressure amplitude is roughly 48 dB, which would put the data center sites far above any residential or rural baseline anywhere in the developed world, including large airports, freeway interchanges, and active industrial sites. This is not plausible for ordinary data center cooling or power delivery, the main way they make sound,. A much more likely explanation: his baseline (Death Valley, random indoor hotel rooms, etc.) is at or below the noise floor of his measurement equipment, while his site recordings are well above it. Dividing “real signal” by “noise floor” gives you any number you want.

This is a recurring issue with amateur infrasound measurements. The noise floor of affordable infrasound-capable equipment is not zero, and the apparent ratio of “loud site” to “quiet baseline” is dominated by the noise floor of the gear. Published peer-reviewed data center acoustic measurements do not find anything remotely like 48 dB elevations over ambient at residential distances.

He’s an audio engineer. He knows what he’s doing here.

17:58–23:15 - The experiment

This is the centerpiece of the video, and is a disastrously bad experiment. I’ll walk through the problems one at a time because each one independently invalidates the claimed findings, and the combination is devastating.

Problem 1: The “haunted painting” priming

19:17 - I improvised a new story of why the owl painting was known to be haunted... I told them that the audio recording that they would be listening to was known to be haunted. I had them sit in this enclosed room while studying the painting and listening to the music for 3 minutes while paying attention to how they felt physically and emotionally.

He has pre-primed every single participant to expect unusual sensory or emotional experiences. This is exactly the Crichton-Petrie nocebo-priming paradigm from the first-video writeup, and it’s been shown repeatedly to produce symptoms on its own, regardless of whether any stimulus is present. His rationale (that varying the story randomizes a bias variable) is wrong. Randomizing the specifics of the priming across groups does not eliminate the priming. I’m confused about why he’s doing this.

A correct design would have one group primed negatively and one group given neutral or no priming, the way Crichton and Petrie actually did it. His design has no unprimed group. Every subject walks in expecting something weird to happen.

Problem 2: He isn’t blind

20:10 - And unless I saw water vibrating in a bottle or something, I often didn’t know if the infrasound was on or off myself.

“Often didn’t know” is not good enough. A study is double-blind when the experimenter does not know the condition, full stop, not when they usually don’t. If the experimenter can sometimes tell what’s up, it’s possible that their behavior, facial expressions, tone, and subtle cueing can leak information. And Jordan explicitly says he can tell via the vibrations. A real study would have a separate operator running the stimulus, ideally out of the room, with the experimenter truly unaware of condition until after data collection.

Problem 3: The stimulus itself

18:04 - I needed to figure out a way to reproduce the infrasound waves at 25 to 30% of the volume or amplitude near the data centers.

He’s playing back infrasound through “a gigantic specialized subwoofer in the room.” Commercial subwoofers, even specialized ones, do not reproduce sub-20 Hz content cleanly. They produce substantial harmonic distortion, port noise, cabinet resonance, and mechanical rattle, all of which show up as audible artifacts above 20 Hz that subjects can hear and that will absolutely affect their reported experience. Without a measurement showing the actual acoustic output in the room across the full spectrum, you cannot claim that subjects were exposed to “infrasound” specifically rather than to audible distortion products from an overdriven subwoofer.

His own video gives this away:

20:23 - Just put my DJI gimbal on the ground here. And Jeez, it’s kind of scary.

This implies the room is shaking. If the room is shaking, subjects can detect when the infrasound is on or off, and are probably going to feel weirder when it’s on.

Problem 4: The self-selection filter

21:33 - The experiment had over a 100 participants, but I disqualified anyone who missed a survey question or someone who seemed suspicious or said that they felt a vibration or where I was privy to whether the infrasound was on or off.

He dropped subjects who “seemed suspicious” or who reported feeling a vibration, based on his own after-the-fact judgment. I’m worried that “Seemed suspicious” is way too much of a subjective call by Jordan, who seems to have a bias in wanting the experiment to show results. And removing subjects who reported feeling a vibration specifically removes the subjects who noticed the confound identified in Problem 3, which is exactly the wrong direction of exclusion. Those are the subjects whose data is the most informative about what’s actually happening in the room.

We also don’t know how many of the original 100+ were dropped, which groups they were in, or what the results look like with them included. A valid pilot reports intention-to-treat results. This one doesn’t.

Problem 5: The effect sizes are tiny and the framing is misleading

To his credit, Jordan half-acknowledges this:

23:58 - the 33% more likely to experience nausea metric, the viewer may imagine a room with 100 people in it and then 33 of them experiencing nausea, which is very much not the case. The average nausea feeling score from 0 to 10 that each user put in for infrasound is 1.2. For the control group, it’s 0.9.

This is “33% increase” from 0.9 to 1.2 on a 0-10 scale. This is an 0.3 difference, in a sample of 74 with no correction for multiple comparisons across what appears to be a dozen or more possible symptoms, in a room where people can probably feel the floor vibrating when the infrasound is on. This is noise. These are the results that caused him to say at the beginning that “the results are terrifying.” He’s even misrepresenting the results of his own study.

24:55 - the average score in the control group for discomfort was 1.2. The average score for discomfort in the infrasound group was 4.8.

A 1.2-to-4.8 shift on a 10-point scale is a large effect, if it’s real. But consider what “discomfort” means in a room where a subwoofer is physically shaking the floor hard enough to move a DJI gimbal, where subjects have been told the space is haunted, where they’ve been warned the audio is spooky, and where the experimenter may sometimes know the condition and cue accordingly. The combo of all of these seems like the clear cause of the “discomfort,” not inaudible infrasound.

The discomfort result is exactly what you’d predict based on the nocebo studies. Prime people negatively, then give half of them a perceptible physical stimulus like a vibration they can feel through the floor or audible subwoofer distortion and they will report more discomfort.

With a dozen comparisons in an n=74 study, you expect chance alone to produce several “significant” differences. There’s no correction, no pre-registration of primary outcomes, no null-hypothesis framing. Some of the results point the “wrong” direction for his hypothesis (infrasound subjects were less creeped out and less spiritual) and he offers a post-hoc story to explain those away while treating the ones that point his way as meaningful.

Problem 6: Multiple comparisons

He reports roughly a dozen outcome measures (tingling, pain, tiredness, nausea, dizziness, creeped-out, spiritual, chills, irritability, eye irritation, lethargy, anxiety, sadness, discomfort), finds a spread of increases and decreases, and presents the increases as findings. With a dozen comparisons in an n=74 study, you expect several “significant” differences by chance alone. There is no correction, no pre-registration of primary outcomes, no null-hypothesis framing. Some of the results point the “wrong” direction for his hypothesis. Infrasound subjects were less creeped out and less spiritual, and he offers a post-hoc story to explain those away while treating the ones that point his way as meaningful.

What the experiment actually shows

If you prime a group of synthesizer-convention attendees by telling them a painting is haunted and some audio is spooky, then expose half of them to a subwoofer producing enough mechanical vibration to visibly shake objects in the room, the exposed group will report more discomfort and dizziness on a survey. This doesn’t tell us anything about data center infrasound. Jordan is just accidentally reproducing the nocebo literature.

23:15–25:43 - YouTube data vs research data

I’ll give him credit for this section, which is better than I expected. The acknowledgment that “33% more likely” on small absolute numbers isn’t meaningful is the right methodological instinct. The nausea shift from 0.9 to 1.2 on a 0–10 scale is, as he notes, not a room with 33 of 100 people vomiting. The recognition that the eye-irritation result is underpowered is correct. If the rest of the video had been pitched at this level of honesty, it would be a very different artifact.

But notice the structure: he does the methodological self-criticism after spending most of the video making the strong version of the claim, and he still ends up leaning on discomfort and dizziness as “profound” findings without acknowledging that those are precisely the measures most likely to be contaminated by the priming and the vibration confound. The honesty is real but selective. He applies it where it protects him from the “33% nausea” line being read literally, but not to the deeper question of whether his protocol measures what he claims.

25:13 - When reviewing that data, it’s hard for even the most skeptical person to come to any other conclusion than the presence of elevated levels of infrasound is likely to lower your quality of life.

Very very very very easy for the skeptical person, actually. The skeptical person concludes: priming produces reported discomfort, perceptible subwoofer vibration produces reported dizziness, and an unblinded single-operator study with post-hoc subject exclusion at a synthesizer convention is not a basis for policy claims about data center health effects.

25:43–end - The call to action

27:12 - Prior to this experiment, I feel like we have seen more than enough data to make infrasound monitoring and regulation just as important as air or water quality.

NOOOOOOOOOO!

Outdoor air pollution kills 4 million people every year. This sentence is elevating a fringe belief with zero evidence to the same level as one of the single largest killers on the planet. Crazy. After 40+ minutes of this guy misrepresenting studies and concepts almost by the second, this moment really made me lose it.

27:21 - Especially when you look at the studies pertaining to the cardiovascular effects, it’s a very real environmental hazard that has been poorly understood and grossly understudied.

Another repeat of his false claim that infrasound is “grossly understudied.” There are no good studies that imply cardiovascular effects. It’s fake.

28:21 - Now would be a great time to set up something like a Raspberry Shake and start storing a daily log of seismic and infrasonic activity. Because the vast majority of the responses to complaints and lawsuits is that there is no way of proving whether the infrasound wasn’t already present before construction.

If communities want to collect baseline acoustic data in advance of data center construction, fine, go ahead. I agree with critics of data centers that they often haven’t been careful about audible noise pollution, that this has caused real quality-of-life harm, and that communities should be able to monitor it.

But communities shouldn’t monitor infrasound for the same reason they shouldn’t monitor wifi signals over fears about wifi-intolerance or cancer. There’s no evidence for either. Monitoring would just waste everyday people’s time and leave them more confused and paranoid when they realized how much infrasound is around normally. It’s like hyping people up about the dangers of infrared light and sending them out to test it themselves. They’ll find a lot!

Conclusion

I found this second video to be way more objectionable than the first. Even though the first had nonstop lies about what every study flashed on the screen said, at least it didn’t have interviews with victims of real noise pollution Jordan was trying to use as evidence of his unrelated baseless conspiracy theory instead without even telling them. I found that pretty gross. I was also repulsed by his comparison of infrasound to outdoor air pollution, one of the great global killers right now.

My claim remains strong. Infrasound harms are fake, data centers or otherwise. People should focus on the real problems of noise pollution and not give an inch to baseless conspiracy theories promoted by chill-seeming guys using fancy-sounding technical terms and unrelated studies flashed on the screen to trick millions of viewers. Infrasounds have been used by all kinds of bad actors to oppose things we should all agree are good, like wind farms. Let’s not give the crazies more power here.

Some stray thoughts

If infrasound exposure near data centers caused this much harm, wouldn’t the absolute worst off people be people working in data centers themselves? They’re in the belly of the beast of data center infrasound, many have been working in data centers for way longer than the current buildout’s been happening, and it’s very easy to identify who works in data centers. They seem like the ideal people to look at for data center infrasound effects, but never get a mention.

Responses to this piece from the authors of the studies Jordan cites

Someone had suggested to me that since Jordan and I disagree about the content of these studies, why not just reach out to the authors themselves?

I reached out a few days ago. So far I’ve received one response from Swen Müller, who wrote the rebuttal to the heart study. Jordan cited the heart study but not the rebuttal, so this isn’t as good as having a direct quote from the authors themselves. Here’s Müller’s thoughts:

I’ll update this section as I get more replies.

Jordan’s response to this article was pretty goofy and reaffirmed that he can’t defend any of his misleading citations

Bluesky back-and-forth

Jordan responded to this post on Bluesky, and it went pretty badly. Opened with this

Then added this. Sure, I’d say people harmed by data center noise are vulnerable, but maybe that was too strong.

Here’s the next thing in the thread + my response, no reply from Jordan here.

He then accused me of hypocrisy here:

Which is a misreading of this post. I never said that it’s invalid to compare sound to light. Like he said, I do it over and over. I think it’s a useful comparison. What I specifically said was that it’s invalid to compare infrasound to the harm of ultraviolet light specifically, and I made that explicit here:

He’s setting up this framing about how things that are invisible to us can still harm us, and then very quickly moves from ‘ultraviolet light can obviously kill us’ to ‘and there’s new disturbing evidence that infrasound can harm us’ without acknowledging the obvious problem that infrared light does not really harm us at all unless it’s so powerful that it heats up our bodies in the way other normal heat sources do. This is the correct analogy to infrasound, not UV light. Just like infrared light can only harm us if it’s powerful enough that we physically feel it in the way we do other heat sources, infrasound can only harm us if it’s so powerful that it causes us to physically feel its presence in the way we do other very loud sources of sound. He’s not so subtly reaching way across this spectrum to say that because something that’s so high energy that we can’t see it can cause damage, maybe something that’s so low energy that we can’t hear it can cause damage too.

I replied making this point and didn’t get a reply. Jordan seems to have a pattern of misrepresenting articles.

I finally got a response here:

No response there so far…

There was one more thread where I got responses:

Instead of answer he just asked this:

We went back and forth for a little while and I asked him for a meta-analysis showing that infrasound was legitimate science. He sent the following links:

This is the same heart study I mentioned above. It’s an individual study, not a meta-analysis. “Cited 5 times” is a weird defense of the paper, it makes it look pretty bad. Two of those citations were the critical responses I posted above basically showing it was useless, and 3 other citations for a paper like this makes it seem like it hasn’t had any impact at all. I’ve been unable to find what those citations were. When I brought this all up he accused me of “moving the goalposts” but couldn’t actually explain why this study was useful beyond that it was a scientific study.

Importantly, he’s the one moving the goal-posts here, because I had specifically asked for a meta-analysis, not a random study.

The next link he sent was this one:

This one’s really weird. It’s not a meta-analysis or a study. It’s a single-author narrative review, basically a long op-ed.

The author is Michael Persinger. He’s best known for the “God Helmet”: a device he built that he claimed could induce mystical, religious, and paranormal experiences in subjects by applying weak magnetic fields to the temporal lobes. When an independent Swedish team ran a proper double-blind replication in 2005, they found nothing. Persinger’s response was essentially that the Swedes hadn’t done it right.

He also published, at length, on telepathy, precognition, “non-local” consciousness, and the idea that the Earth’s geomagnetic field modulates paranormal experiences. A substantial chunk of this guy’s career was in territory that everyone else in neuroscience treats as pseudoscience. He was a professor at Laurentian University until his death in 2018, but his research program was consistently well outside the mainstream and consistently failed when other labs tried to reproduce it.

Benn said “Their validity is supported by convergent quantitative biophysical solutions.” What that sentence actually means, once you trace the paper, is that Persinger’s claims about infrasound are supported by Persinger’s other work on biophysical mechanisms. There wasn’t any research done here.

Actual systematic reviews of infrasound (NHMRC 2015, Baliatsas 2016, van Kamp and van den Berg 2018, Marshall 2023) all converge on the conclusion that infrasound at the levels people are actually exposed to doesn’t produce the symptoms people attribute to it.

So when I asked for his best evidence, the second thing Jordan sent me was a 2014 narrative review, by the God Helmet guy, that vouches for its own conclusions by citing the author’s other papers.

Next was this one:

This paper found that infrasound, when delivered to chinchillas at 100 decibels, simultaneously with a chainsaw-level blast of audible 4 kHz noise, slightly increased cochlear hair-cell damage compared to the chainsaw blast by itself.

These are the paper’s results:

• At 108 dB of noise exposure, adding the 30 Hz tone slightly increased cochlear damage compared to 108 dB alone.

• At the 86 dB exposure, adding the 30 Hz tone did not increase damage at all.

• The authors’ proposed mechanism is that the 108 dB exposure perforates a membrane in the cochlea called the reticular lamina, and the low-frequency fluid movements makes the existing damage worse by mixing cochlear fluids through the hole. The low-frequency exposure only contributes to damage after a separate, extremely loud audible exposure has already wrecked the structure.

So the subjects are not human, the “infrasound” is audible to humans, the exposure levels are not remotely close to what someone living near a data center could encounter, and the finding is that a 30 Hz tone only contributed to damage when a separate exposure had already caused the damage. Alone, it did nothing.

And finally this one (after a mislink of a paper on PTSD that didn’t mention infrasound):

This is a link to a 2016 undergrad paper by Ryan Littlefield that was self-published on Medium and never peer-reviewed or published in a scientific journal. Semantic Scholar indexes it, but it indexes student papers indiscriminately. A paper being on Semantic Scholar tells you nothing about how legit it is.

I pushed back on each. Jordan’s final comment to me was anger that I was calling “published papers pseudoscience.”

He said these were from PubMed, but they’re actually both from PMC (ignoring the Medium article and single-author narrative by the God Helmet Guy he sent). PMC is an entirely separate paper archive with a much lower bar for inclusion, and mixing them up is a very common way to justify pseudoscience.

PubMed is built on MEDLINE, whose journals have to pass a scientific quality review by the National Library of Medicine (NLM). PMC accepts material under looser criteria, and, importantly, it also hosts NIH-funded author manuscripts that get deposited automatically regardless of whether the journal they appeared in has ever been reviewed for quality by anyone at NLM. According to NLM’s own staff, these manuscripts may come from journals that have never undergone scientific review, are outside the library’s traditional scope, or have failed to meet even PMC’s standards. Once a paper’s in PMC, it becomes searchable through the PubMed interface, which is how a paper from a journal no serious biomedical reviewer has ever vetted ends up being defended with the phrase “on PubMed.”

The Yale Medical Library flags this exact move is common enough that readers should watch out for it. A publisher can claim to be “in PubMed” when they’re really just searchable via PMC. It is a well-known laundering pathway for predatory and low-quality journals, and is popular among infrasound pseudoscience.

So yes, this all looks extremely pseudoscientific.

Jordan then blocked me and posted this:

After a lot of back and forth he wasn’t able to stand behind any of the studies he cited in his video. I rest my case that this video is full of fake information and pseudoscience, Jordan seems to know this, and is good at dodging this info and reframing any criticism as a “good guy vs bad guy” thing that will cause a lot of his audience to not look into it further. Jordan is knowingly promoting infrasound pseudoscience and is getting millions of views doing it.

Jordan’s blog post rebuttal of this post

Jordan then posted a blog post reply to this article, and it’s pretty bad. You can read the whole thing here. I’ll go down the list and respond to points from it.

But it seemed desperate and mean. I’m attacked for…creating a sundial and having “scientific looking microphones”.

Jordan is obviously knowledgeable enough about sound to be able to read and understand the incredibly simple language in the studies he’s flashing on the screen. I don’t know how it could be possible that he doesn’t know what they contain. I’m forced to conclude that he does know and is choosing to mislead the viewer with half an hour of wild intentional misinterpretations of very simple studies. So yeah, I think he adds scientific looking stuff to make himself seem more legit.

The author is constantly ping-ponging the people interviewed or featured in my videos as poor, stupid people, or people that are owed more empathy than I allotted. Something is just weird about this.

I don’t do this? I say he talks to poor vulnerable people who have been actually definitely harmed by real noise pollution. They’re not stupid at all. I do think people are owed not having their very real harms rebranded as the result of a pseudoscientific sham yes.

So after some sniffing around, I realize I’m being brigaded by someone deep in the Effective Altruism community.

Oh no how did he notice I was really trying to hide this one.

But please do browse his bibliography.
Andy lives in a parallel universe where datacenters don’t waste water, AI artwork is without victims, and using ChatGPT doesn’t harm the environment.

✅ Data centers don’t waste water.

✅ AI artwork is without victims.

✅ Using ChatGPT is not bad for the environment.

I love the idea that the only way you could possibly believe these is if you’re being paid to. Very intellectually walled-off from a lot of the world. I guess when you’re wrong, the correct people do live in a parallel universe.

This is one of the many takes that perfectly align with the board, contributors, and partners with Coefficient Giving

I only received a grant to write from Coefficient Giving a month ago. All the articles he cited above are from when I was just writing for fun.

Unlike most people with shadowy billionaire benefactors, mine and I are buddies on Bluesky. Here’s his input on my writing:

I spend a lot of time on Twitter interacting with people farther right than I am who think EA’s goal is to destroy all technological progress and stop AI, and people farther left than me on Bluesky who believe EA’s goals are to max out AI progress at the expense of everyone else. The truth is that most EAs think there’s a huge amount of both upside and downside to AI. If CG could, I think they’d slow the data center buildout significantly, but wouldn’t do it via spreading pseudoscience.

formally called Open Philanthropy, but changed after its close association with Sam Bankman-Fried was causing some well-earned skepticism.

This is not at all why Coefficient Giving changed its name. If they changed the name due to associations with SBF, why did it take them four years after FTX collapsed to do it? Open Philanthropy also never accepted any money from SBF.

The effective altruism movement, if we want to be gracious in calling it a “movement”, is a rabbit hole of reciprocal wealth.

Fair tbh

A lot of billionaires unload money into its related NPOs, and oddly enough, that money often goes into buying NVIDIA GPUs for an altruistic AI project, directly to Bill Gates to use philanthropically

Bill Gates isn’t involved really? I don’t know of any EA money that’s been used to buy GPUs or for altruistic AI projects?

or to grants for independent journalists and content creators to attack research that is being used to shift legislation away from things that the partners have interest in. Since it’s 2026, those things are data centers and AI.

I do wish I could get everyone telling me EAs are secretly paid to destroy AI and people who say EAs are secretly paid to max out AI progress in a room together to hash this out once and for all. The idea that EAs are paid to DEFEND AI is very funny. If you look up negative coverage of journalism funded in part by EAs, it usually focuses exclusively on how the journalism is more negative on AI than average.

But how did my infrasound content, of all things, end up as the target of a special interest group?
Well, as luck would have it, It turns out that I’m getting cited a whole lot in legislative meetings that are voting on datacenter zoning and moratoriums.
So while I’ll save my digging deep into the clusterfuck that is effective altruism for another time (I’m inspired now!), below is a quick graphic summarizing where this article came from and why it was written. Feel free to “do your own research”.

Jordan seems to have something of a fantasy of persecution. I didn’t receive any kind of command from above to write this. What happened was I was seeing more and more tweets about data center noise pollution, looked into it, thought “Wow this is terrible, this looks like a real problem” and then stumbled on Jordan’s videos. At first I only noticed a few problems, and was going to make infrasound a small end note of a post about the very real problems with noise pollution. But once I realized just how terrible Jordan was with his sources, and how popular this video was, I decided it needed its own post.

I’ve had a deep hatred of pseudoscience my whole life. In college I Turned Evil at a community meeting about the smart grid (essential for the green energy transition) where I got called a shill by crazies who had swarmed the meeting for not believing in WiFi intolerance. I decided these people absolutely could not be allowed to win anything ever. I respect real community concerns. I do not respect pseudoscience at all, anywhere. Now I get to debunk it it as my job.

The connection between EA and tech billionaires is not something I’m the first to document.

Yup, but the tech billionaires I’m associated with mostly oppose rapid AI progress and the rapid data center buildout.

Jordan shows a picture of local town meetings where he’s been brought up and says “That’s a lot of .gov action for a YouTuber who doesn’t understand science!” Well yeah, he successfully tricked people into believing his pseudoscience. Great.

I’m not going to go through Andy’s novel line-by-line because it’s a waste of everyone’s time. The entire framework of attacks like this are to “machine gun” information in a way that overwhelms the reader so they concede that the writer has an exhaustive understanding of the topic.

There was no way to document all the massive problems with Jordan’s two videos without making this post very long. A lot of people accuse me of either writing posts that are too long, or conveniently leaving things out. I suspect that if this post were shorter, Jordan would bring up every argument he made that I didn’t address. This is just a way of dodging all my very simple specific criticisms that he misrepresented every study he relied on.

1. On-screen graphics are not always citations, but sometimes may appear to be.

There’s a lot of time and tempo change between researching, writing, and editing. My infrasound videos have about 3TB of footage, sliced down into 2 videos running less than 30 minutes per piece. They may seem like elaborate deep dives, but you’re watching an entertaining overview of the actual deep dive. I spend an excruciating amount of time balancing the depth of research versus the probability that anyone will be interested in watching or learning about it. Some may disagree, but I think I’m doing a good job at this. More importantly, I’m always learning how to do it better with each video as I pay close attention to what makes viewers stick around.

My way of breezing through citations is using them as on-screen graphics. Unfortunately, not all graphics are consistent with what I’m talking about in a particular clip of the video. For example, an article making statement A may have a graphic or headline that fits better with statement B. It provides the viewer with continuity and visual confirmation, but if you comb through every on-screen article and compare it to the sentence coming out of my mouth, guess what? You’ll occasionally, if not seldomly, find that the 2 don’t perfectly line up.

But context is important. If a graphic isn’t confirming a point made in the video, that doesn’t mean the point in the video is a nefarious lie. It’s a truly wild stretch to assume that my videos are just factless voids of manipulation and Andy is the first person to catch on. Which brings me to the next segment.

This would be understandable if literally any of the articles he cited did what he claimed. It’s not that the timing was off, it’s that none of them backed up his claims at all and mostly contradicted them. This is an obvious simple dodge.

There is literally no way you can watch this section without believing that the paper he’s zooming in on that’s 100% perfectly timed with every single word he says is about infrasound, whereas in fact it’s entirely about audible sound, as explained here. This wasn’t sloppy editing, this was incredibly deliberate editing that showed a paper that didn’t at all say what he claimed in the moment. He’s just completely dodging these very obvious objections by claiming this was some cool editorial decision.

2. I fact-check. Always.

With the amount I complain about legal expenses, it’s probably not a secret that I often run my entire scripts past attorneys. And they catch a lot, sometimes transforming the video significantly, and in at least two cases, resulting in a video project being shelved. This isn’t always because of poor research, but because perfect is the enemy of good. And I need to be perfect sometimes for all the wrong reasons.

For example, I cannot drive up to a datacenter owned by the wealthiest person in history (who also happens to be a litigious asshole), pull out some equipment, measure infrasound, and make up readings or make claims about their health effects without a really good list of citations. If I make a single mistake in activities like this and when reporting on them, I’ll lose everything I have, including my channel. I do not receive any benefits of doubts when it comes to potential libel.

For months, I’ve had private investigators hired to park in front of my property. Are we to assume the company hiring them hasn’t found out about lawsuits yet?
So the idea that I’m intentionally misleading millions of people and legislators is not something I’m insulted by because in the context of my daily stressors, it’s comedy.

Well the fact-checkers clearly failed here, drastically, and Jordan hasn’t given us any counter evidence of that so far.

Throughout Jordan has what I think is a somewhat self-centered view that I’m just writing this to “stress him out” whereas in fact I would like him to stop promoting the same pseudoscience used to block wind farms. Throughout this exchange, he’s always speaking in “us vs them” language and is trying to identify me as being on a bad team instead of just looking at the facts directly. I think he knows what will happen if he does, so he’s somewhat desperate to just smear me as a tech billionaire stooge.

3. Categorical Dismissal vs. Scientific Uncertainty

Andy’s central thesis that sub-audible infrasound issues are “fake” is a definitive and hyperbolic claim that is impossible to sustain scientifically. In research, there is an infinite distance between “not yet proven without a doubt” and “fake”.
Anesthesia, consciousness, contagious yawning, and a whole library of things we experience are not completely understood. That doesn’t mean they don’t exist.

One thing that irks me is how heavily Andy relies on the “nocebo effect” (the idea that symptoms are caused by negative expectations).
Of course nocebo effect is a real confounding variable, but using it as a "catch all" to dismiss physical data ignores the actual physiological mechanisms routinely observed.

All of this happens within the confines of a world where Andy seemingly doesn’t have the ability to fact-check himself.
Recent research into mechanotransduction suggests that humans can "feel" sound through non-auditory pathways. Low-frequency noise can modulate pressure-sensitive ion channels (like PIEZO1), potentially causing neuroinflammatory responses or cellular stress even if the sound is below the threshold of "hearing" [Armand & Bikaran, 2025; MDPI, 2026].
This is just one of many examples of where infrasound is observed to be related to physiological responses that have absolutely nothing to do with one’s conscious perception or bias. “

Note: Due to IP restrictions, not all papers can be linked, only referenced.

But here is a more recent study pertaining to PIEZO1 and TRP4 channels.

It’s true that infrasound harms could technically exist, but because we haven’t found any after a half-century of serious studies looking for them all over, I’m forced to conclude that infrasound is as fake as other things in this category. Could be real, maybe? But no one (especially Jordan) has produced any evidence so far.

I’m not using nocebo as a catch-all, I’m adding it as an explanation after I show separately that all studies that hint at infrasound harms seem to be pretty quickly invalidated.

He links one MDPI paper by Dastan et al., and references a second “[Armand & Bikaran, 2025]” citation he says can’t be linked “due to IP restrictions.” Peer-reviewed science doesn’t have IP restrictions that prevent citing them, you just paste the DOI. I can’t find any paper by Armand & Bikaran 2025 on infrasound, PIEZO1, or anything like it. If anyone reading this can find it, let me know, but as it stands I’m going to treat it as made up until proven otherwise.

The MDPI paper he did link is a narrative review in Applied Sciences, not a study or meta-analysis, and not a finding of harm. It’s the same type of article as the Persinger paper Jordan sent me over Bluesky.

The paper says the following things:

1. Cellular mechanisms like PIEZO1 and TRPV4 modulation happen in experimental studies at the cellular level.

2. Cardiovascular effects in animals require “higher sound pressures.”

3. Neuroinflammation and memory effects in animals require “prolonged or intense exposure.”

4. Most importantly, this is a quote from the abstract: “Short-term studies in humans at moderate intensities have reported minimal physiological changes, with psychological and contextual factors influencing symptom perception.”

That last line is just perfectly describing my view of infrasound rather than Jordan’s. Moderate intensity infrasound (the kind people experience in their homes) doesn’t have an effect, and people’s experience of it is mostly determined by their psychology. It’s also saying that the cellular mechanisms people get excited about only produce damaging effects at high intensities over long durations in animals, which everyone has agreed on since the 1960s NASA work. Again, Jordan has linked a study that agrees with me, not him.

4. Comparing Light to Sound is Stupid (Except when Andy does it)

As much as I detest surveillance cameras indoors, I kind of wish I had one so I could see the look on my face as Andy repeatedly tried to cross-explain sound and light waves. His article does this 3 times before using the comparison of sound and light waves as a source to discredit my video.

In reality, there’s a lot of false equivalence that needs to be addressed when throwing shade on ultraviolet light comparisons. Light interacts with human tissue at a quantum/chemical level. Sound interacts with human tissue via mechanical resonance. UV light is blocked by skin while infrasound passes through solid structures with ease. This means that every human has their own resonant frequencies for their bones and organs that can be mechanically stimulated by external sources of mechanical pressure regardless of if the source is audible or not.

I already addressed this in the Bluesky section above (and had replied to Jordan on Bluesky saying exactly that, but he either didn’t read it or didn’t care). My complaint isn’t that he’s comparing light to sound, it’s that he’s comparing UV to infrasound when the obvious comparison is infrared to infrasound.

This ”every human has their own resonant frequencies for their bones and organs that can be mechanically stimulated by external sources of mechanical pressure regardless of if the source is audible or not” thing is just a restatement of VAD pseudoscience. The only way to get resonance frequencies to actually harm you is to apply enough power, and there’s just not enough power in infrasound in homes near data centers. Again, a kid on a swing has a resonance frequency for pushing them to get them higher, but if you don’t also push with enough force they won’t move back and forth at all. Same goes for your cells.

5. Wind-Turbines and Oversimplification

While not recently, infrasound research has been poisoned by special interest groups backed by energy companies trying to create arguments against wind turbines. I caught on to this very quickly years ago and had to be especially careful to avoid research papers primarily focused on wind energy.

But it’s also not difficult to do this for some very important reasons unrelated to politics or lobbying:

I’ve spent a gross amount of time playing Othello on my phone while sitting next to a device measuring wind farms and datacenters. Datacenters and cryptocurrency mines have a drastically different acoustic signature than wind turbines. Datacenters utilize high-velocity cooling fans and, in many cases, massive diesel generators in a 24/7, high-density configuration. They’re also typically much closer to residential zones. This is easily enough of a difference to segregate to 2 areas of study.

I think wind farms should be allowed to be built closer to residential zones, to the point that the infrasound from them would be louder than infrasound from data centers, because infrasound harms are fake. Would Jordan be okay with building them this close? He doesn’t give us a way to know. He’s trying to have it both ways here, but pseudoscience is like a biological weapon. You can’t use it to target hyper specific industries you don’t like and spare others. It messes with everything.

But it is tricking out, and we have very new research that directly studies datacenters. A 2026 report by the Environmental and Energy Study Institute [Communities Are Raising Noise Pollution Concerns About Data Centers] points to specific health clusters (vertigo, nausea, hypertension) in Virginia and Texas specifically linked to these industrial cooling arrays, noting that standard decibel meters (A-weighted) fail to capture the low-frequency energy that residents report that is literally “vibrating their homes”.

This article he mentions is good on real noise pollution. It’s a problem. I’ve recently been speaking to some city planner friends and have been kind of shocked at how difficult it is to regulate noise. I’ve upped noise pollution in my worries about the real issues with data centers quite a bit.

But again, this article is about audible low-frequency noise. It is not evidence of inaudible infrasound causing health effects. The Ontario environmental review board found this exact thing about wind turbines in 2011: dB(A) understates how annoying low-frequency audible noise is, which is why the EU moved to including C-weighted measurements. This article also does not support Jordan’s theory about inaudible infrasound. He seems kind of pathologically obsessed with linking articles that don’t show what he claims.

Now before we move on, it’s important to note that this “confusing concept of vibration and infrasound being related” was cited in “Health implications of the rapid rise of data centers in Virginia”, which was published the same week my datacenter video released. It also mentions datacenter water use, so I’m sure Andy has his work cut out for him in reaching to discredit it in the name of altruism.

This is one of the goated papers on data centers. It’s a reasonable mini-review that organizes a lot of the known real concerns about data centers, including air pollution from diesel generators, water stress, and audible noise pollution.

But Jordan is lying about what it says again. I’m coming away from this thinking he’s a pathological liar about research papers specifically.

The paper cites the JLARC report that Virginia data center noise is “between 40 and 59 decibels” and says this “approaches or exceeds” EPA’s 55 dBA day-night outdoor noise guideline. The health harms it then links to this noise exposure are the standard, boring, very real harms of chronic audible noise pollution: heart disease, tinnitus, hypertension, sleep disruption, cognitive and mental health effects. All of these are well-documented consequences of chronic exposure to environmental noise, and this is why I’ve said repeatedly throughout this post that data center audible noise pollution is a real problem.

The paper does not mention “infrasound” a single time, as far as I can tell. It does not mention “ground-borne vibration.” etc.

Jordan’s correct that I mostly disagree with the paper’s assessment of water issues. It basically just says that blowdown from data centers might be an issue and that they use a lot of water in the area. Both are technically true but don’t paint a good picture of the relative harm of either, which I think is pretty small, as explained at length elsewhere.

6. Infrasound and ground-borne vibration are often the same thing.

Andy seems to think that I’ve “confused” infrasound with ground-borne vibration, as if these 2 things aren’t a constant catalyst for one another. I’m actually perplexed that a former physics teacher would even argue this at the risk of discrediting themselves to anyone capable of using common sense.

I have no clue what he’s talking about here? Is this where I said that xAI probably isn’t shaking people’s homes? I have to throw my hands up and await more info.

For any 7 year olds reading, I’ll explain...

For the rest of us, as I explained in the video, it’s really hard to accurately measure the full spectrum of infrasound because [long explanation of microphone diaphragm shelves, vibration sensor weak spots, and Fourier harmonics].

Yeah it’s hard to measure, cosign.

7. The decibel problem.

I’ve already explained how challenging it is to get a quality infrasound measurement… Solving this problem is what I immediately turned my attention to when finishing my last infrasound-related video. I believe that there may be a way to accurately capture the full spectrum of pressure waves via infrared optical sensors and a collimated light source inside a self-contained portable unit. It’s something I’m actively working on. But to be clear…

This is a logistical problem, not evidence of pseudoscience.

This is a non-response to what I said.

Building a new portable infrasound sensor is completely fine. What I take issue with in his video is his experimental design was badly confounded by priming, un-blinded operation, a subwoofer he acknowledges made the room physically vibrate, and post-hoc removal of subjects who reported detecting the condition. None of those problems go away if he builds a better microphone. He could have a perfect measurement of the infrasound in his experimental room and his conclusions would still be unsupported for every reason I gave.

So when Andy read the words “low-quality data” in the World Health Organization’s analysis of infrasound exposure research, he didn’t seem to realize that the “low quality” was in the consistency of measuring the infrasound, not analysing physiological effects.

??? I didn’t quote any WHO “low-quality data” language in my post, and he didn’t link where he thinks I did. If he means the 2018 WHO Environmental Noise Guidelines, the WHO did say the evidence base for health effects at the frequency range they considered was low-quality in the GRADE sense, which is a technical term referring to overall confidence in effect estimates. The issues flagged were about study design, sample size, controls, risk of bias, etc.

His move is to frame every negative finding in the field as a measurement problem and every positive finding as real.

8. This “hit-piece” is good news.

The fact that someone is paid to spend an enormous amount of time pedantically combing through hours of video, reaching in every possible direction to discredit and insult me, my viewers, the cited researchers, and the people whose lives have been devastated by what appears to be infrasound means that our message has reached enough of the general public and their legislators to justify a low blow.

Classic conspiracy theorist move here. “They’re only criticizing me because the cabal doesn’t want me to win.” This is Alex Jones but higher status.

Coefficient Giving didn’t call me one day and say “take this guy out.” I’ve been posting about AI and the environment for a year and a half and now have a no strings attached grant from them to write more. I decide what I want to write about, and I despise pseudoscience and hate when people use stupid tricks like this to push it. I want it gone from the public debate.

Nobody is bringing a tablet to city council meetings and playing my videos. The presented information inspires them to roll up their sleeves, pick and choose what is reputable and relevant to their communities, and then present it formally to legislators.

Inshallah they actually read what the studies say.

Why doesn’t this organization just spend this money on researching and mitigating these things?

Like, wouldn’t actually lowering the water use of a datacenter help legislation pass in their favor?

Wouldn’t actually researching infrasound sources and reducing it where there’s growing evidence of harm make this all go away?

Lowering the water use of a data center often means increasing energy used in cooling, which means more air pollution and greenhouses gases. Don’t do it!

Again with the plea to take his dumb pseudoscience that he knows is fake seriously.

I’ve measured sound sources in hundreds of places, designed low-cost measurement devices, and interviewed hundreds of people suffering from something that I believe to be 100% real. And I’ve done it all on a shoestring budget provided by my videos and viewers under the umbrella of a nonprofit of which I do not claim a salary.

Popular YouTuber funds his own pseudoscience investigations. Hero!

What angers me the most about Andy’s post is that I could have spent this afternoon working on that optical microphone, but instead I’m feeling pressured to defend research that already holds its own ground.

One of the ways tactics like this are effective is by providing a distraction from the research.

I think Jordan should feel some pressure to not abjectly lie to his big audience yes. I’m the one defending the research from his crazy constant misrepresentations.

Response to “Andy Masley doesn’t understand how sound works”

Jordan also shared a blog from someone else claiming that this post misrepresents this science. The post was pretty bad and I wasn’t going to respond to it until Jordan shared it, but since he did I felt like I should put something together. Here’s my complete response (in another post):

There’s a lot of social permission to treat data centers as boogeymen right now

Misinformation is not always easy to identify based on the speaker. If someone looks and acts like Alex Jones, my alarm bells go off, but if someone presents themselves as a well-rounded everyday person who just wants to criticize giant powerful corporations and does it in a chill way, it’s much easier to nod along and not be as alert to ways they might be wrong. More importantly, a lot of people don’t seem to think it’s even worth worrying about the specifics of how they might be wrong if they’re targeting bad people. Who cares about the specific ways data center critics are getting things wrong? Data centers are evil. Jordan is clearly taking full advantage of this and knows that this will get him lots of views.

I think wind farm misinformation is obviously way way worse than data center misinformation. I’d much rather build a wind farm than a data center. But there are two key points here:

• Promoting pseudoscience is like releasing a biological weapon. It’s going to have broad unexpected effects on everything, including things you might like and support. I do not want more infrasound pseudoscience in the world. It will make a lot of people a little more dumb and neurotic and unable to consider real trade-offs well, and cause them to oppose a lot of things I want to see built out, especially wind turbines.

• Decisions about the environment are incredibly complicated, and involve a huge amount of trade-offs. If a pharmaceutical plant or a car factory is proposed near you, it’s important seriously weigh both the real upsides and downsides of both. There are places where building either could help a local community, and places where it can harm it, and getting the real benefits and avoiding the real harms involves understanding very nuanced details of where specifically the harms and benefits are. Throwing pseudoscience into the conversation just incapacitates people from making good decisions. I don’t want people to think of data centers like wind turbines, but I do want them to think of them like they do pharma plants or car factories: things with upsides and downsides that might in specific places be good or bad for local communities.

I want to ask Jordan and his audience “Why do you feel the need to be a goofy goober about this? There’s plenty of trade-offs to worry about with data centers and AI. xAI’s Colossus is a great example of a way a data center harmed people with air pollution, a real and terrible general problem. Why not just make a video about that? Why flatly make things up? Don’t you want to empower your side to know where the actual problems are? Don’t you want to treat your audience like friends and adults, who you wouldn’t lie to and want to empower to think seriously about the world?”

I’d like more people to think of pseudoscience and misrepresenting studies the way Batman thinks about guns.

As data centers become boogeymen for more people, who start to see them as encapsulations of everything wrong in society more broadly, there’s going to be more and more incentive to add on more fake issues, and I think this is mostly just going to incapacitate people from thinking more seriously about the downsides and upsides of the largest industrial buildout of my lifetime and maybe the most consequential new technology.

• “Training GPT-3 produced 552 tonnes of CO2, equivalent to driving 112 gasoline powered cars for a year” — Columbia Climate School

• “GPT-4’s footprint is roughly equal to the annual emissions of 1,550 US citizens” — Shop Without Plastic

• “GPT-3’s emissions are equivalent to the lifetime emissions of 8 cars — or 109 cars’ yearly emissions” — Truthout

I’m going to argue here that 1) The comparisons people make with training are often silly and misleading, and 2) When you make fair comparisons between training AI models and creating other products, the cost of training does not look unreasonably large at all. The way this is talked about often involves comparisons that would make literally any popular consumer product look ridiculous and wasteful. I think these comparisons are obviously goofy, and if I make more reasonable comparisons while keeping the numbers the same, you will see that training AI models is not some unique environmental catastrophe, and actually just blends into all the other ways society uses energy normally.

Contents

• What is training?

• Some bad comparisons

• Two useful comparisons

• A lot of popular coverage of this used terrible comparisons

• State of the art training runs for current models are larger, but don’t emit enough to change my point here, and they have way more users

• Should I also include all the other climate costs of training, like the physical infrastructure and failed training runs?

What is training?

Training an AI model basically means creating it. Untrained models are fed huge amounts of data to pick up more and more subtle background patterns. This involves connecting tens of thousands of specialized AI computers very close together to constantly communicate with each other over months, typically all housed in a single large data center. This uses a lot of energy.

Once a model is trained, it can be used over and over and does not need to be retrained. Newer more capable models are trained instead. Models are often used for months before being replaced by newer ones.

Importantly, spending this much energy is a necessary step to creating frontier chatbot models. We currently do not know of a way of creating models with similar capabilities using much much smaller training runs.

This uses a lot of energy by the standards of everyday buildings, but my argument here is that it doesn’t use much energy or emit that much by the standard of creating any other popular products.

I see training as being analogous to manufacturing a new product for users to buy. Just like physical objects people buy and use have to be manufactured first, AI models people interact with have to be trained first. Both are necessary steps in creating the final products the users interact with.

Some bad comparisons

Your personal emissions

Take a look at this graph, it and graphs like it have been very popular as a way of showing how bad AI is for the environment:

Importantly, this graph is about a very old 2019 model (way before commercial chatbots were available) that didn’t get much use. The stats from this old unused model made it into articles about AI’s environmental footprint 5 years later. Yet it still gets shared a lot to make points about current consumer chatbots. I’ll update the graph with the much much larger estimated training cost of GPT-4. I’m going to start with GPT-4 as an example, because this is the first model where everyone started freaking out about how much energy training uses, and how much it emits:

Much bigger!

I’m using a high-end estimate of GPT-4’s training, partly to include things like the cost of failed training runs. This cost is 15,000 tonnes of CO2.1

Is this a reasonable way to think about GPT-4’s emissions? Is it a useful comparison?

How reasonable does this other comparison look, where instead of training GPT-4 the bottom line shows my rough guess for the emissions of manufacturing every new copy of the iPhone 16?2

Does this tell you anything about whether you personally should or should not purchase an iPhone?

This is my big claim: It is not useful or reasonable to measure the emissions from the creation of a product used by hundreds of millions of people every day against your personal emissions, or the emissions of buying a single plane ticket. That tells you basically nothing about how damaging it is for you personally to use the product, unless you divide by the number of users.

This seems obvious, but almost all reporting on how much energy AI training runs use compare it to individual things people do. A better comparison is the cost of creating the AI model to the cost of creating other products used by hundreds of millions of people. When you do that, it becomes difficult to understand why the emissions of training AI models are receiving so much scrutiny:

Energy of cities

Another common comparison people make with AI training runs is the electricity costs of whole cities. For example, the MIT Technology Review notes that training GPT-4 “consumed 50 gigawatt-hours of energy, enough to power San Francisco for three days.” Is this useful? Well, the electricity homes, businesses, and industry in a single city use doesn’t give us too much useful context for how GPT-4 compares to other consumer products.

Again, take manufacturing iPhones. Manufacturing all iPhone 16s emitted approximately 12 million tonnes of CO2.3 Generating San Francisco’s electricity emits about 890,000 tonnes of CO2 per year. So all iPhone 16s emitted as much as 13 years of San Francisco electricity.4 Does this tell us much about whether you personally should buy an iPhone? Does it add much useful context at all?

Flights

One other common comparison is airplane flights, measured as the total plane’s emissions rather than emissions per capita. GPT-4’s training emissions were approximately as much as 38 full plane flights from San Francisco to Australia5.

NeurIPS is the largest annual ML/AI research conference. This year it’s being hosted in Sydney, Australia. Based on past numbers I’d estimate 15,000 people are going to travel to the conference this year6 from all around the world, enough to completely fill 50 planes. If the average attendee is flying from elsewhere, like San Francisco, this means that a single annual AI research conference will emit more than training GPT-4.

Imagine if an AI research conference was held, and it was so useful for attendees that it singlehandedly created GPT-4 from scratch, where only GPT-3.5 existed before. This seems like a pretty worthwhile get-together. Here, I don’t think people would even bring up the emissions. Right now few seem to be commenting on the emissions of the conference. I think that a headline “AI company emits as much as 38 planes to create GPT-4” is much more attention grabbing than “AI researchers from around the world take 50 planes to all hang out in Australia” even though the second is much worse for the climate and has likely been significantly less consequential for AI as a field, or everyday people’s lives.

Two useful comparisons

Total: Other products

Training is a necessary part of creating a product that hundreds of millions of people interact with every day. One way we can compare training AI models is to the emissions of creating similarly popular consumer products that hundreds of millions of people interact with. Here’s a graph of the total emissions of training GPT-4 vs my best estimates of emissions from the production of other consumer products:

Just like physical CDs and cases had to be manufactured for hundreds of millions of people to play Grand Theft Auto 5, GPT-4 had to be trained before hundreds of millions of people could use it. Both are necessary steps in making a popular product hundreds of millions of people use every day.

Per-user: CDs

I’m old enough to have memories of going out and buying CDs for computer games I wanted to play. One of the coolest objects I owned as a kid was a CD encyclopedia. The whole world’s knowledge at my fingertips!

There are no exact numbers for how many people were using ChatGPT over the time that GPT-4 was available, maybe somewhere around 100-200 million weekly users is a reasonable guess. Dividing GPT-4’s cost of training by this number of users gets to 75-150 grams of CO2 emitted by training per user. This happens to be a little lower than the emissions of manufacturing a single CD. It’s as if each weekly user of ChatGPT had purchased a CD to use it.

This is the cost of training everyone was freaking out about when GPT-4 was released. It’s like the company didn’t have to spend any energy on training at all, and to use ChatGPT you just had to buy it on a CD, which emits a little to manufacture. I don’t think that if GPT-4 didn’t involve training at all but came on a CD, anyone would be talking too much about the emissions of manufacturing those CDs, and yet those emissions are the same as the training cost that people speak so ominously about. This is very small!

A lot of popular coverage of this used terrible comparisons

Here’s a ton of quotes from different articles on the cost of training GPT-4 and previous models. Not one of them compared training to other consumer products used by hundreds of millions of people, or divided by the number of people to get a per-user number. I haven’t found any sources doing either. Read through these and think about whether the reader would come away with a better or worse understanding of the magnitudes involved. Would they know creating a model used by hundreds of millions of people had about the same climate impact as the CDs and cases for Grand Theft Auto 5?:

• “Training GPT-3 produced 552 tonnes of CO2, equivalent to driving 112 gasoline powered cars for a year” — Columbia Climate School

• “GPT-4’s footprint is roughly equal to the annual emissions of 1,550 US citizens” — Shop Without Plastic

• “GPT-3’s emissions are equivalent to the lifetime emissions of 8 cars — or 109 cars’ yearly emissions” — Truthout

• “That’s like a car driving 1.2 million miles” — Carbon Credits

• “500 tons of carbon dioxide emissions is the equivalent of around 600 flights between London and New York” — Cybernews

• “GPT-3 emitted carbon dioxide equivalent to 500 times the emissions of a New York-San Francisco round trip flight” — Analytics Vidhya

• “It’s the same amount of emissions as a single person taking 550 roundtrip flights between New York and San Francisco” — Carbon Credits

• “Training GPT-4 consumed 50 gigawatt-hours of energy, enough to power San Francisco for three days” — MIT Technology Review

• “15,000 tons is roughly the same as the annual emissions of 938 Americans” — Towards Data Science

• “Enough energy to power an average U.S. home for over 120 years” — Truthout

• “Training a single AI model can emit as much carbon as five cars in their lifetimes” — MIT Technology Review (headline)

• “Training GPT-3 consumed 1,287 megawatt hours of electricity (enough to power about 120 average U.S. homes for a year), generating about 552 tons of carbon dioxide” — MIT News

• “Training a single large model like GPT-3 can use over 1,200 MWh — enough electricity to power around 120 U.S. homes for a year” — Climate Impact Partners

• “Training GPT-3 emitted roughly 500 metric tons of carbon dioxide — the equivalent of driving a car from New York to San Francisco about 438 times” — Climate Impact Partners

• “Training GPT-3 produced 552 tonnes of CO2, which is equivalent to the emissions from 110 gas-powered cars over a year” — Smartly.AI

• “GPT-3 emitted carbon emissions equivalent to the lifetime impact of five cars” — Nature Scientific Reports

• “Training the bigger, more popular AI models like GPT-3 produced 626,000 pounds of carbon dioxide, equivalent to approximately 300 round-trip flights between New York and San Francisco — nearly five times the lifetime emissions of an average car” — The Sustainable Agency

• “This is similar to the yearly emissions produced by 120 passenger cars or 600 transatlantic flights per person” — Shop Without Plastic

• “Training AI models can emit more than 626,000 pounds of carbon dioxide equivalent — nearly five times the lifetime emissions of the average American car (including the manufacture of the car itself)” — Learning Tree

• “Training AI models can emit more than 626,000 pounds of carbon dioxide equivalent — nearly five times the lifetime emissions of the average American car” — Supermicro

• “Training a single AI model can emit 626,000+ pounds of CO2 equivalent… about 5x the lifetime carbon emissions of an average passenger car” and “training a single AI model can consume more electricity than one hundred American homes use in one year” — Carbon Credits

• “Training such a model requires… This is equivalent to the annual energy consumption of around 160 average American homes” — Frankly Kranky

• “GPT-3 consumed approximately 1,287 MWh of electricity—enough to power 120 US homes for a year… A single AI model training session can emit as much carbon dioxide as five cars over their entire lifetimes” — AI Energy Calculator

• “Training ChatGPT-3 is estimated to have required the equivalent energy consumed by an average American household for 120 years” — California State University Northridge Library Guide

• “GPT-3 consumed a staggering 1,287 megawatt-hours (MWh) during training. This resulted in about 502 metric tons of carbon emissions, equivalent to the emissions from hundreds of gasoline-powered cars in a year” — PatentPC

• “The process can emit more than 626,000 pounds of carbon dioxide equivalent — nearly five times the lifetime emissions of the average American car” — ACM Communications and separately SPE Journal of Petroleum Technology

None of these comparisons make sense, because basically all of them treat training as if it’s comparable to activities individual people do, instead of creating a product expected to get hundreds of millions of people using it. If instead we treat training GPT-4 like this, it fades into basically nothing. If you think it would have been worthwhile for the climate to prevent GPT-4 from ever being trained, you should also consider stopping the production of Lego for 2 weeks. If you’re not telling everyone to boycott Lego or Coke or Grand Theft Auto because of the emissions involved in creating them, you shouldn’t be telling people to boycott ChatGPT for the emissions involved in creating GPT-4. If you read “Manufacturing all the CDs for Grand Theft Auto 5 emitted as much as 160 American homes” I think you might be a little underwhelmed.

State of the art training runs for current models are larger, but don’t emit enough to change my point here, and they have way more users

Everything above uses GPT-4 as an example because that’s the model that kicked off all the panic about training emissions. But GPT-4 is old now. Current state of the art models are bigger and more expensive to train. Does this change anything?

Training costs are growing at about 2.4x per year for frontier models. The largest training run we have decent estimates for is Grok 4, which Epoch AI estimates used about 310 GWh of electricity and cost around $490 million, roughly 6–7x what GPT-4 cost. But Grok 4 is probably the worst-case scenario for training emissions, because xAI’s Colossus data center in Memphis was powered largely by mobile natural gas turbine generators, which emit about 0.49 kg CO2 per kWh, 1.3x higher than the US grid average of about 0.37 kg CO2 per kWh, which itself is higher than some of the grids where AI models are typically trained, like Oregon which relies more on hydropower. This is why Epoch estimates Grok 4 emitted about 154,000 tonnes of CO2, ten times GPT-4’s estimated emissions, despite using only about six times as much electricity.

Most other frontier labs don’t train on natural gas generators. They primarily train models in large data centers connected to the electrical grid, many of which are in places with more renewable energy. The same 310 GWh Grok 4 training run would emit about 115,000 tonnes on the average US grid, 75% of its actual emissions. On California’s grid it would only emit about 62,000 tonnes, 40% of its actual emissions. This means that a frontier training run of similar size to Grok 4, but run on a typical major cloud provider’s infrastructure, would likely emit somewhere in the range of 60,000–120,000 tonnes.

Not every frontier model uses as much compute as Grok 4, either. Grok 4 was trained on 200,000 GPUs and is probably the single largest training run to date. Other state of the art models may use significantly less compute, bringing their energy use and emissions lower. Hardware energy efficiency has been improving at about 40% per year, so the next generation of training runs won’t need proportionally more energy even if they use more compute.

So what’s a reasonable range for the emissions of training a current frontier model? Probably somewhere between 40,000 and 120,000 tonnes of CO2, with the wide range driven both by differences in the carbon intensity of the electricity and differences in the amount of energy used.

Is this a lot? Well, these models get used for months at least. I could just up the manufacturing numbers to a month each.

AI models still just don’t stand out much compared to other popular products.

For now I’ll use 80,000 tonnes as a round middle estimate for the current emissions of training an average frontier model.

Two things have also changed since GPT-4:

1. There are way more users. When GPT-4 was the main model, ChatGPT had roughly 100–200 million weekly users. Now ChatGPT has 900 million weekly active users. That’s roughly a 5–9x increase in users over the same period that training costs went up maybe 4–6x. The per-user training footprint has likely stayed roughly flat or even declined. 900 million users over 80,000 tonnes of CO2 for training is about 89 grams. This is still about half a CD. Even under Grok 4’s worst-case emissions of 154,000 tonnes, assuming similar user numbers would mean the per-user cost is about 171 grams, roughly one CD.

2. Even at the total level, the comparisons mostly don’t change. The most advanced AI models that huge numbers of users interact with for months at a time use about as much energy to create as it takes to manufacture 3–4 days of iPhones.

My basic point has so far not changed with larger models: creating an individual AI product used by hundreds of millions of people produces emissions that are small relative to manufacturing basically any other product used by hundreds of millions of people.

Training runs are going to continue to use more energy, but models are also getting more capable over time. Eventually this may be more like each user purchasing a piece of computer hardware instead of a CD, but for models so much more capable the investment will, I expect, be worth it.

Should I also include all the other climate costs of training, like the physical infrastructure and failed training runs?

Maybe my comparison to creating other products is unfair here, because there are lots of other analogous steps to manufacturing before the chatbot can be delivered to hundreds of millions of people. OpenAI also had to buy tens of thousands of GPUs, which had to be manufactured in fabs and assembled into servers. Those servers sit in data centers that had to be built out of steel and concrete. What’s the total carbon footprint of all that?

Let’s try to make this number as big as possible. We can include the embodied hardware emissions, the data center construction, the cooling infrastructure, and the experimental training runs that failed before the final model worked. Hugging Face found that when they accounted for all of these factors for the model BLOOM, total lifecycle emissions roughly doubled, from 25 tonnes to about 50 tonnes. The estimate I chose for GPT-4’s training cost is 3 times as high as many popular estimates already, so it likely includes these costs already, but let’s double it anyway to be safe, to get a total of about 30,000 tonnes of CO2 emissions from literally everything required to deliver a model to users.

Even at this extreme, training GPT-4 would still be smaller than building a single cruise ship. It would still be less than one day of manufacturing Nike shoes. It would still be less than a single day of manufacturing iPhones.

But I also didn’t include many of the additional costs of the other products too. I didn’t include building the factories or making the mining equipment for extracting raw materials or transporting iPhones to stores or the cost of the retail stores themselves… many accusations that “you’re leaving information out” are one-directional, and don’t consider that most things we do have lots of hidden costs depending on what we want to compare. Ultimately I think the direct comparison between training and creating other popular products is completely legit.

The per-user comparison doesn’t really change even under the most extreme interpretation. It raises the per-user cost of training GPT-4 from one CD to two. The reason the cost of creating a product used by so many people is so low compared to things like Coke and sneakers is that in comparison, computing is very energy-efficient, and thus it’s very rarely the most promising thing to optimize or cut for the climate.

It’s true that all training across all AI models is forecast to make up a significant fraction of US electricity use in the next few years, maybe rising to whole percentage points of US electricity usage. This is because a huge portion of total AI training will be concentrated in America, and because AI is entirely electrified, while most other industries aren’t. This post was mainly about the training of individual AI models. In the next post I’ll try to paint an overall picture of where training and AI more broadly fit into America and the world’s overall energy and climate situation.

This post will be a very detailed overview for autonomous vehicle advocates to understand the situation in DC and pathways to legalization.

This post won’t have arguments for why we should legalize Waymo. I have a separate post on that, though I’d like to write an update to it since I think there are much stronger cases I can make.

You may have noticed that Waymos are already driving around DC (with humans at the wheel). They’ve been mapping the city and say they’re nearly ready to move forward, but the city currently has no legal path to commercial service. Two decisions are in the way: a driverless testing pathway and a commercial operating framework. Right now both are held up by a missing District Department of Transportation (DDOT) report that was supposed to arrive in October 2022, and by DDOT’s failure to open a permit pathway separate from the report that already exists in statute.

Contents

• What’s blocking Waymo

  • The 2 things Waymo needs

  • The DDOT report and the separate testing permit framework

  • The council and mayor debate once the report is published

• The mayoral race

  • Kenyan McDuffie

  • Janeese Lewis George

  • The mayoral race makes the DDOT timeline much more important, and vice versa

• The federal government

• How to legalize Waymo in DC

  • The most promising levers to pull

  • Lessons from other cities

What’s blocking Waymo

The 2 things Waymo needs

Waymo needs two things from DC’s city government that it doesn’t have.

• Driverless testing permits. Right now, every Waymo in DC is required to have a human safety driver. But Waymos need to be tested without a driver. This has been a standard step in every other city. DC law already authorizes DDOT to run an AV testing permit program that can include remote-operator (driverless) testing, and 2025 amendments specify that driverless testing requires that permit. But DDOT has never made the application for driverless permits available. Until DDOT opens the permit pathway and finishes the associated rules, testing can only continue with a human physically present, and the necessary driverless tests can’t happen.

• A commercial operating framework. Even if Waymo got driverless testing permits tomorrow, current DC law has no structure for a commercial autonomous ride-hailing service. Things like insurance requirements, licensing structures, and rules for how a driverless vehicle interacts with police don’t exist. Autonomous ride-hailing is effectively illegal in DC.

For Waymo to operate normally, they need to get driverless testing permits, then begin commercial operations under a legal framework the council has passed. Neither step is available today.

The DDOT report and the separate testing permit framework

The Autonomous Vehicles Testing Program Amendment Act of 2020 gave DDOT authority over AVs and required the department to produce a report within one year of the law's applicability date of October 1, 2021, meaning the deadline was October 1, 2022. The report was to provide “recommendations to safely accommodate the deployment of autonomous vehicles on public roadways for commercial, personal, and any other use.” The report could optionally include draft legislation or regulations.

This report is more than three and a half years late, and is now the main political obstacle blocking Waymo.

The 2020 law tasked DDOT with setting up a formal AV testing program as a distinct obligation from the deployment report. DDOT hasn't indicated they're working on permits separately, so we may not get clarity on testing permits until the report comes out, if it addresses them at all.

It blocks legislation because Charles Allen, the DC Council’s transportation committee chair, has made the finished report a precondition for holding hearings on any AV bill. Former councilmember Kenyan McDuffie (one of the two leading candidates for mayor) introduced a comprehensive AV legalization bill (the Autonomous Vehicles Amendment Act, B26-0323) in July 2025. It’s been sitting in Allen’s committee ever since without a hearing, because Allen won’t schedule one until the report is finished. Nothing in the law says the council can’t legislate on AVs without this report. Allen’s position is a political choice.

The history of the report

• December 2020: Autonomous Vehicles Testing Program Amendment Act of 2020, giving DDOT authority over AVs and required the initial report. The entire mandate says:

  • “Within one year after the applicability date...the Department shall transmit to the Council a report that provides recommendations to safely accommodate the deployment of autonomous vehicles on public roadways for commercial, personal, and any other use the Department determines. The report may include draft legislation or regulations.”

• January 2022: DDOT publishes a notice of proposed rulemaking in the DC Register to create an AV testing program. This never gets finalized. This is a separate obligation from the report, more on this below.

• Late 2022: The report’s deadline of October 1, 2022 passes with no public explanation. I can find no reporting on what, if anything, DDOT was doing on the report during this period.

• 2023: No report, no public explanation for the delay. There’s no reporting anywhere I can find on the reason it was delayed.

• July 2024: A Wayback Machine snapshot of the mayor’s AV Working Group’s webpage (which has since been removed from the DMOI website) reveals that DDOT was still in the procurement phase for hiring a consultant to help define a regulatory framework. More than two years after the report was due, the work of writing it had not yet been contracted out.

• March 2025: Congress freezes nearly $1 billion in DC’s local funds. Allen later attributes part of the report’s delay to this, but the report was already almost three years late at this point.

• Spring 2025: Some expected the report to be finished around this time. It did not materialize.

• Fall 2025: DDOT told Allen the report would be ready around this time. In September, DDOT backtracked, telling Allen it would be out “in the new year, timeline unknown.” Allen said he had been meeting with trial lawyers, safety advocates, and Waymo in preparation.

• Late 2025: DDOT said the report was one of the programs affected by budget cuts. But this doesn’t explain why it wasn’t finished in 2023 or 2024, before the cuts happened. DC has since entered a new fiscal year and the funds have been restored.

• February 2026: A DDOT spokesperson told Axios the agency expects to propose recommendations “later this year.” Waymo told Axios that DDOT informed the company the report “is being actively worked on, and could be finished by this summer.”

Will the report be finished by summer? I’m skeptical. But there are three reasons to think this time could be different. Political pressure is higher than it’s ever been. The Washington Post editorial board has now published two pieces on this (one literally accusing DDOT of “sabotage”), Waymo is spending a lot more on lobbying, and the company got 1,500 residents to contact city hall. Also, DDOT appears to have finally contracted outside consultants to help write it. And the fact that DDOT gave a specific timeframe to both Axios and Waymo, rather than the usual vague non-answers, suggests at least something is happening.

What happened with that AV testing program?

DDOT published proposed rules in January 2022 to create the AV testing program. This is separate from the report. The 2020 law created two distinct obligations: a testing permit program and a deployment report. The proposed rules would have established how companies apply for testing permits and what the testing framework looks like. If DDOT had finalized them on any reasonable timeline, Waymo could have applied for driverless testing permits years ago.

DDOT never finalized them. The mayor’s now apparently dormant AV Working Group’s own page, as late as mid-2024, said the rulemaking was “still in the works” and they looked “forward to keeping the public informed when there is more to share.”

This is in my opinion more ridiculous than the delayed report. DDOT got far enough to publish draft rules for driverless permits and then just stopped. They should really be held accountable here. Waymo could begin driverless testing with permits while they wait for legislation to be fully legalized if DDOT could just get its act together on this separate thing, but instead they’ve gone completely silent on it.

This created an obvious problem when Waymo and other companies started showing up to test in early 2024. There was no permit system, so there were no formal rules. The council responded to this problem with the Autonomous Vehicle Testing Permit Requirement Amendment Act of 2024 (B25-0710), which Allen’s committee held a hearing on in June 2024. The law was simple: until DDOT’s permit program actually exists, driverless testing is prohibited. Any company testing AVs must have a human in the vehicle and give DDOT advance notice.

Because temporary legislation in DC expires and must be renewed, this had to be passed over and over, as an emergency measure in March 2024, a temporary law in June 2024, another temporary law in March 2025, and a congressional review emergency act in March, before finally being made permanent through D.C. Law 25-312 in the same month. DDOT’s inaction forced the council into a patchwork of reactive legislation instead of operating under the coherent framework that was supposed to exist by 2022.

Even if the deployment report appeared tomorrow, there would still be no finalized testing permit framework from DDOT. The report and the testing rules are separate obligations, and both are unfinished. Anyone who tells you the report is the only thing blocking Waymo is missing half the story. This is something I think a lot of AV advocates are missing in conversations about Waymo in DC.

It’s worth noting that the report could end up addressing testing permits. The mandate says the report “may include draft legislation or regulations,” so DDOT could bundle everything together. But there are reasons to think it won’t:

• The 2020 law created them as separate obligations. The testing permit program was established under a different section of the law than the deployment report. DDOT published proposed rules for the testing program in January 2022, a completely separate regulatory track, and then abandoned them. The fact that DDOT initiated a standalone rulemaking for testing permits confirms that the agency itself originally treated these as distinct workstreams.

• DDOT has never said the report will include testing permits. Despite nearly four years of delay, DDOT hasn’t publicly indicated that the report will address the testing permit framework. If they were planning to combine the two, you’d expect them to say so, especially given how much scrutiny this issue is getting.

• The report and the permits are controlled by different bottlenecks. Allen’s precondition is about legislation: he won’t hold hearings on AV bills until the report is done. But testing permits are an executive function. DDOT could issue them under existing statutory authority without any new legislation passing. These are different problems held up by different branches of government, which makes it less likely that a single document resolves both.

What the report might say

The DDOT report’s content matters a lot. It’s possible that the report recommends heavy restrictions, and that would obviously be terrible for the prospects for AVs in the city anytime soon. There are maybe three possibilities:

• A permissive framework would recommend clear pathways for testing and commercial AVs with standard requirements. This is basically what McDuffie’s bill already proposes. It would give Allen what he says he needs, and things could move pretty quickly.

• A cautious, phased framework might recommend a limited testing period, geographic restrictions, fleet size caps, mandatory human oversight periods, and extensive reporting requirements before any expansion. This would still move things forward but could add a year or more.

• A restrictive or inconclusive framework is the worst case. If the report emphasizes unresolved safety questions, calls for further study on labor impacts, or recommends conditions Waymo can’t practically meet (like requiring a human in every vehicle), it gives official cover to delay indefinitely.

A couple things would really help AV advocates: if the report addresses testing permits alongside deployment (rather than leaving those stuck in a separate bureaucratic track), and if it includes draft legislation (as the mandate allows), which could speed up the council’s work significantly.

The Three Gatekeepers

Three parties share responsibility for this block: DDOT, Charles Allen, and Mayor Bowser.

DDOT

DDOT is more than three and a half years late on the report. The agency has a lot on its plate, and when it received this assignment AVs were much more speculative.

Some people have speculated about internal opposition to AVs at DDOT, but I don’t see strong evidence for this. DDOT Director Sharon Kershbaum hasn’t publicly commented on AVs. Some have inferred anti-AV sentiment from her other unrelated positions that they read as anti progress, but I think that’s a stretch. Transit decisions are complicated and don’t map neatly onto a pro- or anti-tech progress axis. I’d identify as pretty pro-progress, but I share Kershbaum’s lack of enthusiasm for dedicated bike lanes (sorry friends), and I don’t think that means I’m anti-progress on other transit issues.

Stephanie Dock is the manager of DDOT’s Autonomous Vehicles Program and leads the team responsible for overseeing autonomous vehicle testing. She was on a panel on AVs presented by MIT in 2025 and had some useful quotes on the report:

We want to be adaptable, we want to be responsive to what’s going, and the industry is changing a lot faster than our rulemaking structure is allowing us to keep pace.

In order for us to do a permitting program, we have to have done a rulemaking.

Trying to figure it out before it comes. Anybody else who’s struggling with that prospect, you are not alone. We are also trying to figure that out.

These are notable because they’re the closest thing to a public explanation for why DDOT has been slow. She’s essentially saying the bureaucratic process itself (rulemaking requirements) is a structural bottleneck, not just a prioritization failure.

On the deployment report:

A deployment report with recommendations for legislation and regulation... that has to be gone through a lot of signatures, very similar regulations, so I have no promise on when the full report will be released, but I... fingers crossed, we’ll have made it through the procurement process so that... such that we can release the research aspects, kind of the background work.

This confirms the report was still going through procurement/signatures as of this talk in 2025, and that she herself couldn’t promise a timeline.

On industry vs. DDOT’s timeline:

Industry has slightly different plans. I am still looking at recommendations for deployment. Industry is certainly saying we would like to deploy. So they said they’re coming in 2026. I am eagerly awaiting how they’re going to figure out to do that.

But they also have political levers that they can pull that I, as a lowly staffer, do not.

So here it seems like she’s openly acknowledging that Waymo’s 2026 deployment plan doesn’t align with where DDOT actually is, and that the company has political tools (lobbying, public pressure) that she doesn’t.

On DC’s attractiveness and risk for AV companies:

I think everyone knows that the risks of coming into our market are relatively high, right? You’ve got to do it right, because if you want to convince Congress that you need less oversight of the AV industry, you better not be screwing up their travel on a daily basis.

On the depot/workforce concern:

Worried that if we are unable to bring in some of the maintenance and charging aspects of these fleets, that any of the workforce benefits that might accrue associated with this are actually going to go to us, because they’re going to go to Maryland and Virginia, where there is more land.”

“It also has a risk of increasing the zombie miles of empty VMT driving back as they deadhead to get to those facilities.

This shows she’s thinking about empty miles and workforce displacement to the suburbs. She’s not anti-AV here, but she’s clearly concerned about the impacts.

On where they actually are (no deployment framework):

You’ll note that my slides here with the timeline of where we are in legislation and regulation makes absolutely no mention of deployment. We do not have legislation for that at this time.

On the regulatory vs. investment mindset:

I spend a lot of time thinking about what we regulate. I get to spend some time thinking about what we incentivize, and I spend very little time thinking about what we could invest in. I’d like to change that eventually, but first we have to have these base frameworks in place.

She also brings up DC’s objectively weird transit situation:

Lots of places have motorcades. I would argue that we probably have more than most, and they look in all different sizes... it could be everything from the president or the vice president moving on street... but also, we run a lot of unannounced motorcades that are just a couple vehicles moving through, as perhaps a senator is coming up to Congress. There’ll only be two or three black cars running all sorts of red lights.

We are the central city in a tri-state region... on the one hand, we are a state, but at the same time, we’re also at the very middle of a region, and our region involves Maryland and Virginia, and depending on the commute shed that you’re looking at goes as far as West Virginia as well.

DC does not... is not an industrial city at all, right? That’s not why we exist. So we have a real shortage of land that can be used for anything like a depot. We don’t have a lot of big warehouses.

DDOT seems to have expanded the scope well beyond what the law required. The July 2024 Wayback Machine snapshot of the AV Working Group page says DDOT was trying to “procure consultant support to help the agency define a potential regulatory and legislative framework for the deployment of AVs.” That’s already broader than “recommendations” the original law called for. And Allen has added on additional expectations. In his public statements he’s talked about wanting the report to address job displacement, worker transition, and what he calls getting it “right” in a way that clearly extends beyond the safety focus of the original mandate. His December 2025 newsletter explicitly raised “the impact on jobs” as something the report needs to address. The law never called for that.

It’s crazy that after almost four years, we don’t know for sure if DDOT is planning to actually provide a permitting system along with the report. It would be nice if they would just make the general goals for the report clear, because even there we have very little to go on besides the original very short mandate.

Charles Allen

Allen is the single most important person on the legislative side. He’s the chair of the Committee on Transportation and the Environment, so he controls whether any AV bill gets considered. His making the DDOT report a precondition has given the department a total veto over the legislative timeline.

Allen has been careful to say he’s not opposed to AVs. He’s said he doesn’t “think we should be afraid of this technology or throw up obstacles” and that autonomous vehicles will “likely operate in the District” in the near future. But the practical effect of his position is indefinite delay.

His stated reasons are about process and safety. From his December 2025 newsletter:

The idea that we should authorize fully driverless vehicles without DDOT’s analysis in hand is not safe, nor is it even guaranteed to happen if the Mayor and DDOT don’t want to issue permits.

He’s also raised job impacts:

The questions also need a stronger conversation about the impact on jobs — DC residents working as taxi and rideshare drivers today would be replaced, and the money you pay for a ride would no longer go back into the community but rather be sent out of state to corporations largely located in California.

That one sounds especially bad, because there’s no way that Waymo (or buses, or bikes) could be legalized without reducing the number of rideshare jobs at least a bit.

Some other key quotes from Allen on how he’s thinking about this:

Autonomous vehicles hold promise, but DC residents should not be treated like test subjects while companies work through unresolved issues on busy city streets. Given recent nationwide examples of AVs making dangerous mistakes, a careful, data-driven approach is warranted. The questions also need a stronger conversation about the impact on jobs – DC residents working as taxi and rideshare drivers today would be replaced, and the money you pay for a ride would no longer go back into the community but rather be sent out of state to corporations largely located in California.

We’re committed to getting this right – advancing innovation without compromising safety – and to making sure autonomous vehicles are integrated thoughtfully into a transportation system that works for everyone.

- December 2025 newsletter

I don’t think we should be afraid of this technology or throw up obstacles. But my job isn’t to say, ‘How fast can I roll this out?’, but rather, ‘How can we get this right?’ I’d rather get it done right than get it done fast.

- Interview with The 51st

(in response to a claim that AVs are involved in more crashes than human driven cars) This to me is a great example that I don’t necessarily want to be the first into this marketplace. I don’t mind having other cities go first but let’s learn what works and what doesn’t and then figure out what’s best for D.C.

- Interview with FOX 5 DC

Could he change his mind on making the report a condition? Nothing legally prevents him from holding hearings without the report. McDuffie’s bill exists and could be brought to hearing. But Allen has made this a matter of principle, and I’m skeptical that the relatively small number of AV advocates in DC can change his position on it. I also personally just have no way to judge how reasonable his decision is. There’s a legitimate case that the council should have expert analysis before writing complex regulatory legislation. What I can say is that DDOT’s three year delay has turned what might be reasonable caution into an effective block. I do worry a lot about a lot of the quotes he’s shared here on safety and jobs. Both go way beyond a reasonable or fair assessment of Waymo compared to any other vehicles.

Allen has no control over DDOT, but someone else does…

Mayor Bowser

Mayor Bowser also bears responsibility here. DDOT is an executive agency that reports to the mayor. The report is almost four years overdue on her watch. Bowser could direct DDOT to prioritize it. She could also direct DDOT to separately begin designing and issuing driverless testing permits.

Allen has publicly blamed Mayor Bowser for not issuing driverless testing permits. In December 2025, he told Axios “I don’t know what the holdup is.” Bowser’s office responded to a media request on this anonymously. An official who wasn’t authorized to speak to the press told Axios “It’s not us” and characterized DDOT as “being super diligent with their research.” I’m not sure what to infer from this.

The fact that DDOT has been allowed to sit on this for nearly four years without consequence suggests either that Bowser doesn’t consider AVs a priority, or that there’s an active decision to keep this in limbo.

Bowser could unblock the situation from the executive side without needing the council to do anything. Getting DDOT to produce the report would remove the justification Allen is using to hold up legislation.

Bowser has less than a year left of being mayor after 3 terms. A new mayor will take office in January of next year, and the primary that will determine who will be mayor is happening this June. Bowser hasn’t given any sign that she’ll give any priority to AVs in her last year as mayor, so while I wish she’d use her executive authority to push DDOT to prioritize the report more and to separately issue driverless testing permits in the meantime, I don’t expect this to happen. She doesn’t have much incentive to push something new and controversial like this.

Takeaways

• Waymo needs two things from DC’s government to operate: testing permits for Waymos to drive without humans inside, and legalization of commercial AVs.

• The report is the main political bottleneck for both. It blocks legislation because Charles Allen, the transportation committee chair, won’t hold hearings without it. It’s also part of the justification DDOT gives for not moving forward on testing permits, even though the 2020 law established testing permits and the deployment report as two separate obligations, and it’s not even clear the report will address testing permits at all.

• But the report isn’t the only bottleneck. DDOT’s unfinished permit and rulemaking process (a separate track from the report) is the practical bottleneck for driverless testing. The driverless testing permits could also be produced if Mayor Bowser decided to direct DDOT to open the permit pathway that already exists in statute, but it looks like she won’t, and DDOT hasn’t given any indication that they’re separately working on driverless testing permits.

• There are recent signs of progress. DDOT told both Axios and Waymo the report could be done by summer 2026, and they appear to have contracted outside consultants. But DDOT has missed deadlines before and is more than three and a half years late.

The council and mayor debate once the report is published

Once the report is published, this will only clear the way for the DC council to actually make policy on AVs, and we know almost nothing about what each DC councilmember believes. Many other cities in the Northeast have delayed or rejected self-driving cars.

Of the 13 councilmembers, the only one besides Allen who has been publicly vocal about Waymo is Janeese Lewis George, and she’s been skeptical of AVs (more on her below, note that she’s the other main candidate for the next DC mayor). Everyone else seems to not have commented at all. I cannot find any hints of what any of the councilmembers think about Waymo.

One piece of info we do have is who voted for the 2020 AV bill that created the original AV framework and gave DDOT authority over AVs. The bill seems pretty pro AV, it was passed unanimously, and 7 of the 13 councilmembers who passed it are still in office, though some are not running for re-election and will be out of office in January 2027:

Current councilmembers who voted for the 2020 bill:

• Phil Mendelson

• Anita Bonds (not running for re-election)

• Robert White (running for Congress)

• Brianne Nadeau (not running for re-election)

• Brooke Pinto (running for Congress)

• Charles Allen

• Trayon White (currently awaiting a federal bribery trial scheduled for September, creating an unprecedented dynamic where he was unanimously expelled from the DC Council in February 2025, only to win his Ward 8 seat back in a July 2025 special election)

The six councilmembers who weren’t in office in 2020 are:

• Janeese Lewis George

• Christina Henderson

• Doni Crawford

• Matt Frumin

• Zachary Parker

• Wendell Felder

If a councilmember hasn’t thought about AVs, this might be a great opportunity for DC citizens and groups to influence their opinion early before the issue becomes polarized, or before the anti-AV interest groups reach them first. This actually seems really urgent in a way that hasn’t been covered as much, because even when the report is passed, the decision to legalize Waymo will be in the hands of these 13 councilmembers, and the only one who’s spoken about Waymo specifically has been negative. I think we should actually be somewhat pessimistic about how the council will vote. As noted in Understanding AI, Waymo has had a lot of success in Republican-dominated states where the regulatory environment is more favorable, and has hit barriers in more Democratic states. As a baseline, because DC skews very left, and this pattern of delay in more left of center places is becoming more consistent, we should expect a higher chance of bumping into more political opposition here.

One other note is that the two council votes running for Congress (Pinto and White) will be heavily influenced by how organized labor (a vital constituency in a citywide/district-wide congressional primary) views the AV issue.

The mayoral race

I really don’t think people should vote for DC mayor based on how they think about AVs, but it’s also important to understand that the two main candidates for DC mayor seem to be on opposite sides of the AV debate and it’s likely that this election will be extremely consequential for the future of self driving cars in the city.

The mayoral race in DC is basically decided by the Democratic primary on June 16 2026. Because DC is overwhelmingly Democratic, whoever wins that primary will almost certainly be the next mayor, taking office in January.

The two leading candidates in the primary are Janeese Lewis George and Kenyan McDuffie. McDuffie was the most pro-AV member of the DC council, and Lewis George is the most anti-AV.

Here’s a prediction market for who will win:

Kenyan McDuffie

McDuffie is the former councilmember who introduced the Autonomous Vehicles Amendment Act of 2025 (B26-0323), the bill that would create DC’s complete AV operating framework. The bill was introduced on July 11, 2025 and referred to the Committee on Transportation and the Environment on July 14, 2025. It would allow fully autonomous vehicles to operate without a human driver; establish insurance and financial responsibility requirements; require companies to submit law enforcement interaction plans; create an on-demand AV network under existing for-hire transportation law, with human-driver-only provisions carved out; and designate DDOT as the sole agency implementing the act.

McDuffie was regularly described as one of the most “pro-business” councilmembers. If elected mayor, he would likely direct DDOT to finish the report on a hard deadline, possibly issue driverless testing permits independently, and use his relationship with Allen and other councilmembers to push for hearings.

Surprisingly, McDuffie hasn’t done or said anything else related to AVs that I can find.

Janeese Lewis George

I want to flag that I’m going to be pretty critical of Lewis George’s coalition’s attitude toward AVs here, but I’m otherwise unsure of who I’m going to vote for and friends I trust have wildly different takes on which candidate is preferable in general, though basically everyone I know agrees that we’d like Lewis George to change her mind on AVs. Among other things Lewis George got the endorsements of prominent housing groups in DC who I tend to agree with.

Lewis George is the councilmember who has been most publicly skeptical of AVs. She said in a podcast interview in February “I don’t think our city is ready for Waymo at this moment.” She’s framed her concern as primarily about safety, but she also spoke about worker displacement, saying “we’re gonna create a balanced approach” because “I want to make sure we are not displacing workers.”

Lewis George describes herself as a democratic socialist and has built her career around labor advocacy. In December she got early endorsements from five unions with significant local organizing presence: SEIU 32BJ (which represents property service workers including janitors, security officers, and airport workers), ATU Local 689 (transit workers), UFCW Local 400 (grocery, retail, and healthcare workers), and Unite Here Locals 23 and 25 (hospitality and food service workers). At that endorsement rally, she pledged that as mayor she would “take on companies” engaged in union-busting and protect workers from displacement fueled by artificial intelligence. The Working Families Party and Washington Teachers’ Union have also endorsed her.

Axios reported that “a prominent local labor union” endorsed Lewis George in part because of concerns about job losses from autonomous vehicles specifically. The Washington Informer identified this union as SEIU 32BJ (property service workers), whose executive vice president Jaime Contreras said they endorsed Lewis George because "She's anti-Waymo, which is something that we oppose coming to the city because it's going to displace working-class people who do Uber or Lyft or DoorDash…for extra income.” This mirrors the pattern in other cities where organized labor has emerged as the primary opposition force to Waymo’s expansion.

Lewis George hasn’t called for banning Waymo or said autonomous vehicles should never operate in DC. Her stated position is that the city isn’t ready “at this moment” and that she wants to ensure workers aren’t displaced. This sounds reasonable, but is actually quite extreme as a reaction to new vehicles. Any rule that says we can’t introduce a new transit technology if it threatens to cause rideshare drivers to lose their jobs is an abysmal way to think about transit policy. Among other things, if buses didn’t exist, this same rule would ban buses. This same rule would have also banned rideshare apps in the first place, because they mostly destroyed the (better regulated, better paid, easier to organize) taxi industry in DC1. I have more on that argument here.

Lewis George’s position on Waymo closely matches the positions of leaders in cities that have stalled or effectively banned it:

• In Boston, the city council’s proposed ordinance uses identical framing: study the impact on jobs first, require a human safety operator in every vehicle, and don’t allow commercial operations until an advisory board gives the green light. That advisory board would be required to include representatives from the App Drivers Union (a rare example of unionized rideshare drivers), the Greater Boston Labor Council, the Teamsters, and the UFCW. Eight city councilors collaborated on the ordinance. The Teamsters organized a coalition called “Labor United Against Waymo” which was the first mass-mobilization effort outside California to push back against AV expansion. Waymo’s spokesperson called the proposal the “first major city in the world to ban fully autonomous vehicles based entirely on vibes.”

• In New York, Governor Hochul withdrew her robotaxi legalization proposal in February after failing to secure legislative support. Her spokesperson said “Based on conversations with stakeholders, including in the legislature, it was clear that the support was not there to advance this proposal” which in practice meant that organized labor made the political cost too high in an election year. The proposal would have allowed limited robotaxi deployment upstate while leaving New York City decisions to the mayor and city council. It died anyway. Waymo has testing permits for a handful of vehicles in Manhattan, but can’t charge passengers or operate commercially without new legislation that now doesn’t exist.

• In Minnesota, SEIU chapters started organizing against AV legislation before Waymo even announced plans to expand to the Twin Cities. By the time Waymo began testing, there was already organized opposition pushing for legislation that would require human drivers in every vehicle. Multiple Minneapolis city councilmembers have signaled interest in passing a citywide ordinance with the same requirement.

• In Washington state, law allows testing but requires a human safety operator in all vehicles and doesn't offer a clear path to commercialization. Seattle has its own additional testing permit program on top of the state requirements.

The same pattern shows up again and again where politicians express openness to AVs in principle, insist on studying job impacts first, and structure the study process so that it can never actually conclude that it’s okay to proceed. The “study jobs first” framing often functions as a ban.

Lewis George hasn’t gone as far as Boston’s councilors, who proposed requiring a human in every vehicle. But her framing seems to track the same playbook, her coalition includes the same types of unions driving opposition in those other cities, and the political incentives point in the same direction. The Teamsters’ national position is a permanent human-operator requirement for all autonomous vehicles. ATU Local 689, which endorsed Lewis George, has lobbied against AVs alongside its national chapter.

That said, Lewis George does have room to eventually support AV deployment. She could condition it on workforce transition programs, labor protections, or union consultation requirements. There’s a version of AV legalization that includes strong worker provisions and that she could plausibly support without reversing her position. The question is whether she’d actually pursue that as mayor, or whether the political incentives from her labor coalition would push her toward indefinite delay.

This doesn’t mean AVs would never happen under Lewis George. She hasn’t called for a permanent ban, and external pressure would keep building: Waymo would be operating in 15+ other cities, Baltimore might have the service 40 miles away, federal legislation could change the landscape. But nothing in her current positioning suggests she’d prioritize this, and the most likely outcome is that legalization gets pushed back by multiple years.

The mayoral race makes the DDOT timeline much more important, and vice versa

The mayoral race matters for AVs, but how much it matters depends almost entirely on whether the DDOT report comes out before the new mayor takes office in January 2027.

If the report comes out before January 2027, the mayoral race becomes potentially less decisive. Allen’s stated precondition would be removed. The McDuffie bill already exists. Allen could hold hearings in the fall. The council could potentially vote before the end of the year. If legislation passes before January, the new mayor inherits a legal framework, and unwinding legislation the council has already passed is much harder and more politically costly than just preventing it from ever being introduced.

Bowser could also use the report as cover to issue driverless testing permits in her final months. If DDOT hands her a finished product, the political calculus changes. She could claim credit for moving the city forward on her way out.

If the report still isn’t finished by January 2027, the mayoral election becomes the single most important variable in whether Waymo is legalized in DC anytime soon. The next mayor controls DDOT, and the report’s absence is what keeps everything frozen. Under Lewis George, the stall could extend for years, especially if she adds requirements to look into effects on jobs and halts legalization until there are guarantees that rideshare drivers won’t lose work.

Under McDuffie, things would likely move faster, though he’d need to navigate the council’s labor concerns.

This is why pushing for the DDOT report to come out before the election is the single most important thing AV advocates can do. Every month of delay past this summer makes the mayoral outcome matter more.

The federal government

Congress has direct authority over DC. While the local political dynamics I’ve described above are the most important near-term variables, federal action could eventually change the landscape, though right now there are no especially promising bills. Two bills that would in part pre-empt DC’s decision making on AVs are the SELF DRIVE Act of 2026 and the Autonomous Vehicle Acceleration Act of 2025 (S.1798), but both look very unlikely to become law. The SELF DRIVE Act is being talked about as maybe possibly being folded into the Surface Transportation Reauthorization, which would make it more likely to pass, but this is all pretty speculative. Technically the federal government could eventually pre-empt DC law if they passively disallow or actively ban Waymos, though I wouldn’t count on it anytime soon.

How to legalize Waymo in DC

The most promising levers to pull

I don’t know a lot about the specifics of influencing city government, so take these with a grain of salt. I’ll update them as I get feedback. The three main things to do right now seem to be:

• Apply public pressure to DDOT to finish the report on time, and then to create the separate necessary testing permit framework. This could be through public writing or writing to representatives.

• Make a plan for how to sway the councilmembers who haven’t expressed any opinions on AVs to support a legal framework for AVs in the city once the DDOT report is finished. It’s likely that like other cities that are pushing back against Waymo, interest groups will be eager to reach undecided councilmembers as well. They’re probably already trying to.

• Push for “shall issue” permits in any AV legislation. This might be the most important detail to get right, and I haven’t seen it discussed publicly. The 2020 law gives DDOT discretionary authority over testing permits. DDOT decides whether to make the application available, how to evaluate it, and when to approve it. There’s no requirement to actually issue a permit once criteria are met. This is exactly how we got here: the law says DDOT can create a permit program, DDOT chose not to, and nothing can force them.

  If the council passes AV legislation and keeps this discretionary structure, the same thing happens again at the deployment stage. A hostile administration sets up a permitting process that technically exists but moves at whatever pace it wants. Applications sit in review. Additional information gets requested in endless rounds. The framework exists on paper while no permits are issued. DDOT’s own AV program manager said in a 2025 MIT panel that “the industry is changing a lot faster than our rulemaking structure is allowing us to keep pace” and that’s without a mayor who’s actively trying to slow things down.

  The solution would be that once an applicant meets clearly defined conditions (safety benchmarks, insurance, data reporting, law enforcement plan), DDOT must issue the permit within a defined timeframe. No discretion on qualified applicants. No sitting on applications.

  Under McDuffie this probably doesn’t matter much. Under Lewis George it’s the difference between a framework that works and one that becomes another tool for delay. Someone with more knowledge of DC government than me pointed out that this might be the most important thing the council could do besides passing the legislation itself. I think they’re right. If the legislation gives the executive branch unlimited discretion over whether to actually issue permits, we end up right back where we are: a legal framework that exists in theory and doesn’t work in practice.

These seem less promising:

• Convincing Charles Allen to change his mind on using DDOT as a prerequisite for any policy. Allen seems to have made up his mind on this, I’m skeptical that he can be swayed with the admittedly limited number of AV advocates in DC, and I also personally just have no way to judge how reasonable his decision is. It does make sense to me that councilmembers ought to rely on government agency expertise. A lot of what he’s shared on DC looks silly and overly critical of the cars compared to most other vehicles though, and the standards he’s using to judge them (jobs and safety) would imply every other type of vehicle in the city shouldn’t be legalized. On that maybe we can make some progress.

• Relying on Mayor Bowser to take action. She doesn’t seem interested in prioritizing AVs and I’m not sure how much this could change.

Can we change Lewis George’s mind?

A strategy I’m unsure of is convincing Lewis George to change her mind on her current stance on AVs. She says she wants to ensure rideshare workers aren’t displaced, but this same rule would also ban buses or bikes. Her union endorsement coming in part because of her stance on AVs makes it seem difficult to change her mind. That said, Lewis George describes herself as a democratic socialist, and there are arguments for AVs that map onto things democratic socialists care about:

• Fewer traffic deaths. DC sees about 40 traffic fatalities a year, disproportionately in lower-income wards with fewer protected pedestrian crossings.

• Every Waymo is electric. DC has climate goals and environmental justice commitments. Waymo’s fleet is entirely electric, while a significant share of Uber and Lyft drivers in DC drive gas-powered cars. Replacing gas-powered ride-hail trips with electric AV trips is a direct emissions reduction, concentrated in the neighborhoods with the worst air quality. For someone who cares about environmental justice, this should matter. It won’t be decisive on its own, but it makes the “let’s wait” position slightly more costly in environmental terms every month it continues.

• Ride-share jobs aren’t union jobs and probably never will be. This is the argument I find most interesting, though I’m least sure it would land politically. The jobs Lewis George is protecting (Uber and Lyft driving) are difficult-to-unionize gig work. They’re not the kind of jobs that democratic socialists typically celebrate. To my knowledge, DC does not have any rideshare unions. These are rare in general but do appear in cities where labor is especially powerful, like Boston and Seattle. It’s likely that the unions pushing against Waymo are actually worried about future automation of buses, trains, or trucks, and see automated rideshares as the beginning of a wave of automation that will eventually come for them too.

• Constituencies who otherwise have trouble getting around the city might benefit from AVs. Elderly people among others.

I want to be honest that I don’t know if any of these arguments would actually move Lewis George. Politicians rarely reverse positions that are tied to endorsement relationships, especially during a campaign. The more realistic hope might not be changing her mind before the election, but rather creating enough cross-pressure from interest groups, environmental advocates, and safety organizations that, if she wins, she feels politically constrained from actively blocking AVs indefinitely.

The reason this matters is that if Lewis George’s position is immovable, then whether AVs are legalized in DC hinges almost entirely on the June primary. Any progress on softening her stance would reduce the stakes of the election and make the path to legalization less dependent on a single vote.

Lessons from other cities

Every city that has Waymo got it because at least one of three conditions was met:

• A state-level framework that preempted local objections (California, Texas, Arizona)

• An executive who personally championed the technology (Phoenix, Atlanta, Denver)

• A regulatory process that moved on a defined timeline (California’s phased CPUC system)

Unfortunately, DC currently has none of these.

Here are some case studies:

California

San Francisco’s city government was deeply skeptical of Waymo. Its transportation agencies formally protested expansion, and the city actually sued the California Public Utilities Commission to try to block it. The CPUC approved Waymo’s commercial deployment in August 2023 over San Francisco’s explicit objections, and a state appeals court upheld the decision.

Public opinion in San Francisco shifted dramatically once the service was actually available. In mid-2023, net favorability for robotaxis was negative 7%. By mid-2025, it had swung to positive 38%, with 67% of residents supporting continued operations. People tend to like AVs once they can actually try them.

Texas and Arizona

Both states created permissive frameworks before Waymo arrived. Arizona’s governor issued an executive order welcoming AV testing in 2015. Texas passed legislation in 2017. When Waymo was ready to launch, the legal framework was already there. DC’s 2020 law was supposed to be that proactive step. DDOT’s failure to follow through is why DC is now years behind.

Boston: The closest parallel to DC

Boston is the most instructive comparison. The Teamsters organized “Labor United Against Waymo,” the first mass-mobilization effort outside California to push back against AV expansion. Eight city councilors collaborated on an ordinance that would require every AV to have a human safety operator (effectively banning the whole thing) and commission a study before any commercial operations could begin. The advisory board overseeing the study would be required to include reps from the App Drivers Union, Greater Boston Labor Council, Teamsters, and UFCW.

The Boston situation is still unresolved. Competing state-level bills are being debated. One allows driverless vehicles, the other requires human operators in all vehicles. Waymo has returned to Boston for further testing but faces serious organized opposition.

This Boston city council meeting drove me completely crazy to listen to, and might be a hint of the bad dynamics at play in coming debates about AVs in DC.

Minnesota

In Minnesota, SEIU chapters started organizing against AV legislation before Waymo even announced expansion plans. By the time testing started, there was already an organized opposition pushing for human-operator requirements. This is almost certainly already happening in DC.

Maryland

Governor Wes Moore is publicly enthusiastic about Waymo. The Maryland legislature is actively considering bills to allow fully autonomous vehicles, and hearings have gotten surprisingly little pushback from lawmakers. Waymo already has cars in Baltimore being driven by professional drivers to map the city. If Maryland passes its legislation, DC residents will be able to see the technology operating 40 miles away while they can’t use it at home. Maybe this will have some impact?

Atlanta and Austin

In both cities, Waymo launched partly through a partnership with Uber, making rides available through the existing Uber app. This partially neutralized the “Waymo vs. rideshare drivers” framing by positioning Waymo as an option within the existing ecosystem rather than a replacement for it. Whether this model could help politically in DC is unclear, but the narrative doesn’t have to be adversarial.

The national labor strategy

The Teamsters have called nationally for a regulatory standard requiring a human operator in all autonomous vehicles, with that operator subject to commercial driver’s license requirements. Teamsters president Sean O’Brien testified before Congress that “allowing the unfettered and unregulated operation of autonomous vehicles, ultimately seeking to replace human drivers with robots, is unequivocally a threat to safety on our roadways and the existence of good jobs in the trucking industry.” The labor impacts framing is part of a coordinated national strategy.

Federal momentum is actually moving in the opposite direction from the labor strategy. The SELF DRIVE Act passed the House Energy and Commerce subcommittee on a narrow 12-11 vote and could be folded into this year's Surface Transportation Reauthorization. Congress seems more receptive to AVs than blue-city local governments are. There’s no serious national ban proposal on the table. But labor opposition is likely to grow.

DC matters in the federal picture for a specific under-discussed reason: it’s where members of Congress, their staffers, federal regulators, and lobbyists live and commute. If Waymo operates in DC and these people experience it working well in their daily lives, it might normalize the technology among the people who write federal policy. If it’s anything like the other cities where Waymo’s operating, we can expect the public perception of AVs among policymakers to meaningfully improve.

I’m writing this as an emergency post because a new contender for drastically wrong common wisdom has just popped up.

There’s a new paper being shared and reported on called “The data heat island effect: quantifying the impact of AI data centers in a warming world.” It is the single worst writing and research on AI and the environment that I have read.

The central claim of the paper is that in the places where data centers are built, land surface temperatures increase by an average of 2°C (3.6°F) after the data center begins operations, creating what the authors call a “data heat island effect” that affects over 340 million people worldwide. In some extreme cases, data centers are found to increase land surface temperature by up to 9°C (16°F). The paper claims that a warming effect can be measured up to 10 km from the data center. This all sounds really bad! 340 million people affected by heat islands.

The paper also has alarming looking graphs that I suspect will do well in the media coverage:

There are three problems with this paper:

• The methodology is completely off-the-wall goofy and the study does not at all show what the authors claim. This will be the focus of this post. The authors very strongly imply this temperature change is due to data center waste heat, but I think it’s almost certain that it’s instead entirely due to the fact that the surface of any building, a data center or Walmart or house or Starbucks, is hotter than the grass that was there before. I think I can show conclusively using high school physics and math that the data centers are not heating the land around them. Data center heat exhaust could only heat land by at most 1% of what the authors imply. The paper itself doesn’t even consider this very obvious reading as a possibility.

• The paper’s framing is wildly misleading and will create a lot of additional confusion on top of the methodology itself being bad. The author’s suggestions from this paper is to “build more energy efficient AI algorithms” but the actual conclusion should be “Don’t build buildings if you don’t want those buildings themselves to get a little warm, even though they won’t affect anything around them.” There is a lot more incredibly shifty language the authors use over and over.

• I’m worried that the media takeaway from this is going to be that data centers are warming the air this much. If they were, that would be a complete disaster. To put this in context, if (another) data center were built near me in DC, and it heated the air by an average of 9°C, this would change the DC climate to the climate of Miami. A 2°C would be equivalent to moving DC halfway to Georgia, it would be like the region had fast forwarded through 50-70 years of climate change. In reality, the data centers are being measured to warm the land around them, which is very very different (explained below). It seems likely that a lot of people will misinterpret this. Look again at the graphs from the paper. The Y axes say “Normalised temperature increase,” not “Normalised land temperature increase” and if they’re shared without context, I suspect people will draw the wrong conclusions.

Contents

• The paper

  • A summary of my argument

  • What is land temperature?

  • The methodology

  • My core claim: this is literally just measuring hot surfaces of new buildings, and the soil and land around those new buildings probably hasn’t changed temperature at all

  • How much could a data center’s waste heat raise the land temperature around it?

  • The one remaining question: why the clean step change?

  • Other candidates for warming land

  • A few other bizarre methodological mistakes

  • The incredibly shifty framing the authors use

• The media is already running with this

• A parting thought: AI is useful for research

The paper

A summary of my argument

• The measurements in this paper are just showing the fact that buildings are hotter to the touch than grass. The data centers are most likely not heating the land around them. They themselves are just hot because they are buildings. If you include hot buildings in a very general measurement of the average total surface temperature of a large area, that average will go up slightly without telling you anything about the undeveloped land.

• Using simple high school physics, it’s easy to show that there is no way data center heat exhaust could account for more than at absolute most 3% of the observed average land heating, and any realistic assumptions drive that down to basically 0. The only other mechanism by which they could be heating the land fails. This leaves the other explanation: the data centers themselves are hot because they are buildings, the land around them remains at the same temperature, unless more buildings have been built on top of it to accommodate the data center.

• You would think the authors would have anticipated my criticism, but they don’t even consider it as an issue with their methodology at all.

What is land temperature?

NASA defines land surface temperature (LST) this way:

Land surface temperature is how hot the “surface” of the Earth would feel to the touch in a particular location. From a satellite’s point of view, the “surface” is whatever it sees when it looks through the atmosphere to the ground. It could be snow and ice, the grass on a lawn, the roof of a building, or the leaves in the canopy of a forest.

We need to understand land temperature as opposed to air temperature. They can be very different from each other. An obvious example is stepping onto black asphalt on a summer day. Even though the air temperature may be warm, if the air were as warm as the asphalt you’d be dead. Stepping onto a light-colored concrete sidewalk instead would be much more cool. Land temperatures can deviate a lot from the temperature of the air.

Changes in land temperature of 2-9°C don’t really impact human welfare directly. What matters for human welfare is air temperature. This temperature range invokes the low to very extreme catastrophic ends of the spectrum of what could happen with climate change, but that spectrum is about air temperature. Land temperatures change all the time without a corresponding significant effect on air temperature. If the most extreme land temperature changes happening in the paper were happening around you, the air around you would likely warm by a fraction of a degree at most. It would probably too small to measure or distinguish from normal day-to-day temperature variability.

How much does land temperature matter for the ecology of a region? Soil temperature directly affects plant health, microbial activity, moisture retention, and which organisms can survive in an area. If data centers really were warming the land surface by 1°C out to 4.5 km away, as the paper claims, that would be worth paying attention to ecologically even if it didn’t matter much for the people living nearby. A 1°C rise in soil temperature is pretty minor, but a 9°C would change which plants and microbes could survive. This would be a problem if it were real, but as I’ll try to show below, it’s not.

The methodology

The researchers used satellite data from NASA satellite sensors to measure land surface temperature at data center locations worldwide from 2004 to 2024. They looked at how land temperature changed at each site before and after the data center began running, and found a clear jump of about 2°C on average right at the moment the data center came online, as shown on this graph (the orange region shows the range from the very minimum to maximum measured temperature changes, the red line is the average, and the error bars represent the 95th percentile bounds, meaning the vast majority of measurements fell within those much smaller bounds):

The paper also claims that this effect extends up to 10 km from each data center, with a measurable 1°C land temperature increase as far as 4.5 km away using the same MODIS sensor:

The authors present this as evidence that data centers create broad climate zones comparable to urban heat islands.

The authors are somewhat ambiguous about what’s causing this temperature increase. They say that their study “relies on the assumption that AI hyperscalers might have an impact on the LST of their locations because of the heat that they would release as a result of the high power demanding applications.” So this makes it sound like they mean to imply that it's the heat exhaust of the data centers (evaporated water or from air cooling systems) that's causing this increase. However, they never explicitly state this, and they never consider the alternative explanation that the temperature increase is simply caused by the construction of the building itself. The possibility that replacing grass with a large building and parking lots might explain their results is completely absent.

Their mitigations section confirms what they think is going on, though. Their proposed solutions are all reducing operational energy use. They recommend more efficient AI algorithms, adiabatic circuits, and passive radiative cooling coatings for data center buildings. These are solutions to the problem of “servers produce too much heat” not “a building was built.” If the authors believed these numbers were primarily driven by land cover change, the solution would be “paint the roof white” or “plant vegetation around the perimeter.” Instead, they propose redesigning AI models themselves. This tells you they believe operational waste heat is the cause, even though their methodology can’t actually support that conclusion. But their methodology is goofy goober.

This is in some ways worse than if they’d made an explicit causal claim, because there’s nothing specific to argue against. The most obvious alternative explanation for their findings, that buildings are hotter than fields, isn’t mentioned in this paper. They don’t control for it as a possibility.

My core claim: this is literally just measuring hot surfaces of new buildings, and the soil and land around those new buildings probably hasn’t changed temperature at all

My argument here is going to be that this paper is just measuring the fact that buildings are hotter than grass. The land around the data center that isn’t either buildings or road has probably not warmed. It doesn’t matter at all that this study is about data centers. I’d expect similar measurements for Walmarts.

If you build a building, whether it’s a data center or Walmart or house or Starbucks, and you point a NASA satellite at the building, you are going to measure a warmer surface temperature exclusively caused by the material the building made of, not any heat exhaust from what’s happening inside the building.

However, the difference between the surface temperature of buildings and grass is much larger than study’s finding of 2°C. It’s on the order of 20-35°C on a sunny day. Why is the study’s number so much smaller? The satellite sensors this study used (MODIS) can’t actually look at an area as small as a data center. MODIS pixels are roughly 1 km² (or 0.5 x 0.5 km in the enhanced product the paper claims to use). A data center and its parking lots might occupy a fraction of that pixel, with the rest still being undeveloped land, roads, or vegetation. So what the satellite sees is a blended average, maybe 10-20% data center and parking lot surface, and 80-90% unchanged land.

Let’s assume buildings and roads are on average 20°C warmer to the touch than grass. To get an average land temperature change of 9°C (the most extreme outlier in their data) on a 0.5 x 0.5 km unit of grass, you would need to build 112,500 m² of new building and road surface, about 28 acres. Many hyperscale data centers are larger than this. For example, QTS Atlanta's campus covers over 95 acres, DC BLOX's Atlanta West campus is 55 acres, and the Stargate campus in Michigan will occupy 250 acres. Even a typical hyperscale data center occupies around 10 acres of building footprint alone, before accounting for parking lots, substations, access roads, and security perimeters, which can easily double or triple the total paved surface. 28 acres is a mid-sized hyperscale campus. This on its own would explain the most extreme outliers.

What about the 2°C average they find? To get a 2°C increase, you would only need about 25,000 m², or roughly 6 acres of building and road surface within the satellite's field of view. That’s a pretty normal size for a non-hyperscale data center.

What about the increase in temperature farther from the data center, up to 10 km away? Well, two things:

• Data centers are mostly not built in very isolated places. They need power infrastructure, fiber connectivity, and road access. Places with this stuff are also attractive for warehouses, logistics hubs, office parks, and residential development. When you look at the area within 10 km of a data center, you’re typically looking at a region that has a lot of new building in general, and that all registers as warmer to a satellite’s thermal sensor, regardless of what the buildings are used for. The paper’s smooth spatial decay curve is exactly what you’d expect from a development footprint that’s densest near the infrastructure hub and thins out with distance.

  

  Importantly, this is not what you would expect if the effect were being driven mainly by waste heat from a single facility. Waste heat in air does not spread outward in a neat, slowly fading circle. It behaves more like a plume: it rises, mixes, and disperses through an ever larger volume of atmosphere, causing its temperature effect to fall off in a strongly nonlinear way with distance. The exact decay depends on wind, turbulence, exhaust height, and mixing conditions, so there’s no single universal graph here. But we know for sure that if this were really a direct waste-heat signal, you would expect a pronounced curved drop-off, not a broad, almost linear decline over many kilometers like what we actually see. The paper’s red line looks much more like a development gradient than a plume. A smooth, nearly linear decrease is exactly the sort of pattern you would expect if the land closest to the data center were also the land most likely to pick up new roads, roofs, parking lots, warehouses, and other warm impervious surfaces, with that development footprint thinning gradually as you move outward.

• Data centers also frequently trigger or coincide with significant new infrastructure construction in the surrounding area. A hyperscale data center consuming 100 MW or more needs dedicated power infrastructure. These are large industrial installations with their own footprints built within a few kilometers of the data center they serve. Access roads also need to get built or widened. Fiber corridors are trenched. Supporting commercial services like equipment suppliers, security contractors, food service for construction crews and staff set up nearby. The data center brings an ecosystem of development with it. All of that replaces vegetation with built surfaces, and all of it would register as increased land surface temperature to a satellite, having nothing whatsoever to do with the thermal output of the servers.

The paper doesn’t control for any of this. They don’t compare their data center sites against other types of commercial construction. They don’t attempt to separate the surface change from operational heat output. They don’t even discuss the possibility that what they’re measuring is a land cover change rather than a thermal pollution effect. The entire paper treats the LST increase as if it were caused by the servers running inside the building, when the far more parsimonious explanation is that it was caused by the building itself. And if it is caused by the building itself, there’s no reason to expect the land around it to be heating at all, for 2 reasons:

• Land mostly doesn’t absorb heat from nearby buildings. If you step from a hot asphalt parking lot into cool grass, that grass is likely the same temperature as grass a mile away from the asphalt.

• If the land were heating up, this would imply that the buildings themselves would need to be much cooler to keep the average as low as it is. Data centers and the roads and infrastructure around them would need to be significantly cooler than all other normal buildings, or just be very small, and we know they’re not small.

There’s some very minor effects big buildings have with the flow of air and wind around the land around them, but it’s pretty marginal, and again this would change the study to be about any large buildings at all.

I think I already have a strong case here, but I have some high school physics and math in the next section that in my opinion completely confirms it.

How much could a data center’s waste heat raise the land temperature around it?

MODIS satellites take their measurements around 10:30 AM and 1:30 PM. During the day, the sun is shining directly on the ground. The ground absorbs solar radiation and gets hot. The air above it is cooler than the ground surface. This means heat is basically always flowing upward, from the ground into the air.

Data centers emit heat into the air. The way they would heat the land is by raising the temperature of the air enough that less heat from the ground flows into the air, and stays in the ground instead. The colder the air, the more heat is lost from the ground, and the warmer the air, the more heat stays in the ground. It’s like if you touch an ice cube to your hand, and then touch a lukewarm object to your hand. In both cases, your hand is losing heat energy, but it loses it much faster when in contact with the ice cube.

How much would the data center need to heat the air to cause significant amounts of heat energy to stay in the ground?

The math (skip if you’d just like the conclusion)

MODIS is measuring land surface temperature. The graph in the study shows that over an area of 1.6 km, land temperatures were elevated by 1.5°C. Could a data center’s waste heat plausibly deliver enough extra heat to keep the surface around it about 1.5°C warmer over a radius of roughly 1.6 km?

Consider a large hyperscale data center drawing 100 MW of power. Nearly all of that electricity eventually becomes waste heat, that’s 100 million joules every second leaving the facility. A circle with a 1.6 km radius around the data center would have an area of about 8 million square meters.

Imagine that every single watt of heat the data center produces somehow makes it to the ground surface within that circle, spread perfectly evenly. None of it rises into the upper atmosphere. None of it blows beyond 1.6 km. Every joule goes straight into the ground. How much heating would each square meter of ground receive?

100,000,000 watts ÷ 8,000,000 m² ≈ 12.5 watts per square meter

Now, does 12.5 W/m² produce 1.5°C of surface warming? No, because the ground doesn’t just absorb heat and get hotter forever. As the surface warms, it loses heat faster, it radiates more infrared energy and sheds more heat into the air through convection. The surface will only warm until the extra heat loss matches the extra heat input, and then it stabilizes at a new temperature.

How quickly does the surface shed extra heat? Engineers quantify this with a “heat transfer coefficient” that measures how many watts per square meter the surface loses for each degree it warms. For outdoor surfaces with even a light breeze, this coefficient is at least 10-15 W/(m²°C), and often higher. This means that for every degree the surface warms, it loses an additional 10-15 watts from every square meter.

So to hold the surface 1.5°C warmer than it would otherwise be, you need to deliver:

1.5°C × 10 W/(m²·°C) = 15 W/m² (using the most favorable coefficient)

or

1.5°C × 15 W/(m²·°C) = 22.5 W/m² (using a more typical value)

Our absolute ceiling of 12.5 W/m², which already assumed 100% of the data center’s heat reaches the ground, falls short of even the most favorable estimate of what’s needed. And in reality, the vast majority of the heat rises, disperses, and never reaches the ground surface within 1.6 km. A generous estimate for daytime conditions would be that 2-5% of the data center’s total heat output reaches the ground surface within 1.6 km. That gives us:

12.5 W/m² × 0.02 to 0.05 = 0.25 to 0.625 W/m²

Which would produce a surface temperature increase of:

0.25 to 0.625 ÷ 12.5 = 0.02 to 0.05°C

That’s roughly 1-3% of the observed 1.5°C signal. And that’s for a data center way larger than average and almost no wind.

Conclusion of the math

Even if we assume that the average data center in this study was a 100 MW hyperscaler (which it definitely wasn’t, since the vast majority of the 11,000+ facilities in the study’s database are much smaller enterprise and colocation centers drawing closer to 10-20 MW), and the wind is always gentle, the math still doesn’t work for waste heat. With a 100 MW data center, only about 1-3% of the observed temperature increase could plausibly be attributed to waste heat warming the surrounding ground. The remaining 97-99% is explained simply by the building and its pavement being warmer than the grass they replaced, which raises the satellite’s blended average temperature reading without actually warming the surrounding land at all.

And 100 MW is an extremely generous assumption. The study’s database spans 2004-2024 and includes facilities of all sizes. A typical facility in this dataset likely draws 10-20 MW or less. At 20 MW, waste heat could account for less than 1% of the observed signal. In other words, for the actual average data center in this study, the temperature increase is almost entirely, over 99%, explained by the fact that a building exists where a field used to be. The ground around these data centers almost certainly hasn’t warmed in any meaningful way. The remaining temperature change to the land itself caused by heat exhaust from the data center is so tiny that it cannot have any meaningful effect on the ecology or human wellbeing in the region.

The one remaining question: why the clean step change?

There’s one aspect of this that’s still somewhat confusing and doesn’t square with my story. Look again at this graph:

This jump is somewhat significant. An average increase of 2°C is about 1/10-1/20th of the difference between grass and a building, but also we’re assuming the building is taking up a small part of the satellite image. It is weird that this jump doesn’t occur until after the data center becomes operational. If this were construction, you’d maybe expect more of a slow gradual curve up?

The strength of the other evidence leads me to suspect that something’s off here. There’s no smoking gun, but I have a few explanations for what might be happening:

• The stuff that creates the large majority of the building’s new hot surface area is usually added at the very end of construction: roofs and parking lots. It would make sense to me that during construction a roofless building where the ground hadn’t been paved wouldn’t become as hot as a building with a full roof and large paved parking lot. If these are added in the last month and add the major part of the heat, it would appear like a step change. But this could be wrong. Some people who know more about architecture than me reached out to say the roofs are likely not what’s causing this, they’re often cooler than the construction site. I’m kind of at a loss here, could this be the parking lot? It’s hard to find reliable data on which parts of construction absorb the most heat. I need good sources on this but can’t find many. If you have tips, send them!

• The authors say they are measuring temperature changes relative to when each data center became “operational,” but that may be much less precise than it sounds. They don’t seem to know if operational here means “first server energized,” “first meaningful IT load,” “certificate of occupancy,” “roof completion,” or “substantial completion.” They’re just using a database date from the IEA-linked source they cite, and then centering all of their analysis on that single date. It would make sense to me that a lot of what’s listed in the data base might just be when the data centers wrapped up construction, which would align with when the roof and parking lot were added. The database they say was provided to them by IEA isn’t publicly available, so I have no way to check this. Data Center Map, the largest collection of public info on data centers online, says they track data center lifecycle stages including Land Banked, Planned, Under Construction, and Operational, but then immediately warns that these lifecycle stages “should only be considered indicative, as it is very hard to track when stages change.” If data center map considers its own data on this to be very hard to actually pin down, I’d suspect that IEA doesn’t have a significantly better private collection of all 11,000 data centers.

So my best guess is that the actual operational opening time varies a lot compared to the numbers the researchers are using, there’s going to be a clear jump in heat around the time the roof and parking lot are built, (←I’m now convinced that crossed out part’s wrong and this graph is a deep mystery to me) and therefore it’s likely that if you’re just looking at month by month snapshots, the roof + parking lot jump might appear to occur after the data center “became operational.”

One other important point is that this step change actually doesn’t look like what you’d expect to result from a data center’s energy draw, because most data centers are opened and then slowly filled with servers and powered on more and more over a period of months or even years. If this were the result of emitting more heat, you would expect the line to continue to go up and up as the data centers drew more power, but the actual average line appears to be flat after only a month of operations. The entire industry's ramp-up pattern is gradual, not instantaneous, at every scale, even for smaller colocation (not hyperscaler) data centers.

Data centers also vary enormously in their power draw, but the error bars where almost all observed heating effects occur are incredibly tight along a narrow band. I would expect any heating caused by data center power consumption to vary as much as the power consumption does itself, but this graph doesn’t show that at all. Instead, regardless of the size, data centers seem to have mostly shockingly similar effects on warming. Again, this is a sign that it’s the full physical infrastructure buildout of data centers and the stuff around them that might be causing this instead, though obviously data center physical size also varies a lot. This one’s very confusing in general.

Looking into this more, I learned that there are very few studies like this on other large buildings that emit a lot of heat. The clearest comparison I can think of is nuclear power plants, which can emit 10 to 20 times as much waste heat as a 100 MW data center. I’ve never heard of a nuclear power plant’s heat emissions raising the temperature of the ground around it, even though by the same logic it should be having 10-20 times the effect of the data center. However, there seems to be no info on this in either direction as no one’s seriously studied it.

I’m unsure here so will circle back if I get better evidence.

Other candidates for warming land

I would be pretty shocked to find out that the heating exhaust is able to warm the land this much after all this converging evidence, but my building explanation isn’t super satisfying as an explanation of the step change mentioned above. Other candidates for ways the data center could actually be warming land include:

• Groundwater and irrigation changes. Data center construction often involves significant grading, drainage modification, and sometimes groundwater pumping. If the local water table drops or irrigation patterns shift in surrounding agricultural land, vegetation dries out and LST rises. This could plausibly create a gradient extending several km, especially in arid or semi-arid regions where many data centers are built. Would be interesting if this were about water use, though I think this is pretty unlikely as there are a few aspects of water that don’t fit the patterns we’re seeing here.

• Vegetation stress from construction dust and runoff. During and after construction, dust deposition and altered drainage can stress vegetation in a radius around the site. Stressed or sparse vegetation has higher LST. This is temporary but could persist for years if the site continues expanding.

• A statistical artifact from MODIS point spread function. MODIS doesn’t have a perfectly sharp pixel boundary, its effective spatial response bleeds signal from bright/hot pixels into adjacent ones. A very hot data center pixel could artificially elevate apparent LST in neighboring pixels purely as an instrument artifact. This would produce a smooth radial decay that has nothing to do with actual ground temperature. MODIS was designed for global-scale monitoring rather than facility-level analysis, so this could maybe be a nontrivial contributor.

A few other bizarre methodological mistakes

Thanks to Rohit for pointing these out:

• They measure population affected by counting residents within km of each data center measured, but they don’t seem to check for overlaps at all. There are a lot of places like Loudoun county where data centers are all within 10 km of each other, so you’d get a lot of overlapping circles and people double counted in each one. The population affected number is likely inflated by double counting.

• The paper says it used "a reconstructed MODIS LST dataset (produced by NASA) acquired worldwide from 2004 to 2024 over an enhanced 500m resolution grid" and cites Metz et al. But the Metz et al. paper describes a European product with 250m spatial enhancement, not a global 500m product. So the citation doesn't match what they claim to be using.

The incredibly shifty framing the authors use

Over 99% of the observed land temperature increase seems to be coming from an omission in the author’s methodology. They’re assuming the land has warmed, but it hasn’t, and they’re only observing that there are additional warm buildings in the areas they are analyzing. They did not control for this at all and don’t even reference it as a possibility.

The authors strongly imply multiple times that the causal mechanism for this temperature rise is the high energy usage of the data centers. Here’s every time they imply that the effect here is due to heat emissions caused by high energy usage:

• Title: “The data heat island effect: quantifying the impact of AI data centers in a warming world”

  • This frames the phenomenon as caused by AI data centers specifically, not by construction or land development. A better title would be “quantifying the impact of large buildings.”

• Abstract: “we focus our attention on the heat dissipation of AI hyperscalers”

  • Explicitly names heat dissipation from operations as the focus of the paper.

• Abstract: “the land surface temperature increases by 2°C on average after the start of operations of an AI data centre”

  • “after the start of operations” implies the operations are causing it, not the construction.

• Introduction: “the type of human activities established and operating dramatically influence the impact of UHI on environment and communities”

  • Frames the effect in terms of activities operating, not buildings existing.

• Introduction: “data centres are expected to be one of the most power-hungry activity in the next decade”

  • Connects the phenomenon to power consumption.

• Introduction: “AI data centres are in vast majority relying on fossil fuel use”

  • Connects to energy source and emissions, implying operational energy is the driver.

• Introduction: “the steep growth of AI training and use for various applications would directly translate into high net impact on emissions”

  • Links AI workload growth to environmental impact.

• Introduction: “the inefficiencies and nonidealities of AI hyperscalers operations would cause their emissions to rise even more”

  • Again, operations.

• Section 2: “This study relies on the assumption that AI hyperscalers might have an impact on the LST of their locations because of the heat that they would release as a result of the high power demanding applications that they would be used for”

  • This is the most explicit statement. The assumed mechanism is heat released by computational workloads.

• Section 3: “These results are dramatically impressive, especially considering that the typical LST increase caused by the quintessential example of compound of anthropogenic activities – the urban heat island effect – has been estimated in the 4 and 6 °C interval”

  • Comparing to UHI, which is understood as an operational/activity-driven phenomenon.

• Section 3, Bajío case study: “The stable climate, low seismic activity, and proximity to North American markets made the Bajío region a great hub for AI data centres. Nevertheless, it has been recorded a serious LST increase trend”

  • Implies the data center operations caused the regional warming. In fact, this region fits the Andy Masley theory of More Buildings = More Hot Buildings much much better. In addition to building some data centers over the last 20 years, the Bajío underwent one of the most dramatic industrial buildouts in Latin America. The region received $37.6 billion in accumulated foreign investment from 2004 to 2019, with nearly two-thirds going to manufacturing. While national industrial activity grew at 1.7% per year from 2003-2016, Bajío industry expanded at 4.6%. Toyota, Honda, General Motors, Bombardier Aerospace, and hundreds of other manufacturers built large-scale operations across the region. Industrial parks, automotive plants, logistics centers, highways, rail networks, and supporting commercial infrastructure were constructed at an enormous scale during the exact period the paper attributes its warming trend to data centers. The LST increase in the Bajío is far more plausibly explained by the massive conversion of agricultural and rural land to factory rooftops, warehouses, parking lots, and paved roads than by the waste heat of data centers. The paper sees “data centers + warming” and draws a causal arrow between them, while completely ignoring the fact that this region was transformed from an agricultural center into one of North America’s most important manufacturing corridors during the same period.

• Section 3, Aragón case study: “The region is becoming a critical node for AI, cloud computing, and, increasingly, specialized server manufacturing. At the same time, the region has recorded an anomalous increase of approximately 2°C”.

  • Same story as Bajío, different country. Aragón has undergone a massive energy and industrial buildout during the study period. The region has become a multi-sectoral hub (from data centers to logistics and automotive) with companies like AWS, Microsoft, Blackstone, and others developing strategic projects there. In the last year alone, 651 new megawatts of wind and solar photovoltaic capacity were installed, a 7.1% increase over 2023. Aragón has been described as potentially the “Saudi Arabia of solar power in Europe” and bp alone has around 1.1 GW of solar projects at various stages of development or construction in Aragón. A single solar cluster near Zaragoza installed 615,000 bifacial solar panels across 650 hectares of land, with two overhead transmission lines of 18 km and 20 km constructed to connect it to the grid. That’s hundreds of hectares of dark solar panels, wind turbines, substations, transmission infrastructure, access roads, and associated industrial development blanketing a previously rural, sparsely populated region, exactly the kind of massive land cover change that would produce a clear LST increase when viewed from a satellite. The paper attributes the warming to data centers and ignores all of this. The Andy Masley theory of Buildings are Hotter than Grass is more parsimonious with the data.

• Section 4: The entire mitigation section proposes solutions to reduce operational energy consumption: more efficient algorithms, adiabatic circuits, model pruning, dynamic power response. These are solutions to “servers produce too much heat,” not solutions to “a building was constructed and is hotter than grass.”

• Section 5: “We focus our attention on the heat dissipation of data centres, which is directly connected to the energy consumption required for the operations of the AI hyperscalers”

  • Explicitly links the effect to operational energy consumption.

• Section 5: “Since the trends of data centre energy consumption are expected to show a steep growth in the foreseeable future, the data heat island effect could solidly become an additional factor for environmental and industrial sustainability”

  • Implies the effect will worsen as energy consumption grows, which only makes sense if operations are the cause. If it were land cover change, the effect would be determined by building footprint, not energy consumption.

The paper also repeats the claim over and over that 343 million people are “affected” by the data heat island effect. They find this by just counting everyone who lives within 10 km of a data center in the study’s database. They don’t bother to see whether those people experienced anything bad at all, like a change in air temperature, health outcomes, energy costs, or anything else. By the same methodology, you could count everyone living within 10 km of a Walmart and announce that billions of people are “affected” by the Walmart being a hot building regardless of whether it’s affecting anything else. In reality, I think 343 million people are being affected by the “If you build a building the building gets hot in the sun and doesn’t really affect anything else” effect.

Another incredibly shifty move the authors make is using "AI hyperscalers" and "AI data centres" throughout the paper, but the database they use (from the IEA) contains data centers of all types. Most data centers built between 2004 and 2024 weren't doing AI workloads at all, especially in the earlier years. The framing makes it sound like this is about AI specifically, but almost all of what they’re measuring is traditional non-AI focused data centers. It’s incredibly jarring to read “across all AI hyperscalers analysed during the 2004-2024 period” as someone who knows anything about the timing of the data center buildout. AI-focused hyperscaler data centers didn’t appear until after 2020.

I don’t understand the sociology of how something like this gets published. These are serious people! Going down the list of authors:

• The lead author Andrea Marinoni is an associate professor of applied remote sensing at the Arctic University of Norway with a visiting fellowship at Cambridge.

• Benjamin Horton is the Director of the Earth Observatory of Singapore at Nanyang Technological University, a leading sea-level rise researcher, IPCC author, Fellow of both the American Geophysical Union and Geological Society of America, and winner of the EGU Plinius Medal. His research was cited by President Obama in the 2015 State of the Union Address. He has 260+ peer-reviewed publications including in Science, Nature, and PNAS. He’s a very serious climate scientist!

• Erik Cambria is a Professor of AI at NTU, IEEE Fellow, Visiting Professor at MIT Media Lab, Provost Chair in Computer Science, 80,000+ citations on Google Scholar, listed in Clarivate’s top 1% of scientists, and was featured in Forbes as one of “5 People Building Our AI Future.”

• Pietro Lio’ is a well-known computational biology and AI researcher at Cambridge.

• Jocelyn Chanussot is a prominent remote sensing researcher at Université Grenoble Alpes, IEEE Fellow, with expertise in signal/image processing for remote sensing. He’s arguably the author whose expertise is most relevant to the methodology.

What happened here? I’m somewhat cynical and think that academics want attention on their work and personas, media attention is good for them, and making hyperbolic claims about data centers is a really great way to get media coverage right now, even if they have to use goofy methodology to get there.

The media is already running with this

Despite this paper not being peer reviewed and only being up on arXiv for 10 days, the media is already running with it. Notice that most of these headlines leave the reader with the impression that it’s the air that’s warming:

• CNN: Scientists have found an alarming environmental impact of vast data centers

  • CNN adds that data centers are “making life hotter for more than 340 million people.” They also add “The findings are particularly alarming, the scientists say, because AI data centers are set to boom over the next few years, and these temperature rises come as planet-warming pollution is already making heat waves more extreme around the world.” This is so incredibly deceptive. This study told us nothing about heating air. “Already making heat waves more extreme” paints a picture of people’s lives getting warmer as a result of this effect.

  • There were also a lot of local CNN affiliates that ran copies of the story, like here and here and here.

• Benton Institute for Broadband & Society: "AI data centers can warm surrounding areas by up to 9.1 degrees Celsius"

  • WHAT?! They ran with the absolute craziest outlier in their headline?!

• Bode Living: “AI Data Centers Are Generating Significant Local Heat, Study Reveals”

• New Scientist shares this story on their social media feed

• More Perfect Union wins the most deceptive presentation award, simply announcing “Major data centers are creating “heat islands” that warm the surrounding area by up to 16 degrees Fahrenheit, according to a new study.” They did exactly what I thought they’d do: leave out that this is a land effect and let the reader infer that this must be talking about the air temperature. They do nothing but lie on this topic.

• Gadget Review: "Scientists Find That Vast Data Centers Are Creating Dangerous Heat Islands" Opens with "Those ChatGPT queries and smartphone photo enhancements come with an unexpected side effect: they're literally heating up the planet in ways you've never considered"

• Slashdot: "AI Data Centers Can Warm Surrounding Areas By Up To 9.1C"

• AOL UK: "Data centers are creating 'heat islands' on land around them – warming them by up to 16 degrees, researchers warn"

• Gizmodo: “Data Centers Create Heat Islands Stretching 6 Miles, Study Finds.”

  The subtitle is: Heat islands can worsen air pollution and increase heat-related deaths.

• Fortune: “Data centers are so hot their ‘heat island’ effect is raising temperatures up to 6 miles away and impacting 343 million people worldwide, study finds” This one does link to this post as criticism at least!

• Futurism: "Data Centers Causing Huge Temperature Spikes for Miles Around Them, Study Suggests" This one also quotes me, though the title is clearly wrong.

What is going on with the media ecosystem around data centers? It’s crazy. The average person’s understanding of AI has already been wildly warped by bizarre obviously wrong coverage of data centers over and over. I’d like to make sure more new crazy wrong ideas don’t seep into the public’s beliefs. I want to stop this one from escaping containment if I can.

A parting thought: AI is useful for research

This paper didn’t pass the sniff test for me when I first read it. The first thing I did after I read it and had drawn some conclusions myself was upload it to Claude and ask it for anything that seemed off about the methodology. Claude responded like this:

This paper has some pretty significant methodological problems. Let me walk through the main issues.

The core identification problem is fatal. The paper’s entire causal claim rests on showing that LST rises ~2°C after a data center begins operations. But they’re measuring land surface temperature at 500m resolution from MODIS satellite data — and what happens when you build a data center? You replace vegetation or bare soil with a large dark-roofed building, parking lots, and paved access roads. That land use change alone would produce exactly the LST signature they observe, completely independent of waste heat. They’ve essentially rediscovered that buildings are warmer than fields when viewed from space. LST measures the radiative temperature of the surface itself, not ambient air temperature, so a rooftop baking in the sun will read dramatically hotter than the grass it replaced. They never attempt to distinguish this from actual thermal pollution.

No control group. A basic difference-in-differences design would compare data center sites to similar sites where, say, a warehouse or distribution center was built instead. They don’t do this. Any large commercial construction project — an Amazon fulfillment center, a Walmart, a factory — would likely show a similar LST step-change. Without controls, they can’t attribute anything specifically to data centers’ operational heat output versus generic land development.

The spatial decay curve is suspicious in the wrong direction. They find the effect “reaches up to 10 km” and drops to ~30% at 7 km. But think about what actual waste heat dissipation looks like: data centers exhaust warm air that mixes rapidly with ambient air. The thermal plume from even a large facility would be negligible beyond a few hundred meters in terms of measurable air temperature change. A 10 km radius of influence would require an implausible amount of heat. What would produce a gradually decaying LST signal over kilometers? The ancillary development (roads, commercial buildings, housing) that tends to cluster around data center campuses. They’re likely picking up the general suburbanization/industrialization footprint.

The 2°C figure is misleadingly framed. They compare their 2°C LST increase to the urban heat island effect’s 4-6°C range as if these are comparable phenomena, but UHI studies typically measure ambient air temperature differences (or at least carefully distinguish LST from air temperature). A 2°C LST increase in a single MODIS pixel that went from grassland to a commercial roof is trivially explained and tells you almost nothing about whether nearby residents are experiencing warmer temperatures.

The 343 million affected people claim is essentially meaningless. They count everyone living within 10 km of any data center globally and associate them with whatever LST change was observed. But they haven’t shown that these people actually experience any temperature change. LST at a data center rooftop doesn’t translate to air temperature in a neighborhood 5 km away. The population figure is just “people who live somewhat near data centers,” repackaged as an impact metric.

The case studies are cherry-picked and don’t support the argument. They mention the Bajío region in Mexico, Aragón in Spain, and Ceará/Piauí in Brazil as places with “anomalous” warming trends that coincide with data center presence. But these are regions with well-documented climate trends driven by broader factors — land use change from agriculture, deforestation, urbanization, and regional climate variability. Pointing at regions that have both data centers and warming trends, without controlling for anything else, is textbook confounding.

They conflate “AI hyperscalers” with generic data centers. The paper uses “AI hyperscalers” and “AI data centres” throughout, but the database they use (from the IEA) contains data centers of all types — colocation facilities, enterprise data centers, telecom facilities. Most data centers built between 2004 and 2024 weren’t doing AI workloads at all, especially in the earlier years. The framing makes it sound like this is about AI specifically when their data can’t support that distinction.

The seasonality removal and filtering methodology is underspecified. They mention removing seasonality and outliers but don’t describe how. With 500m MODIS LST data, cloud contamination, emissivity variations by land cover type, and viewing angle effects are all serious issues. The paper doesn’t discuss any of these, and “aggregating to monthly scale” doesn’t solve them — it just smooths over them.

The “step function” in Figure 2 could easily be an artifact of construction timing. If the “start of operations” date is close to when the building was physically completed, then the step change in LST simply marks when a large impervious surface appeared in the satellite’s field of view. You’d expect exactly this kind of abrupt transition regardless of whether the servers inside were even turned on.

The mitigation section is a grab bag that undermines the paper’s seriousness. They pivot from their empirical findings to discussing adiabatic circuits, model pruning, carbon-aware inference, passive radiative cooling coatings, and a “Matryoshka model of intelligence” — a philosophical framework by one of the coauthors. None of this connects rigorously to their findings. It reads more like a literature review of the authors’ other work than a coherent response to the specific problem they’ve identified.

Fundamentally, this paper presents a land-use-change signal as a climate impact finding. A rigorous version of this study would need to measure actual ambient air temperature effects (not just LST), include matched control sites, and disentangle construction from operations — and it would likely find a much smaller and more localized effect.

I didn’t rely on any of this being true. I did a deep dive on each to see if it held up and came up with additional arguments (like the math for how much energy the ground could take in), but ultimately I was given most of the issues with this paper within about a minute of uploading it to an AI chatbot, and major news organizations like CNN apparently missed these. My skills at finding these errors have in large part been automated away and you can download the same skills by using Claude or ChatGPT or Gemini. I claim that this is strong evidence that AI is, in fact, sometimes useful for research.

• A popular book summary of everything important in the IPCC report, written for a mass audience. Best I can find are the summaries published by the IPCC itself, which don’t seem to be reaching many people.

• A clear customizable visualization of how any one activity compares to your total daily and annual emissions, to help you figure out what to cut.

  • Edit: I made it!

  • The visualizations that do exist are pretty clunky.

  • A lot of people I know worry about the emissions of flying in planes, but almost none can tell me how those emissions compare to their daily lives (a flight from NYC to San Francisco emits in expectation about as much as 2 weeks of your regular lifestyle, or about 1/10th of a car’s annual emissions).

  • More importantly, few people seem to know how advocating for systematic change to the energy grid can pay off in ways that have hundreds of thousands of times as much impact on the climate as any lifestyle change you could make. I’ve been vegan for 10 years, but that basically rounds to zero compared to my friends working on smart grid tech. More people should know about how big this gulf is.

  • It is kind of an overwhelming thought that in the fourth decade of serious climate communication, the average well-educated person is still basically spinning a roulette wheel in their head to make random lifestyle cuts for the climate with no real sense of how the options compare to each other. Eat less meat or fly less? Cut chatbots or recycle? So few people actually know anything about how these compare.

• A map of new smart grid and green energy infrastructure in your area you could go and advocate for.

• An updated version of Sustainable Energy – without the hot air. My favorite climate book, partly because when you read it you become confused about why so little climate communication is able to achieve anything like the same level of simple clarity.

• An 80,000 Hours-like comprehensive website trying to rigorously compare the actual impacts of different climate careers specifically (I know the Effective Environmentalism network aims to do something like this).

• A big public estimate of how much emissions have been prevented, in total, by the climate movement, by government action, by new technology vs. policy vs. lifestyle changes etc. I asked Claude and it estimates based on some papers that the climate movement specifically has prevented maybe 8 Gt of CO2, a little over a year and a half of the US’s emissions. I think this is way too low because it’s not including government policy, which Claude estimates to have prevented 34 Gt, and surely the movement is having lots of indirect impact there. Is any of this correct at all? Zero clue, I’d like a better source on this, but I can’t find one!

Most of these don’t seem that hard to make and maintain. I might try to make the second one if no one beats me to it. In general, a lot of people thinking at high levels about this stuff seem to be asleep at the wheel when it comes to how much they can change the conversation by just putting out clear, accessible, but also comprehensive and highly numerate info about climate change.

In a more sane world, I wouldn’t have been able to build the big audience I have by saying over and over again “The emissions of a computer program aren’t really in the same ballpark as driving a car,” but too many people who know about this stuff weren’t saying anything. I got lucky and had the time and energy to type it down, and was also motivated a lot by the effective altruist observation that almost no one is bothering to publicly compare very simple numbers involved in big global problems, and that those numbers do matter quite a bit and should be more action-guiding than they often are. The basic EA intuition is that a lot of people who talk about big high-status issues are coasting on saying the correct social passwords in order to be seen as good people, that we easily deceive ourselves with these passwords into thinking our own beliefs are way more thought out than they actually are, but also that the issues themselves are often very real, getting them right matters, and taking the numbers seriously is a way of breaking through the status games we play with ourselves and others.

You should strongly defer to actual expert consensus unless you have an extremely good reason not to, but you need to figure out what the actual expert consensus is and not trust that social passwords align with it. I was lucky in college to learn a lot about climate change and what the actual expert consensus was, so I could take the problem seriously while also ignoring the people confidently telling me that the US would fall into a civil war over water by the early 2020s. That was just a password, and the actual expert consensus was available to me if I was willing to seek it out instead of coasting on this new fake way of signaling that I was a serious clear-eyed person. So much of my success in blogging in the last year came from the simple decision to not go along with the social password “You should definitely be mindful with your chatbot use.”

I’m not bothered at all if people don’t like EA, but I do think this simple story of the world it tells is basically true and a way of springboarding yourself way above the fray in big debates about global problems like this. Don’t call it EA if you don’t want to, don’t feel like you need to give EA itself any status, but if you’re thinking about diving into climate communication, keep this basic story in mind. You can break out of the mental stupor of coasting on high-status passwords with a blank Google doc, a calculator, and a goal to seriously communicate how simple quantities compare to each other (with humility and strong deference to expert consensus where it exists). As I’ve learned over and over, there’s a huge appetite for this way of thinking, and you should assume that so many people are coasting along without bothering to look for the gaps in the discourse that these gaps are actually very easy to find and fill as soon as you start seriously looking for them. If you step out of trying to say the right social passwords about climate (or another big issue you care about) and just start free writing about what everyday public resources could exist for it but don’t, I think you’ll be surprised at how easy it is. Even just a simple rule like “Make the resource you wish your past self had” on its own goes a long way.

As an aside, Our World in Data is a light in the dark on good climate stats, and if you go back through my posts you’ll notice how reliant I am on a lot of the climate data they’ve compiled. Draw inspiration there! The Effective Environmentalism network also think about this stuff a lot. Project Drawdown’s another good example. So is this Founders Pledge report on climate philanthropy. There are lots of others, but not enough.

This talking point is crazy. It’s so obviously wrong that it’s disorienting to me whenever I see grown adults saying it. This is one of the pieces of common wisdom that’s congealed about AI that collapses when you poke at it for even a moment.

Knowledge is justified true belief.1 Can AI give me a justified true belief that no one else has had before? Yes, obviously. Google searches can also generate new justified true beliefs. It’s not actually difficult at all to generate new knowledge. It doesn’t require magic.

Here’s a very simple example: I asked Claude “Please do a BOTEC of how many pieces of avocado toast in restaurants in DC equal the cost of renovating DC’s Union Station”

This has given me a new justified true belief. It’s justified because it provides sources, and I can clearly see that there are good reasons to believe each of the premises that lead to the conclusion that the Union Station renovation costs roughly half a billion avocado toasts. It appears to be true. And it’s entered my mind as a belief I hold. And it’s new. I don’t think anyone’s held this specific justified true belief before. AI can create new knowledge. Why would anyone say otherwise?

This could have all been done with 2 Google searches: the cost of the Union Station renovation, and the cost of avocado toast. Doing a few Google searches can also generate new knowledge. It’s very very very easy to generate new knowledge! Why would AI models as they exist not also be able to do this?

I think that when people say this, they don’t actually mean new knowledge, they mean new concepts. AI has been trained on basically the full corpus of human text, rewarded for adhering to our conceptual universe, and punished for straying from it. It’s hard for me to imagine a deep learning model as they exist independently inventing the concept of a negative number if its training data had not ever included any implication that negative numbers exist.

So I probably agree with LLM skeptics that there’s a good chance they don’t scale to AGI, because the way they’re trained might mean that they’re fundamentally not able to generate radically new concepts for thinking about the world, which seems like a prerequisite for full human intelligence. They can merely approximate almost all currently existing human concepts that can be expressed in text, and combine wildly disparate information using those concepts to help us generate huge amounts of new knowledge. Still seems useful to me.

Update 11/17/2025: the author took the time to respond to me below. While I’m very grateful, the materials she sent actually seems to confirm my main criticism and I’m now very confident a key number in the book is 1000x too large and needs to be revised. I summarize everything in my reply here.

Intro

I was taking a break from posting about AI and the environment, but after reading parts of Karen Hao’s book Empire of AI, I’ve stumbled on such wildly misleading claims that have so far gone unaddressed that I’ve felt the need to counter them here. Within 20 pages, Hao manages to:

• Claim that a data center is using 1000x as much water as a city of 88,000 people, where it’s actually using about 0.22x as much water as the city, and only 3% of the municipal water system the city relies on. She’s off by a factor of 4500. This is the single largest error in any popular book that I’ve found on my own, and to my knowledge I’m the first person to notice it.

• Imply that AI data centers will consume 1.7 trillion gallons of drinkable water by 2027, while the study she’s pulling from says that only 3% of that will be drinkable water, and 90% will not be consumed, and instead returned to the source unaffected.

• Paint a picture of AI data centers harming water access in America, where they don’t seem to have caused any harm at all.

• Frame Uruguay as using an unacceptable amount of water on industry and farming, where it actually seems to use the same ratio as any other country.

• Frame a proposed data center in Uruguay as using a huge portion of the region’s water where it would actually use ~0.3% of the municipal water system, without providing any clear numbers.

These are all the significant mentions of data centers using water in the book. Read in this light, the chapter becomes somewhat ridiculous, because the rest includes descriptions of brutal acts of torture and plunder under colonialism, and then frames data center water use as a continuation of that same colonialism. If instead you see data centers using water in other countries as part of a simple trade the countries are making to get more taxable industry in the area, and that doesn’t seem to harm water access, the central narrative thrust of the chapter becomes false.

This book has been very popular, and influenced the AI/environment conversation. One of the most common replies I’ve received to my water arguments is that I should read it. So I did, and I came away kind of shocked at how badly it covered the issue. Hao’s points about water are all in the chapter “Plundered Earth.”

My only ask for people writing about AI and the environment is that at the end, readers are left with a more accurate picture of how energy and water is being used overall in the regions covered, and not left with a contextless impression of AI as a huge environmental offender in places where it’s not. Every single time water’s mentioned in this book, the reader is left with a worse understanding than they came in with.

I make my case for each with easily accessible numbers. All quotes and page numbers are taken from the Kindle edition.

Contents

• A misleading projection of how much water AI is expected to use

• A massive factual error: Hao claims a data center would use 4500x as much of a city’s water as the actual value

• A weird framing of Uruguay’s water use

• Misleading presentations of data center water issues in America

• Conclusion

A misleading projection of how much water AI is expected to use

The first time the book goes into detail on water is here:

The land and energy required to support these megacampuses are but two inputs in the global supply chain of data center expansion. So, too, is the extraordinary volume of minerals including copper and lithium needed to build the hardware—computers, cables, power lines, batteries, backup generators—and the extraordinary volume of potable—yes, potable—water often needed to cool the servers. (The water must be clean enough to avoid clogging pipes and bacterial growth; potable water meets that standard.) According to an estimate from researchers at the University of California, Riverside, surging AI demand could consume 1.1 trillion to 1.7 trillion gallons of fresh water globally a year by 2027, or half the water annually consumed in the UK.

(pp. 277-278)

The study mentioned is “Making AI Less Thirsty.” The study does not say that AI demand will consume 1.1-1.7 trillion gallons of water annually. Hao seems to be getting this number from this part of the study:

Even considering the lower estimate, the combined scope-1 and scope-2 water withdrawal of global AI is projected to reach 4.2 – 6.6 billion cubic meters in 2027

4.2-6.6 billion cubic meters = 1.1-1.7 trillion gallons.

Withdrawal is very different from consumption. Withdrawal means the amount of water taken from a local source. Consumption is the amount of water taken and not returned to the local source. Many of the ways we use water (especially power plants) withdraw water temporarily from local sources, use it briefly, and then return it unaffected. This is called non-consumptive withdrawal. It’s like diverting part of a river to run near a mill where you have some water wheels, and the diverted water then returning to the main flow after. Consumptive withdrawal is like sucking up and evaporating the water. The main water issue for most regions is water consumption, not just water withdrawal.

How different is consumption and withdrawal for AI? Very! The next sentence in the study Hao cites says:

Simultaneously, a total of 0.38 – 0.60 billion cubic meters of water will be evaporated and considered “consumption” due to the global AI demand in 2027.

This is equal to 100–158 billion gallons, only 10% of the number Hao reports. If someone’s writing a book that covers AI water use in depth, I would expect them to know the difference between consumption and withdrawal, especially when the consumption number is given in the very next sentence of the study. It’s very weird that Hao misidentifies withdrawals as consumption here.

Why are withdrawals so much bigger than consumption for AI? It’s mainly because the authors of the study are measuring how much water offsite power plants use to generate electricity for AI as part of its total withdrawals number. The vast majority of water that power plants use is withdrawn and then returned to the local source (non-consumptive use on this graph).

So the withdrawal number is going to be way bigger than the consumption number. Withdrawal is just not nearly as much of an issue for local water access as water consumption.

But the way this is being measured gets weirder. Even just measuring consumptive water use, the amount of water actually removed permanently from a local source, the study authors still find that the vast majority of water consumed by AI is offsite in nearby power plants that data centers draw from. For the US average, only ~15% of the water AI is consuming is actually happening in data centers themselves (the on-site water column).

Basically none of the water power plants use is potable. The only potable water used for AI is in data centers themselves. So the study Hao is citing is not only saying AI will consume just 10% as much water as she says, only 15% of that 10% will be drinking water.

Read Hao’s paragraph again. It strongly implies that AI will be using 1.1-1.7 trillion gallons of drinking water:

The land and energy required to support these megacampuses are but two inputs in the global supply chain of data center expansion. So, too, is the extraordinary volume of minerals including copper and lithium needed to build the hardware—computers, cables, power lines, batteries, backup generators—and the extraordinary volume of potable—yes, potable—water often needed to cool the servers. (The water must be clean enough to avoid clogging pipes and bacterial growth; potable water meets that standard.) According to an estimate from researchers at the University of California, Riverside, surging AI demand could consume 1.1 trillion to 1.7 trillion gallons of fresh water globally a year by 2027, or half the water annually consumed in the UK.

(pp. 277-278)

In reality, the study she’s citing specifically says that the water used by AI data centers themselves (the only place potable water is used) could consume 150 – 280 billion liters. This is 40-74 billion gallons. This is just 3.6% of Hao’s number. Further, the study notes that only 80% of the water used by Google’s data centers is potable, brining the number down 32-59 billion gallons, just 3% of Hao’s number. Here’s how much of Hao’s number is non-drinkable water returned to the source unaffected (blue), non-drinkable water consumed by power plants and data centers (red), and drinkable water used in data centers themselves (yellow).

The paragraph compares water to half the UK, and leaves the reader to infer that this is all potable drinking water. London uses 2.6 billion liters of water every day, 690,000,00 gallons, which is 252 billion gallons per year. So the actual amount of potable drinking water this study Hao is citing is projecting that all global AI will use is at most 20% of just the London water system, and 1.5% of the UK’s water use.

Hao repeats this misconception in her Atlantic article AI is taking water from the desert:

Public data hint at the potential toll of this approach. Researchers at UC Riverside estimated last year, for example, that global AI demand could cause data centers to suck up 1.1 trillion to 1.7 trillion gallons of fresh water by 2027.

This leaves the reader with the very strong impression that the physical data center buildings themselves will “suck up” and not return up to 1.7 trillion gallons. In reality, 90% of this “sucked up” water will be returned to the source it was drawn from, and only 3% of it will be in physical data centers themselves.

I have some disagreements with the study itself. Among other things, a lot of the water it measures AI “using” offsite is actually water evaporated in lakes dammed by hydroelectric plants to generate electricity, but it ignores the water these lakes recapture from rain. In similar studies this evaporated water ends up being 2/3rds of the total water use estimate of AI in general, so I think the actual offsite use should be significantly lower. The study is also two years old and I think we just have better estimates based on way more data now, specifically this report. We also have better numbers for chatbots measured by companies themselves, which show them using significantly less water than the estimates we had available in 2023. I also argue here that whether water is potable doesn’t really matter, because what actually harms the availability of drinkable water is access to total freshwater, not potable water. It’s relatively easy to turn freshwater potable.

A massive factual error: Hao claims a data center would use 4500x as much of a city’s water as the actual value

The chapter later turns to Chile, focusing a lot on the municipality of Quilicura on the outskirts of Santiago. The chapter covers ways that colonialism has seriously harmed the people and local environment and water there in the past, and then turns to the Google data center built there. The Google data center is framed as a continuation of the colonialism. It then goes on:

This unique combination—a history of neglect and a precious water source—created fertile ground for the blossoming of several environmental activist groups who were used to being watchdogs and were fiercely protective against the extraction of their resources. That summer, as Google filed a report with Chile’s environmental agency for approval of its data center—a largely rubber stamp process—MOSACAT, a water activist group, began combing through all 347 pages of the filing. Buried in its depths, Google said that its data center planned to use an estimated 169 liters of fresh drinking water per second to cool its servers. In other words, the data center could use more than one thousand times the amount of water consumed by the entire population of Cerrillos, roughly eighty-eight thousand residents, over the course of a year. MOSACAT found this unacceptable. Not only would the facility be taking that water directly from Cerrillos’s public water source, it would do so at a time when the nation’s entire drinking water supply was under threat. In 2019, as with Iowa and Arizona, Chile was already nine years and counting into a devastating and historically unprecedented megadrought.

(pp. 288-289).

Look at this line again:

In other words, the data center could use more than one thousand times the amount of water consumed by the entire population of Cerrillos, roughly eighty-eight thousand residents, over the course of a year.

Hao justifies this number in the notes section at the end of the book:

In other words, the data: The Google environmental impact report to SEA stated that the data center could use 169 liters of potable water a second, or 5,329,584,000 liters a year. According to the water service authority in Cerillos, the municipality consumed 5,097,946 liters in all of 2019, the year Google sought to come in; 5,329,584,000 liters a year divided by 5,097,946 liters a year equals 1,045.

(p. 454)

Something isn’t adding up here. 5,097,946 liters a year is 13,966 liters per day. Dividing by 88,000 residents says that each resident is using 0.2 liters of water per day. That’s about this much:

This implies that a city of 88,000 people is using as much water per day in total as a single shower head left running. Adults need to drink ten times as much as this, 2-4 liters of water per day, to stay alive. The average citizen of Chile buys 180 liters per day from their municipal water systems. I think the actual amount of municipal water supplied to people in Cerrillos is 900 times as much as Hao is claiming here.

I can’t find the study Hao is referencing, but I did find this document from the local government saying that the potable water consumption for the region of Maipú, Cerrillos, part of Estación Central (which the water system seems to serve) in 2019 (the year the study was conducted) was 54,148,639 m³. That’s 54,148,639,000 liters in a year, 148,352,000 liters per day. The total population the system is serving seems to be ~650,000 people.

So each person using this system is using ~230 liters of water per day. That’s way more in line with Chile’s average water use. The reason this is larger is probably that it also includes commercial buildings.

Hao got this number wrong by 3 orders of magnitude and reported the data center as using 1000 times as much water as a city of 88,000 people. I can’t get over how crazy this is. She implied that a single building is using as much water as a city of 88 million people. That’s over 4x the entire population of Chile! Readers are leaving this book assuming that Google built a single building in Chile that’s using 20% of all the residential water on the whole continent. No wonder everyone’s freaking out so much about AI and water use.

I think I know what happened: this municipality seems to report their water use in cubic meters. Each cubic meter is 1000 liters. Hao’s number reported in liters is noticeably very close to the cubic meter value I see in my sources. I suspect she got her wires crossed and somehow recorded m^3 as liters, making the city appear to use 1000x less water than it does. But she and others should have caught this in editing. I’m not aware of any building anywhere that uses 1000x as much water as a nearby city. That would be crazy! Note: Since writing this Hao replied here, and the material she sent confirmed to me that this is actually what happened, I explain here. I expect the book to be corrected.

But it gets worse! This is not the only misleading thing about how this number is presented.

This 169 L/second number was reported by a local paper as the maximum permitted amount. Nowhere in Hao’s writing does it make it clear that this number is the very maximum upper bound of how much water the data center will use, not the average normal water use. Reporting the maximum permit for water draw as the normal amount it will actually draw is very misleading. Here’s my explanation for why from my main post on AI and water:

Many articles about current or future AI data centers report the number in the water permit they apply for as the amount of water they actually use day to day. But this almost never happens.

When a data center is being built, the company needs to obtain water use permits from local authorities before construction. At this stage, they have to estimate their maximum possible water consumption under worst-case scenarios:

• All cooling systems running at full capacity

• Peak summer temperatures

• Maximum IT load (every server rack filled and running)

• Minimal efficiency from cooling systems

The permit needs to cover this theoretical maximum because regulators want to ensure the local water infrastructure can handle the demand and that there’s enough water supply for everyone. It’s easier to get a higher permit upfront than to come back later and request more, so data centers are incentivized to aim high.

Actual water usage is always significantly lower than what the permits allow, because they’re designed with the absolute worst conditions in mind. But many popular articles about how much water data centers use give the number on the water permit, not how much the data center actually uses.

We don’t have information on how much water the data center was actually expected to use, but we can at least compare it to other Google data centers permitted for the same amount of water to get a rough guess. Google’s data centers in The Dalles Oregon were permitted to draw the same maximum amount of water per day. The actual amount they used in a year was 275 million gallons, which is 0.75 million gallons per day. So another data center with this same permitted amount ended up using just 20% of its actual permit.

Maybe the data center would use more water because of different climates? The only times when data centers seem to use significantly more water is during heat spikes at the hottest times of the year, and The Dalles’s hottest month is on average warmer (88 F average in the Dalles, 71 F average in Cerrillos).

This all points to the actual expected water usage of this data center being around 1 million, not 4 million, gallons per day. 1 million gallons per day is 3,800,000 liters per day, or 1,767,000,000 liters per year.

So in a place where the local water system sells 54,148,639,000 liters per year, this data center’s normal operations would have raised the total water system’s demand by 3%.

Compare Hao’s sentence on this:

the data center could use more than one thousand times the amount of water consumed by the entire population of Cerrillos, roughly eighty-eight thousand residents, over the course of a year.

To what I think is the correct description:

A municipal water system serves 650,000 people. If a data center is built in the region, it would raise the system’s annual demand by 3%, equivalent to the water used by 19,500 residents.

Still significant, but drastically different from the number Hao gives. This is the main story of a data center harming water access in the book, and the main number given for its water usage is 4500 times as high as the correct value. Ridiculous!

This book has been reviewed almost 1000 times on Amazon:

This also shows up in an interview did with Tech Policy Press:

That’s where Google wanted to add its data center and then tap into that freshwater resource to cool its data centers. The amount of water that they were proposing was to consume more than a thousand times the amount of water that residents in that community would typically consume. They went through, they fought tooth and nail just to get Google’s attention because they not only had to make enough noise to pressure Google Chile, they had to make enough noise to then get that all the way back to headquarters in Mountain View.

And from what I can tell not a single person has noted “Oh hey the central story of a data center harming water access is assuming its water use is 4000x as big as it actually is.” Way, way, way too many people are reading about AI water issues uncritically. Why is it so easy for me to so quickly stumble on these gigantic errors in popular writing, that it seems like no one else has found? This book has been really popular with environmentalist critics of AI. Surely someone would have noticed this mistake?

Hao says here that she spent time with activists in Chile focused on data centers. Surely she would have noticed somewhere that these numbers were off. She specifically says they were “fighting tooth and nail to stop these data centers from taking all their drinking water” so conversations must have focused on the amount of water the data centers would actually use. How did this go so wrong? Further, she has a degree in mechanical engineering from MIT with a minor in energy studies and was a senior editor on AI at the MIT Tech Review. Surely she knows that there’s just no way any single physical building we’ve ever built would use as much water as 1000 times a city of 88 thousand people. The data center with the most water used in the world as of writing seems to be Google’s Iowa data center, with a billion gallons used every year. Even this one is just using half as much water as Cerrillos. This new data center would have to be 2000 times as large as any previously existing data center to make her claim true. How did someone covering AI for a living for a decade, with an energy studies degree from MIT, who spent time in the region itself with water activists, not immediately clock that this is way too big?

A weird framing of Uruguay’s water use

The next section covers Uruguay. It opens with a discussion of drought in the country, and includes a strange observation:

The water crisis emerged from the compounding effects of climate change and a failure of the state’s allocation of freshwater resources: In Uruguay, more than 80 percent of the country’s fresh water goes to industry instead of human consumption—most notably, cash crop agriculture. These include industrial farms for soybeans and rice, and for trees that feed into paper production. Most such farms are run not by local companies but by multinationals that export what they grow and show little accountability for Uruguay’s natural environment. Their activities deplete the nutrients in the soil, making it more difficult to grow actual food, and pollute the country’s water streams with a volume of fertilizers that makes Uruguay one of the world’s largest per capita fertilizer consumers and causes unusually high rates of cancer.

(p. 293)

The reason I think this is strange is that in basically every country, including the US, 80% of water is used on industry and commercial buildings rather than household consumption. I think a lot of readers don’t know that. This framing implies Uruguay is holding back water from locals who need it more. In reality they have the same split of water as every other country. Water’s very important to agriculture, industry, and commercial buildings. Hao presents this as a sign that Uruguay’s water is being used by big evil industries and being kept from the people. In reality it seems to have the same split of water as any other country.

The chapter then goes to a sociology researcher named Daniel Pena.

So when Google arrived, Pena was vigilant. During his regular scans of the Uruguayan environmental ministry’s website, which lists major industrial projects, he came across the company’s proposal for the data center. Pena had read about hyperscalers using potable water, even during major droughts, and the activism of communities like MOSACAT that had resisted the projects. But when he downloaded the details of the project, the water numbers were marked as confidential. After submitting a public information request, which he had successfully done around twenty times, the ministry continued to withhold the numbers, saying they were proprietary information. Pena wondered what they were hiding and worried about the precedent it would set for other cloud companies that would inevitably begin to eye Uruguay, following Google’s lead, for their own expansion. So he evoked the water clause in the constitution. With the help of a lawyer friend who was willing to work pro bono, he sued the ministry.

In March 2023, four months later, Pena won the case in a surprising victory. The environmental ministry revealed that Google’s data center planned to use two million gallons of water a day directly from the drinking water supply, equivalent to the daily water consumption of fifty-five thousand people. With much of Montevideo receiving salt water in their taps not long after, the revelations were explosive. Thousands of Uruguayans took to the streets to protest Google and all of the other industries that had led the government to squander the country’s precious freshwater resources.

….

Near the end of 2023, Google silently updated its proposed data center in Uruguay to use a waterless cooling system and said it would reduce the facility to a third of its size.

(pp. 294-295)

I’m not going to lecture locals in a far away country what they should and should not build in their country, but I do want to know how nefarious Google was being here. Again, I suspect this 2 million gallons per day was an upper limit, and if it’s anything like other Google data centers the number’s probably closer to 400,000 gallons per day. The municipal water system serving the city of 1.7 million people there seems to use 500,000 m³ per day. This is 132 million gallons. So if the data center had been built, it would be using about 0.3% of the municipal water system. Again, not nothing, but the reader is left with zero sense of scale here. It should be pretty easy to provide that.

Misleading presentations of data center water issues in America

Hao repeatedly mentions data centers in America built in water stressed areas. Each mention I think is misleading. Take this example from Iowa:

Altman and other executives never brought up the data centers’ environmental toll in company-wide meetings. As OpenAI trained GPT-4 in Iowa, the state was two years into a drought. The Associated Press later reported that during a single month of the model’s training, Microsoft’s data centers had consumed around 11.5 million gallons, or 6 percent, of the district’s water. GPT-4 had trained there for three months. (A Microsoft spokesperson said the company is working to increase its water efficiency by 40 percent above its 2022 baseline and to replenish more water than it consumes across its global operations by 2030, with a focus on the water-stressed regions where it works.)

A month of using 11.5 million gallons works out to about 383,000 gallons per day. Corn in Iowa uses between 0.1 and 0.2 inches of water per day through evapotranspiration, with 0.2 inches/day being typical at peak growth. One acre-inch of water is 27,154 gallons, so an acre of corn at peak ET consumes about 5,430 gallons of water per day. That means OpenAI was using as much water as roughly 70 acres of Iowa corn, or about 0.11 square miles. The average Iowa corn farm is 346 acres, so this is equivalent to Sam Altman buying about 20% of a single Iowa farm to grow corn for his employees.

Does Hao’s paragraph get this magnitude across? If you heard a company had bought a fifth of one corn farm, how big of a problem would you assume this is for regional water access? What if the tech company were using that fifth of a farm to grow something that half a billion people would use every single week for a year?

The book turns next to Arizona.

Arizona, too, faces a severe water crisis. In 2022, as Microsoft laid the groundwork for Phase 3, a study in Nature Climate Change found that the Southwestern US had been facing the worst drought it had seen in over a thousand years. That drought, combined with severe mismanagement, has drained the Colorado River, which Arizona and six other states rely on for fresh water, to dangerously low levels. Without drastic action, the river could cease to flow. The shortage compounds a power crisis, as climate change has slammed the region with relentless record-breaking temperatures and families have cranked up their air-conditioning. The region relies in part on hydropower from the Hoover Dam and water-cooled nuclear power plants. In other words, it needs water to produce more energy. In 2023, the Phoenix metro area hit multiple new heat records as well as the worst year for heat-related fatalities, which surged at least 30 percent from 2022 to over six hundred dead. “All things,” says Tom Buschatzke, the director of the Arizona Department of Water Resources, “are converging in a challenging direction.”

(pp. 280-281)

Notice that Hao does not give a number for how much water Arizona data centers are using. Here’s a section of my main water post where I look at how much water data centers in Maricopa County (the main place they’re being built there) are using compared to the total county water and golf courses, and how much tax revenue they’re bringing in per unit water:

Take Maricopa County in Arizona. The county is home to Phoenix, and is in a desert where water is pumped in from elsewhere. It’s also one of the places in the country where the most new data centers are being built.

Circle of Blue, a nonprofit research organization that seems generally trusted, estimates that data centers in Maricopa County will use 905 million gallons of water in 2025. For context, Maricopa County golf courses use 29 billion gallons of water each year. In total, the county uses 2.13 billion gallons of water every day, or 777 billion gallons every year. Data centers make up 0.12% of the county’s water use. Golf courses make up 3.8%.

Data centers are so much more efficient with their water that they generate 50x as much tax revenue per unit of water used than golf courses in the county:

So even though data centers are using 30x less water than golf courses, they bring in more total tax revenue:³

Some people see this, and react with something like “Well I don’t think golf courses OR data centers should be built in the desert.” At some point this becomes an argument against anyone living in deserts in the first place. If you want to have a gigantic city in the desert, like Phoenix, that city needs some way of supporting itself with taxes, and giving jobs to the people who live there. Most industries use significant amounts of water. If Phoenix is going to exist, it’s going to need private industries built around it that are using some water. We have two options here:

• Build industries that generate huge amounts of tax revenue relative to the water they use. Data centers fall into this category (though they don’t provide many jobs).

• Do not build cities in the desert in the first place.

Arguments against data centers existing in the desert because they harm water systems there also often apply to building cities in the desert in the first place. It’s fine and consistent to say that Phoenix shouldn’t exist because it’s unnaturally pumping water from hundreds of miles away, but it’s inconsistent to say that Phoenix should exist, that its water bills should be kept as low as possible, but also that no industries that use any water should be built there.

Simply reporting data centers as “being built in water scarce areas like Arizona” I think leaves out way too much important context to leave readers more informed.

Hao opens the water section with this observation:

Another study found that in the US, one-fifth of data centers were already drawing that water before the generative AI boom from moderately or highly stressed watersheds due to drought or other factors.

(p. 278)

This is true, but data centers are subject to the laws of supply and demand like any other business. In places where water’s more scarce and expensive, data centers are more likely to use other methods of cooling. In Arizona, where lots of data centers are being built, they’re using very little water relative to many other industries, and the tax revenue they’re bringing in. Overall, I haven’t found a single place where the normal operation of a data center has caused any issues for water access anywhere in America. We have plenty of other industry in medium and high water stress areas. Those communities also benefit from the tax revenue industry and commercial buildings bring in. But you wouldn’t know that from the book’s coverage.

Conclusion

Hao is smart. She has a degree in mechanical engineering from MIT with a minor in energy studies and was a senior editor on AI at the MIT Tech Review. If she wanted to give readers a full complete picture of where AI data centers are fitting into the broader environmental picture, this would be extremely easy for her. But every individual mention of water in Empire of AI leaves the reader less informed.

More broadly, this is a terrible sign for just how bad the public understanding of AI water use is. This book has been out for 6 months, received 1000 reviews on Amazon, was recommended by Time, The New York Times, Vulture, The New Yorker, The Economist, Financial Times, and Kirkus Review. Hao also thanks her fact-checking team in the acknowledgements:

To my incredible fact-checking team: Lindsay Muscato, Matt Mahoney, Rima Parikh, and Muriel Alarcón. All four of them fastidiously combed through the draft, cross-checking the labyrinth of details against documents and sources, and stress-testing my word choices. Matt also supported early research in my book, and Lindsay fielded many calls from me to serve as the most patient sounding board, while Rima somehow turned her fact-checking notes into standup comedy. They are all lifesavers.

(p. 424)

And yet after all these people read the book, I’m somehow the first person to notice and comment on the fact that no building, anywhere on Earth, is using anywhere close to 1000 times as much water as a community of 88,000 people use. How did this happen? Why did no reader, anywhere, let the author know about this massive glaring issue?

I think this happened because almost no one covering this issue is actually just looking at the numbers on AI and water use. The people who do are mostly experts less engaged in the public conversation. Almost everyone covering this seems to just be going with vibes. The fact that so, so many journalists, self-identified environmentalists, and members of the public could just nod along to the claims here and let them slip by provides a useful intuition for how it could possibly be that after all the ink spilled on it by so many professional people, the AI water issue is still somehow fake.

More by me

• All my writing on AI and the environment

• The AI water issue is fake

• Data centers don’t harm water access at all anywhere in America

• More Perfect Union videos are wildly deceptive on data center water use

• Contra the UK government, please don’t delete your old photos and emails to save water

In the past few months I’ve spoken to a lot of people facing objections to using chatbots, including a surprising number of people who want to buy chatbot access for their large organizations and have been shot down because of worry over the environmental impact of individual prompts. I think it’s crazy that this is still happening and want a much shorter post readers can send to people who are still misinformed about this. Here it is!

Using ChatGPT isn’t bad for the environment

Using chatbots emits the same tiny amounts of CO2 as other normal things we do online, and way less than most offline things we do. Even when you include “hidden costs” like training, the emissions from making hardware, energy used in cooling, and AI chips idling between prompts, the carbon cost of an average chatbot prompt adds up to less than 1/150,000th of the average American’s daily emissions. Water is similar. Everything we do uses a lot of water. Most electricity is generated using water, and most of the way AI “uses” water is actually just in generating its electricity. The average American’s daily water footprint is ~800,000 times as much as the full cost of an AI prompt. The actual amount of water used per prompt in data centers themselves is vanishingly small.

Because chatbot prompts use so little energy and water, if you’re sitting and reading the full responses they generate, it’s very likely that you’re using way less energy and water than you otherwise would in your daily life. It takes ~1000 prompts to raise your emissions by 1%. If we assume each response is ~100 words, and you read at the speed an average American does, and writing the prompts and waiting for the response took you no time, it would take you 6 hours and 30 minutes to read all the responses. So you would use half your waking day on an app that in total caused 1% of your emissions. If you sat at your computer all day, sending and reading 1000 prompts in a row, you wouldn’t be doing more energy intensive things like driving, or using physical objects you own that wear out, need to be replaced, and cost emissions and water to make. Every second you spend walking outside wears out your sneakers just a little bit, to the point that they eventually need to be replaced. Sneakers cost water to make. My best guess is that every second of walking uses as much water in expectation as ~7 chatbot prompts. So sitting inside at your computer saves that water too. It seems like it’s near impossible to raise your personal emissions and water footprint at all using chatbots, because using all day on something that ends up causing 1% of your normal emissions is exactly like spending all day on an activity that costs only 1% of the money you normally spend.

Different models use different amounts of energy and water. To see current estimates, you can look at EcoLogits’ list here. For each, compare it to your total emissions (you can estimate your emissions using my calculator here) and see how many prompts it would take you to raise your emissions by 1%.

There are no other situations, anywhere, where we worry about amounts of energy and water this small. I can’t find any other places where people have gotten worried about things they do that use such tiny amounts of energy. Chatbot energy and water use being a problem is a really bizarre meme that has taken hold, I think mostly because people are surprised that chatbots are being used by so many people that on net their total energy and water use is noticeable. Being “mindful” with your chatbot usage is kind of like filling a large pot of water to boil to make food, and before boiling it, taking a pipet and removing tiny drops of the water from the pot at a time to “only use the water you need” or stopping your shower a tenth of a second early for the sake of the climate. You do not need to be “mindful” with your chatbot usage for the same reason you don’t need to be “mindful” about those additional droplets of water you boil.

Some people think tiny parts of our emissions “add up” when a lot of people do them. They add up in an absolute sense, but they don’t add up to be a larger relative part of our overall emissions. If AI chatbots are just a 100,000th of your personal emissions, they are likely to be around a 100,000th of global emissions as well. We should mostly focus on systematic change over personal lifestyle changes, but if we do want to do personal lifestyle changes, we should prioritize cutting things that are actually significant parts of our personal emissions. That’s the only way we could reduce significant amounts of global emissions too.

The reason AI is rapidly using more energy is that AI is suddenly being used by more people, not that AI stands out as using a lot of energy per person using it. Personal chatbot usage is a tiny fraction of AI’s total energy energy and water footprint, it’s being used for way more. It’s like if the internet had been invented a second time and people were rapidly coming online.

The reason AI data centers use a lot of energy is that they are built to collect huge amounts of individually tiny computer tasks in a single physical place. This makes them more energy-efficient than other ways of doing the same things with computers. If we’re going to do things with computers, we should prefer that data centers manage a lot of it. Every time you interact with the internet, you’re using a data center in the same way you use any other computer. Globally, the average person uses the internet for 7 hours a day, but data centers only use 0.23% of the world’s energy. It’s a miracle of optimization that something we spend half our waking lives on can use less than a 200th of our energy. Computers in general have been ridiculously optimized to use as little energy as possible, so we should assume that the things we do on them will not be significant parts of our carbon footprints. It does not matter for the climate where emissions happen. If I’m right that individuals using chatbots are emitting way less than they would doing other things, then all the emissions caused by chatbots in data centers would have actually still happened, and there would have been a lot more of them, if people boycotted chatbots instead. So chatbots in data centers are often reducing emissions, they just concentrate the reduced emissions so we can see them all in one place. This makes them look bad, but they’re often preventing way more emissions that would just be more dispersed.

Data centers do put more strain on local grids than some other types of buildings, for the same reason a stadium puts more strain on a grid than a coffee shop: the stadium is serving way more people at once. Data centers are building-sized computers that tens of thousands of people are using at any one time. The reason they stand out is that they gather a large amount of aggregate energy demand into a tiny place, not that they’re using a lot of energy per user. In the equation (Total Energy) = (Energy per Prompt) x (Number of Prompts), energy per prompt is low, but the number of prompts in a data center is extremely high, so the total energy they use is high. This means that your personal use of AI is adding extremely tiny amounts of energy demand, and of all the things you can cut to reduce your emissions, it’s one of the very least promising. The fact that chatbots as a whole are using a lot of energy tells you nothing about whether you using it personally is wasteful, for the same reason that tens of billions of dollars are spent on candy bars globally, but you purchasing a candy bar isn’t financially wasteful. Deciding that you’re going to stop using AI for the sake of the climate is like going around your home and randomly unscrewing a single LED bulb, or pausing your microwave a few seconds early to save the planet. It’s so small that it’s a meaningless distraction.

The vast majority of AI’s effects on the environment will come from how it’s used, not from what happens in data centers. Amazon and Google Maps both have big impacts on the climate. Amazon might help or hurt a lot, and Google Maps optimizes a lot of car trips, but also might encourage more driving. But no one in debates about Amazon or Google’s climate impact says “The most important issue is the energy costs of running this website in data centers.” That would be crazy, because the websites are tools that cause people’s behavior to change, which leads to much larger changes in the physical world. If you’re concerned about AI’s impacts on the climate, the main question should be how using AI can help or hurt the climate, not the (tiny) costs of running AI in the first place.

You can compare our current best estimates for very specific AI models and outputs against other things in your life here.

Louis Anslow who runs the Pessimists Archive coined a useful simple extension of this idea: the lump of cognition fallacy: the idea that there is a fixed amount of thinking to do. I see this come up a lot in AI discourse. Like the lump of labor fallacy, the reason this is wrong is simple: thinking often leads to more things to think about.

I was recently sent a funny article in the Guardian about how the author isn’t dating anyone who uses chatbots in part because they’re bad for the environment. Articles like this generate a few easy clicks for my posts. But I was struck by the author’s broader complaint, shared by many people she interviewed, that AI somehow causes people to think less throughout the day. That it’s literally always intellectually lazy to use. Take this quote:

Pereira thinks that using ChatGPT “shows such a laziness”.

“It’s like you can’t think for yourself, and you have to rely on an app for that,”

The article lists a few things that I agree are bad to use ChatGPT for, like writing messages on dating apps. But the author extends this to strongly imply that using ChatGPT at all is causing people to think less, because any cognition the chatbot performs leaves the user with fewer thoughts to think. I would brush this off as silly rage bait if I weren’t also so regularly bumping into the same idea elsewhere. I’ve written about this a bit before, but really want to get across just how strange and obviously wrong this is as a model of how human thought works. I also want to popularize Louis’s name for it. I find myself muttering “the lump of cognition fallacy” pretty regularly now. I want to get across a few intuitions about why there isn’t a limited amount of cognition to do, and why outsourcing cognition is often just a way of allowing yourself to think more deeply and meaningfully rather than avoiding thought.

Is it lazy to watch a movie instead of making up a story in your head?

Choosing to watch a movie is a massive outsourcing of thought. You could just close your eyes and imagine the whole thing in your head. There are so, so many mental tasks involved that you’re choosing to have someone else do for you:

• Making up a story

• Writing the music

• Choosing the lighting, set, decorations, costumes, etc.

Have you ever seen someone watching a movie and thought “they’re so lazy for not just imagining a story?” If not, why?

The reason is that there is not a limited amount of thinking to do about the story. The fact that other people have put in so much mental effort into so many parts of it opens up way more potential ways to think about it compared just making up a movie in your head, for the same reason a large complex economy has way more job opportunities than a simple small economy, even though in the large economy more work has “already been done.”

I remember watching Lord of the Rings as a kid and being totally overwhelmed. I didn’t think about anything else for days. The fact that so many adults had already done so much complex thought about the movie, thought that wasn’t available to me as a kid, just meant that my own thoughts about it exploded in a ton of directions they wouldn’t have if I were just reacting to something I made up in my head. Just like we benefit from specialization in labor, I was benefiting from the cognitive specialization of people who had spent decades thinking about story, images, music, and sets, and this left me with way more things to think about, in the same way the wild level of economic specialization that goes into making a computer just leaves you with way more you can do with it after. Similarly, buying a pencil is a way of outsourcing physical labor to thousands of other people. Just like outsourcing labor to other people is mainly a way to allow yourself to do more in total (buying a good laptop vs. trying to build one yourself), outsourcing cognition to a movie leaves you with way more high-level thoughts to think.

The extended mind

This leads into one of my favorite ideas from philosophy of mind, the extended mind thesis: much of our cognition isn’t limited to our skull and brain, it also happens in our physical environment, so a lot of what we define as our minds could also be said to exist in the physical objects around us.

One simple example is how we use our phones to store information. Storing our friends’ phone numbers is similar to storing them in our memory. In both cases, they mostly stay out of our conscious minds. When we want to retrieve them, they are easily available. Over time, dialing a friend’s phone number might become so second nature that you don’t actively think about it. In the same way, having the number stored might mean you never actually have to look at it, you just select the friend to call. In both cases, you’re causing something like cognition to happen in order to achieve some goal, most of it not happening in your conscious experience. It’s behaving as if it’s a part of your broader mind. It seems kind of arbitrary whether it’s happening in the neurons in your brain or in the circuits in your phone. It’s true that you could lose your phone and therefore lose the stored knowledge, but you could also have a part of your brain cut out. Our thinking is often supported by hugely complex background processes both in our skulls and outside of them. The extended mind seems like a useful analogy to help us see how to get the most out of our thought. Offloading our thought onto the environment is in some sense a method of radically expanding and freeing up our minds.

The built environment

To get more extreme, most of our physical environments have been designed specifically to minimize the amount of thinking we have to do to achieve our daily goals. The reason many parts of society are standardized is to reduce the cognitive load of navigating them. Grocery stores are all laid out in similar ways, keyboards are arranged in the same order, street addresses are systematized clearly, all to prevent us from having to expend precious conscious thought on navigating them. When I approach a door, I have no conscious thoughts at all about how to open it. My active thinking is occupied by other stuff, my arm just subconsciously reaches out to the correct location. In these moments, I have “outsourced” my thinking to the door design, because there was a possible world where I had to actively think about opening the door, and the reason I don’t is how the door was crafted.

Try just walking around your daily life and observing how much of the built environment exists to minimize the amount you need to consciously think about getting what you want. Try to imagine how much additional thinking you would need to do if things were designed differently. You’ll quickly notice that huge swaths of our lives are designed to quickly become intuitive to us, so that we don’t constantly need to occupy our thoughts with navigating our built environments or figuring out simple tasks like making payments or traveling or eating. The fact that I have outsourced so many possible thoughts to my built environment liberates me to think about higher level stuff, the things I actually find deeply valuable about the world. I can spend more time thinking about my friends or my job or interesting ideas or media I like or just pausing to take in the beauty of a tree or a building. I don’t need to constantly plan out how to move through the world, because those decisions have often been “outsourced” to civilization.

Other people

Thought is incredibly social. We learn through imitation. Thought uses shared language and symbols. Culture transmits old tacit knowledge. Every thought I’ve had has just built off the complex thoughts of other people. In some sense I’ve offloaded the learning required to be part of an advanced civilization to all prior humanity. This is all extremely positive-sum. There isn’t less to think about now that humanity has thought through the last 10,000 years of transition and left us with the results at the end.

Institutions

More broadly and abstractly, much of the “reasoning” happening in society in any one time might actually be happening via the mechanisms of market or government institutions or culture, evolving over time, maybe to achieve some higher way of being. This is kind of what Hegel was talking about.

Civilization as offloading thought

This is all so obvious it seems silly to write, but it actually doesn’t make sense under the lump of cognition fallacy. Our ancestors had to expend way more cognitive power just navigating their day to day lives. The modern world has more or less completely removed that need compared to most of human history. With so little of that conscious cognition happening, people who believe in the lump of cognition should predict that people just think way less often now than they did before. But that’s clearly not true. With our minds freed from huge amounts of minor inconveniences, we can spend our days thinking about more interesting stuff. The philosopher Whitehead described this process well when he said:

Civilization advances by extending the number of important operations which we can perform without thinking of them.

I think people who worry about how chatbots literally always involve outsourcing some mental task might not be noticing the gigantic mountain of mental tasks we have already outsourced to civilization, and how this has only liberated us to think more deeply and meaningfully more often. The lump of cognition fallacy causes people to see this backwards: instead of cognition leading to more and more thought, they see it as draining a finite pool, and nothing will be left to think about once it’s drained.

When is it good or bad to outsource thinking to chatbots?

So there are plenty of examples of where it’s good to offload our cognition, because that only frees us up to live more meaningfully and think in more complex ways about what actually matters to us. But there are also clearly cases where it’s very bad to offload our cognition. Things like:

• Homework.

• Messages on dating apps.

• Summarizing a valuable complex book instead of reading it (assuming you had the time and energy to read it and would have benefited from it).

• Personal connection and close conversation.

What’s the difference between these and all the good ways we have outsourced our thinking? When is it bad to outsource?

It seems pretty obvious to me that there are actually a few key obvious places where outsourcing your thinking is bad. They all overlap with places where outsourcing your labor is bad. It’s bad to outsource your cognition when it:

• Builds complex tacit knowledge you’ll need for navigating the world in the future. Homework is valuable because it trains your brain to see more and more complex patterns in the field you’re trying to learn about. Marinating in cognitive work in a field can give you subtle, hard-to-communicate knowledge about it, or at least gives you knowledge you can easily recall in the future instead of having to look it up. It’s bad to outsource cognition on homework for the same reason it’s bad to hire someone to take your place as a student in a class.

• Is an expression of care and presence for someone else. If a friend or partner asks for your help with a problem, they often want to feel your mental presence just as much if not more than they want a technical solution to the problem itself. Paying someone else to make something nice for your partner feels less special.

• Is a valuable experience on its own. I wouldn’t want to outsource the thoughts I have after a movie, the simple appreciation of a nice day, or the thoughts in a night with friends. Somewhat outsourcing these can be really valuable (hearing what someone else thinks about a movie might set off my thinking in wildly new directions), but I still want the experience of thinking about these a lot, because the experience of thinking about them is itself valuable and nice.

• Is deceptive to fake. If someone’s messaging you on a dating app, they want to know what you’re actually like.

• Is focused on a problem that is deathly important to get right, and where you don’t totally trust who you’re outsourcing it to. I don’t hand off very high-stakes decisions to chatbots.

The places where it’s good to offload your cognition to chatbots, where I use them over and over again, are the places where it’s good to offload cognition in any other circumstance, where we rely on the built-up knowledge of other people and civilization, where I won’t gain anything from putting in cognitive effort, and where there are massive positive-sum spillover effects of other people being involved in the thought process.

I was recently around someone complaining about how people aren’t thinking for themselves anymore because of chatbots. They had mentioned that multiple people they knew were using them to find recipes and make grocery lists. Most of my best cooking experiences have been following recipes I find online. My willingness to offload the mental effort of putting the recipe together means I get to do much more ambitious things in my cooking. I don’t think this person would have ever been upset if I had mentioned I Googled a recipe, but because a chatbot was involved, the lump of cognition fallacy was operating, and they had the sense that having a recipe given to you took away a finite opportunity to think for yourself.

Among other things, I use chatbots as research assistants. They do the tedious, repetitive work of assembling sources on a relevant topic, I read and check the sources themselves. I get the benefit of reading about and thinking through the key ideas, they do the drudgery of long boring internet searches and digging through academic literature for relevant information. For the same reason human research assistants don’t turn humans stupid, chatbots aren’t turning me stupid either, even though both involve massive offloading of cognitive effort. They free me up to do the type of thinking that actually helps me learn a lot about the world. In all the other ways I use them, they imitate a good conversation with a subject-matter expert, or a simple assistant to arrange text in a way I want. The lump of cognition fallacy implies that anyone using a research assistant is missing out on some of the thought available for their project. Using the research assistant must make them stupid and lazy. But obviously this isn’t true. Research assistants leave people to think deeper and more thoroughly about the problems they’re tackling. The incredibly positive-sum nature of thought is obvious when the research assistant is a human. But when it’s a chatbot, people suddenly sense some zero-sum way in which, if the chatbot is thinking, they’re not.

When you’re in conversations about chatbots “replacing our thinking” see if the person is legitimately concerned about the chatbot removing some important experience, or if they’re just operating under the lump of cognition fallacy. Articles about this abound with examples. Take this from the same Guardian article:

Noijeen stopped using AI to code, and uses it very sparingly in his personal life. He will make fun of friends who use it too much. He recently met up with an old friend who lives a three-hour train ride away. They decided to meet in the middle. The friend said he would use ChatGPT to find the right spot, but Noijeen just looked at a map. “There’s a city exactly between us,” he said. “Why do you need to ask ChatGPT for that?”

If you’ve never worried about Google Maps “replacing thinking” but you find yourself reacting negatively to a chatbot offering directions, or you regularly check restaurant reviews before going but find yourself repulsed at asking a chatbot for recommendations, consider that you’re falling victim to this weird selective application of the lump of cognition fallacy that seems to exclusively but consistently appear when chatbots are involved. It depresses me that so many people seem to want to swamp their day-to-day lives in the meaningless cognitive minutia of long fruitless internet searches or working solo without any input from outside, because they fear that if they offload the minutia, they won’t have anything left to think about. There’s a whole world out there.

I want to avoid three categories of fake catastrophe here:

• Things that only fringe people believe are catastrophes, like vaccines being worse than COVID. You should be able to go to a party of educated adults and find at least a few people who believe them, and when they talk about them others just nod along without questioning them.

• Things you can only believe aren’t catastrophes if you have a very specific political or religious outlook. I want to avoid things like “It’s bad that people are less religious now” because that’s too dependent on the values of the person speaking.

• Things without a clear expert consensus. For example, while I enjoyed The Economists’ coverage of the “myth” of the decoupling of wages and productivity, this is actually pretty contentious, and there are a lot of good counter-arguments to how they present it.