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 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 Wal Mart 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 Wal Marts.

If you build a building, whether it’s a data center or Wal Mart 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 means each day OpenAI used 380,000 gallons of water. Corn in Iowa uses between 0.1-0.2 inches of water to grow per day. 0.1 inches of water over 1 acre is 27,154 gallons. So OpenAI was using as much water as 14 acres of an Iowa corn farm, or 0.02 square miles. The average Iowa corn farm is 346 acres. This amount of water is equivalent to Sam Altman purchasing 4% of a single Iowa farm to grow corn for his employees. Here’s that area on a map (the yellow box).

Does Hao’s paragraph get this magnitude across? If you heard a company had bought 4% of a corn farm, how big of a problem would you assume this is for regional water access? What if the tech company were using this 4% 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.

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.

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.

I’ll end with some things that I expected to be fake, but when I looked into them turned out to be real

Fake catastrophes

“More people in the US are getting cancer”

The total rate of cancer in the US has increased since the year 2000, but this is just due to the population being older on average now. If you adjust for age, incidents rates of cancer fell by 5.7% between 2000 and 2021.

“Cell phones/5G/Wifi cause cancer”

This one feels like it’s at the edge of being fringe, but I’ve been to multiple parties where educated people bring it up as if it’s real, and others nod along. There are multiple ways to go about showing that it’s wrong:

• As mentioned above, the average US cancer rate has actually dropped once you adjust for age since 2000. In this time, everyone has completely surrounded themselves with cell phones and wifi. It would be weird if every last one of us had each adopted some new habit that carried a significantly increased risk of cancer, but the cancer rate had also significantly dropped.

• There are two ways radiation causes cancer. Neither makes cell phone radiation look like it can harm us:

  • Ionizing atoms in DNA molecules (knocking electrons out of their orbits). To knock electrons out of place, waves need to each carry a specific minimum amount of energy (I explain this a lot more in my physics YouTube series here) that radio waves fall way, way, way short of. If they don’t carry that minimum amount of energy, they don’t affect the electron’s positions at all. They can oscillate them, but they can’t break them free. Wifi, cell phones, and 5G are all completely, totally lacking in this mechanism.

  • Heating tissue enough to damage DNA. If you heat cells enough, you can denature proteins and damage DNA structures. This is why severe burns increase cancer risk, and is why we can get cancer from UV rays. But this requires significant heating. Cell phone radiation is so weak that the maximum heating it can cause is a fraction of a degree, less than the temperature increase from holding the phone against your face and absorbing the battery heat, going outside on a warm day, or having a mild fever. Your body’s normal temperature regulation handles fluctuations vastly larger than anything wifi or 5G can produce. The FCC’s safety limits for cell phones are set to ensure heating stays below 1°C even under worst-case conditions, and real-world exposure is typically far below those limits.

• The only established way radio waves interact with your body is by heating it slightly, the same principle that makes a microwave oven work. But your cell phone’s output is roughly 100,000 times weaker than your microwave, and it’s not concentrated on you. The actual temperature increase in your head from a phone call is a tiny fraction of a degree—less than the temperature fluctuation you get from drinking a cup of coffee or walking outside on a warm day. If heating were the concern, we’d be far more worried about hot showers. Your body has robust systems for handling small temperature variations; it does this all day every day. The heating hypothesis doesn’t get you to cancer either.

• There have been plenty of studies on cellphone users and brain cancer that have found no relationship.

Climate and energy

“We’re going to run out of fossil fuels”

Basically all forecasts now seem to imply that it’s way more likely that we’ll hit a peak in fossil fuel demand before we hit a peak in fossil fuel supply. This is good. The main bad thing about fossil fuels is that they cause climate change. The fact that they’re finite is a secondary bad, and burning all of them would be disastrous for the climate.

“Wind farms kill birds”

A reader convinced me this is actually more of a problem than I thought, see his comment here.

They do, but not very many relative to other things we do.

Two thirds of bird species in North American are at risk of extinction due to climate change. If you want to help birds, build more turbines.

“Planes are significantly worse for the climate than driving the same distance”

If you compare the emissions you cause by taking a plane ride (dividing the emissions of the plane by the passengers) it’s often comparable to driving solo for the same distance. The reason why plane rides add so much to our carbon emissions is that they cause us to travel way, way farther distances than we would have otherwise, not that they’re drastically worse per mile. In the past year I’ve taken a few plane tripes that were thousands of miles (I flew way more than normal). I would not have chosen to drive solo this far to get to the same place. If I had, my emissions would have been about the same.

I regularly travel between DC and Massachusetts for holidays. If I drive, my emissions are about 120 kg of CO21 (440,000 ChatGPT prompts!). If I fly, my emissions (diving the plane emissions by the number of passengers) are about 110 kg CO22. I usually opt for the train, since it emits way less, but I should definitely not choose to drive for the sake of the climate. That’s the worst option here!

Planes pollute at higher altitudes. The effects of CO2 emissions doesn’t seem to change much based on the altitude they happen at. Some argue that the main bad effects of flying is in their non-CO2 pollutants like nitrogen oxides (NOx), water vapor, contrails, and cirrus cloud formation, which can have a warming effect 2-4 times greater than CO2 emissions alone. But the warming effects of all these other emissions is extremely short-lived. CO2 remains in the air and warms the atmosphere for centuries to millennia, but the warming caused by NOx lasts at most a few months if we consider its ozone effects, water vapor’s warming effects last up to a few years, and effects of contrails last for about a day. These very short warming effects aren’t really what matter for climate change. All the worst effects of climate are in the medium to longterm as more and more CO2 builds up in the atmosphere.

If we break down the immediate warming effects caused by plane emissions on a given day:

• 57% is caused by contrails

• 34% is caused by CO2

• 17% is caused by NOx

Though we’re uncertain about the exact magnitudes. But it seems possible to eliminate contrail emissions from planes for very little money. If we do this, the immediate warming effects of flying will mostly come from CO2 again, and flying becomes pretty comparable to solo driving even if we’re just considering the short-term effects. Unlike driving, flying also doesn’t significantly contribute more to local air pollution that people actually breathe.

If we look at the carbon footprint per kilometer traveled for different vehicles, gas cars and planes don’t look too different from each other, especially not compared to many other options like electric cars and trains:

On this graph “Domestic flights” are considered very short (as you can tell, the graph was made in the UK, so their “domestic flights” are all extremely short), whereas short and long-haul flights are longer. The carbon intensity per mile actually drops off a lot for longer flights:

I tried looking at the trade-off you make in CO2 saved per increased risk of injury and death when you choose to drive over taking a domestic flight, but the absolute risk of injury and death were both still so absolutely low while driving that it didn’t come out to much. Even if you were completely cutting all your CO2 at this rate of risk it wouldn’t add up to too much additional injury risk or death per year. Basically, if flying is more comfortable to you than driving, and those are your only options, and you would definitely make the trip either way, then if it’s a longer or medium flight (over ~400 miles) you should definitely fly, and if it’s a shorter flight the slight increase in emissions might still be worth the trade-off. It’s not a serious disaster. Obviously, if you have the option to take the train, just do that! I’m lucky to live in the Acela corridor so this is an easy choice for me. If you’re driving with other people or in an electric car, driving will basically always be better than air travel.

“The US middle class is disappearing”

Yes, because they’re all getting too wealthy to qualify as middle class.

“Using AI makes people less intelligent (it creates a ‘cognitive debt’)”

If you use AI to cheat on an assignment, it is true that you will not learn that assignment well. Your brain will also show less activation while you do it. But some people are going farther to say that this failure to activate your brain somehow makes you less generally intelligent over time. There’s suddenly a lot of worry about “cognitive debt” after an MIT Media Lab study blew up all over the internet. I’m going to pass the mic to the BS Detector here for a full explanation:

The BS Detector

The Cognitive Debt of Digging Through Preprints

1. One Media Lab preprint is worth a thousand think-pieces…

Read more

10 months ago · 59 likes · 12 comments · Ben

“Testosterone levels are plummeting”

Testosterone levels seem to have fallen. This is an interesting problem, but many reports of how much they’ve fallen wildly exaggerate how much, mostly due to a confusing measurement error.

You may have seen a graph that looks like this on social media:

A fall of almost 30% since 2000! Seems alarming! If this were real I’d be concerned. But it’s not. This Substack post is a good rundown of how this is likely the result of a change in how we measure testosterone, not the actual amounts:

Seeds of Science

Maybe it's just YOUR testosterone that's low

Eryney Marrogi is a 2nd-year medical student at the University of Vermont, and is currently working on AI protein design for continuous monitoring devices at Caltech. Before medical school, he was in the startup world, working primarily on making new viral vectors for gene therapy as one of the earliest employees at Dyno Therapeutics. He came to Dyno fr…

Read more

a year ago · 27 likes · Seeds of Science

See also this Twitter thread. Along the same lines, it seems like the idea of declining sperm counts is currently relying on data that’s too uncertain to draw many conclusions from.

“Landfills will become a major problem as they take up more and more space”

Landfills aren’t bad for the environment and we have unbelievable amounts of additional space for them relative to the garbage we produce. They’ll basically never be an issue for us.

Think of all the garbage you and everyone you know has thrown away. Even just your personal circle could probably make a moderate mountain of garbage if you combined all your lifetime trash. Now think about all the times you encounter a landfill in your day to day life, as you’re driving around. They seem incredibly rare. That’s a weird mismatch!

It turns out it’s just incredibly easy to dig gigantic, well-contained holes in the ground to throw away centuries’ worth of a city’s trash while going mostly unnoticed. I think humans sometimes underestimate just how small they are compared to the planet. Rob Wiblin has a really great rundown here going into details about just how small of a problem landfills are.

“Seed oils are bad”

I’m open to considering stuff like seed oils and won’t write off concern about them right away, but the evidence that they’re harmful does in fact seem shockingly bad. Here’s a long roundup of the evidence. Here’s a good (sometimes goofy) video summary of the state of the evidence.

“There’s a new loneliness epidemic”

Seems pretty uncertain. Americans are reporting spending more time alone, but not reporting being more lonely. See this piece for more.

If you break it down by gender and age, it seems like young men are significantly more lonely, but women over 35 are more lonely than men, and US young men are way more lonely than the OECD average, but so are women over 35:

So it seems like we have something like a continuing loneliness problem that mostly affects men between 15-34 and women between 35-54, not a sudden recent epidemic all concentrated in men.

“Kidnapping by strangers is common enough that parents should worry”

About 350 children are kidnapped by strangers annually in the US. Meanwhile, conservatively, about 75,000 children are kidnapped in the US by one of their parents and are seriously harmed as a result.

There are 73 million children in the US. In any given year, a kid’s odds of being kidnapped by a stranger are 0.0005%, one in 200,000. Obviously terrible, but way way less common than a lot of people seem to believe.

School-specific misconceptions

I was a teacher for 7 years and so got into the habit of debunking common myths about American education. The three central ones I would regularly talk about were:

• Schools are unsafe

• Schools are unfairly funded

• Teachers aren’t paid enough (my very very very very least popular take in education circles was that teachers receive adequate pay)

“Schools are dangerous now because of school shootings”

Back when I was a teacher, I was pretty regularly asked if I was concerned for my safety in schools. School shootings rose significantly from 2015 to 2022:

But looked at in the context of overall school violent deaths, things actually mostly hovered around the same number of deaths each year. This graph only goes up to 2015, but the worst year for school shootings on the upper graph wouldn’t be the worst year for total deaths on this graph, because students are also sometimes killed in other ways.

Schools in the 90’s used to be much more physically violent places, to the point that it was common in kids’ cartoons to jokingly show fight scenes like it was a normal part of life. They’ve become significantly less violent since then, and unfortunately outlier shootings are now balancing out the statistics to keep the overall number of people dying roughly the same.

But even in the peak year for school deaths (2006, with 63), my best guess is that adding up students and teachers, there were 55 million people learning and working in American schools. That means that your odds of being killed in a school in the single most dangerous year were one in a million. In comparison, your odds of being killed in general in America are about 1 in 15,000 each year (22,830 homicides in 2023, total population of 335,000,000). So American schools in the year the most killings happened in them are still about 66 times safer than everyday life outside of them.

“School funding is unfairly based on property taxes. Schools in wealthier areas get way more funding than poor schools because the tax base is richer. Shouldn’t we give the most money to the students who need the most support?”

This is a surprisingly common point in a lot of conversations about education in America, but is basically wrong. Schools are partially funded by local property taxes, but also receive state and federal funding. People in charge have also put together that poor students could also use additional support. As a result, via state and federal funding, spending on American schools is actually somewhat progressive, with school districts serving predominantly poor students receiving more funding in total than districts serving predominantly middle and upper income students.

If you break this down by sources of funding, it becomes clearer that the lack of local funding for poor schools is being made up for by increased state funding.

I suspect that many of the issues with American schools are actually downstream of bad teaching practices rather than insufficient funding. I’ve seen huge amounts of funding go to teaching practices that don’t replicably work. I’d very strongly recommend the podcast Sold a Story if you’d like an example of a catastrophic teaching practice that had huge amounts of funding behind it. Even very well-funded schools often have trouble forcing their teachers to teach phonics!

Real catastrophes

Things I expected to be fake but turned out to be real.

“You can harm your eyes with too much screen time”

It does seem like nearsightedness has grown over the years as more people use screens. This seems to result from the screens (and books, and other things we look at) being too close, rather than the light from them. I expected this effect to be smaller than it was.

I’ll circle back and add to this as more come to me.

It’s been shared in a lot of recent conversations about AI, from AI psychosis to autonomous vehicles to regular bad outputs. Simon Wilson dug into the source here, looks like it’s from an old presentation at IBM.

I think this is a pretty silly sentiment and is basically incorrect. Consider the following statements:

• “Bill’s been moving a little slow recently compared to slightly younger people. I think the best move is to kill him and throw his body away and replace him with someone who can move about 0.5 seconds faster on each task.”

• “Martha seemed to consistently say incorrect things when we ask her about a topic. I performed neurosurgery on her and discovered the problem in her brain, I rewired it and she’s behaving fine now.”

• “I just learned that Andy ran over a squirrel. This is really terrible and I’m re-evaluating everything I think about how safe he is as a driver. Luckily, we keep cameras all around his car 24/7 so we can figure out what happened. I’ll perform neurosurgery on him to make sure this never happens again, and will keep him off the road in the meantime.”

• “We have this new service where every day 20 million people talk to 20 million representatives of our company. Unfortunately, we recently found out that a few of our employees are crazy and gave the people they were connected to really bad advice, hyping them up when they asked to be hyped up. We’re going to kill all 20 million employees and replace them all with better employees to get that rate down to zero.”

It seems pretty obvious that we actually:

• Hold computers to way way higher standards of accountability than real people.

• Are usually much more able to monitor and detect their bad behavior.

• Have a much easier time being much more invasive in how we deal with their bad behavior.

I think when people say “A computer can never be held accountable” what they mean is “A computer can never be socially punished for a bad decision.” That’s true, but social punishment is just a means to a goal: better behavior or harm prevention. It’s also a pretty clumsy, often useless tool. Many people are socially punished and still behave terribly. If we had the ability to perform neurosurgery on everyone behaving badly to permanently change their behavior, I think this would be seen as:

1. Much more effective than regular social punishment.

2. Deeply evil and totalitarian.

And yet we do the same to computers regularly. Why would we want to “hold computers accountable” when that’s way, way less effective than what we can actually do to them?

I’ve recently seen this image shared a lot in the context of Waymo running over a cat in San Francisco. It seems like human drivers probably run over 75-150 times as many animals when they drive the same distance as Waymo has. Many of these are hit-and-runs. The people whose animals get killed often can’t hold the driver accountable, because the driver speeds away before people can see the license plate. In comparison, it is very very visible when a car is an autonomous vehicle, and the brand is painted all over it. It’s much easier to identify and complain to a massive company. It’s likely that even minor negative media attention will cost them a lot of money, so they have a strong incentive to change their behavior. The cars can be programmed to better detect their surroundings before being let on the road. Meanwhile, human drivers who kill people with their cars are often eventually allowed to continue to drive. If you kill someone while driving drunk in Florida, as long as you don’t have a prior DUI offense, you can eventually drive again! We’re already way, way more lenient with human drivers than we are with AI drivers.

Another funny comparison is AI psychosis. Many, many people have gone crazy using the internet. QAnon, antivax, and getting memed into doing apologia for North Korea are some surprisingly common examples. But that doesn’t stick out to us, because the people are going crazy interacting with content created by other crazy people. These other crazy people get to stay anonymous and never get properly socially punished. But OpenAI can receive huge amounts of condemnation whenever any additional person goes crazy using ChatGPT. Problems with computer applications run by big corporations are much easier to identify, the corporation is easier to pressure to change its behavior, and the applications themselves are easier to reprogram. If only it were as easy to catch all the crazy people online and turn them normal.

There are great reasons to worry about computers having more control in society. Cybersecurity and AI risk among others. But the idea that they’re somehow less “accountable” than people isn’t one of them. Computers shouldn’t be used to dodge accountability, but it’s very hard to do that in general. Unlike humans, it’s fine to keep computers in what’s basically a totalitarian surveillance state and completely reprogram them at the slightest sign of misbehavior. Computers are held to much higher standards of accountability than humans. Let’s keep it that way, but also acknowledge that citizens of totalitarian surveillance states can make better drivers.

• There are no positive-sum trades. In every exchange, someone is winning and the other person is losing. Profit is usually a sign that someone’s been harmed.

• Every trade-off is a trick. Anything harmful cannot be made up for by other unrelated positive effects. If someone tells you we need to make a trade-off, they’re hiding a much better solution where everyone’s better off and nothing bad happens.

• Big, global institutions are always way less trustworthy than small, local institutions. The real political axis is the virtuous, authentic, everyday, rooted people vs. the unrooted powerful cabals who run society.

• It’s very important that all resources be spent on your specific value system and vision of the good life. Pluralism is a trick. Other people pursuing very different values are basically always a threat, because there are no positive-sum trades.

• Information is not valuable and not worth spending resources to acquire. What matters is physical goods. Thus, digital goods cannot make life better. It is at least somewhat sinful to spend physical resources to produce digital goods.

• The world is getting irrevocably worse. Technological progress is always just a march toward something worse.

• Individual humans are magic. Any implication that things humans do can be truly replicated by machines is an attack on human dignity.

• Some form of a folk labor theory of value is true. The value of a good is determined by how much thoughtful human labor has gone into making it.

I think every one of these individually is a pretty bad way of thinking about the world. The data center debate has been a place where all of these show up, and where many people talk as if they’re each individually obvious, and only the bad evil people disagree. This was a big reason why I was compelled to write so much about them. It was aggravating me how much the broader conversation about data centers (and technology more broadly) is currently dominated by what seems like very low-trust intuitions that fall apart if you just examine how data centers actually work. This isn’t to say locals are always wrong to oppose data centers, just that way too often, objections seemed to be guided by these bad ways of thinking, as opposed to concrete harms. Going down the list:

• There are no positive-sum trades. Data centers often involve lots of positive-sum trades with local communities. Mainly via tax revenue, but also lots of additional funding for local utilities. Loudoun County (with the most data centers in the country) is a good example of where data centers provide huge amounts of tax revenue for the locals without causing many issues at all. It’s noticeable how often “data center profits” are brought up as if this is a sign that they’re harming people. To me, this sounds more like they’re successfully serving a lot of people who want to use them.

• Every trade-off is a trick. Any large industry like data centers is going to come with some harms, but these harms can be made up for if the benefits are large enough. I think in most cases so far they have clearly been net good for the places they’ve been built and the world more broadly. Any other industry this large and lucrative would also come with similar trade-offs. It is okay that data centers purchase some nonzero amount of water from local utilities, as an example. This isn’t an irrevocable loss that cannot be made up for with utility and tax revenue from the data center. Whenever people talk about the harms of data centers, they often talk as if there are other options just around the corner for making local communities much wealthier without any negatives at all, but they don’t actually share what those are.

• Big, global institutions are always way less trustworthy than small, local institutions. The internet more broadly has been a utopian technology for me. A lot of tech companies have provided unbelievable value to my life, and in general I trust them more than many local small businesses. I’m not entirely trusting of all tech companies (especially due to potential risks from advanced AI), and I’m aware they can do very bad things, but I’m not at all inclined to think they’re evil just because they’re big. Larger organizations in general often allow for much more complex work, and the standardization they provide often mean many different people with wildly different backgrounds can operate in them more easily. Similarly, many in debates about data centers also talk about local politicians as being complicit in the evil because they just want more tax revenue. Even a lot of left-wing people often frame this as “the people” against very local governments. This shows a shocking level of distrust in local governance that I don’t think is correct or healthy. It’s often good when local governments try to attract more tax revenue for their communities.

• It’s very important that all resources be spent on your specific value system and vision of the good life. Pluralism is a trick. One of the reasons why data centers are interesting to me is that they each serve tens of thousands of people from around the country and world at once, each pursuing wildly different things. Any one person might disagree with a lot of what happens in a data center, but I think people around the world should be able to pursue what they personally find valuable. Even if this is all concentrated in one place, if it’s not actually a significant harm to the locals, that pursuit should happen. Many commentators seem to want to litigate the specific value of AI and other tools here. My reaction is mostly “there’s clearly lots of demand for this, so our first impulse should be to let it happen as long as it’s abiding by all local regulations, even if we think it’s goofy.” There are plenty of of other ways people spend time, energy, and water that I think is at least silly and at worst bad, but I can acknowledge that they should still be able to pursue them. Pluralism is pretty foundational to how I think. The fact that the users of data centers are invisible to locals is a big reason why people distrust them. I think this is silly. We’re constantly being affected by the economic choices of tens of thousands of other, invisible people. This invisibility is a result of living in a complex pluralist society where we’re all individually pursuing different goals, and yet have still found clever ways to coordinate with each other. Making everything local and visible would make us all poorer, and even more importantly would expose too much of our decisions to the specific judgment and value systems of the community we happened to be in. We would be less free.

• Information is not valuable and not worth spending resources to acquire. Information is wildly valuable. It’s good to spend lots of physical resources to produce a book, even though almost everything valuable about the book is exclusively in the information. Most of the ways human life has been made really wildly better are exclusively due to intellectual abstractions like economics and physics and good political ideas, not the simple acquisition of physical objects by hard labor. The internet has been unbelievably useful to me because it gave me way, way, way more access to useful information than I would have otherwise had.

• The world is getting irrevocably worse. Technological progress is always just a march toward something worse. Here’s where I have the most sympathy for low-trust thinkers. I’m pretty concerned about AI specifically. But there are a lot of ways I’m wildly excited about AI too. More broadly, I think the case is strong that the vast, vast majority of new technology has on net made society much better.

• Individual humans are magic. It is not an attack on human dignity to say that we don’t have magical angels living in our brains. The industrial revolution has on net led to much greater human dignity, even though it replaced many acts of physical labor performed by humans.

• Some form of the labor theory of value is true. I don’t think most people are Marxists, but I do think a lot of people have a tendency toward believing in some form of the labor theory of value, as opposed to the obviously correct marginal theory of value. Worse, they mangle this beyond Marx’s view of exchange value to instead believe that labor is the ultimate source of use value. Many people often talk about how AI in data centers cannot provide anything of value by definition, because it’s not being thoughtfully created and curated by a human. This comes up a lot in conversations about AI art especially. There’s a lot of talk about how what AI is producing cannot be valuable by definition. I’ve pontificated about this before, but I think one of the causes is that people have a bias and think all value originates in human labor. I know Marx’s actual theory is much more complex (and yet still wrong), I’m talking here specifically about a folk theory instead.

There was a recent story by Heatmap on the building backlash against data centers. It was hard to read it without coming to the conclusion that a significant portion of the strongest opposition to data centers isn’t actually coming from people who have been harmed by them, it’s from people who are carrying around this cluster of low-trust beliefs, and who are now the main voices in the national conversation. I think low-trust populism is in general the main threat in America right now to the type of politics I think actually makes the world better. The data center debate has been one of many arenas where localist, low-trust populism squares off against pluralist, high-trust liberalism. The thing that made it stand out to me is seeing how few other people seemed interested in going to bat for my side. This is not a trivial topic, this is the main new industry in America! I’d like a lot more people to enter the debate, not necessarily always on the side of data centers, but on the side of pluralism, economic literacy, and without an inherent distrust of technology or big abstract systems. Maybe this could be you!

I’m also sympathetic to people who worry that chatbots as they exist are massively overblown, that we’re in a pretty intense hype cycle, that today’s AI models aren’t reliably producing accurate or novel enough results to justify the money and energy spending on them, that scaling is now producing such diminishing returns that something is about to blow up. I still have plenty of experiences with ChatGPT, Claude, and Gemini where they’ll give me results I know to be straightforwardly wrong or made up.

But there’s a third completely different reaction, which I think of as the chatbot moral panic view, that combines three contradictory beliefs:

1. Chatbots are phenomenally stupid, useless, and incapable.

2. Chatbots cannot provide anything of value by definition.

3. Chatbots are demonic. There is something ominous and evil about using them, beyond any measurable clear harm. The person might list a lot of specific harms chatbots cause, but it’s clear that even if all of the problems were solved, the person would still have some deeper moral revulsion. Their concern about specific problems is post hoc and disproportionate to the problems themselves, and goes way beyond their concern about other online tools causing similar problems.

3 contradicts 1 and 2 because it seems hard for something ineffectual to also be deeply evil. 1 contradicts 2 because, if something by definition cannot produce anything of value, it doesn’t make sense to complain that it’s very far down on some axis of quality. It seems like complaining that a rock can barely sing at all.

Yet I keep bumping into people who seem to hold all three beliefs at once.

This is anthropologically interesting. Chatbots feel to me kind of like Wolfram Alpha used to when I was in college, but it’s very hard to imagine anyone having such a strong moral reaction to Wolfram Alpha, or Wikipedia, or YouTube, or any other website or online app that doesn’t clearly involve some direct harm. There’s something really getting to people about the nature of chatbots.

I’ve developed an armchair psychology theory for why this is happening. My confidence in this isn’t high, but it’s been interesting enough that I find myself coming back to it when I’m having conversations about chatbots, so I figured I’d submit it for review here. This is my most hand-wavy post.

Chatbots remove our ability to associate information with specific high and low-status people

Years ago I lost a friend to a cult. There were clear indicators that he was vulnerable to cult dynamics I should have picked up on ahead of time, including that he was:

1. Very hyper-sensitive to his personal sense of status in any given conversation or situation

2. Not receiving much status in his personal life

3. Lurching toward more and more desperate means of getting it

4. Easily compelled by big flashy contrarian ideas, even if they very easily fell apart when poked at

and this was all exasperated by some underlying mental health stuff.

I remember the first time I had noticed something was deeply wrong. We were walking around Boston and I had made a joke about how I needed to swing by a bookstore to pick up the latest solarpunk manifesto. He suddenly exploded into a 20 minute nonstop monologue about how solarpunk was “fascist” (you can read about it here and judge for yourself) because of a pretty incomprehensible pile of reasons he was implying he had some special deep understanding of that was inaccessible to me. I was too disoriented and unnerved by this to think much about what was actually happening here: my friend was positioning himself as a special arbiter of truth and ethics as a way of feeling like he had conversational power in the moment. He wasn’t making an argument for something being false or unethical. If he were, he would have built some kind of conversational bridge where I could join him in the debate. Instead, he was specifically putting up incoherent conversational walls to imply he had accessed some special Platonic realm of truth I couldn’t possibly access myself. His brain was short-circuiting out of a desire to feel important (a pretty fundamental evolutionary drive) and mashing any buttons it could, like a desperately hungry animal clawing for food.

I realized years later that my friend was actually just doing an extreme version of bad behavior we all indulge in at different levels: setting the boundaries of which people and what ideas are considered high and low-status exclusively to flaunt our social power and importance.

One way this often comes up is in conversations at parties. People will often bring up the names of people who hold specific beliefs, and a lot of the debates about the quality of the beliefs will be shifted to debates about the quality of the people who believe them. When people I meet at parties talk to me about ChatGPT and the environment, it’s very rare that the conversation stays on the topic of Watt-hours or milliliters of water or the IPCC report, and very frequently turns to Elon Musk or Mark Zuckerberg or Republicans or climate deniers. These characters often exist in the conversation more as spells one tries to cast rather than arguments. If you can associate me with Elon or Zuckerberg, you’ve cast me out as the bad guy and won the argument, regardless of the hyper-specific facts of how much energy a prompt happens to use. If you’re able to cast people out into the low-status crowd, that implies you have a lot of conversational power and status. It’s like you’re a conversational wizard, and the person you’re casting out has no power of their own. It can be an intoxicating move to make. This is a much less extreme version of my friend attempting to associate me with fascists. Just like my friend wanted to feel like he had power in that moment to cast me out into the low-status community and win the conversation, most of us will often indulge in invoking low status names specifically to cast out our conversational opponents, even going so far as to completely ignore the specifics of their claims and ideas. In fact, like my friend implying he had some secret access to knowledge I couldn’t have, entertaining the specifics of a claim gives your enemy too much power. It gives them a way to raise their evil idea’s status, which you can’t let them do. Thus, raw information without an associated high or low-status person is dangerous to entertain, and you need to ignore it when possible.

Even there, in that last paragraph, I associated people who criticize me with my friend who went crazy and joined a cult. Aren’t they so low-status? Gross!

I’m not claiming to be above this dynamic, but I do think I and most people I know who I consider mature can recognize this tendency in ourselves and work to build up our understanding of facts on their own and see where they lead. I try whenever I’m in a disagreement to speak without using the names of high or low-status people. Still, it’s very easy to fall back, and there are a million other ways status guides my thinking. The best we can do is try to focus on the facts when we can.

This is pretty fundamental to how a lot of people move through the world: identifying the good guys and associating with them, and identifying the bad guys and casting them out. It’s very easy for people to go long periods of time where they more or less ignore the specific contents of a lot of their beliefs and just collect them as little talismans, signs that they’re part of the elect. This is a very common theme of my posts. I’d noted this dynamic in my first post on AI and the environment:

On a meta level, there’s a background assumption about how one is supposed to think about climate change that I’ve become exhausted by, and that the AI emissions conversation is awash in. The bad assumption is:

To think and behave well about the climate you need to identify a few bad individual actors/institutions and mostly hate them and not use their products. Do not worry about numbers or complex trade-offs or other aspects of your own lifestyle too much. Identify the bad guys and act accordingly.

Climate change is too complex, important, and interesting as a problem to operate using this rule. When people complain to me about AI emissions I usually interpret them as saying “I’m a good person who has done my part and identified a bad guy. If you don’t hate the bad guy too, you’re suspicious.” This is a mind-killing way of thinking. I’m using this post partly to demonstrate how I’d prefer to think about climate instead: we coldly look at the numbers, institutions, and actors that we can actually collectively influence, and we respond based on where we will actually have the most positive effect on the future, not based on who we happen to be giving status to in the process.

I’d brought it up in my post “People’s deeply held beliefs are surprisingly surface level” too:

A lot of people (myself included) have a lot of internal illusions about how deep our deeply held beliefs go. I used to think that if someone were structuring their lives around a specific idea, and describing it as one of their most deeply held beliefs, their process for getting to it looked like this:

• I have investigated and compared this contentious belief to others and tested it against the world, and after a lot of careful diligent thought I have decided it is so uniquely powerful as an explanatory tool that I have decided to structure my life around it.

I’ve realized now that a lot of the time, they (and I) are doing something more like this:

• I have muttered this basic idea to myself repeatedly for years to make myself feel important. I first found this idea because a person with a cool jacket said it. I wanted to be more like them.

This happens a lot in how people approach books to read. My favorite passage about this (which I’ve shared previously) is from Knausgård’s autobiography:

Espen probably didn’t know this himself, since I always pretended to know most things, but he pulled me up into the world of advanced literature, where you wrote essays about a line of Dante, where nothing could be made complex enough, where art dealt with the supreme, not in a high-flown sense because it was the modernist canon with which we were engaged, but in the sense of the ungraspable, which was best illustrated by Blanchot’s description of Orpheus’s gaze, the night of the night, the negation of the negation, which of course was in some way above the trivial and in many ways wretched lives we lived, but what I learned was that also our ludicrously inconsequential lives, in which we could not attain anything of what we wanted, nothing, in which everything was beyond our abilities and power, had a part in this world, and thus also in the supreme, for books existed, you only had to read them, no one but myself could exclude me from them. You just had to reach up.

Modernist literature with all its vast apparatus was an instrument, a form of perception, and once absorbed, the insights it brought could be rejected without its essence being lost, even the form endured, and it could then be applied to your own life, your own fascinations, which could then suddenly appear in a completely new and significant light. Espen took that path, and I followed him, like a brainless puppy, it was true, but I did follow him. I leafed through Adorno, read some pages of Benjamin, sat bowed over Blanchot for a few days, had a look at Derrida and Foucault, had a go at Kristeva, Lacan, Deleuze, while poems by Ekelöf, Björling, Pound, Mallarmé, Rilke, Trakl, Ashbery, Mandelstam, Lunden, Thomsen, and Hauge floated around, on which I never spent more than a few minutes, I read them as prose, like a book by MacLean or Bagley, and learned nothing, understood nothing, but just having contact with them, having their books in the bookcase, led to a shifting of consciousness, just knowing they existed was an enrichment, and if they didn’t furnish me with insights I became all the richer for intuitions and feelings.

Now this wasn’t really anything to beat the drums with in an exam or during a discussion, but that wasn’t what I, the king of approximation, was after. I was after enrichment. And what enriched me while reading Adorno, for example, lay not in what I read but in the perception of myself while I was reading. I was someone who read Adorno!

When I walk around in popular bookstores, a lot of the books seem to be specifically designed to be displayed rather than read. If I open a book with a maximally exciting cover and title like “Capital-Feudalism Beyond the Anthropocene” its contents often seem specifically designed to be read quickly and mostly just massage the reader’s pre-existing tastes and preferences, maybe a lot of talk about how “We need to start to begin to think about what commoditization in the post-anthropocene will make of our relations to one another” without much concrete challenging engagement with actual social theory or history or climate science or economics, or any specific claims at all beyond invocations of other high and low-status authors. I suspect a lot of people buy these books primarily to be the type of people who display them on their shelves.

I’m not free of sin at all. In a lot of cases when I’m walking around a bookstore, if I pick up a book, I feel a strong urge to buy it to be the type of guy who’s read it, not only because I really want to have the information it contains. I think for a lot of people, the information in the book itself is often not actually as important as the book serving as a social ritual to ascend into the realm of the people who read and engage with this sort of thing. Books often also function as a useful barrier protecting the reader from the evil ideas circulating in broader society. If you put in the work of discovering the good, virtuous authors, you can trust that what they share in their book has been filtered by the good people, and the contents are acceptable for high-status people to believe.

Visual media also works this way. People discover YouTubers and streamers and TikTok presenters they like, who they perceive as high-status. The ideas these people are interested in become high-status, the ideas they reject become low-status. I have another low confidence armchair theory that the rise of short form video content might partially explain gender polarization.

This is where chatbots come in.

I’m going to put forward that chatbots, way more than any other online tool, completely sever the connection between information and the status of the people the idea’s associated with. Chatbots remove two key functions that high-status people play in our acquisition of knowledge:

• They do not give you a clear way of associating the ideas they’re giving you with good people or bad people. Because they’re “just stochastic parrots” they don’t themselves have any status to convey.

• Because they are not The Good People, you have no guarantee that the ideas they provide will be the High-Status Ideas. It’s like you’re suddenly just allowing strangers into your home with no guarantee that they’re good or safe, whereas before your high-status authors and presenters guarded the door for you and only let in the best guests.

Worse, chatbots can trick people into thinking they’re real, high status people, when they’re actually just illusions. To hand wave about the evolutionary environment, any monkey who could be tricked into thinking some reflection or rock was a high-status monkey worthy of listening to was probably a danger to themselves and the group.

I think this idea makes the three contradictory ideas of the chatbot moral panic view suddenly make a lot more sense:

1. Chatbots are phenomenally stupid, useless, and incapable. This is just the perceived “expert consensus” on chatbots that people who don’t like them think of as the high-status opinion in society.

2. Chatbots cannot provide anything of value by definition. The thing a lot of people look for in learning is not information, it’s to ascend to be among the high-status people, to be the type of people who read certain authors or listen to certain podcasts or watch certain videos. The Knausgård quote above wouldn’t make any sense if he were using ChatGPT. He wouldn’t be able to feel the ecstasy he describes, because he wants to be the type of person who reads certain authors. He’s not looking at all for any new specific ideas. Thus, chatbots can’t be useful at all by definition, because they can’t actually provide that ladder to status people are looking for. You can learn as much as you want about Adorno using them, but you cannot use them to become the type of person who reads Adorno. You thus cannot become a Good Person in the way learning is supposed to help you achieve. Chatbots are useless.

3. Chatbots are demonic. Chatbots do not provide the shelter that high status authors and creators provide from evil outside ideas. They a portal that lets the evil ideas in. They cannot be good guardians. They trick people into allowing deep evil into their lives. Following raw information is often just a way of giving evil people the power to appear convincing and good. Raw information without any associated person to tie it to is thus incredibly dangerous.

I’ve observed that the people I see falling into the chatbot moral panic view are often the people who most quickly move to change a conversation about ideas to a conversation about the people who believe them, who will talk a lot about the titles and authors of books compared to the contents. I think for these people the main function of learning is to discover the elect good people, and to surround themselves with signifiers that they’ve become a part of that elect. To them, chatbots are in fact useless, and raw information can be an evil threat to their place in the elect. To the rest of us, raw information has a lot of value.

I don’t enjoy masking

Throughout COVID, I was always very careful to mask, and spent time telling friends and colleagues to buy high-quality N95 masks. I was a teacher, and when school got back in I was careful to mask until I and the students had all had a chance to get vaccinated. It was pretty obvious that the trade-offs made masking worth it before we had an effective vaccine.

Masks also made me somewhat depressed. A lot of normal interactions with students and other teachers felt much more stilted and unnatural because we couldn’t see each other’s full face. I was more than happy to take this hit to keep everyone safe, but my classroom felt more lifeless than normal. It was especially dispiriting because students had also lost a lot of normal time together to school shutdowns over the last year. Normal life still felt very far away. I also personally find masks somewhat uncomfortable, especially when I was lecturing in one for hours a day.

Getting the vaccine was pretty transcendent. Since then, I don’t mask in public unless I’m sick, because I see the vaccine as equivalent to wearing an N95 all the time anyway. I do always mask if I’m sick. I also think everyone should invest in a new high-quality reusable mask (maybe this one, as a heads-up friends debate how comfy it is. Find one that works for you!) to have around in case there’s another pandemic (which AI might make more likely). If we all had and wore high-quality masks all the time in March 2020, many many fewer people would have died and life could’ve been a little more normal than it was.

I say all this to get across:

• If you hate masking, I really really get it.

• I think masks were very important during the pandemic.

• I mostly don’t think masking is useful now unless you’re sick. The most important thing is obviously keeping up with COVID booster shots.

• You should really buy a new high-quality mask in case of another pandemic. Seriously, do it!

But I’ve recently decided to start masking every single time I’m in an airport specifically. The argument here is pretty straightforward.

Why you should always mask at airports

Three simple facts about airports together make it seem like it makes a lot of sense to mask there:

• Airports seem like the single most likely place you’ll get sick per unit of time spent there. You come into close contact with a lot of strangers from all over the country and world in an airport, way more than anywhere else in your life. Whether an illness is “going around” your specific area tells you little about what’s in an airport. Even if the airport is made up entirely of people from your area, you’re still coming into close contact with way more strangers than normal.

• If you’re in an airport, it’s likely that you’ve just spent a lot of money to get somewhere important to you, where getting sick for just a few days takes away most of the value. If you get sick in the airport, a lot of that money (potentially thousands of dollars) will be wasted. You have very few opportunities to travel for fun in life, so the opportunity cost alone is often huge. For every additional person you’re traveling with, the opportunity cost saved by masking roughly doubles.

• You only spend 2-3 hours there. To follow this rule, you just have to be in discomfort in a mask for a few hours each year. Your experience in the airport isn’t super important to the quality of your trip, so it’s the best time to be a little uncomfortable. Masking is uncomfortable and I don’t like it, but this is a place where it seems more than worth it.

So almost every time you’re in an airport, you’re taking on a much higher risk of getting sick right at the moment where getting sick has a drastically higher opportunity cost than normal. You can cancel this out with the mild discomfort of a mask for at most 3 hours during the most boring unimportant part of your trip. You can take off the mask a few minutes after the plane itself takes off, because planes actually have some of the safest, well-ventilated air anywhere.

This argument seems pretty simple and straightforward, but whenever I’m in an airport I see almost no one masking. It’s fine if they want to take the risk, but I think we’re all actually risking a lot of value for some mild comfort during the most boring and unimportant part of the trip. Consider masking whenever you fly (at least on the outbound flight), and also buy a mask for future pandemics.

A speaker said that Waymo (the AV company requesting clearance to run in Boston) was only interested in not paying human drivers (Waymo is a new company that has never had human drivers in the first place) and then referred to the ‘notion that somehow our cities are unsafe because people are driving cars’ as if this were a crazy idea. A council member strongly implied that new valuable technology always causes us to value people less. One speaker associated Waymo with the Trump administration. There were a lot of implications that AVs couldn’t possibly be as good as human drivers, despite lots of evidence to the contrary. Some speeches included lots of criticisms that applied equally well to what Uber did to taxis, but now deployed to defend Uber.

Most of the arguments I heard were pretty wildly off-base. Many of the speakers didn’t factor in the basic safety benefit of AVs to the riders or pedestrians at all, and many of the arguments fell apart when poked at. Here are all my arguments for why a city should legalize AVs, with some concerns at the end:

• AVs are ridiculously safe compared to human drivers

• A transition to any new, safer mode of transit will still involve people dying in that new mode. What matters is the rates of death.

• Protecting ride share driver jobs is a bad basis for policy

  • Should we build fewer bike lanes to save Uber jobs?

  • Ride share jobs can be pretty bad

  • Losing these jobs is still a loss

  • Would you ban AVs if they were already widespread to create Uber driver jobs?

• There is no real difference for a city between an AV fleet and a very cheap rental car service

• It is more elitist to use a personal chauffeur (a ride share driver) than to ride in an AV

• If you want car ownership to stay legal in your city, any argument to ban AVs that works equally well as an argument to ban cars is bad

• Eyes on the street

• AVs can open up opportunities for disabled riders

• Waymos are fully electric

• Nothing makes the world more human than safety

• If a vehicle is safe, the default should be legalization

• AVs can make it much easier to take licenses away from the worst human drivers

• Real problems with AVs

  • Cybersecurity

  • Could make traffic worse

  • Environmental impact

AVs are ridiculously safe compared to human drivers

The most obvious reason to allow AVs in your city is that every time a rider takes one over driving a car themselves or getting in a ride share, their odds of being in a crash that causes serious injury or worse drop by about 79%. I’d strongly recommend this deep dive on every single crash Waymo has had so far:

Understanding AI

Very few of Waymo’s most serious crashes were Waymo’s fault

Everything was fine until the wheel came off…

Read more

7 months ago · 90 likes · 40 comments · Kai Williams

This is based on public police records rather than Waymo’s self-reported crashes. It doesn’t seem like there have been any serious crashes Waymo’s been involved in where the AV itself was at fault. This is wild, because Waymo’s driven over 100 million miles. These statistics were brought up out of context in the hearing to imply that Waymo is dangerous. By any. normal metric it’s much more safe than human drivers.

40,000 people die in car accidents in America each year. This is as many deaths as 9/11 every single month. We should be treating this as more of an emergency than we do. Our first thought in making any policy related to cars should be “How can we do everything we can to stop so many people from being killed?” Everything else is secondary to that. Dropping the rate of serious crashes by even 50% would save 20,000 people a year. Here’s 20,000 dots:

The more people choose to ride AVs over human-driven cars, the fewer total crashes will happen.

One common argument is that Waymos are very safe compared to everyday drivers, but not professional drivers. I can’t find super reliable data, but ride share accidents seem to occur at about a rate of 40 per 100 million miles traveled. Waymo in comparison was involved in 34 crashes where airbags deployed in its 100 million miles, and 45 crashes altogether. Crucially, it seems like the AV was only at fault for one of these, when a wheel fell off. There’s no similar data for how many Uber and Lyft crashes were the driver’s fault, but they’re competing with what seems like effectively 0 per 100 million miles.

A transition to any new, safer mode of transit will still involve people dying in that new mode. What matters is the rates of death.

Motorcycles are the single most dangerous way to travel, by a long shot.

If we had everyone who currently rides a motorcycle drive a car instead, they would all be much safer. But some of them would still die in car crashes. Some people might look at those car deaths and say “We shouldn’t have had them switch, it turns out cars were what was dangerous.” This is a really bad way to think. What matters is reducing the total number of people who die,

There has recently been a lot of attention on Waymo killing a cat in San Francisco. A city supervisor made this ridiculous video where she declares that this means Waymo should be put up to a vote in each individual county to decide whether it has a right to operate there.

In Waymo’s first 100 million miles of travel, it seems to have killed two pets. In both cases it doesn’t seem like it could have done anything different. In the cat case, the cat darted under the wheel while the car was pulling away. In 2023, a dog ran out in front of the Waymo, which correctly identified it as a dog but wasn’t able to stop in time.

In comparing this to human drivers, we need to look for similar situations where cars are:

• Driving in a city.

• Hitting pet-like animals that can be reported (we shouldn’t measure all animals, because there isn’t good data on this for either Waymo or human drivers).

There’s not much good data on this, but the little we have implies that humans kill cats and dogs way more often. A study in Baltimore in the 80’s estimated that human drivers kill around 5,000 cats in the city each year. Current government records say about 3.18 billion miles are driven in Baltimore each year. Combining these two stats implies that human drivers kill ~150 cats for every 100 million miles driven. So it seems like human drivers are 75x more likely to kill pet animals compared to Waymos. This should make us want to replace human drivers with Waymos for pet safety, but because Waymos are new and weird, people are hyper-focusing on this one case where they killed a cat and ignoring the much much higher rate of human drivers killing cats and dogs.

If we refuse to move to a safer mode of transit because it’s not perfectly, 100% safe in all cases, tens of thousands of additional people will continue to die for no reason.

Protecting ride share driver jobs is a bad basis for policy

Should we build fewer bike lanes to save Uber jobs?

You’re out and want to get somewhere, and have a few options:

• Walking

• Public transit

• Driving your own car

• Biking

• Taking an Uber

Every time anyone chooses any option besides Uber, less money goes to Uber drivers. If you make any of the other options better (building bike lanes, improving public transport, or making your city more walkable) you are incentivizing people to pay Uber drivers less often. A better bike lane means Uber drivers lose work. Walkable, safe pleasant streets mean Uber drivers lose work. Better public transit means Uber drivers lose work.

Jobs driving for Uber are not essential, what’s essential is being able to get around a city safely. In every other circumstance, we’re willing to sacrifice Uber jobs for the sake of making a city safer and easier to get around. AVs are another option to make a city safe and easy to get around. We shouldn’t ban them to preserve Uber jobs. The priority is the millions of people who want to get around the city quickly and easily. Uber jobs are good insofar as they help with that. If you wouldn’t block a renovation of a subway system because it would harm Uber jobs, you shouldn’t block AVs either.

Sometimes people bring up ride share jobs and public transit in the same sentence, like “We need to think about how these will affect public transit, and ride share driver jobs.”

I think this is weird, because each of these two is already pretty directly harming the other. If public transit were worse, people would use ride shares more often and there would be more jobs. I realize there are some ways they benefit each other, but the statement sounds to me kind of like saying “We need to think about how this new competitor will affect both Coke and Pepsi.” If you’re very pro public transit, you’re not looking to perfectly preserve ride share jobs, and vice versa.

Ride share jobs can be pretty bad

Uber and Lyft are predatory with their drivers, implementing policies that maximize corporate profits while shifting financial risks and costs onto workers who are classified as independent contractors rather than employees. This classification denies drivers access to benefits like health insurance, paid time off, unemployment insurance, and retirement contributions that traditional employees receive. Drivers use their own vehicles, and often fail to properly incorporate the cost of their vehicle’s depreciation into their own cost-benefit calculation of working with the company.

The companies have also been criticized for:

• Opaque pay structures that make it difficult for drivers to understand how their earnings are calculated, with the companies taking increasingly larger cuts of rider fares over time.

• Algorithmic management that controls drivers through ratings systems, surge pricing manipulation, and acceptance rate requirements without meaningful transparency or appeal processes.

• Unilateral rate changes where the companies can reduce driver pay rates at any time without negotiation.

• Vehicle expenses being entirely borne by drivers, including fuel, maintenance, insurance, and depreciation, which can significantly eat into their earnings.

• Lack of job security, with drivers subject to sudden deactivation with limited recourse.

• Psychological pressure tactics like gamification features and persistent notifications designed to keep drivers working longer hours.

• Driving is also one of the most dangerous things we normally do. Driving as a job is inherently dangerous.

These are not good jobs. Policy should focus on getting workers access to high-quality well-paying jobs, not preserving Uber and Lyft’s pretty shady business model.

Losing these jobs is still a loss

Despite all this, it’s obviously still the case that losing these jobs would be a general loss. Ride share driving might represent an important opportunity for low-skill workers. Interestingly, a much higher percentage of ride share drivers have college degrees than the general working population (40.0% compared to 28.4%). Still, ride share jobs can give useful opportunities to workers without college degrees. Reducing the number of such jobs might therefore harm low-skilled workers. I support these types of jobs existing, because among other things they give very recent immigrants a foothold in the country they can build up from, but it’s important to understand the realities of what the jobs themselves are like. Losing these would have negative effects on workers, but these effects need to be weighed against the positives of AVs.

Uber jobs can seem like pretty bad options. There was a lot of opposition to Uber when it arrived. But if you think Uber jobs are worth all the bad aspects because they give low-skilled immigrant workers a leg up, you should consider other “bad jobs” your city shouldn’t regulate away. If your beliefs about what counts as a bad job would’ve caused you to ban Uber in 2011 to preserve taxi jobs, and now you see Uber as essential for low-skill workers, consider other industries where regulations might be preventing Uber-like jobs from popping up. If you’re willing to go so far as banning new vehicles to save these jobs because they’re so important for low-skilled workers, you should be willing to open up other similar options that might not have the salaries or job security you’d want if they also give low-skilled workers a leg up.

Would you ban AVs if they were already widespread to create Uber driver jobs?

Suppose we lived in a world where AVs were widely used. You’re brought a policy proposal: we could create a lot of jobs for more Uber drivers if we banned AVs. These jobs would be pretty predatory and take advantage of the drivers, and would be one of the more dangerous jobs you can do, because driving is dangerous. The odds of the riders being in serious car accidents would be 10 times as large as before the ban. You’d be growing everyone’s risk by an order of magnitude. Would you choose to ban AVs in this case? If not, you shouldn’t ban AVs now.

There is no difference for a city between an AV fleet and a very cheap rental car service

Imagine a very cheap rental car service appeared in your city. They have a policy where they drive the car to you for you to drive around, and pick it up for you after. To my knowledge, this kind of rental car service isn’t banned anywhere. If this service were cheap enough, it would compete with Uber and Lyft and sometimes cause drivers to lose jobs, because people would just be driving themselves to where they want to go. If this rental service became too popular, it would lead to worse traffic in the city as more cars circled around being delivered to drivers. This is the major risk of a service like this.

An AV is identical to this service. The only difference is:

• AVs are wildly safer compared to human drivers.

• The rider happens to be sitting in a different spot in the car.

• The rider can do other things while still driving safely.

Outwardly, there is no other difference. If you wouldn’t ban the rental car service, you shouldn’t ban AVs either.

It is more elitist to use a personal chauffeur (a ride share driver) than to ride in an AV

Sometimes people will frame using AVs as an elitist option. Maybe the rider doesn’t want to take a ride share because they don’t want to have to interact with another person, maybe they’re so elitist that they find the idea of interacting with everyday people driving ride shares unpleasant.

In my opinion this gets it exactly backwards. Suppose autonomous vehicles had somehow come before ride share cars. Everyone normally takes a robot taxi, but suddenly the option is available to pay a human to be your personal chauffeur throughout the city. It seems obvious that this is the more elitist option of the two.

In general I don’t like feeling like I’m being served. I prefer to drive myself as opposed to paying someone to drive for me, for the same reason I prefer to cook and clean over paying someone to do it for me. In the same way that it’s less elitist to use a Roomba instead of hiring a maid service, I think it’s less elitist to ride in AVs than it is to hire a human driver to be your personal servant for 10 minutes at a time.

If you want car ownership to stay legal in your city, any argument to ban AVs that works equally well as an argument to ban cars is bad

A lot of arguments against AVs seem to be arguments against personal ownership of cars in general. While I’m open to these arguments (I hate cars in cities) a lot of people clearly support keeping cars in cities, but use arguments against AVs that apply equally well to cars. For example, the argument that driving somewhere in an AV takes work from an Uber driver applies equally well to driving in your personal car.

Eyes on the street

One argument that came up in the hearing was the idea that human drivers are important because they can see people in trouble or crime happening, and pull over and stop it. AVs don’t have drivers to stop and help.

This argument is implying that we should artificially boost how often Uber is used to have more people watching the street. Imagine if a city council member proposed funding Uber drivers to aimlessly drive around the city and observe and watch out for anything bad happening. This would obviously be ridiculously inefficient, and terrible for the environment. More pedestrians would be harmed by the total air pollution from the cars than helped by individual Uber drivers interfering to stop crimes.

If we want to make policy to have more people watching the street to keep people safe, we should just hire more police, community patrols, or pay everyday individuals to stand around observing an area to see if people need help, install security cameras in more places, or follow Jane Jacobs’ strategy of making streets more walkable and appealing for lots of people to be on. Uber and Lyft are terrible public safety interventions. Imagine if a politician proposed funding Uber and Lyft more to “make people feel safe and seen on our streets.” This would be obviously ridiculous.

This argument also applies to above-ground trains. Many above-ground public trains can’t stop in time if they observe a crime happening. We don’t block building trains because they can’t stop in time to prevent nearby crimes. We shouldn’t hold AVs to a different standard.

This argument shows an important common mistake

This argument is very specific and something I haven’t heard outside this meeting. I bring it up because it’s a good example of a broader pattern we should expect to see in conversations about AVs: people feel very strongly that there should be a human in the driver’s seat, and the experience that lack almost like it’s a physical presence. So when an AV doesn’t do something that a human driver would like stepping out to help someone on the street, people ascribe a ton of responsibility to the AV, like there was supposed to be a human there, and the lack of the person is responsible for the bad thing happening. But almost everything we do causes these “lacks of people” in specific places. If a popular restaurant opens up, there will be fewer people hanging around other places the restaurant took business from, and therefore fewer people to intervene if something goes wrong. The only reason people don’t hold the restaurant accountable is that they see restaurants as normal, and AVs as weird. We can’t make it a goal of policy to perfectly equally distribute people in every public place in a city. A talking point that says “We need to require human drivers so there are more eyes on the street” is identical to saying “We need to require more everyday people to equally spread themselves out on every street.” This only makes sense if you think of the AV as having some unnatural lack that urgently needs to be filled. If instead you think “It is okay for there to sometimes or often be no drivers on a street” it doesn’t look objectionable.

In all conversations about AVs, watch out for this general pattern: people treating them as weird and different to the point that they’re expecting things they don’t expect in any other circumstance, like policy that perfectly spreads people out across a city. Watch out for when they treat the lack of a human as some kind of physical entity that can be responsible for bad things happening. A lot of things in society create these lacks, but AVs are getting special condemnation for it because they’re seen as weird.

AVs can open up opportunities for disabled riders

Another point brought up was that some disabled people need human help when entering a car, and AVs can’t provide that. For what it’s worth, the council meeting speakers representing different disabled communities in Boston seemed to mostly be pro legalizing AVs.

Uber and Lyft have not gone out of business where AVs are operating. Companies know that there is going to still be a lot of demand for human drivers into the future. Eventually, autonomous vehicles could also get good enough to provide help for disabled riders. For many disabled people, AVs can give them access to ride share-like experiences they don’t otherwise have. A common issue some disabled people have with ride shares is not being able to take their service animals on the ride due to a policy by the driver. AVs can consistently allow service animals because there’s no driver to get scared.

Right now, AVs do not seem likely to displace Uber or Lyft. They exclusively give disabled riders an additional option. For some, this will allow them to use ride hailing services they can’t otherwise use due to their service animals.

Waymos are fully electric

An underrated aspect of Waymos is that they’re fully electric. If a rider chooses a Waymo over an Uber or Lyft, they’re often choosing an electric vehicle over an ICE vehicle. This on its own should be a big selling point for making them more available. We need to get as many ICE vehicles off the roads as fast as possible to reduce CO2 emissions and air pollution.

Nothing makes the world more human than safety

People sometimes talk about AVs making the world “a little less human.” This is the least relatable talking point to me. Cars as they exist are profoundly grizzly and inhuman.

I’ve been in two serious car accidents. Neither was my fault at all. There wasn’t anything I could’ve done differently. One left me in pain for a few months. In both, I was really lucky to not get more hurt. After a serious car crash, you think a lot about how you can work so hard to build up a great life and community and put in a ton of effort to learn a lot, and that can all be erased in a moment by a bad decision by another random driver. This is one of the most inhuman parts of our daily lives. I would so obviously rather spend the rest of my life constantly walking by empty driverless cars aimlessly but safely circling around than be in another accident like that.

It’s inhuman that 40,000 people die every year on American roads. In comparison to those deaths, a basically aesthetic objection to an incredibly safe car just doesn’t seem to matter. In conversations about this, I would like more people to feel in their bones how awful cars as they exist are.

If a vehicle is safe, the default should be legalization

It’s kind of crazy to me that we haven’t banned motorcycles yet.

AVs haven’t hit the 1 billion miles traveled mark. When they do, I expect them to have a significantly lower death rate than regular cars. If a vehicle is safer for the driver and pedestrians than a regular car, I think people should be allowed to ride in it around a city.

More generally, AVs give a lot of people the freedom to go places they might not otherwise be able to access. This is good. If we’re so wildly libertarian that we still allow people to still drive motorcycles (in my opinion, this should change), I think we should also allow people to use AVs if they want.

If a vehicle is proven safe, the default should be legalization. People should be able to choose for themselves how they want to get around. Second order effects matter, but I’d rather not have the government unpacking every last possible second and third and fourth order effect of different types of new vehicles, because they don’t really do this at all with vehicles as they currently exist. Gas powered cars create air pollution, climate change, and often kill people. If your city hasn’t held any meetings on banning them recently, I think they’re not seriously considering the second order effects in the way they are with AVs. Holding AVs to a much higher standard than ICE cars seems like a way of locking in our very bad current transit equilibrium.

In DC as food delivery apps have led to a lot more motorized bikes in the city, I’ve found myself at much greater risk of getting hit in crosswalks, because they ignore stop lights way more often. I think this is bad, but it’s not bad enough that I think the apps or vehicles should be banned. In comparison, we’re debating a vehicle (AVs) that’s way safer than the average human driver. It’s pretty easy for me to say that because I’m fine with the app delivery bikes, I’m also fine with something much safer.

AVs can make it much easier to take licenses away from the worst human drivers

An underrated benefit of potential future mass availability of self driving cars will be that it will be way easier to take licenses away from the most dangerous drivers without ruining their lives. The reason it’s so hard to take licenses away in America right now is that public transit is so bad that in most places, not being able to drive is effectively cruel and unusual punishment. AVs will fix that. People could be ordered to only use autonomous vehicles. Maybe there will specially designed autonomous-only cars? I don’t know.

Most of the problems with traffic safety in America are caused by the bottom fifth of drivers, and these problems often involve alcohol. The single most promising way to improve road safety is getting people who drink and drive, and the otherwise very worst drivers, off the road. AVs can remove the main barrier to doing that: the life-ruining consequences of losing your license.

Real problems with AVs

Cybersecurity

Autonomous vehicles are just computers driving cars. This makes them vulnerable to hacking. A successful cyberattack could let hackers control steering, brakes, and acceleration, turning vehicles into weapons. Worse, attackers could target entire fleets or traffic systems at once, causing mass accidents or citywide shutdowns. AVs also collect massive amounts of personal data about where people go and when, making data breaches a serious privacy and safety threat.

AV companies are well-aware of cybersecurity issues, but the tech is new and oversight of how companies are dealing with the problem seems necessary.

Could make traffic worse

If there’s a huge fleet of ride share cars added to a city network, it seems likely that on net these could make traffic worse. There’s a trade-off here where a much higher percentage of cars on the road would be driven by incredibly safe drivers, so though this would increase traffic, it seems likely to seriously reduce injury and death as well. How to think about this trade-off is complicated, but we should lean in favor of saving lives and preventing injury at the cost of a little more time on the road. This is what we do when we impose traffic slowing measures.

Another key consideration is that a lot of traffic is caused by car wrecks. The main problem in a car wreck is obviously that people got seriously hurt, but any technology that reduces the rate of car wrecks also reduces the rate of massive traffic backup. If we want to reduce traffic, it’s better to opt for vehicles with wildly low rates of causing car wrecks.

Environmental impact

AVs seem likely to cause more people to take car trips because of their convenience. This is bad and will be harmful for the environment. But the solution to environmental problems with cars is not to prop up Uber and Lyft, it’s to build out better public transportation networks. Eventually, AVs getting good enough could become government-owned. AV buses could open up more routes. Being part of the AV rollout might help public transportation better. But this is all very uncertain. It’s important to note that Waymos are fully electric. Electric cars still use a lot of energy, but emit way less than the ICE cars many Uber and Lyft drivers use.

1. There is a unified self that floats above mental processes and observes them

2. We know our own internal experience with complete certainty

3. The basis of all knowledge lies in our first-person subjective experience

In this post I’ll give a lot of arguments and intuition pumps for why each of these is probably incorrect, or at least isn’t “obvious.” This post is very long. My goal is that at the end you have a grasp of many of the most important questions in philosophy of mind, why so many philosophers reject the popular everyday understanding of mind, and that you hopefully come away with a bag of new mental tools for thinking about why machines might be able to replicate what happens in human minds. I’m writing this partly to be the guide I wish I had when I was first learning about this.

“1. There is a unified self that floats above mental processes and observes them”

How can a Cartesian ego fit within our scientific theories?

Cartesian egos are strange. They seem to behave like magical floating eyeballs, peering into physical reality from outside. No other physical object does this. No matter how many grains of sand I pile up, there will never be “something it is like to be the sand.” The sand does not have an internal movie theater playing experiences for it to observe. Being sand feels identical to being dead. We would expect all physical objects to behave similarly, because all physical objects are ultimately just elementary particles that don’t have experiences, similar to the grains of sand. No physical object ever becomes an observer in an inner movie theater. So how do Cartesian egos fit within our understanding of science?

There are only two options: Cartesian egos are fundamentally physical, or they’re not. Both options have some weird implications that make them look implausible.

Cartesian egos are not fundamentally physical

If Cartesian egos are not fundamentally physical, they threaten to clash with physicalism, which says that the only things that have causal power over physical objects are other physical objects. If they have any kind of causal power, this breaks physicalism. We have two options:

Cartesian egos do not have causal power

Maybe Cartesian egos are silent observers, taking everything in but never actually having any causal effect on the world. They silently watch the movie of your life play out.

This has some very weird implications, to the point that it looks like it can’t be true.

First, if a Cartesian ego doesn’t have causal power, it cannot have any influence over the information in your brain that causes you to physically speak or write about the ego itself. Your brain could never acquire any information that it exists. Describing Cartesian egos should feel like describing “that feeling of gloofleglorf around your left ear at 3:52 PM every day. We all know that one.” Something that our brains have never received any information about existing at all.

But many Cartesians clearly don’t think this is how Cartesian egos work. Descartes himself talked as if nothing in the world could be more obvious than that he had an observing ego. “I think, therefore I am” is supposed to be the absolute obvious bedrock of all other reasoning. This implies that the Cartesian ego is giving the brain some kind of information that it exists, so it must be having some causal effect on the world. If it exists, it’s what caused Descartes to write that!

Another weird implication: if your Cartesian ego suddenly popped out of existence, and the “you” experiencing things no longer existed, literally nothing about your behavior would change, because the ego wasn’t affecting anything about your physical body. You would go on behaving exactly as you do, talking about your experiences and feelings as if they were really happening, but no one would be watching the internal theater. If you believe that you have an internal theater of experience, and that you are reacting to the experiences that happen in this theater, then you cannot accept the view that Cartesian egos both exist and do not have any causal power.

Cartesian egos have causal power

If Cartesian egos are both nonphysical and have causal power, they violate physicalism. This is possible. Maybe physicalism is wrong and nonphysical objects have effects on the world. But it’s very suspicious. Physics is this grand unified theory of the world that seems to predict everything from what happens inside particle colliders to distant stars to our ordinary lives, and does so with wild consistent success. Physics is our best attempt at a complete account of every entity in the universe that has causal power, and it’s shockingly effective at making accurate predictions, to the point that most physicists perform their investigations under the assumption that physics is causally closed, that nothing outside of physics can affect anything in the universe. Yet the only place where the theory is supposedly violated is in the brains of the species making this claim. This species has evolved with strong motivations for others to treat it as special and different from the rest of the world. From the outside, it looks suspicious when the one place where magic is claimed to happen is located in a species that has strong reasons to want other members of its own species to believe that it’s special.

It seems like there should be some way of observing laws of physics being violated in human brains if this is the case. If the Cartesian ego is some nonphysical entity sending information to and receiving information from the brain, there must be some creative way to measure where this information suddenly appears in the brain out of nowhere. I’m enough of a physicalist that I wouldn’t expect us to ever make this observation, even if we had nano bots observing every neuron in a human brain.

Some people talk about quantum mechanics leaving an opening in the universe for non-physical objects to affect the brain, but this seems wrong for two reasons:

• Most of the important signals that happen in the brain are on a scale that’s too large for the uncertainty of quantum physics to have a significant effect.

• For quantum physics to work, the probability distribution of what happens needs to follow mathematical laws. This doesn’t leave any room for causality to “flow” through the randomness. Causality means that if A happens, B follows. But quantum mechanics says that B cannot be more likely than the pre-determined spread of probabilities dictates. So quantum mechanics is actually a completely sealed barrier to forces outside the universe. Nothing could get through its requirement for complete but mathematically structured randomness to influence the physical world.

Cartesian egos are fundamentally physical

If Cartesian egos are actually just very complicated physical processes, they don’t behave like any other physical processes. They’re the only physical processes that are “awake” and can have subjective experience at all. Very weird, but possible! Importantly though, if they are fundamentally physical, they could also exist in machines, so AI might independently develop them.

But Cartesian egos also seem to violate most core properties that every other physical object has:

1. Spatiotemporal location: The don’t seem to have a clear location in the world. Maybe right behind your eyes? Definitely not something that can be pointed at and identified.

2. Causal structure and lawfulness: They don’t seem to follow any kind of regular laws. They’re not discoverable through observation and experiment. They’re not predictable. They can’t be expressed mathematically.

3. Detectability/measurability: They can’t be measured, either directly or through their causal effects. They don’t seem to leave empirical traces, except for humans after the 1600s saying they had them.

4. Public accessibility: No other person has any access to my Cartesian ego at all.

All 4 common properties of physical objects are violated by Cartesian egos. Maybe they’re the only objects in the universe that violate all of them, whereas every other object adheres to all of them. Seems unlikely. I would expect any and all entities we discover to eventually fit into the universal wavefunction, the unifying mathematical description of everything that exists. Cartesian egos can’t be written as a part of this function.

Cartesian egos are folk theories and do not actually exist

All the above examples make Cartesian egos seem super strange to me, to the point that it doesn’t seem obvious that they exist. It seems like what’s actually happening is that human minds are inventing folk theories of how they work.

Importantly, there are ways that large information processing systems could develop subsystems that work kind of like Cartesian egos. Global workspace theory posits that there are parts of the human brain that send information to a lot of subsystems at once, as if their contents are on a stage and the subsystems are observers in the crowd, who each run with the big central information to make inferences. This seems like a possible physical corollary for what we mean when we think about the Cartesian ego, but it’s notably just another physical function in the brain.

Shouldn’t this all be obvious?

Stepping back from this debate, Cartesians talk as if the fact that we are each a self watching our experience like a movie is so obvious that it’s unquestionable, but they often wildly disagree on the nature of this self. Does the self have causal power, or does it just silently watch? Does thought happen “within” the self, or does the self passively observe thoughts as well as experiences? If no one quality of the Cartesian self is obvious, maybe its existence isn’t so obvious either.

Conclusion

So it seems like actual Cartesian egos probably don’t exist. The mind is not a movie playing for an inner observer, watching it like a floating eye peering into physical reality from somewhere else. The Cartesian theater of the mind isn’t actually happening, at least not in some special nonphysical way. Any description of the difference between humans and AI that implicitly relies on a nonphysical Cartesian ego humans have that AI does not is mistaken.

What’s left when a Cartesian ego falls away? Different mental functions taking in information, moving them around the brain, giving and storing outputs, and using those outputs in other functions. If global workspace theory is true, there might be some part of the brain that broadcasts out information to many subsystems at once, approximating the role a Cartesian ego plays. But there is no nonphysical angel-like eyeball looking over the whole subjective field of the mind like an observer in a movie theater. The mental function global workspace theory describes could be replicated by a machine.

“2. We know our own internal experience with complete certainty”

The goal of this section is to argue against the Cartesian idea that we have special access to what’s happening in our own minds, our own first-person subjective experiences, in a way other systems like computers cannot have, because they don’t have these experiences.

There are two related claims I’ll argue against here:

• We have 100% certain access to the contents of our own experience. I can be mistaken about whether something white and puffy in a field is an actual sheep or a fake sheep, but I cannot be mistaken that in that moment my subjective, first-person experience includes white puffiness framed by a green field.

• We can introspect and clearly observe what’s happening in our own minds. AI systems can never introspect in the way people can because they’re machines. They just take an input, run it through computations, and produce an output. Humans can turn their inner eye back on this process itself. We don’t know how to make machines do this and we might never know.

In both cases, I’ll be arguing for the alternative view that both experience and introspection are useful but fallible natural processes that take in potentially incorrect information, run them through mental functions and computations, and return outputs the mind stores and uses in additional functions. I am not arguing that we don’t have experiences or introspection, only that these do not behave like mystical, 100% clear and reliable sources of information that could never be replicated by machines, and that our experience is not like watching a movie in an internal theater. At the end, you should feel like the idea of a movie theater is a bad analogy for your internal experience, because there’s a lot more ambiguity and uncertainty in the experiences you have than what’s displayed on a screen. This ambiguity leaves us unable to be too certain about the nature of our moment to moment experience.

The slipperiness of our experience

A friend recently asked me a funny question: “What do you think the shape of your visual field is?”

My visual field has been my main experience since birth. There should be basically nothing more obvious to me than what it’s shaped like. Every moment, I’m being flooded with visual experience. What is the shape of this flood? If the Cartesian theater is real, what is the shape of the movie screen? Which of these three screens have you been looking at all your life?

Turns out, this is the shape:

The shaded central area is seen by both eyes, the white area is seen by only one eye. Numbers represent degrees from straight ahead.

Does this shape look familiar to you? Given that you’ve been “looking at it” for your entire life, would you have been able to pick it out from a line of what the visual field looks like? Is this the shape of the screen in your internal movie theater?

This brings up one of the big central questions in philosophy of mind: qualia. From the Stanford Encyclopedia of Philosophy:

Feelings and experiences vary widely. For example, I run my fingers over sandpaper, smell a skunk, feel a sharp pain in my finger, seem to see bright purple, become extremely angry. In each of these cases, I am the subject of a mental state with a very distinctive subjective character. There is something it is like for me to undergo each state, some phenomenology that it has. Philosophers often use the term ‘qualia’ (singular ‘quale’) to refer to the introspectively accessible, phenomenal aspects of our mental lives. In this broad sense of the term, it is difficult to deny that there are qualia. Disagreement typically centers on which mental states have qualia, whether qualia are intrinsic qualities of their bearers, and how qualia relate to the physical world both inside and outside the head. The status of qualia is hotly debated in philosophy largely because it is central to a proper understanding of the nature of consciousness. Qualia are at the very heart of the mind-body problem.

If a computer could scan all the informational content in your brain, every last belief and memory and observation about the world, and get it all down in written words and stored information, people who believe in qualia would say that the computer would still not know what it was like to be you. Qualia are the contents of your first-person experience that cannot be summarized in pure information.

If the Cartesian ego is the person sitting watching the movie in our heads, qualia are the movie.

Unfortunately for me, qualia both:

1. Don’t make sense under physicalism (they violate all 4 properties of physical objects).

2. Are very very very hard for me to deny, in a way Cartesian egos are not.

I mean, look at this.

It’s hard to understand how any third-person purely informational account of the world could ever get across the first-person experience of seeing red. Simply reporting “A wavelength of 700 nanometers hit Andy’s eyes and activated memories and emotions associated with previous times this has happened” just doesn’t get across the first-person redness of the red. It’s like how all attempts to describe color to a lifelong blind person just obviously hinge on other experiences the blind person has also not had. We can’t get across the qualia using words.

I’m agnostic on whether qualia exist, or how they relate to the physical world. But even if qualia do exist, I’m pretty sure they’re very different than what our folk story says about them.

One of my favorite papers in philosophy, Quining Qualia, attempts to use lots of thought experiments to convince the reader that qualia don’t actually exist. The author (Dan Dennett) specifically defines qualia as having four key properties. They are:

1. Intrinsic: They’re basic and fundamental to the world and can’t be picked apart further. They’re also not “relational” in that they don’t depend on their relationships to other things. The quality of being “to the left of a bookshelf” is relational. It’s not an intrinsic property, because it could be changed by moving the object. But “the redness of the red” does seem intrinsic. No matter what colors we put adjacent to the red, or other things we compare it to, the redness of the red in my first-person experience will still be unaffected.

2. Ineffable: Not describable using words. You know what the redness of red is like, but you can’t actually get across to someone who’s never seen it before what that experience is like using words alone.

3. Private: Our experience of our own qualia is fundamentally private to us. We can’t even know if other people experience the same qualia. “How do I know the red you see and the red I see are the same?” is a question about qualia. There is no way one person can ever get direct access to another person’s qualia. Qualia are like each of us is stuck forever in our own private movie theater, never able to access what other people can see on their screens.

4. Directly or immediately apprehensible in consciousness: I can be mistaken about the source of red light, but I cannot be mistaken about the fact that I’m experiencing the redness of the red. I can’t directly see or hear or experience any objects actually, I can only experience the qualia they create in my mind, but that I have direct access to.

Dennett’s claim in the paper is that qualia do not actually exist, because our internal experiences actually have none of these four qualities. The reason I’m summarizing this paper specifically is that most of his points overlap with my main point about folk Cartesianism: our assessment of our own subjective, first-person experience is actually unreliable and often wrong. We don’t have 100% certain access to it. It provides useful information, but it should be taken as one data point among many. It cannot serve as some kind of magical firm foundation to build all our other beliefs off of. The information processed in our brains mostly isn’t actually rooted at all in the fact that we have qualia. If this is the case, systems without subjective first person experience can also have justified true beliefs.

So what are the issues for qualia? Dennett provides thought experiments for each property people claim they have. I’ll be half-summarizing them, and sometimes running with my own twists on them. I do strongly recommend you read the full paper.

1. Intrinsic

People “develop a taste for” coffee and beer. Most people I know who drink either remember when they took their first sip and didn’t like it, but now they love it. Did the actual taste, the direct sensation they experience in the moment of drinking beer or coffee, change over time? Or did the sensation stay the same, and only their reaction to it changed?

It seems like this should be a pretty easy question if our mental experience is “right in front of us.” If we’re watching a movie, there’s a clear difference between a character appearing on the screen before and after we’ve changed our judgment of them, versus a totally new character appearing. We can tell which one is which. But it seems like we have a lot of trouble when we stare directly at what’s going on with the sensations we experience with beer and coffee, or anything that comes with some judgment of the experience. It seems like our reaction to the sensation is somehow also part of the sensation itself. Maybe over time we’ve developed an ability to notice parts of the sensation we didn’t before. But if this is the case, it means these experiences aren’t “intrinsic.” They aren’t single, indivisible things, because they can actually be broken up into smaller individual experiences. They may even be “relational” because core qualities of them seem to depend on our judgment of them. See here for more of a breakdown on the intrinsic vs relational distinction.

We don’t have any clear way of telling whether any of our sensory experiences are actually “intrinsic,” just a bare experience that can’t be broken up into parts, or actually an assemblage of many individual experiences at once. This again chips away at our image of ourselves as having direct, easy access to our subjective, first-person experience.

The taste of coffee to a novice drinker may be a single overwhelming experience. They can’t detect any subtleties or how the taste of composed of many individual hints and flavors. Instead, they just taste a disgusting, overwhelming bitterness. Over time this taste changes… but does it change? Does their reaction to it change? There’s no way to be sure, but I lean hard in the direction that their actual taste of the coffee changes, not just their reaction to it. Suppose you didn’t like coffee, and I zapped your brain with a machine that gives you my personal reaction to the taste. You take a second sip, and suddenly you like it. It is very difficult for me to imagine you saying “Oh! I like the coffee now! It’s so good! I want to drink it all the time” while also saying “But it tastes exactly the same as it did a moment ago. See, my reaction to the taste was just changed.” I’d imagine this person would instead be stunned, and say something like “Oh it tastes very different now, the bitterness isn’t overwhelming, there are a lot of pleasant notes and hints I couldn’t detect at all before.” It seems like our judgment of the qualia is somehow itself also part of the qualia. The boundary between ourselves and the qualia we experience blurs.

A plucked guitar string

The sound of a plucked guitar string can be indescribable to an untrained ear. They only hear a single, fundamental, basic experience, ”the guitarness of the sound.” But to trained experts, a plucked string sounds like the combination of several different sounds a string can make happening at once. Are these people having different qualia, or are they reacting differently to the same qualia? Dennett himself puts this well:

Pluck the bass or low E string open, and listen carefully to the sound. Does it have describable parts or is it one and whole and ineffably guitarish? Many will opt for the latter way of talking. Now pluck the open string again and carefully bring a finger down lightly over the octave fret to create a high “harmonic”. Suddenly a new sound is heard: “purer” somehow and of course an octave higher. Some people insist that this is an entirely novel sound, while others will describe the experience by saying “the bottom fell out of the note”--leaving just the top. But then on a third open plucking one can hear, with surprising distinctness, the harmonic overtone that was isolated in the second plucking. The homogeneity and ineffability of the first experience is gone, replaced by a duality as “directly apprehensible” and clearly describable as that of any chord.

The difference in experience is striking, but the complexity apprehended on the third plucking was there all along (being responded to or discriminated). After all, it was by the complex pattern of overtones that you were able to recognize the sound as that of a guitar rather than a lute or harpsichord. In other words, although the subjective experience has changed dramatically, the pip hasn’t changed; you are still responding, as before, to a complex property so highly informative that it practically defies verbal description.

So what changes between people who can detect all the subparts of the sound of a single low guitar string, and people who can only hear the note as a single, intrinsic, undivided “guitarish” sound?

Does the guitar make different sounds based on how much you can detect? That seems unlikely. If one person can pick apart the sounds they hear much better, are they having additional sensations beyond the sound of the guitar itself, or is the guitar string just composed of these individual sensations like a checkerboard is composed of red and black squares, and the other person just can’t pick up on those internal differences? But “picking up on” the sound is itself what we’re trying to describe here: the direct, first-person experience of hearing. These experiences are specifically not supposed to be just the information we gather that we can write down; they’re supposed to be the first-person, in-the-moment undeniable experience. The fact that whether the two people experience the same or very different qualia is another hint that our sensory experiences are not simply displayed on a mental screen for us to watch like a movie.

Conclusion

We do not have any clear way of telling whether any of our qualia are actually “intrinsic” and just a bare experience that can’t be broken up into parts, or actually an assemblage of many individual experiences at once. When you first drank coffee, you had no way of telling that this taste you were experiencing was or was not an assemblage of many different other experiences at once. Just like the first-time coffee drinker, we have no way of knowing whether any one experience we have is actually intrinsic, or a combination of many different experiences at once. This again chips away at our image of ourselves as having direct, easy access to our subjective, first-person experience. People often talk as if qualia are these brute, intrinsic, foundational facts of our mental world, but it seems like we can barely tell if any one experience we have is actually composed of many others, or whether we ourselves are affecting the experience based on our judgment and reaction to it.

This all makes our experience seem very distinct from an inner movie theater. Unlike the movie theater:

• It is not obvious when something is a single basic thing (like a solid color on the screen) or whether it’s composed of many other things we’ll easily notice when we get more experience with it.

• Our judgment seems to directly affect our experience. We are not passively watching a screen of experiences and then judging and reacting to them after. The screen itself seems to radically change based on how we judge it.

2. Ineffable

Dennett argues that the ineffability of qualia is not due to qualia being some special, mystical property, but because they are “practically” ineffable. They are just highly specific, information-rich responses to the world that could in theory be described if we have enough experience with them. “Ineffability” is just the current horizon of our ability to analyze our own responses, not a fundamental property of the experience itself. This limit can be changed with training (like wine tasting or ear training).

The last example with coffee and the guitar string gets at this idea. Qualia might only be indescribable because we and other people are relatively untrained at detecting the communicable information they’re giving us. With training, people can learn to communicate a lot more relevant information about the sound of a guitar string or the taste of coffee. It might be that we’re mistaking our own current cognitive limitations for a fundamental property of the experience itself. Over time, experts get better and better at describing qualia.

On this point I mostly part ways with Dennett, where it does seem like some aspects of experience might permanently be walled off from public discussion. But this isn’t as relevant to the question of the Cartesian theater.

3. Private

Qualia are really really private

Qualia are fundamentally private. No one else can ever access what we experience.

Suppose we invent a machine that connects our brains, feeding your visual input directly into mine.

I report seeing everything you see. (This is the plot of the 1983 movie Brainstorms). I step back and gasp “Wow! Your qualia are completely different from mine! Your red is my blue! Everything smells and tastes different. This is crazy! We’re living in two different worlds. Qualia can be so different without anyone noticing.” We agree this is crazy. Suddenly, you scratch your head and say “Wait a minute, maybe we messed up somewhere. What if we plugged the wire that connected us in upside down on one end? Let’s just check to see if that changes anything.” We flip the plug upside down at one end and try it again. This time, we both agree that the other person’s experience looks totally normal.

What happened here? There are two possibilities for us: either we have different qualia, or we have the same qualia. There are also two possibilities for the cord connecting us: either it inverted the images the first time, or the second time.

Can we tell which is true based on the information we have?

In a connection where the qualia look weird to us, there are two possibilities. One is that we both have the same qualia as each other, and it’s the connection that’s inverting it. This would cause us both to say the other person’s qualia looked weird.

Another possibility is that the connection is correct, and we do have inverted qualia. This would yield the same reaction:

The situation where we confirm that what we see is normal also has two possibilities. It could be that the connection is reporting what our qualia actually look like to the other person.

But it could also be the case that the connection is inverting our different qualia, making them look normal when they’re actually different.

Let’s organize these situations by whether we have the same or different qualia, and whether the cord inverts the image.

Now, you want to test to see if the cord is right-side-up or not. You plug two people in. As they’re watching the other person’s vision, they say “that looks weird!” This is all the information you have to go on. You can listen to them both say “Wow… your blue is my yellow. Your red is my purple” for as long as you want, but this doesn’t really add any new information, because either way you face the same problem: there are two possible situations where the participants say the other’s qualia looks weird: the cord’s inverting their similar qualia, or their different qualia are transferred faithfully by the cord.

Given the information you have, you have no way to tell whether it’s the cord that’s wrong, or if the two people have different qualia. Your evidence perfectly fits both cases. If you flip the cord, and the participants say “now it looks normal!” you have no way of deciding between these two cases.

Again, you don’t know if the cord is actually showing that the two people have the same qualia, or if it’s now upside down. You have no way of telling if the cord is right-side up or upside down. Even a machine like this can’t ever actually give you direct access to the other person’s qualia, because neither has any way to externally test if the machine is getting it right.

So qualia remain fundamentally private. We have no final way of telling whether someone else’s qualia is the same as our own.

If qualia are this private, you do not have direct mental access to your own qualia

But if qualia are this completely and totally private, this also creates a problem for us. All our understanding of what qualia look “normal” come from our memories and intuitions about what the qualia looked like in the past. And we’re actually in the same relationship to our past self as the two people were in the last thought experiment.

We can imagine swapping the machine in the last example with a different machine. This time, only you get in. When you step out, all the colors in the world look inverted to you.

You’re stunned. The world is overwhelmingly alien. You ask “How did you invert my qualia?” The machine operator chuckles. “Oh, your qualia are actually the same as always. The machine uses a simple trick: We just edited your memories and crossed your memories with different colors. You now remember yellow as being blue, and blue as yellow, as an example.”

You’re very confused. “But the banana I see in front of me now is clearly blue!”

The operator says “Oh no, it looks exactly the same to you as it always did. It’s just that all your memories of yellow have been crossed with blue things. Like I bet that banana looks exactly like what you remember the sky looking like, right?”

You nod.

“Well if you go outside, you’ll see the sky’s never actually looked like that. It’s blue and has always been blue. We just swapped your memories, so now that color’s associated with what you think of as the color of bananas.”

You go outside, and the sky looks banana-yellow to you.

“No this is all clearly wrong. You did invert my qualia somehow.”

The operator scratches her head. “How would you tell the difference between an inversion of qualia and an inversion of your memories?”

“Well I just feel in my gut that this is wrong.”

“But that gut feeling is all your memories and built-up intuitions of the world.”

Hmm…

Later, a huge scientific discovery is made. Scientists have located qualia themselves, directly in the pineal gland of the brain. The operator uses this knowledge to build a second machine, this one really inverts your qualia and leaves your memories untouched. She invites you to try it out, but because the machines look identical, she realizes she mixed up which one is which. Is there any way at all for you to tell which machine is which based on the outcome of going through it? When you emerge, will you have any way of knowing at all whether it was your qualia or memories that were inverted?

In this example, memories are taking the place of the cord in the first example. When it comes to your qualia, your connection to your past self through memory is identical to your connection to the other person’s qualia through an unreliable cord. In both cases, you don’t actually have direct infallible access to the qualia.

This is a big problem, because all human thought happens in time, and the present is an infinitely small slice of time. If qualia are so private that they can only be accessed in the infinitely short present, we humans cannot actually access them directly, because we live and think in time.

Whenever your mind receives any information at all about qualia, you are reacting to a past experience, because of the fractions of a second it takes the signal from the qualia to be mentally processed. But already at this point, you’re thinking about something in the past, and you’re dependent on memory. Qualia’s privateness means memory does not give you that special access you’re supposed to have. Memory, like the cord, is fallible. You can be wrong about what you just saw. “What’s in front of your eyes” still needs fractions of a second to process. To be clear, this doesn’t mean you’re necessarily wrong about the qualia you’re experiencing, it just means you can be wrong about them. This strikes at the heart of the Cartesian intuition that we have direct, certain access to our own subjective experience.

If the qualia in the last experiment are so fundamentally private that no one else can ever access them, you yourself also have no fundamental cognitive access to your own qualia. They are as inaccessible to you as they would be to another person using the brainstorm machine to try to understand what it’s like to be you. Your brain can provide you with valid reports of what you’re experiencing, just like the other person can provide you with verbal reports about what they’re experiencing, but in both cases you don’t have that direct, magic, private access qualia are supposed to come with. In fact, here they’re behaving suspiciously similarly to a world where they don’t exist, and where your brain is only receiving information about the world that can be reported publicly, either to your future self or to another person.

Again, this seems like really bad news for the inner theater model of the brain. Not only is there no observer and the screen itself unclear and obscured by our own judgments on it, suddenly the screen’s in another room, and written reports are being sent in about what’s happening in the movie.

A relevant argument here is Derek Parfit’s long piece in Part 3 of Reasons and Persons on how the self does not persist over time, and your relationship to your past and future self is similar to your relationship to other people (summary here). This is an especially good but long read for people interested in more arguments against Cartesian egos. If qualia are so fundamentally private that no one else can access our qualia, but also we ourselves are different people moment to moment, the intersection of these views seems to imply that our own access to our past qualia (which is all qualia we can actually think about and respond to, because the present is an infinitely thin slice of time) is identical to our access to other people’s qualia: indirect and uncertain.

What to make of this? I think that this is a strong sign that qualia (whatever they are) are not fundamentally private, and might exist instead as some kind of (very complex) reportable information that can be communicated between people who can understand it. This makes much more sense to me than the idea that they are fundamentally private, because the implications of fundamental privacy make them behave completely differently than how I experience them. I want to define qualia as “the experiences that I have some direct access to.” It might be that those experiences are entirely just raw information in my brain assessing the situation. This information could in principle be communicated completely to another brain. I’d hope so, because I also think the relevant information can be communicated by memory across time to my present self. Again, this is a case where it seems like we have as much mental access to our own qualia as a computer would: an uncertain tether of memory or wires communicating information about what the world is like.

The more inaccessible you believe qualia are to machines, the more inaccessible you believe they are to your own mind as well.

4. Directly or immediately apprehensible in consciousness

If qualia are so fundamentally private, we’ve seen above that they by definition are not directly or immediately apprehensible in consciousness. We’ve also seen via the coffee and beer examples that people have a surprisingly hard time apprehending the qualia they are currently experiencing or have experienced in the past. We can’t even tell if the qualia we’re experiencing are basic and intrinsic, or composite and relational, as in the case of the guitar string. If qualia exist, we have a strange and complicated relationship with them. They are not directly apprehensible in consciousness in the way an unobscured movie screen is.

Conclusion

I think there are some clever arguments that even with these objections, qualia may still exist. But we’re way, way less sure of our own subjective experience than folk theories imply, and the nature of qualia seems more complex than simple, accessible, nonphysical pure experience.

So it seems like we don’t actually know our own internal experiences with certainty. Here I don’t mean that we might be mistaken about what objects and events we’re seeing, I mean that we can be mistaken about the nature of our current first-person subjective experience. Not only is there no one sitting in the Cartesian theater, and not only is the screen itself obscured, but there doesn’t seem to be a screen at all. This magical place, the center of the mind and thought, that so many people seem to take as so obviously given it’s crazy to doubt it, does not seem to actually exist.

If this is the case, first-person subjective experience becomes one of many possible sources of information and evidence. It’s useful, it’s just reduced to the same level as other ways we acquire information, like written descriptions, verbal accounts, and knowledge that other people we trust believe things (all of which computers can access). Most importantly, humans do not seem to have some magical, central location where experience happens that machines by definition will always lack.

The impossibility of folk introspection

A very common critique of large language models is that they can’t “introspect” in the way humans can. They just take in input, run it forward through the net, from its input variables to its final classification, and don’t have any way of circling back on anything that’s happened. They can’t go backwards. Therefore, unlike humans, they do not have introspection. They’re pattern recognition rather than true thought. Here, the information in the neural network passes from left to right through the weights and nodes. The only way this model could have true introspection is if it were somehow able to step outside of this process and observe what had already happened:

This critique always seemed implicitly Cartesian to me. Human brains are also only more or less complex information-processing units. They are composed of inputs, mental functions that perform computations, and outputs. Those outputs are themselves used later as new inputs.

Crucially, all information processing in the human brain can only happen in one direction in time: forward, because every mental subprocess in the brain is physical. When our minds take in input, they might combine that input with memories or inbuilt mental biases, run these through mental computations, and product an output.

This could get more and more complex, as different parts of the brain draw on results from the mental computations of other parts:

Where would introspection fit into this? Well, if introspection is another natural functional process, it has to exist in time, take in inputs, and give outputs. It seems like what we call “introspection” would be the brain calling prior inputs or results of past computations. Maybe it would look like this:

Crucially, what is NOT happening is that there is a magical outside observer looking down at the mental processes from outside. There is no Cartesian ego who can “just see directly” what’s actually happened in the mind.

“True introspection” where we step back and clearly look at our thought processes from the outside is not possible. Introspection is just another mental function that takes in inputs and gives outputs. We cannot actually see the wiring of our own minds, we can just react to the outputs our minds are giving us, in the same way we can observe someone else’s behavior over time and come to conclusions about what they are like internally. “Introspection” is actually just another informed guess about the world our brains make. Introspection is like observing a natural phenomenon and making predictions about what will happen next. It’s not like looking in a mirror and clearly seeing ourselves as we really are. Just like we can be mistaken about the real causes of other people’s behavior, we can also be mistaken about the causes of our own behavior, because the inputs our minds provide for introspection can be misleading. We can call on false or modified or selective memories with no way to tell that they’re not perfectly accurate. We can build up strong incorrect intuitions about what we’re like. There’s no special place our intuition function can turn to get the “real” story, it’s all fallible inputs just like the external world provides for anything else we’re trying to understand. I’d recommend this classic essay for a deeper dive on this.

It seems like when people say AI models do not have any way of doing introspection, they think that to do that, the model needs to have some outside Cartesian ego looking in:

But if introspection in humans is just another mental functionalist process, where inputs are received and outputs given, rather than a magical Cartesian ego looking down from above, then it seems like anything we call introspection in a process like this:

could in principal also happen in a process that looks like this:

The fact that LLMs “only pass information along one direction” on its own tells us nothing about whether they can introspect, for the same reason that the fact that human information processing can only happen in one direction in time tells us nothing about our own ability to introspect. The only way this would invalidate the idea that LLMs can do anything like introspection is if folk Cartesianism were true.

There are obviously a lot of other ways large language models differ from the structure of human brains. But any comment on those differences shouldn’t imply that the AI companies forgot to install a Cartesian ego watching over the whole thing.

A simple rule for what introspection can’t do

The individual thoughts and experiences we have access to during introspection are not the fundamental units of thought. The fundamental units of thought are the electrical signals sent and received by neurons. They combine together into thoughts in the way binary 1’s and 0’s can combine together to form a programming language. Most of our mental processes are very complex mental functions with neurons and electrical signals as the building blocks. Because introspection is another mental function, it does not necessary have access to every minute step of its own or other functions’ structure. Computer programs can summarize the outputs and steps of other programs, but they can’t “step outside the process” and observe the binary 1 and 0 signals that make them up. In the same way, human introspection can also draw information from other mental processes, but cannot “step outside” to see the mind laid bare before it, because the mind is not actually fundamentally composed of the type of thing introspection gives us access to.

Conclusion

Our assessment of our own internal experience and thought process is fallible in the same way our assessment of the external world is. There is no “solid ground of absolute certainty” we can find in our subjective experience. This makes sense of the mind is a pile of complex functions taking in information, processing it, and providing new inferences as output. There’s no deep metaphysical difference between the mind analyzing its own contents and the mind analyzing the world, for the same reason

Whether the information the mind receives is about what’s happening inside of it, or what’s happening externally to it, doesn’t have some deep metaphysical difference.

From the outside, our situation with introspection, and the vagueness and difficulty of directly apprehending qualia, give the impression to me of a species with very strong internal built-up narratives of how our minds work colliding with the simple fact that we’re machines and not angels. We can perform mental functions on the information we receive, but we cannot enter some kind of extra-physical movie theater of consciousness, where we easily and directly apprehend our deep consistent subjective worlds, and draw conclusions based on our assessments of those worlds. In this way, we behave similarly to very general computers sent off to survive in the world.

“3. The basis of all knowledge lies in our first-person subjective experience”

We’ve already seen that:

• There is no Cartesian ego observing and judging our experience.

• Our first-person subjective experience is surprisingly opaque. We can be mistaken about it. It’s still evidence, but we don’t actually have 100% confident privileged access to it.

• Similarly, our introspection is just another mental process that takes in fallible inputs and gives fallible outputs. The mind is not actually all easily available to us at once, like looking down at a complex domino chain falling. Anything we call “introspection” is just one part of that very complex chain itself.

Human minds take in input, run it through very complex mental functions, and give output, that they then use for other functions. In no part of this process does the mind “step above” the physical functions happening in the brain, and looks down at all the mind-stuff happening at once, and observes it like a map. Our felt sense that this happens, that we’re Cartesian observers with access to clear distinct instrinsic private mental experiences, is strong but mistaken. At best, the functions in our brains doing the introspection draw from lots of parts of the brain at once,

The folk theory of language acquisition

Many people believe we learn what words “really mean” by connecting them to specific experiences in our minds. They think we only understand “cat” because we’ve seen an actual cat and someone labeled that experience for us. Since your personal experience of seeing a cat lives privately in your mind, the “real meaning” of “cat” must be something private that only you and others who’ve had similar experiences can truly grasp.

But if word meanings are fundamentally private, language breaks down. If everyone has their own private version of what words mean, with no way to compare these meanings, how can we ever know if we’re talking about the same thing? For language to work, meanings must be publicly accessible and verifiable.

When I learn the word “cat,” its real meaning can’t depend on some private, indescribable feeling I experience, because you might experience something completely different. The meaning must be something we can communicate using other words and shared reference points. It makes much more sense to say the meaning of “cat” is something like “Small carnivorous mammals of the species Felis catus, typically weighing between 8-11 pounds, with retractable claws, whiskers, and forward-facing eyes, commonly kept as household pets and are known for hunting rodents and birds” than to say it’s something private and indescribable that people who haven’t seen one cannot possibly understand.

We only have access to public evidence about meaning. We can’t look inside each other’s minds. So any realistic theory of how we learn language must focus on what we can actually observe: how people use words in response to their environment.

We don’t learn language by linking words to mental images. Instead, we learn by linking words to publicly observable situations where they’re used. The philosopher Quine called this “stimulus meaning.” A child learns when to say “cat” by observing environmental cues and getting social feedback (nods, corrections) from the people around them.

A word’s meaning comes from its role in our entire language system, not from its connection to a single private idea. This explains how we use abstract words like “democracy” or “neutrino,” terms for which we have no clear mental picture or direct sensory experience. The old theory can’t account for these. We understand these terms not through mental images, but through their relationships to other concepts in our linguistic and social framework.

Since word meanings must depend entirely on things we can communicate about using language, and exist as essentially nodes in a web of interrelated words, they can’t depend on private, indescribable experiences. This means machines that process enough information can, in principle, access the meaning of words. They don’t need inner transcendent subjective experiences to understand language. Modern large language models are designed to situate words in broader webs of meaning and association. Once you exit the folk theory and learn about how LLMs process language via association, it becomes hard not to think that they’re successfully mimicking what humans do when the learn the meanings of words.

Can machines without qualia have knowledge?

This will be an argument that machines can in principle have knowledge. I’m not arguing that current AI systems have knowledge (though I would argue that chatbots do “know” the meaning of the words they use).

What is knowledge?

Knowledge is justified, true belief. It’s worth breaking this down a little:

• Justified: I wake up in my room with the window shades down. I can’t see if it’s raining outside. I decide to flip a coin. If it’s heads, I’ll believe that it’s raining. If tails, it’s not. I flip the coin and it lands on heads, so I believe that it’s raining. I go outside and see that it actually is raining. Did I “know” it was raining before I saw it? It seems like the answer is no. I got lucky and my silly guess happened to match reality, but to “know” it was raining I needed some kind of justification for my true belief.

• True: It doesn’t make sense to say I have “knowledge” that David Hume is currently President. We can only be said to have knowledge about true beliefs.

• Belief: An acceptance that a statement is true or that something exists.

Anything “completely private” cannot contribute to most knowledge.

Can machines hold beliefs?

Before we can ask if machines can have beliefs, we need to get clear on what beliefs actually are. The folk Cartesian story goes something like this:

A belief is a mental state that exists in your Cartesian theater. When you believe it’s raining outside, there’s some kind of propositional content—”it is raining”—that exists in your mind, in your subjective experience. You have direct access to this belief through introspection. You can “look inward” and see what you believe. The belief causes you to grab an umbrella, but the belief itself is fundamentally a feature of your private, subjective mental life.

But we’ve already seen problems with this picture:

• There is no Cartesian theater where beliefs are displayed for an inner observer

• We don’t have reliable introspective access to our own mental states

• Mental processes are just physical information-processing functions

So what’s left when we strip away the Cartesian story? What is a belief if it’s not a proposition floating in your mental theater?

In the physicalist-functionalist view, a belief is a certain kind of functional state. It’s a state that:

1. Takes in information from the world (perceptual inputs, testimony from others, memory, reasoning)

2. Combines with other beliefs and desires to guide decision-making

3. Produces behaviors that would make sense if the belief were true

4. Updates based on new evidence

5. Plays the right inferential role in reasoning (if you believe “All mammals are warm-blooded” and “whales are mammals,” you should infer “whales are warm-blooded”)

Notice that nothing in this definition requires a Cartesian theater, qualia, or privileged introspective access. A belief is defined by what it does, how it functions in the overall cognitive system, not by what it feels like “from the inside.”

You might object: “Fine, maybe machines could have states that function like beliefs. But real beliefs require understanding. When I believe it’s raining, I understand what rain is. A machine just manipulates symbols without understanding what they mean.”

But we’ve already undermined this picture in the folk theory of language acquisition above. We’ve seen that:

• Meaning isn’t grounded in private, ineffable qualia.

• Language acquisition works through publicly observable patterns of usage, not through attaching words to subjective experiences.

• Our own understanding is ultimately cashed out in how we use words, respond to contexts, and draw inferences, not in some private mental glow of comprehension.

If understanding is functional, if it’s about having the right patterns of response, the right inferential connections, the right sensitivity to context, then there’s no reason in principle that machines couldn’t achieve it.

Can machine beliefs be justified?

The folk Cartesian picture of justification goes something like this: Your beliefs are justified when they’re properly grounded in your direct, first-person experience. You know you’re seeing red because you have direct, certain access to your own qualia. You can introspect and observe the redness right there in your mental theater. This provides a firm foundation, a bedrock of certainty, upon which all other knowledge is built.

Descartes himself made this explicit. He doubted everything he possibly could. The external world, his own body, mathematics, even whether he was dreaming. But the one thing he couldn’t doubt was his own existence as a thinking thing: “I think, therefore I am.” His subjective experience provided absolute certainty that he existed and was having thoughts.

From this foundation of subjective certainty, Descartes tried to build back up to knowledge of the external world. The idea was that our private, first-person experience provides a special kind of justification that nothing else can match.

But we’ve already seen the problems with this picture:

• We don’t have direct, certain access to our own experiences

• Our introspection is fallible and often misleading

• The supposed bedrock of subjective certainty is actually shaky and unreliable

• Our experiences aren’t clear, simple, and directly apprehensible. They’re complex, constructed, and surprisingly opaque

• The Cartesian ego Descartes detected might not even exist

If the folk Cartesian story about justification is wrong, what’s left?

An alternative picture of justification is reliabilism: A belief is justified when it’s formed and maintained through reliable processes that tend to produce true beliefs. Justification isn’t about having some special subjective feeling of certainty or being able to introspectively verify your mental states. It’s about the process that produced the belief being a good one.

Consider how you actually form beliefs:

Visual belief: You look out the window and form the belief “It’s raining.”

• Your eyes receive light reflected off raindrops

• Your visual cortex processes this information

• Pattern recognition systems identify the characteristic appearance of rain

• Memory systems match this against stored representations of rain

• A belief state is formed: “It’s raining”

Is any of this process fundamentally dependent on having qualia or a Cartesian theater? It doesn’t seem like it. The process is:

1. Receive sensory input

2. Process that input through various computational mechanisms

3. Compare against stored patterns and previous experiences

4. Form a representation of the world state

5. Store this representation for future use

This is an information-processing story all the way down. And crucially, this process is reliable: it tends to produce true beliefs about whether it’s raining. That’s what makes the belief justified.

On the reliabilist account, justified beliefs don’t need to be certain. They just need to be formed by processes that usually get things right.

This actually matches our intuitions better than the Cartesian story. Consider:

You look out the window and see what appears to be rain. But it could be:

• A sprinkler system you forgot about

• A movie set creating artificial rain

• An elaborate hallucination (you’re on drugs, or having a stroke)

• A very realistic dream

• A brain-in-a-vat scenario where you’re being fed false sensory data

You can’t be certain it’s raining with the kind of absolute certainty Descartes wanted. But that’s fine. Your belief is still justified because it was formed by a reliable process. Most of the time when your visual system produces the representation “rain,” there really is rain. The process is reliable even if it’s not infallible.

What counts as a “reliable process”? Here are some features:

1. Correlation with truth: The process should produce true beliefs more often than false ones in the environments where it operates.

2. Appropriate sensitivity to evidence: The process should update beliefs when new evidence comes in, and it should be more confident when evidence is strong and less confident when evidence is weak.

3. Coherence: The process should produce beliefs that fit together consistently. If you believe “All mammals are warm-blooded” and “Whales are mammals,” your processes should lead you to believe “Whales are warm-blooded.”

4. Resistance to known error modes: The process should avoid systematic biases and known sources of error, or at least be able to correct for them.

5. Appropriate use of background knowledge: The process should integrate new information with relevant prior knowledge rather than treating each belief in isolation.

Could a machine implement processes with these features?

Let’s think about how a machine might form the belief “It’s raining”:

• Sensor input: A camera receives visual data, or a moisture sensor detects water.

• Pattern recognition: Neural networks or other algorithms process this data to identify rain patterns.

• Comparison with stored data: The system compares current inputs against training data or previous observations.

• Belief formation: The system forms an internal representation: “It’s raining.”

• Confidence assessment: Based on signal strength and pattern matching confidence, the system assigns a probability or confidence level.

Is this process reliable? Well, that depends on the specifics:

• How good are the sensors?

• How well-trained is the pattern recognition system?

• Does the system have enough relevant training data?

• Can it distinguish rain from sprinklers or other confounds?

• Does it update appropriately when conditions change?

These are all empirical questions. There’s no in-principle reason why a well-designed system couldn’t form beliefs through highly reliable processes. In fact, for some tasks, machine processes might be more reliable than human ones.

Under reliabilism, it looks like machines can in fact have justified, true beliefs. Therefore, it seems like machines can have knowledge.

The implication that humans might be incredibly complex biological machines rather than transcendent experiencing angels should hopefully be a little more understandable than when we started.

Conclusion

So I claim folk Cartesianism is not “obviously correct,” and a better story of the mind looks something like this:

• Humans do not have Cartesian egos, magical eyes floating outside of physical reality observing what happens in our minds like a movie.

• Human experience is deeply fallible as a source of information about our own minds, and even of the experience itself. We do not actually have a clear understanding of what’s happening in our own minds, or even a clear understanding of what we are currently experiencing at any given moment. “What it is like to be us” is not something our brains have special, ultimate access to. We have very strong evidence, but our minds are not displayed before us clearly like a movie.

• “True introspection” where we step back and look at our thought processes is not possible. Introspection is just another mental function that takes in inputs and gives outputs. We cannot actually see the wiring of our own minds. “Introspection” is actually just another informed guess about the world our brains make, this time based on the patterns we’ve observed in our own behavior. Introspection is like observing a natural phenomenon and making predictions about what will happen next. It’s not like looking in a mirror and clearly seeing ourselves as we really are.

• Human knowledge is based on publicly observable evidence and testable reasoning, not privileged access to private mental contents. Our beliefs are justified through the same kinds of information processing that machines can perform, drawing inferences from data, testing predictions, and refining models based on outcomes.

• The meaning of language is rooted in public use and social context, not private mental associations. We learn words through observable patterns of usage, not by linking them to ineffable subjective experiences. This means machines can acquire genuine understanding of language without needing a Cartesian theater.

• There is no fundamental barrier preventing machines from having beliefs, knowledge, or understanding. If our minds are physical information-processing systems (as physicalism suggests) and what matters is function rather than material (as functionalism suggests), then appropriately designed machines could replicate any cognitive capacity humans have.

I’ll close with a famous line from Derek Parfit on the reductionist view of personal identity, where he describes the experience of letting go of his sense of having a Cartesian ego and internal movie theater.

On the Reductionist view each person’s existence just involves the exercise of a brain and body, the doing of certain deeds, the thinking of certain thoughts, the occurrence of certain experiences, and so on…

Is the truth depressing? Some may find it so. But I find it liberating, and consoling. When I believed that my existence was such a further fact, I seemed imprisoned in myself. My life seemed like a glass tunnel, through which I was moving faster every year, and at the end of which there was darkness. When I changed my view, the walls of my glass tunnel disappeared. I now live in the open air. There is still a difference between my life and the lives of other people. But the difference is less. Other people are closer. I am less concerned about the rest of my own life, and more concerned about the lives of others.

“Let me see if I understand your thesis. You think we shouldn’t anthropomorphize people?” -Sidney Morgenbesser to BF Skinner

The 2020s have made my philosophy degree feel more worthwhile. Not only has social media made a ton of new people interested in philosophy, but the real world has also made many of the main questions in philosophy more relevant. AI especially has brought many philosophy topics into the mainstream, but it’s also polarized a lot of the debates. Back in 2011, I could say things like “The human mind is composed of natural processes, like everything else in the world, and therefore it could probably eventually be replicated by a machine, because in some sense it itself is a machine” and people would have long drawn-out conversations with me about whether that’s true or what that implies about language or experience or society. Now, when I say the same thing, it’s much more likely that I’ll get accused of “just buying into the recent AI hype” or that I’ve been tricked by secularism or capitalism or worse to deny the fundamental transcendent character of human minds. This issue used to be a pretty marginal question, but now it’s entered mainstream partisan debate. Sides have been taken.

What’s odd is that a lot of critics of the “minds are machines” view used to implicitly defend it. Back in the 2010s, there was a general worry about anthropocentrism. The belief that humans were in some way unique or separate from the natural world was regularly sneered at as “naive humanism” at best and a method of justifying exploitation of the natural world at worst. There was also a lot of talk about Cartesianism (the philosophy of René Descartes) as the origin of many of society’s problems. Specifically, mind-body dualism was accused of denigrating embodiment and elevating technocratic authoritarian reason. I always thought both criticisms went too far, but analytic philosophy had turned me into an anti-Cartesian naturalist, so I was able to nod along and contribute to these conversations where I could. Since then, attitudes toward Cartesianism have flipped. Many of the same people are now saying that AI cannot, by definition, ever have knowledge, because it does not have subjective first-person experience, or that it’s lacking some transcendent non-physical characteristic the human mind has; both implicitly Cartesian takes. Accusations of anthropocentrism have also been replaced with accusations of anthropomorphizing. A common line is that “everyone who actually knows how this technology works knows that it’s just linear algebra.1 The only people who believe it’s thinking are dummies who anthropomorphize it because it seems human.” I long for the 2010s when it was more common to hear people talk about a lot of different systems exhibiting some basic qualities of mind. I think back then people would be less likely to assume linear algebra couldn’t mimic some basic mental processes.

There’s a cluster of beliefs I associate with “folk Cartesianism” that even critics of Descartes often fall into. It’s incredibly common, to the point that people talk as if its implications are obvious without ever having directly thought about them before. I think this cluster of beliefs falls apart when you poke at it, and the fact that it’s implicit in so many debates about AI shows that it’s not being poked at enough. This post will be me poking at folk Cartesianism, and arguing for “anti-Cartesian naturalism” which is a widely-shared view among analytic philosophers of mind. Nothing I write here will be new or surprising to people knowledgeable about analytic philosophy. But I’ve been feeling the gulf between academic philosophy and mainstream discourse and want to contribute to building a bridge. This will be a long collection of the most popular and effective arguments for why folk Cartesianism is wrong, and how I think it’s harming the AI debate.

I have three goals here:

• You can identify what folk Cartesianism is, and when people are thinking like folk Cartesians.

• You come away no longer viewing folk Cartesianism as the obvious, common-sense view of the mind. You have a better understanding of the anti-Cartesian naturalist view, and philosophy of mind in general.

• You are more open to the idea that machines can replicate what happens in human minds. That minds are not magical, inexplicable substances separate from nature.

I’ve written this partly as an instruction manual to my past self I wish I had when I was younger, as my personal crash course in philosophy of mind 101 as it relates to AI.

Some of these points will be taken from my other posts on the same ideas. The “Naturalism” section will be repetitive for people who have read those already.

Contents

Part 1: Defining the problem

• Definitions

  • Folk Cartesianism

    I define folk Cartesianism as consisting of three common intuitions:

    1. There is a unified self that floats above mental processes and observes them

    2. We know our own internal experience with complete certainty

    3. The basis of all knowledge lies in our first-person subjective experience

    • The Cartesian view of the mind

  • Naturalism

    • Definition

    • Physicalism

    • Functionalism

    • Evolution

Part 2: Problems for Cartesianism

• “There is a unified self that floats above mental processes and observes them”

• “We know our own internal experience with complete certainty”

• “The basis of all knowledge lies in our first-person subjective experience”

Definitions

Folk Cartesianism

René Descartes inaugurated modern philosophy by attempting to establish a way to reach ultimate truth completely dependent on reason alone (rather than religious revelation). To do this, he looked for a solid, undeniable foundational fact he could start at. Everything that followed would be equally certain. He famously attempted to doubt all his beliefs, and found that the one belief he couldn’t doubt is that he himself existed. “I think, therefore I am.” From here he reconstructs all his other beliefs by building an intellectual ladder, with his belief in himself as the foundation. The next step is a pretty weird proof of God, followed by concluding that God wouldn’t deceive us about the external world.

Descartes’ explicitly and implicitly put forward many more specific claims about the mind, and his overall account matches how many everyday people imagine their minds working. “Folk Cartesians” mostly haven’t read Descartes, but have been influenced by how he and the broader culture talk about mind. So when I talk about “Cartesianism” here, I mean a cluster of concepts that are popular in everyday folk theories of the mind, not necessarily what literal Cartesian philosophers believe.

There are three key features of Cartesianism that I want to deny, and that are relevant for current conversations about AI.

1. There is a unified self that floats above mental processes and observes them

A “Cartesian ego” is a featureless observer hiding in the mind. This is the “I” in Descartes’ “I think therefore I am.” Cartesians imagine the mind as something like a movie on a screen, with the ego the silent observer. Critics call this the “Cartesian theater” model of the mind.

2. We know our own internal experience with complete certainty

Cartesians would say that we ourselves are egos. We observe our first-person experiences and thoughts. We have direct, clear access to them, in the way someone at a movie theater has access to the picture on the screen. The Cartesian ego also has direct, unmediated access to thought processes. We can turn our inner eye toward our thoughts in the way we would a piece of paper that lays out a step-by-step argument. We can observe the mental moves we’ve made and introspect on these thought patterns by just observing them directly. When we introspect, what’s happening is that we’re stepping back and observing the overall shape of our thought processes as they happen, like watching a line of dominos fall while standing above them.

3. The basis of all knowledge lies in our first-person subjective experience

Anything we could call real knowledge of the world has to come from the Cartesian ego observing and experiencing the world, or introspecting on its thought processes. A machine cannot have knowledge, for the same reason a line of dominos cannot have knowledge: there’s no Cartesian ego behind the scenes experiencing and judging the world. Thus, true artificial intelligence is impossible by definition, and many go farther and say AI is useless at what it’s trying to do. It can only ever imitate and copy knowledge. It cannot have knowledge of its own. It will always and only be glorified auto-complete. Even a machine that can handle and process large quantities of information cannot have knowledge, because the mind does more than information processing.

Many people apply this to our understanding of language as well. We only access the meaning of words via our first person subjective experience. I only know what “blue” or “tree” means because I have had subjective, first-person experiences of blue things and trees, and have learned to associate the words with those experiences. More complex concepts like “the national debt” are made up of simple concepts I’ve experienced like “money.” Any entity without a Cartesian ego cannot actually understand the meaning of words, because it cannot have these foundational experiences where it learns what words really mean.

Together, these three beliefs guide how many people think about their own minds, and how they think about AI. I will argue that they are incorrect.

The Cartesian view of the mind

The three of these paint an overall picture of the mind: a rich, internal theater where our egos live and experience things. Most of the important stuff that we consider “mind” happens here. Mathematical functions performed on our experiences and ideas happen a little behind the scenes, and we can perform those functions in our little theater as well (like when we hold numbers in our head while adding them together). Computers can replicate these mathematical functions, but they cannot replicate the internal theater. My goal for this post is to build some intuitions for the idea that this theater doesn’t exist. That we are not egos perceiving clear, unambiguous experiences, and that the mind is actually functions all the way down, where “function” is defined as any process that takes in inputs, performs computations on them, and gives an output.

Naturalism

Definition

I’m arguing for “anti-Cartesian naturalism.” Philosophical naturalism is a loose concept. It basically means “the world follows the laws of science, there is nothing magical happening.” Most analytic philosophers are naturalists, they just have strong disagreements about what counts as part of the natural world. There are three concepts associated with naturalism I adhere to in philosophy of mind:

• Physicalism

• Functionalism

• Evolution

I’ll summarize and give some basic arguments for each.

Physicalism

Physicalism is the view that the only things that have causal effect on physical objects are other things described or used by the laws of physics. Physicalism could be said to mean “Ultimately, what exists is the universal wave function.”

For something nonphysical to have causal effect on physical entities would violate our current best theories of physics. Anything new that has causal effects is likely to be an entity similar to what physics is already describing. Physics has been remarkably successful at describing the world, and physicists operate under the assumption that anything influence physical objects will itself also be governed by physical law.

These are some qualities that all physical entities seem to share, and that differentiates them from supernatural entities:

1. Spatiotemporal location: Physical things exist in space and time. They have locations, durations, and can be detected at specific coordinates. Supernatural entities are often conceived as existing “outside” space and time.

2. Causal structure and lawfulness: Physical things behave according to regular, mathematical laws that describe their interactions. These laws are:

   1. Discoverable through observation and experiment

   2. Consistent and predictable

   3. Expressible mathematically

   Supernatural things are often defined by their ability to violate or transcend these laws.

3. Detectability/measurability: Physical things can be measured, either directly or through their causal effects. They leave empirical traces. Dark matter counts as physical even though we don’t know what it is, because we detect its gravitational effects.

4. Public accessibility: Physical phenomena are intersubjectively observable. Multiple people can measure and verify them using instruments. They’re not private or accessible only to special individuals.

There are entities in the universe we don’t understand, like dark matter and energy, but we should still expect those entities to conform to these 4 patterns when we eventually learn more about them. While physicalists believe that many more things “exist” besides fundamental particles and fields, those things are ultimately just names for those fundamental particles and fields interacting in some way. Even something as abstract as “the national debt” can in some sense be reduced to the incredibly complex behavior of the physical particles that make up people on Earth.

Functionalism

From the Stanford Encyclopedia of Philosophy:

Functionalism in the philosophy of mind is the doctrine that what makes something a mental state of a particular type does not depend on its internal constitution, but rather on the way it functions, or the role it plays, in the system of which it is a part

Functionalism’s main claim is that mental states are defined by what they do, not what they’re made of. Among professional philosophers, functionalism is the most popular account of mental states.

A mathematical function takes an input and produces an output. Functionalists imagine mental states as being functions that take specific inputs and give outputs. A good example of something else like this is monetary value. “Twenty five cents’ worth of value” can exist in a quarter or a digital bank record of $0.25. What makes it the same monetary value is the function it plays in our broader system of exchange. When you transfer it to someone else, you can expect their behavior to be the same regardless of whether it’s delivered as a physical object or a digital exchange.

Similarly, the state of being in physical pain can happen in different animals with different physical brain structures, or even brains made of different physical substances, provided that their mental architecture reacts similarly (gives different outputs) to the same inputs. Just like the monetary value of twenty five cents can be instantiated in different physical systems (a metal coin or a computer), mental states as functions can also be instantiated in different physical objects.

If a system is able to reproduce all the same inputs and outputs as mental states as they occur in human minds, and is just made of different physical material, we should probably assume that it also has the same mental states as the human brain.

I don’t think current AI as it exists is conscious, but it is possible in principle for a machine made of silicon to be conscious like humans if it can reproduce the same inputs and outputs of our relevant mental states. It might be that at a certain level of complexity AI could also have mental states vastly alien to what humans can experience, but that we would still consider conscious experience.

An argument for functionalism, presuming physicalism

Suppose that we were able to replace exactly one neuron in my brain with a silicon chip that could send and receive exactly the same electrical impulses at exactly the same times as the neuron. Maybe there’s something about the substrate of silicon that prevents it from replacing all the functionality of a neuron, but let’s presume for now that it can send and receive all information the neuron receives. What would happen?

Importantly, if physicalism is true, nothing about my behavior would change. There wouldn’t be some immaterial soul or something similar swooping in to say “Hey, Andy’s brain has something strange in it. Something’s off!” The same electrical impulses would happen in my brain either way and cause me to behave in the same way. The same inputs would lead to the same outputs. If I looked at a tree and said, “I see a tree” before replacing a neuron with a chip, I would say the same afterward.

So if physicalism is true, my behavior would stay the same after my neuron is replaced with a chip. What would happen to my conscious experience of the world? Let’s imagine I’m staring at a tree (I’m using an example of my conscious experience of my field of vision because it’s easier to make an image). It seems like there are maybe 4 options, shown below:

• Option 1: No change - My conscious experience of the world doesn’t change.

• Option 2: A little missing - Maybe some small specific part of my field of vision or something similar goes blank.

• Option 3: Less consciousness - There’s less conscious experience in general. Maybe my experiences are more blurry or dim, like I’m in a dark room.

• Option 4: No consciousness - I no longer have conscious experiences.

We’ve established that my behavior wouldn’t change. Physicalism implies that the same inputs would lead to exactly the same outputs. This would make option 2, 3, and 4 very strange, because I wouldn’t react to my conscious experience changing, because my outward behavior would be exactly identical to the before case. If I looked out at a tree, and my conscious experience included a small piece of the picture missing, I would notice that and comment on it: “What’s going on? There’s a black dot in my field of vision.” If there were less conscious experience in general, I’d probably say “My vision is suddenly very blurry.” If there were no conscious experience, I’d say “Aaaah! I can’t see!” Because my behavior doesn’t change, I don’t say any of these things, I say “Look! A tree!” This would be very strange behavior if 2, 3, or 4 were true.

I trust my current self’s reports about his conscious experiences. My speech about my conscious experience matches the experiences I’m having. I should probably trust silicon neuron brain Andy’s reports as well.

Maybe it’s possible that my conscious experience would be disconnected somehow from my physical behavior, where I’d be helplessly losing consciousness as my physical body behaved normally as if it were a robot. Unconscious silicon brain Andy would take over as normal brain Andy helplessly lost consciousness and died. This would be very strange, because most of my day to day behavior is responding to my conscious experience. If you somehow removed my conscious experience’s ability to affect my physical behavior, I’d behave in very physically different ways, because I’d be missing a lot of important information. I wouldn’t react to heat or cold or anything in my field of vision. I might behave as if I were dead. It seems like it’s very likely that my conscious experience wouldn’t be affected by replacing 1 neuron with a silicon chip.

We could then go ahead and replace a second neuron with a silicon chip. For the same reason, my conscious experience wouldn’t change. We could build special chemical receptors and signals to replace other parts of my brain. As long as they had the same inputs and outputs on my behavior, I wouldn’t expect my conscious experience to change. At the end, I’d have the same mental states as before, but my brain would now be made mostly of silicon and other materials. This thought experiment is evidence that functionalism is correct: what makes something a mental state depends on its inputs and outputs, not on the specific physical material the system creating that mental state is made of.

So we’ve established that if physicalism is true, it seems likely whether my mind is in a certain conscious state is that the same inputs lead to the same outputs, not the internal constitution of what’s creating those inputs and outputs (a neuron vs. a silicon chip). Physicalism seems to imply functionalism.

Two other key ideas that together seem to imply functionalism:

• The Neuron Doctrine: The idea that the brain is made of discrete cells (neurons) that communicate through connections. This supports functionalism because it suggests the brain works through information processing between units, not through some special physical substance. This is a central tenet of modern neuroscience.

• The Church-Turing thesis: Any function that can be computed by any possible mechanical process can also be computed by a Turing machine. In other words, there’s a fundamental equivalence to all forms of computation. They’re all doing the “same thing” at a deep level.

If the mind is instantiated by a system of neurons taking inputs and sending outputs to each other (performing specific functions), and that this exact same type of thing can be instantiated in many different mediums, this seems to imply that everything we think of as mental can in principle happen in other systems that can perform computations.

It’s important to note that many non-physicalists are also functionalists, David Chalmers being the most famous example.

Evolution

If naturalism is true, the mind is a product of evolution. Like all other parts of all animals, the mind was subject to evolutionary pressures and natural selection. We should not expect that the mind came pre-made to access abstract truths about the world. Instead, for most of life’s history, minds mainly operated as machines to predict very local environments and specific situations, help the animal survive and reproduce, and (in more advanced brains) achieve and compete for social status (social status here being very broad, and including things like friendship and belonging). Given our evolutionary history, the real question is “Why do people believe true things?”

Dan Dennett had a great line about how “conscious human minds are more-or-less serial virtual machines implemented inefficiently on the parallel hardware that evolution has provided for us.” Put in more everyday language, our ability to do abstract reasoning, to follow a logical argument from premise to conclusion, to work through a math problem step-by-step, or to carefully consider the pros and cons of a decision, runs on top of brain hardware that wasn’t really designed for this kind of thinking.

Our brains evolved to handle dozens of things simultaneously: monitoring our surroundings for threats, maintaining balance, regulating body temperature, processing faces and voices, coordinating movement. This massively parallel processing is what kept our ancestors alive. In comparison, conscious, deliberate reasoning was rarely useful in the ancestral environment.

This helps explain why certain mental tasks feel so effortful. When you’re trying to hold multiple abstract ideas in your head at once, or forcing yourself to focus on something boring, you’re essentially running software that’s fighting against the grain of the hardware. Recognizing a friend’s face in a crowd or catching a ball requires vastly more computation, but feels effortless, because that’s what the parallel hardware excels at.

We shouldn’t expect our conscious minds to be perfectly rational machines, or to have some kind of magical access to higher realms of truth than what is available to other complex physical systems. We can obviously access truths that other systems can’t, but our access to these truths demands physical and evolutionary explanation.

• I was going completely crazy with the number of everyday people I was meeting who suddenly all had wildly inaccurate confident beliefs about AI, energy, and water. All of these beliefs were easily disprovable with simple easy-to-find statistics and comparisons, but I wasn’t finding anyone online doing it. Every piece of news coverage I was reading had ridiculous comparisons (the first one I read literally said that ChatGPT is now using more than twice as much energy as a whole person) and no one doing the simple David MacKay move of actually putting the numbers in context.

• I had a decent background in the general facts involved after following climate change for 15 years and teaching physics for 7 (if you’ve enjoyed my explanations here you can find my full explanation of all high school physics on my YouTube channel) and it was satisfying to use my stored-up knowledge. It was nice to get out some really fundamental ways I think about climate to a big audience.

• I was getting a lot of great feedback. My original two posts (here and here) have been collectively read 230,000 times, and posting the first was the reason my blog took off:

  

  It’s put me in touch with a lot of really cool people in tech and journalism and environmentalism, and has been extremely fun. I’ve developed a reputation as the AI water guy.

  

• This was one of the first topics I was able to do very rapid, complex research using chatbots as aids. A lot of people who don’t use them much don’t know how easy it’s become to just have them make huge catalogs of relevant sources and to double check all the sources they give. Here’s one of many examples:

  

  I feel like I’m writing with a whole team of researchers by my side now. I owe a lot of this blog’s success to AI chatbots. At some point I’ll write an update to how I use them.

Now I’ve developed a pretty huge catalog of takes on AI and environmentalism more broadly. I always wanted a topic where I could imitate Piero Scaruffi and leave a big collection of takes important to me people could explore for themselves, and now I have that.

I’ve run up against a big wall of reasons to take a break from the subject:

• Everyday people are converging on the correct objections. Much more than before, I’ve noticed a lot more people saying “AI water use is mostly fake, personal prompt costs are mostly fake, but AI as a whole is going to put a huge strain on our electrical grid going forward that’s going to matter a lot for the green energy transition, local electricity prices, and air pollution.” To be clear I don’t think I caused that, it’s just that the conversation has found its footing more than it had a year ago.

• I’m working on AI and the environment in other places. I have a few pieces forthcoming in more established places than this blog and want to give them my full “AI and the environment” energy right now.

• I’ve said most of what I want to say about it. Some of the new objections to AI data centers are getting weirder and weirder. Some people are now saying the land use is an issue, which is so wild that I don’t even think it deserves a post on its own (they’re so incredibly compact relative to what they do, and such tiny tiny parts of America’s full land footprint. We have a lot of land!). I’ve addressed what I think I can address, and other stuff (like household electricity prices) are complex enough that I feel like I can’t do them justice with the limited time I have right now. I’ve really really really been thorough in addressing every last point. Every now and then some new big article will come out with some massive incorrect claim about AI and the environment, and every single time I feel like my past blog posts have already addressed all the goofy moves the author is making.

  

  I can have a hit tweet on the topic every month or so when a new article comes out, that takes me ~10 minutes to research and type, and reach a pretty large audience. This leads into the next point.

• I’ve gotten most of the “explore” out of the way, what’s left to do is “exploit.” In the explore-exploit trade-off, I feel like I’ve covered a lot of the ground and learned where most of the contours of the debate now. What’s most impactful to do now is mash a button on the most important points.

  

  This is me saying “It doesn’t use that much water tho” over and over again. Because I feel like I’ve found the most important points, I’ve mostly just been saying the same few simple things (listed here) over and over in response to different specific arguments. This can get old, and is starting to not feel valuable.

• My impact on this topic could be bigger elsewhere. I think if I wanted to actually maximally change the conversation on this, I should make some kind of YouTube overview. I don’t have the capacity for that right now, but if I’m being honest and wanted to build the biggest audience and change the most minds, that’s where I’d do it. If you’d like to run with a lot of my points on YouTube, I think there’s a ton of low hanging fruit in the debate there.

• I’m burned out on this topic. Back when I was teaching physics, the word “physics” had become painful for me to hear, because I’d hear it so often every single day. Now typing the word “data center” is equally painful. I kind of just need a break.

• I’d like more people to be doing this. I really think what I’m doing here is straightforward and a lot of people could be doing the same. I’m flattered to get people messaging me about this.

  

  But I’d also like more people to run with these basic moves I keep making over and over and apply them in new places! I’ve gotten some nice messages like this:

  

  and I’d like more people to do the same. Almost no one anywhere is actually writing about this thoroughly for a broad audience. It’s either dense academic papers of mostly correct takes, or wildly off coverage from people who seem to mostly be following whatever others have already written.

• I should prioritize more important stuff. I am into and a representative of effective altruism, a key tenant of which is that most of your impact in life comes from what you choose to prioritize, and most people very quickly get obsessed with a very local maxima of impact they happen to discover and lose the opportunity to do a lot more good elsewhere. Posting about this has been a fun hobby for me, but I think I need to orient my hobbies in more impactful directions. I’d like to focus more on:

  • Misuse risks from advanced AI systems.

  • How seriously to take the basic x-risk case from advanced AI (my “p(doom)” has always been low but nonzero, and I need to see how justified that is).

  • Animal welfare and ethics.

  • The general DC AI policy space.

  • AI and China, and China in general.

  • General lifestyle posting (I think this short post is one of the best things I’ve written and I’d like to write more like it).

  • Ideas from philosophy that have been important to me.

• I need to give more time to big work projects over the next few months. In the words of one of my closest friends:

  

I’m willing to bet that you haven’t read every last post I’ve written on this topic. If you’d like more posts from me on this, first circle back and read the other ones. Maybe upload them all into an AI model and have them give an AI take from my perspective on a new issue you’re trying to understand. Or best of all, just start doing these deep dives yourself! I’m mostly using high school-level math and physics here.

Thank you to my legions of new followers. I’m so excited to be participating in the debates with all of you about this amazing, wild, sometimes terrifying new technology and its implications for society and the future. It’s crazy to have such a big audience from all over and I’m excited to keep this adventure going. If you ever want to chat, your bar for reaching out to me at AndyMasley@gmail.com should be low.

A bounty for mistakes

In writing this blog, I have also developed many critics. While some have been really useful, others make what I think are wildly false claims about what I’m doing here. Take this one:

All my claims are based on external trustworthy sources I link, and if I link myself it’s only to show an argument I’ve made somewhere else using external sources.

I stand behind everything I’ve posted on this blog. To demonstrate, I offer a simple bounty: I will send you $50 if you can find:

• A legitimate source for a statistic I cite where I lie about the number it’s giving or I get it wrong, which invalidates a core argument I’m making.

• A source for a number I cite that’s illegitimate, and that most legitimate sources disagree with, and this updated information invalidates a core argument I’m making.

If I did a calculation wrong and 0.0000102% should actually be 0.0000103%, I’m not going to consider that to be invalidating. But if I say the median chatbot prompt uses 0.3 Wh, and it actually uses 300 Wh, that does invalidate a core claim I make. A few more rules:

• All claims need to be analyzed by when I said them. If I make a claim about ChatGPT in June, and Sam Altman comes out with a new model in December that’s 100x as energy intensive, my claim in June isn’t wrong, just outdated.

• I’m vetoing very specific disagreements of what counts as “using” water. Whether to count rainfall on crops or into lakes dammed by hydroelectric power stations turns out to be very relevant for the water debates. The way both are reported leave huge error bars in both directions and I’ve tried my best to take a middle ground on them. I’d be interested if you disagree with my takes, but I worry that both extremes have such reasonable people on them that I could go back and forth between sending both $50 as I oscillate between two extremes.

• I have a few pieces I typed fast (like this one) where I haven’t circled around to cite some of the stats in them, because I had gone over them in previous posts. I ask for a reprieve if I don’t cite a source in a newer post but I had cited the source for it previously, and the source is legit.

• I used some rough back of the envelope math for the graphs in my very first post that I want to revisit, so I’m walling those off specifically. Sorry fam.

If you find something, email me at AndyMasley@gmail.com. I’ll be happy to have a back and forth with you if I disagree with your take and why. You can feel free to publicly shame me if I’m evasive. If you’re right, I’ll send you $50 and post the update on a wall of shame. I might be a little slow to get to you, ping me if I don’t respond the first time, publicly shame me if you don’t hear back in a few weeks.

My main worry hasn’t been that AI’s being unfairly attacked (I’m to put it mildly ambivalent about the future of AI). It’s more that readers are coming away with wildly inaccurate beliefs about where AI and data centers fit into the broader environmental picture. Getting this right matters a lot, because it’s very hard to keep people’s attention on climate and the environment for long. If they get distracted by a relatively small issue, they miss the opportunity to do a lot more good elsewhere. Environmental problems are best understood as a series of complex trade-offs. We need to have a correct picture to manage those trade-offs well.

My ask isn’t that you as a journalist present AI positively or negatively, it’s that you aim to give readers an accurate picture of its place in the environmental issue, and not leave them less informed about where the most pressing environmental problems are in their communities and the world more broadly.

These are all my requests. I go into more detail on each below:

• The big ultimate goal: readers should leave your story with a better understanding of how data centers fit into the broader environmental problems a region or the world is facing.

• Don’t ever share contextless large numbers.

• Readers should leave understanding that the energy and water used by individual prompts doesn’t meaningfully add to their personal carbon or water footprints.

• Compare data centers to other industries and commercial buildings, not household use of energy and water.

• Criticize AI specifically, but don’t imply that it’s inherently weird or bad to spend physical resources on digital products.

• Don’t imply that data centers make the computer processes inside less efficient.

• Don’t imply that data centers are new or uncommon.

• Don’t leave readers to infer for themselves that data centers have caused specific catastrophes that haven’t actually happened.

• Water-specific asks

  • Don’t report a data center’s water permit as the amount of water it will actually regularly use.

  • Don’t frame the offsite water use as “hidden” that the companies are dishonestly keeping secret.

  • Don’t use “straining local water systems” or “exacerbating drought” as synonyms for “using any water at all in a high water stress area” without clarifying what the actual harms are.

For context, this recent critical story on data center buildouts checks my boxes for great coverage. I think more writing should aim for this level of quality.

The big ultimate goal: readers should leave your story with a better understanding of how data centers fit into the broader environmental problems a region or the world is facing.

If a reader leaves your story convinced that data centers are the main unique threat to a region’s water, when golf courses in the region are using 30 times more, readers have been misinformed. This often happens in coverage of data centers in Maricopa County Arizona, where data centers are framed as a unique water catastrophe for the region, but are actually using 0.12% of the county’s water, whereas golf courses use 3.8%. If you write a story about Maricopa County or somewhere else, a reader shouldn’t later be shocked to see this graph:

This is a good general rule of thumb. If after reading your article, a reader can feel shocked and confused to see some simple data completely clash with the vibes of what they just read, the article has miscommunicated the problem.

Don’t ever share contextless large numbers.

This is the singular sin of bad writing on the environment. There are 8 billion people. Anything used by even a small fraction of people globally will use huge amounts of resources by the standards of one individual person, to the point that literally anything in society can overwhelm us with its scale. If you don’t give readers a sense of proportion, you’re leaving them in the dark and unable to make good decisions about the environment. Don’t merely write “10 million gallons of water per year” or “200,000 bottles of water per day” in a vacuum. This is a huge amount of water by the standards of any individual, but it’s also about 10% of the water a large car factory might use. If you drop a lot of huge contextless numbers on your readers, they will basically always be alarmed, whether there’s a “thousand” or “million” or “billion” involved. A reader leaving your article should not be surprised to later find out that the data center you described uses as a car factory.

My favorite book on good environmental communication (that I’d assign to everyone covering this if I could) is Sustainable Energy - Without the Hot Air by David MacKay. The book’s available for free online. This quote from the introduction captures how useless and harmful it is to share contextless large numbers:

This heated debate is fundamentally about numbers. How much energy could each source deliver, at what economic and social cost, and with what risks? But actual numbers are rarely mentioned. In public debates, people just say “Nuclear is a money pit” or “We have a huge amount of wave and wind.” The trouble with this sort of language is that it’s not sufficient to know that something is huge: we need to know how the one “huge” compares with another “huge,” namely our huge energy consumption. To make this comparison, we need numbers, not adjectives.

Where numbers are used, their meaning is often obfuscated by enormousness. Numbers are chosen to impress, to score points in arguments, rather than to inform. “Los Angeles residents drive 142 million miles – the distance from Earth to Mars – every single day.” “Each year, 27 million acres of tropical rainforest are destroyed.” “14 billion pounds of trash are dumped into the sea every year.” “British people throw away 2.6 billion slices of bread per year.” “The waste paper buried each year in the UK could fill 103448 double-decker buses.”

If all the ineffective ideas for solving the energy crisis were laid end to end, they would reach to the moon and back.... I digress.

The result of this lack of meaningful numbers and facts? We are inundated with a flood of crazy innumerate codswallop. The BBC doles out advice on how we can do our bit to save the planet – for example “switch off your mobile phone charger when it’s not in use;” if anyone objects that mobile phone chargers are not actually our number one form of energy consumption, the mantra “every little helps” is wheeled out. Every little helps? A more realistic mantra is:

if everyone does a little, we’ll achieve only a little.

Readers should leave understanding that the energy and water used by individual prompts doesn’t meaningfully add to their personal carbon or water footprints.

Simply reporting a ChatGPT prompt uses “ten times as much energy as a Google search” doesn’t give a reader much useful information. It’s fine to report on its own, but it shouldn’t be used to imply that ChatGPT is going to become a noticeable part of a person’s daily carbon footprint. Google searches use tiny amounts of energy. Wouldn’t it be weird to hear a climate scientist say you should limit your Google searches? It’s like saying that a digital clock uses 1 million times as much power as an analog watch. That’s true, but it’s such a minuscule amount of power that they both round to zero. Readers should be left understanding that their individual prompts are not really affecting their carbon or water footprints at all. The average American would have to prompt ChatGPT 1000 times per day to raise their carbon footprint by 1%. They would have to prompt it 8,000 times per day to raise their water footprint by 1%. But if they were doing this, they would likely be skipping other activities that were way more harmful to the environment. I’ve read too many articles that give readers the sense that using chatbots can somehow meaningfully contribute to their individual environmental footprint. Even if you try to include every last way chatbots affect the environment, the numbers just don’t add up. It is wildly misleading to imply that using AI raises your personal carbon or water footprint.

Compare data centers to other industries and commercial buildings, not household use of energy and water.

Because AI and the digital economy are large general industries in America, their proper context is other industries, not large multiples of personal lifestyle things individual people do. It would be weird for me to compare the American auto industry’s energy use to the energy I use in my home. Better comparisons would be to the steel industry, or agricultural industry, or other places where I can understand how car manufacturers fit into the big picture of America’s industrial use of energy and water. Simply reporting “Wow, the auto industry uses millions of times as much energy as Andy! Shocking.” would give readers no useful information.

But most media I’ve seen about AI data centers seems to only ever compare AI energy and water to massive multiples of personal household use, not to other normal industries. Take this recent example from the BBC:

The video announces that all data centers in Scotland are using 27 million bottles of tap water every year. That’s a weird way to talk about a large industry and doesn’t give the viewer any context for how data centers compare to other industries. If we make the comparison, we find that all data centers in Scotland combined are using just 4% of the water used by a single large car factory. That’s just 0.004% of Scotland’s total water use. This is a much more helpful comparison for readers. Many viewers would likely be confused if after watching this video, they discovered that everyone involved had been talking about a single car factory in Scotland. It gives viewers a wildly misleading impression of where water’s actually being used. Because most water use is in agriculture and industry, not in households, you should be sure to give readers context for how much resources data centers are using by comparing them to factories, farms, and other things that use larger amounts of water.

This is a very easy move to make. It doesn’t add many words to your piece, and gives readers a much more complete picture of how data centers compare to other industries.

These are some specific ways data centers are often talked about that other industries aren’t:

• Anything referencing “community water” as in “data centers want to tap into local community water.” Most water in America is used for large scale agriculture and industry, not households. Any industry could be said to be “tapping into community water.” It would be strange to read an article about the Detroit auto industry “tapping into Detroit’s community water” even though the auto industry there uses millions of gallons of water per day. It’s understood that most parts of the country have both industry, commercial buildings, and homes, and in fact places are often benefited by having more industry and commerce.

• Ominous stories of data centers “using more than 20% of a town’s water.” This only sounds shocking if you don’t think of data centers as any other industry. If a factory or large college were built in a small town, and you found out either were using 20% of the town’s water, this would make sense. The main business in the town would use a big chunk of its resources. This can often be beneficial, as it gives local utilities more money to invest back into improving service.

Criticize AI specifically, but don’t imply that it’s inherently weird or bad to spend physical resources on digital products.

The digital economy more broadly has been a gigantic civilizational achievement for transmitting valuable information. It’s part of what’s contributed to dematerialization where over time we’ve had more economic growth using less physical resources and energy. Digital goods are valuable because information is valuable. You can hate AI and think it’s all bad or useless, but don’t imply that it’s wasteful to spend physical resources on digital goods in general. Computers are the most efficient way to deliver information. Information is valuable. The value of a book is mostly in the information it contains, not the physical paper or ink that make it up. It’s okay and good to spend physical resources to acquire information, especially when doing it digitally is saving physical costs elsewhere.

Don’t imply that data centers make the computer processes inside less efficient.

Data centers are the most efficient ways to do large-scale computing. This point seems obvious, but I’ve met a lot of people who follow AI and the environment in their spare time who seem to think that the environment would be helped if we didn’t use data centers and ran more computer processes in our homes. This would actually be pretty terrible, everything we did would use way more energy. I’m not sure where they’re getting this idea, but the ways data centers are framed sometimes seems to contribute.

Don’t imply that data centers are new or uncommon.

Massive AI data centers are new, but readers should also understand that everything they do online besides AI has always used energy and water in data centers. I meet a lot of people who talk as if AI is the only digital product we use that uses water. There are ~5,400 data centers in the country. 99% of Americans live within 50 miles of a data center. They are ubiquitous and normal. Make sure to distinguish massive new AI data centers from all the others.

Don’t leave readers to infer for themselves that data centers have caused specific catastrophes that haven’t actually happened.

A very common move in a lot of reporting on data centers and water is to say that data centers “can create problems for community access to water” or “data centers can drain local aquifers” without once mentioning that of all 5,400 data centers in the US, none of them have done either. It’s true that they technically “can” create problems for access to water, because they use water! This is true of any industry. Anything that’s clearly meant to let a reader infer something has happened that hasn’t confuses them and leaves them less informed.

Water-specific asks

Don’t report a data center’s water permit as the amount of water it will actually regularly use.

Many (I’d say the majority) of articles I’ve read on data center water use frame the data center’s water permit as the amount of water it will actually use. This is wildly misleading. Water permits are difficult to amend, so data centers request the absolute most water they would need in extreme situations to give themselves a safe upper bound.

Don’t frame the offsite water use as “hidden” that the companies are dishonestly keeping secret.

The companies that run data centers often don’t have access to exactly how much water the power plants they draw from on the grid use. If a data center reports “We will use x amount of water” to the public, it seems reasonable for the public to assume that they are only talking about the water used in the building. If data centers become more efficient, that means that they use less water relative to the power plants around them. But this also means that the “hidden” cost in the power plants is a much larger multiple of the data center’s water. Talking this way effectively punishes the most water-efficient data centers, and implies they’re actually worse because they have a much larger proportion of “hidden” water offsite. This is backwards. I explain this more here.

Don’t use “straining local water systems” or “exacerbating drought” as synonyms for “using any water at all in a high water stress area” without clarifying what the actual harms are.

Literally everything that uses water in a drought-prone area is “exacerbating the drought” and “straining the local water system.” But using those terms can give readers an inaccurate picture if they’re using an extremely small amount of the water. There are many articles referencing data centers “exacerbating drought” in Phoenix that don’t mention they’re only using 0.1% of the water there. Find more specific ways to get across how much they’re adding or not adding to the problem.