This post is written for anyone already kind of into EA or thinking about EA comms, or who just wants to read my perspective on it. I have 3 thoughts on EA comms I want to get across. For what the specific deal is with EA I’d read the first part of my longer series on it here.

Promoting the ideas should not have to mean promoting any one specific community or individual associated with EA

I think of effective altruism in the same way that I do other big ideas that have influenced me, like specific ideas in philosophy or economics or science. I can talk your ear off about Hilary Putnam or Quine or markets, but I can’t guarantee at all that any one person or group of people who are into Putnam, Quine, or markets will be good to hang out with, professional, or safe. Similarly, while I’m obviously excited to promote EA ideas, I don’t want this to be read as endorsing any one person or org who happens to be into EA. It’s the same reason I’d like to be able to write a lot about why capitalism is so useful without having it read to also mean “And you should like and trust people who identify as capitalists!” I don’t actually believe that.

EA is in a weird place right now. The term is used to mean both the cluster of ideas and the “community” around it, and both are drawing scrutiny at the same time. I was personally involved in building up the EA network in Washington DC, so I’m intertwined with specific communities of people into EA. But even as someone that intertwined, I never meet someone into EA and immediately think “Ah, they must be a good person, I should trust them, and other people should too.” It’s the same experience as meeting a self-identified capitalist or physicalist. All that tells me is that I’ll have a lot to talk to them about on a very very specific topic. So in my writing, my goal isn’t to get my readers to vibe with any one person or org in EA either, but to get across the ideas I like and want to spread.

This all seems so straightforward that it shouldn’t need to be said, but I’m worried that a lot of people are hesitant to write explicitly about what’s been useful to them about EA ideas, because they’re rightly ambivalent about specific ways parts of the EA community have behaved, now or in the past, and they have an understandable fear that writing about the ideas means endorsing some or all of what’s actually been done in the name of those ideas.

I think a lot of us should do a lot more work to separate promoting the ideas from endorsing the people as much as possible. I see the specific people who happened to get into EA as somewhat incidental, and as a small slice of a potentially much larger group of people thinking with the basic mental tools the idea cluster provides. To be clear, there are a ton of people and orgs in the “community” who I’m very honored to be associate with, but also many I’m not at all. Any suggestion that by identifying with the ideas you’re also identifying with the specific people who hold them seems harmful to taking the ideas themselves seriously.

None of this is to say that anyone should feel obligated to promote EA ideas if they’ve been put off by the people involved. It’s only to say that if you’re considering communicating EA ideas that have been useful to you, you should know that you have zero obligation to defend or associate with any one of the people or orgs currently associated with EA (or even most of them). This is one of many reasons I’d personally like to drop the “community” framing of EA that I mentioned in this other post. I’ll go into that in a lot more detail in the next few.

I’m writing more posts in this series on how EA has influenced my thinking. If you, dear reader, have enjoyed “my thinking” in general, it’s difficult to get across just how many of the mental moves I’m making have their roots in the EA intellectual scene. I’m hoping the rest of the series will make that clearer. I see the brand of effective altruism as basically a useful bridge to these ideas, and I want to keep that bridge solid so that other people can get the specific value out of them that I did. What I’m not trying to do is argue that anyone else who’s crossed the bridge must be a great person you should trust with power and influence.

I do want to clear up a lot of misconceptions about us, about that bridge, and about the cluster of ideas it leads to. Most of all, I want to try to show why I and so many other people continue to be attracted to an idea that many have come to associate with simple-minded, one-dimensional utilitarian bullet-biting social games that no serious person could entertain. Underneath that surface-level impression there is in fact a very dynamic idea cluster that’s wildly different from the average person’s understanding of it. I think we could use a lot more well-rounded adults who are willing to try to write clearly about what that underlying idea cluster is actually like, who feel zero obligation to defend “the EA community,” and who know they can speak freely about any people or orgs they don’t like or agree with in it.

The good quadrant of EA comms

I think of a lot of effective altruist comms as existing in two of four quadrants:

If you can’t tell, I’m not a big fan of the bottom left quadrant. I really don’t think we need to write more books or blog posts about how we “just want to do good” and how EA is just about “doing the most good,” because it’s not. The value I personally get out of the idea cluster feels far removed from “doing the most good” in the abstract, because I realize how philosophically contentious that idea is.

The kind of thing critics mean when they talk about effective altruism isn’t the (still under-discussed) fact that you can compare charities to each other and some are wildly more effective than others. They specifically mean the glued-together cluster of ideas around ethics, philosophy of mind, evolution (for thinking about animals and AI), evolutionary psychology (for trying to understand why people, including people into EA, often fall short of their lofty moral language), markets and economics, history, and trying to get at truth in general that make up what both I and critics of EA see as the core of the idea. I think this idea cluster looks completely insane when you only look at its strange outputs (AGI might happen soon, we should probably donate way more abroad or to animals than we are, and a lot of people are getting these questions wrong), but compelling once you’ve seen the full picture and the reasons smart, well-rounded adults might be drawn to it.

I think there are a lot of great examples of this type of communication (the 80,000 Hours podcast and article series, Cold Takes, plenty of blog posts, etc.), but I consistently feel like I don’t have enough resources on these ideas that I can send to my own smart, well-rounded friends in their 30s to help them understand what I specifically see as so special about the idea cluster, despite seeing what I’d personally say if I had time so clearly. A lot of the best EA writing isn’t even labeled as EA, so people leave without making the connection. That’s why I’m taking a shot at writing the series I’d like to send those people. I’d basically like to contribute to the very top of the top left quadrant, and I think this is the main thing a lot of EA comms is missing right now.

To be blunt, I think it looks a little ridiculous at this moment to start skeptics off in the shallow end of the pool, guiding them with baby steps through “Well, suppose you wanted to do MORE good, where would you go next?” This is condescending to smart people. My goal in my own writing is to basically start way way way in the deep end, and just trust that people will follow me if I use clear, accessible language to get across what’s going on in that deep end and what’s interesting to me about it. I know a lot of other people who have been motivated by these ideas are also capable of this, but I’m worried they’re limiting their vision of EA comms to object-level “See look at this individual neat thing I did that’s broadly popular, AND I’m into effective altruism!” I’d personally really love to hear a few people who have done lots of interesting stuff go into much more depth about what the actual idea cluster has been like and done for them in a lot of specific detail, that could make their entire honest thinking about what they’re doing clear and compelling to someone who hadn’t bumped into EA before.

I’d like to fill more of that top left quadrant with what I’d want to say to a smart friend in their 30s about what’s actually up with the ideas popular in EA, and why the cluster is interesting to me. Right now I can see other people trying to do this, but I still find there’s a huge amount I’d want to say that isn’t being said. I personally know a lot of people I’d trust to do a good job walking any smart person I know through what’s up in the EA idea cluster, but they’re mostly too busy to write or otherwise communicate. I cannot tell you how much comms about EA ideas needs more hands on deck to preserve the bridge to these ideas that I think are so useful. (And remember, I’m talking about the ideas here. I’m really not asking you to defend every last part of “the community.”)

If you’re on the fence about trying to communicate what’s up in EA, I’d very strongly encourage you to try your best at writing something for the top left corner of that top left quadrant. Write something that would’ve been clear and useful and direct for your past self about what’s actually up at a high level, and where the most useful ideas are, without holding back disagreement or disapproval or anything else you actually think. You might be surprised at what you have to say.

I’d like a lot more people writing about what they actually think about EA ideas at this high level, that would fit in this top left quadrant, and less stuff that sounds like a sales pitch.

Building bridges to weird beliefs

A lot of people first stumble on EA ideas when they’re in their 20s, because so much of the package is oriented around careers. In your 20s, it’s easy to be attracted to ideas that make you stand out, that make you feel ascended beyond everyday people, and that you can use as a kind of conversational spell to signal to “normies” that you’re so above caring whether they think you’re weird. EA supplies these in bulk, and since it’s so appealing to people in their 20s, you often get people into EA flashing the weirdness in everyone else’s faces. I basically think this is a unique disaster. It destroys the very easy bridge we could build to everyday smart people, and treats those people as so stupid that they couldn’t possibly be worth reaching.

In my last year of teaching, I got the chance to teach a philosophy class (“Theory of Knowledge” in the IB curriculum, for those of you in the know). Toward the end of the year, after the IB tests were done, we had some free time, and I decided to present the drowning child thought experiment. I was averse to sharing anything about my life or ideology with my students, so they didn’t know anything about EA. But I found that over the course of the conversation, they kind of reinvented EA from first principles. At one point, one student said “Well obviously, everyone’s obligated to donate almost all of their money,” and then another said “No wait… what if you’re working on something so important that you need to stay comfortable enough to work on it?” That conversation was a microcosm of the history of good and terrible ideas in EA. Turns out these ideas are easy not only to communicate but to arrive at independently!

More recently, now that AI existential risk has gone mainstream, it’s become incredibly easy to have high-level conversations about basically anything related to AI with anyone I meet. This is a good time to build bridges!

I’m worried that a lot of people who are thinking about EA comms right now are basically thinking in terms of managing the “weird” beliefs that everyday people won’t get, or they think of their “weird” beliefs as something that they need to just accept most people will never think about, and instead just weather the storm of public criticism and remain unpopular. I’ve personally become an optimist on this, and think we’re living in a time when people are much, much more willing to entertain most if not all of the “weird” ideas that fall out of the EA ideological cluster, like the now apparently common view that AI could be radically, maybe dangerously, capable soon. I’d prefer people just try to write directly and clearly for a general audience to build bridges to our weird ideas, instead of treating them as landmines to be avoided in public comms.

Thinking about every conversation as full of landmines that “normies” won’t get is basically a recipe for condescending to your audience, and it’s very, very easy for most people to pick up on when they’re being condescended to. Instead of writing the most watered-down, PR-compliant, vague post that says absolutely nothing interesting or new, it might be good to just rip off the bandaid and try writing the way you’d write to a smart, skeptical friend. Way, way more people are like your smart, skeptical friends than you might think, and communicating with them well means thinking hard about actually building bridges to what you think.

I’m attracted to a particular aesthetic of weathering the storm of societal change to allow fundamentally new things to happen that we couldn’t have imagined before, and of slowly waking up to the fact that most of what we see as foundational, normal, and eternal is actually shockingly contingent, provincial, and recent. There was a time in the not-too-distant past when pizza restaurants and movie theaters didn’t exist, and in 50 years there will be new aspects of the world that feel at least as fundamental to life as pizza restaurants and movie theaters do to me. I associate this vibe with a particular type of minimalism that leaves music, images, and buildings open to being interpreted in wildly new ways as the society around them changes. Some examples are the abstract brutalist monuments to World War 2 in Yugoslavia, which itself no longer exists:

Or specific songs by Brian Eno where he lets a simple, almost silly melody repeat and build until it becomes overwhelming:

Or a lot of New Order songs with such simple melodies and general lyrics that they feel timeless, even though listening to them in 2026 is probably a wildly different experience for me than it was for someone in Manchester in the 1980s:

I like the idea of keeping a part of myself a little detached from the particularities of culture at this exact moment, in the same way these songs and buildings are, so I can learn to let those particularities go as culture changes.

For all these reasons, I’m very attracted to the view that AI will not endanger our survival but will make the world unbelievably weird and new. That’s a world I feel emotionally prepared to handle. I can calmly adjust myself and hold onto the vibes and feelings I experience as most important, which depend so little on the specifics of the here and now.

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I find that I’m also tempted by this intuition when I’m walking around DC, thinking about what could change. When I walk by my local cafe or bookstore, I feel this deep sense that the writing I was reading earlier about AI risk or potential dystopian futures with AI is so abstract that there’s something fundamental about the world it can never touch. The bookstore or cafe feels much more fundamental to what I experience as real, with its ties to my core memories of other cafes and bookstores far away and in the past. It’s like these everyday things flow from some deep, wise center of the world, whereas conversations about AI risk are just surface-level. While I expect the world to change a lot, it feels like this flow from the “real” world will be what changes it. I can’t really describe the feeling better than that, but it’s a very strong and noticeable experience. It’s as if the deep normality of these everyday buildings around me is forming a barricade protecting me from the weirdness of speculative AI risk. I want to be the type of guy who can respond to the subtleties of navigating a bookstore more than I want to be the type of guy who can write and argue about AI risk. Knowing about some specific relation between two obscure used continental philosophy books while sipping a coffee and wearing a tasteful overshirt feels like I’m accessing some deep part of the world that was always there waiting for me to find it, whereas AI risk feels like something shallow and two-dimensional, or like a language game I’ve learned to play with myself.

However, when I step back and ask what’s attracting me to this view, what’s making me feel protected by the everydayness of my specific experience in DC, and what’s making me feel like AI risk is low, it seems pretty obvious that thinking about society-level catastrophe just doesn’t fit with the type of guy I want to be. It feels a lot like I’m looking down at a buffet of options, choosing a bit from effective altruism, a bit from Tyler Cowen-type beliefs about capitalism and change, a bit from the normal techno-optimism that’s defined a lot of my life so far. I put all these vibes on my plate and enjoy the combination of flavors and what they say about my own ideological taste.

I don’t think any of this is an adult way to think about the next 10 years or my relationship to them. All these same impulses would’ve caused me to downplay the threat of the World Wars before either happened, or nuclear risk during the Cold War. I have a strong aversion to thinking seriously about catastrophe. It’s very emotionally intense for me, but also (maybe more importantly) it just doesn’t give off the vibe I want. I suspect a lot of other people have a similar experience.

I’m pretty proud of how I’ve resisted this impulse in the past. In the weeks before COVID hit, I became the local prophet of doom at the school where I was teaching. About a week and a half before we shut down, and before other teachers were giving the same advice (most of them were still laughing off the idea that school would actually close), I started announcing to my class that they should all take their stuff home with them every day, because I expected school to close pretty soon. I wasn’t excited to play that role, but it was pretty easy to see then that COVID was spreading very fast, was gonna be all over the US very soon, and had a reported death rate high enough that it was probably going to cause a lot of things to shut down for a while. I had a record of never crying wolf. I had been pretty cool-headed about other past threats and cataclysms. One day when an anonymous school shooting threat came in, I went into work anyway, knowing the base rates of dying in schools are so low and the false positive rate for threats is so high that being there was still safer than my drive to and from work. I also ended up spending more time than I expected talking students through their unreasonable levels of climate doom, trying to show them that it was possible to be very concerned about climate without believing it would wipe out society in just a few years. But I was able to override my fun, upbeat, cool-headed vibe for COVID because the trends became so obvious that I couldn’t carry them forward without seeing schools close. I was willing to risk crying wolf and break with my normal persona. The buffet option of staying chill was tempting me the whole time, and I had to be more adult, ignore it, and try hard to think about what I actually believed. That turned out to be the right decision!

None of this is to say that our mental aversion to thinking about big cataclysms is any evidence that AI will actually cause one. That’s a separate argument. But in thinking through what you actually believe about AI and its effect on the world, it’s extremely important to avoid this specific move of simply choosing the option that coheres best with your vibe. Instead, you need to look for things to grip onto in the real world. The future of AI might be impossible to fully understand or predict, but we can at least try to think through what our best knowledge of AI, economics, geopolitics, government, and human behavior tells us, and be honest about what they’re actually implying along every step.

This is one reason I’ve been pretty disappointed to see the phrase “I’m not dooming” about AI come to mean “I’m not giving in to negative emotions.” Beliefs about AI shouldn’t come from your emotional state either way. Most people I know who worry about AI catastrophes (myself included) are not predisposed to “doom” about other things. If anything, we tend in a techno-optimist direction otherwise. This general implication that “doomers” believe what they do because they’re neurotic just doesn’t fit my own experience. I constantly have to drag my own mind kicking and screaming to think through AI risk arguments, because they just don’t match my emotional temperament or vibe at all. And yet many of the arguments are so straightforward and compelling to me that I’d easily be classified as a “doomer” (in the sense that I think there’s a real chance AI destroys civilization) despite having pretty low neuroticism. Of course I could be getting those wrong, but my and your emotional state don’t really help in figuring that out. As a claim about your emotional state, “I’m not dooming” is pretty uninteresting, because your emotional state gives you basically no access to the actual future of AI.

Many people understandably think of a lot of talk about AI risk as a kind of social game, and their own belief as something like a specific style of clothes to wear in this game. That might have made more sense 10 years ago, but the trends in near-term AI are looking uncertain and potentially wild enough that it’s time to start thinking about it at the level of seriousness you would have wanted in the early days of the Industrial Revolution or the Cold War. Simply testing out different beliefs about AI as if you’re sampling buffet options isn’t a sufficiently adult way of thinking about the next 10 years of your own life.

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• Basically everyone would like the world to be better in obvious ways, like fewer people dying early or living in poverty. But it’s very easy to get caught up in our own lives and problems.

• Sticking your neck out to say that something is badly wrong costs you something, and actually trying to fix it costs a lot more.

• So a lot of ongoing simple and obvious catastrophes go unaddressed, like deadly diseases in poor countries that can be easily prevented with cheap vitamin supplements, or the treatment of animals on factory farms, or the possibility that highly intelligent, capable machines could be uniquely dangerous, because they’re far away, or weird, or hard to fix from scratch, or because nobody gets much credit for working on them.

A lot of our sense of what we should work on, what’s most worthy of good hardworking people, comes from what the people around us admire and notice, which I talk about a lot in my last post on EA. That works fine for lots of big problems, but it also means urgent, terrible problems far away or outside the view of our normal social communities tend to lose out to much smaller ones close to home.

Effective altruism (EA) is basically an attempt to push back against this. It’s a cluster of ideas oriented around using our best evidence and careful reasoning to find the biggest problems that the rest of the world is neglecting and figure out how to actually help in the most quantifiably good way, plus the loose network of people trying to act on them. (If you’ve heard of it lately, it was probably as an insult in a political fight over AI).

Here’s the short version of the EA I’d like to defend:

• We should take the world’s worst problems seriously, even when they’re far away or strange or unpopular. It is terrible that hundreds of millions of people live in extreme poverty, factory farming is a clear ethical disaster, and the threat that we might lose control of society to AI systems vastly smarter than us needs to be taken more seriously. There are strong moral reasons to devote significant resources (money, time, organizing) to solving these problems, including at some personal cost.

• We ought to use numbers and explicit cost-benefit analysis where they work… Giving $10,000 to one charity means not giving it to another, so it’s important to compare charities that do similar things. Charities that provide anti-malarial bed nets save more lives per dollar than many other global health charities. Simply counting lives respects people’s basic dignity and freedom, because their personal choices or values don’t come into the equation for whether they deserve help.

• … but pluralism and respect for other people as free agents are foundational starting points we can’t override. We should among other things reject confident exchange rates between cause areas. We all live surrounded by free people with wildly different moral views, and society works because each of us makes compromises in our own ethics so that everyone can pursue their own vision of the good life. A totalizing utilitarianism ignores this background compromise. A corollary is that choosing which causes to prioritize can’t be reduced to utilitarian math.

• We should have a strong presumption toward helping others in ways they’re free to turn down (like bed nets, vitamins, and unconditional cash transfers). Utilitarian comparison works best as a way of choosing between interventions that expand people’s freedom and dignity, including their capacity for self-determination. We should also have a strong presumption against biting the bullet of any ethical system that would override people’s decisions about the course of their lives.

• Ordinary rules about honesty, money, power, and personal/professional boundaries still apply, no matter our perceived ability to rationally analyze our choices. Society’s rules are often good in themselves, and people are too fallible and too swayed by their social scenes and status to safely reason their way past them. No one should feel obligated to justify longstanding social rules and conventions around basic decency from first principles. They often exist for reasons much deeper than we can immediately grasp using reason.

• EA works best as a cluster of ideas and a loose professional network (a lot like the YIMBY movement) rather than a “community.” When one set of beliefs decides your job, funding, housing and friends, changing your mind or speaking up gets far too costly.

Part of the reason this cluster is so important to me is that it still often works magic and feels like one of the most intellectually vibrant and fruitful scenes in the world. Our work on factory farming is a good example of the good aspects of EA at its best. Taking it seriously is unpopular (many people don’t know how bad it is, or wave away the idea that animals can feel pain) and inconvenient (nobody wants to give up cheap meat), and EA treats it as one of the biggest problems in the world anyway.

Other things EA has managed to do:

• It has prevented a major war’s worth of deaths, mostly from malaria (GiveWell, a charity evaluator, estimates that the donations it has directed have saved more than 340,000 lives).

• It helped push the share of American egg-laying hens that are cage-free from under one in ten in 2015 to almost half today.

• It started building a whole professional field around the potentially massive risks from advanced AI most of a decade before ChatGPT came out. Some EA thinking about AI from years ago still feels shockingly fresh and relevant.

Because of its track record, and how compelling the EA intellectual scene has been, I’ve been publicly associated with and representing effective altruism for the past 5 years, and was a longtime follower of EA debates and ideas in the 10 years before that. It’s shaped my thinking in the same way my other core political and philosophical beliefs have.

Of course, effective altruist ideas have also led to a lot of harm. Sam Bankman-Fried, a major donor who linked himself closely to EA, defrauded depositors of billions of dollars. EA spaces have sometimes tended toward social and intellectual insularity. EA has taken a lot of criticism over the last few years, and while some of it has been in bad faith, some of it has been completely fair.

The same source of EA’s biggest successes is also the source of its biggest risks: if you step outside what the people around you take for granted, that is both how you identify gigantic, obvious problems no one else is working on, but it’s also how people convince themselves that the normal rules don’t apply to them anymore and how they become intellectually insular and isolated. The principles above are the version of EA I’m excited to defend, one that preserves the first and aims to be much better at avoiding the second. This view is rooted in my specific view of ethics and politics, that differs from totalizing utilitarianism, places much more emphasis on human choice and freedom, but still takes many of the trade-offs and choices EA pushes us to consider deeply seriously.

What I’m describing is somewhere between my favorite version of EA as it already exists and a new clear direction I’d like it to move in, learning from its successes and failures so far, and my views on both the value and limits of utilitarian thinking and comparisons. Plenty of people in EA already live by this version, and I’d like it to be easier to point to. This post is meant to plant a basic flag for my high-level principles. I’ve articulated my basic case for EA in much more detail in my last post on it. In future posts I’ll explore in much more detail how I think about EA, what I love about it, the specific version I want to represent, and where I’d like us to reform and change.

Like the last post, this one’s long. This series is me imagining I had a few hours with a smart friend to get across what I actually think about AI and philosophy of mind, and trying to do my very best.

To get to my full argument, I first need to argue that it’s possible in principle for computer programs to think and understand, and only then can I claim that specific AI programs can. This post argues for that first claim, which is pretty contentious on its own, and the next post will argue for the second.

It’d help to have Part 1 fresh in your mind before going on, or at least the section on introspection, the argument that physics is causally closed (and what that means for phenomenal consciousness), and the concluding summary.

I’m not claiming in this post that current AI models think (that’s Part 3). I’m not claiming the brain is a computer, which I’ll explain below. And I’m not claiming AI is conscious. I don’t think there’s anything it’s like to be a current AI model. In Part 1 I argued that the phenomenal consciousness question doesn’t affect whether AI models can think anyway.

Here’s how my argument in this post will go. I argue for each of these premises, and the conclusion follows:

• Premise 1: Things that think and understand are reducible to physical processes. There’s no extra nonphysical ingredient.

• Premise 2: What makes a physical system think is how it processes information, and what it’s made of only matters insofar as it can do that processing.

• Premise 3: A computer can simulate the brain’s information processing to whatever level of detail matters.

• Premise 4: A detailed enough simulation of an information process is that process, running in a new substrate.

• Conclusion: A computer process can, in principle, think and understand.

Most people who reject the conclusion reject premise 2 or premise 4, so those are what the majority of the post will focus on.

I won’t claim that AI models are or are meant to be simulations of human brains. This argument is only to show that because a simulation of a human brain could think and understand, computer processes can in principle think and understand. Once I’ve shown it’s possible, the burden of proof will still be on me in Part 3 to show that current AI models are sepcific examples of computer programs that can think and understand.

Part 1 took qualities we tend to assume are fundamental to minds and tried to demystify them a bit to show that they might be available to machines. I started high up in our conception of what humans can do and slowly worked us down to a more natural and nuanced picture of the human mind. This and the following post will build up from the ground instead. I’ll start with the basic building blocks of mind in physical reality and work my way up from physical systems (premise 1), to non-carbon-based material (premise 2), to computer programs specifically (premises 3 and 4). Part 3 will then argue that the internal workings of AI models specifically meet the definition of thought.

Why computers can in principle produce thinking and understanding

What are minds made of?

Can physical objects think?

The first and simplest step in figuring out whether computers can think is to ask whether merely physical objects can, or whether thinking requires something nonphysical, something more than particles, forces, fields, and the ways they relate to each other. I discussed this a lot in Part 1 but will revisit the core points.

I see 3 possible answers:

1. Everything about the mind is physical, including thought and understanding.

2. Some parts of the mind aren’t physical, but thought and understanding aren’t among them, and don’t depend on anything nonphysical. Maybe our phenomenal consciousness (what it’s like to be us) is nonphysical, but our thought and understanding are a kind of physical information processing.

3. Thought and understanding themselves depend on something nonphysical. They happen in part outside of the physical world.

Number 3 would mean we probably can’t build machines that really think and understand, while 1 and 2 leave the possibility open. I argued in Part 1 that while phenomenal consciousness is still a deep mystery, real thought and understanding don’t seem to depend on it. The only kind of consciousness they need is what philosophers call “access consciousness,” which is a specific, elaborate kind of information processing. So for the purposes of this series, I’m going to go on assuming #1 or #2 is true, and both say that it’s possible in principle to build a purely physical object that thinks and understands. Under either view, human brains are proof that this kind of object can exist.

I also see lots of reasons to think that physics is causally closed. As I explained in Part 1, it seems suspicious to assume that the one place in the world where physics is regularly violated is inside our own heads. This would also seem to break basic important physical rules like the conservation of energy, that would turn the world topsy turvy if they were regularly broken. And it seems weird that a purely physical, unguided process like evolution by natural selection could have stumbled on some way of interacting with nonphysical stuff. This makes 1 and 2 seem especially likely to me. A lot of people I meet who believe 3 believe it for religious reasons, and that’s fine, but if that’s your main reason for believing that AI can’t think, it’s important to say so directly instead of making some unrelated argument about the science of physical brains and computers. Some also believe #3 for scientific and philosophical reasons, and that’s also fine, but I think the burden of proof is on them to demonstrate why they do.

Under the view that the mind is reducible to physics, our thoughts and understanding are high-level patterns that emerge in our physical brains. Here a mind is kind of like the national debt, which isn’t a physical entity itself but is ultimately the result of huge numbers of specific tendencies and patterns in the behavior of purely physical systems.

So I think the answer is either the first or the second, and either way, things that think and understand are reducible to physical processes. There’s no extra nonphysical ingredient. That’s premise 1 of my full argument. The fact that minds happen in physical objects has a lot of additional implications for philosophy of mind.

Implications of minds existing in purely physical objects

In classical Newtonian physics, everything that happens is predetermined by the initial state of the physical system you’re in. If you had absolutely perfect information about the beginning of the universe and infinite time to calculate everything, you could in principle perfectly predict everything that would happen, down to the fact that you’d be reading this blog post right now. If you could rewind a Newtonian universe, the exact same thing would happen when you played it back.

We now think this probably isn’t true. On the standard reading of quantum mechanics, events at the quantum level are fundamentally probabilistic. They aren’t determined in advance, but they can be described and estimated using probabilities. If you rewound the universe and started it over, different things could happen. Some interpretations of quantum mechanics (like my favorite, many-worlds) circle back to a kind of determinism, but even here they involve what looks like randomness from the observer’s point of view. Either way, physical reality reduces to strict laws plus randomness.

Everything we know of in physics either follows strict deterministic laws or is random in the ways quantum mechanics describes. This has an important implication for brains and thought, assuming that they are ultimately physical: any brain activity or mental process is either predetermined by physics, random, or some combination of the two. If your requirement for real thinking and understanding is that it can’t fundamentally rely on either purely mechanistic systems or randomness, then either humans have never thought or understood anything, or human thought transcends physics. Both seem wrong to me, so it looks like real thinking needs to be able to be broken down into purely mechanistic systems or include pure randomness, and there’s no secret third thing involved.

This gives us a quick way to check a lot of arguments that AI can’t think, which I’ll call the brain test. Take the argument and apply its reasoning to the human brain instead. If the reasoning also applies to the human brain, the argument proves too much, because it would also show that you can’t think. I’ll come back to this simple test a lot.

Another way of saying this, which I described in Part 1, is that any system that understands is ultimately made up of individual parts that don’t:

Unless thought and understanding are supernatural and happen outside of physics, any system that understands or thinks is ultimately made up of fundamental particles and forces. These on their own don’t understand or think. Thinking is an emergent property of arranging them in specific ways to perform specific functions. At a higher scale, individual neurons also don’t “think” or “understand.” They are building blocks for a specific system that thinks and understands. All that human neurons do is receive basic chemical signals, change their electrical activity, and release some chemicals. Any system that understands will be made up of parts that “only” do simple things. That’s a basic part of living in physical reality.

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The role of access consciousness in thought, like anything else in the world, also needs to be completely explained by individual processes that are not conscious. If thinking requires some higher-level awareness, that awareness also has to be explainable in the language of physics (again, unless you believe it happens in a separate nonphysical world that interacts with ours). Dennett once observed (in his book Brainstorms) that explaining an intelligent system means breaking it into smaller, dumber systems, then breaking those into even dumber ones, until you get down to parts so simple that they can be “replaced by a machine.” I think this is the only way minds can ever be explained.

Information theory

My argument later on will be that thought and understanding are fundamentally both specific ways of processing information, so it’s important to explain how information fits into a purely physical view of the mind. There’s a field of math and physics called information theory (started by Claude Shannon, who the AI Claude is widely thought to be named after) that gives us a precise way to measure information. Information is anything that reduces uncertainty, and it’s measured in units called “bits,” where 1 bit is the answer to a yes or no question when both answers are equally likely. If I have a number in my head between 1 and 8, it takes 3 bits of information to pin it down, because you can ask “Is it in the top half of the range?” and then “Is it in the top half of the remaining range?” and then repeat that second question one more time. If I thought of 6, the answers would be yes, no, yes. Each bit doubles the number of possibilities you can tell apart. One bit can tell you if a number is 1 or 2, two bits can tell you which of 1, 2, 3, or 4 it is, three can tell you which number from 1 to 8 it is, and so on. This is why the game 20 Questions can get you to one specific thing, person, or place out of all the options, because 20 bits can narrow things down to 1 option out of about a million, 2²⁰.

Under this definition, to say that a physical system “carries information” means that its state changes reliably with the state of the other thing. Tree rings carry information about a tree’s age, for example, even if a human never actually counts them. To say a system “processes” information means that its information-carrying states cause each other to change in ways related to the information that changes and shapes what the system does. Sometimes people use “information processing” to mean the very specific way that a computer manages its own information flows. Computers work very differently from the human brain, so people will sometimes say that brains “don’t process information.” This is true in the limited sense that they don’t behave exactly like computers (your memories for example aren’t perfectly preserved in clear places for retrieval), but false in the broader sense that information theory uses the term. This semantic overlap leads to a lot of problems I’ll discuss more below.

Each bit needs to be stored in or carried by something physical (and since nothing that carries a signal can move faster than light, information can’t either), but the information itself is independent of the specific thing it’s stored in. It can be transferred from one thing to another. For example, the words on your screen right now started off as information in my brain, which became patterns in how I typed the words into my computer, which became both words on my screen and electric charges in my laptop’s memory. When I added them to my blog, the information traveled as radio waves to my router, then mostly as light in fiber optic cables to a data center somewhere, where it was stored again as electric charges in the chips there. Then it reached your device the same way, came out of your screen as light that you read as words when it hit your eyes, and now it’s being stored in your brain. This was the same information all along. It could be carried by patterns in very different physical stuff, whether brain tissue or light or electricity or the movement of my hands on my keyboard. But this information isn’t some special nonphysical substance. It’s just a very complex, specific pattern in physical material that influences patterns in other physical material, the same way an ocean wave is a pattern in water that shapes the water ahead of it without being anything magical over and above the water. A wave can even cross an ocean while each particle of water just bobs in place.

Does an object need to be made of a specific material to think?

Here I’ll argue for premise 2: what makes a physical system think is how it processes information, and what it’s made of only matters insofar as it can do that processing. This is often the most controversial part of the argument.

A belief that’s very popular among everyday people is that thinking depends on biological material. A lot of people believe that there’s something so fundamentally special about the material the brain is made of that a similar object made of anything else couldn’t “really” think or understand. At best it would just be an imitation. Obviously, to make my case I need to convince you that thinking and understanding can happen in non-biological material like silicon.

The word “substrate” will be useful here. It comes from a Latin word meaning “spread underneath.” In philosophy of mind, it means the physical stuff a process happens in. The substrate my computer programs run on is silicon, and the substrate my mental processes run on is my brain tissue. “Substrate dependence” is the view that mental events like thought or understanding can only happen in very specific substrates, and (on most versions of the view) silicon isn’t one of them.

There are two possible reasons why mental activity might depend on specific substrates:

1. Only specific substrates can play the causal, functional roles needed for specific mental processes. For the same reason that you can’t make a calculator out of a puddle of water, you can’t make something that thinks and understands out of just helium. A lot of substances don’t allow the kinds of complex organization and structures that minds need. Maybe carbon-based biological substances are the only ones that can form the types of causal structures that thought and understanding require.

2. Specific substrates have some inherent quality that lets things made of them think or understand, over and above the causal roles they play in the systems they’re in. Maybe only biological brains can really understand anything, because the material they’re made of is somehow tied to what understanding is, beyond any functional role it plays. This view says that even if you could perfectly remake every last process happening in the brain using a different material, the artificial brain would only be imitating understanding and thinking.

To see the difference between these two views, we can imagine (following a famous thought experiment from David Chalmers called “fading qualia”) that scientists have made a surprising discovery: they can replicate literally every process that happens in your brain using silicon-based material rather than carbon-based material. There is no causal difference in the brain’s behavior when they replace a carbon-based neuron with a silicon-based one.

Suppose that they replaced a single neuron in your brain with one of these silicon neurons. What would happen to your consciousness and thinking? Would you notice? What would happen if they slowly, over the course of months, replaced more and more of the neurons in your head with these silicon-based neurons?

If you hold belief #1, your mind would remain intact, because these artificial neurons can (for the sake of the thought experiment) in fact play the same functional role that carbon-based neurons play. There would be no change in your perception, thinking, or understanding. The only thing about the carbon-based neurons that mattered for your mind was the specific role they played in your overall brain processes. Replacing them with these new neurons would be like replacing individual transistors in your computer with new ones that work the same way. It could still run all the same programs.

If you hold belief #2, replacing your biological carbon-based neurons with silicon neurons would slowly remove key parts of your mental activity. Maybe your field of vision would fade or shrink. Depending on what you believe is reliant on carbon, by the end you either wouldn’t be conscious, or wouldn’t be able to really think and understand, or both. Your outward behavior would stay exactly the same the whole time, because the cause-and-effect relationships that made up your brain would all still be running, but there would be no real thought, understanding, or consciousness behind it. This is almost exactly one of the scenarios John Searle described in his 1992 book “The Rediscovery of the Mind,” where, as your brain is replaced with silicon, you internally want to cry out that you’re going blind while your voice, completely out of your control and now run by the silicon neurons, keeps describing what’s in front of you.

I’ll call belief #2 “the carbon view,” and I think the thought experiment itself shows that it’s wrong. I’ll also argue later that belief #1 is correct in some sense (you can’t make a mind out of helium because it couldn’t maintain the right cause-and-effect relationships), but that silicon (and probably a lot of other materials) checks all the boxes for the specific causal relationships and functions we’d need for real thought and understanding.

What would actually happen in this thought experiment?

I want to start to poke at the carbon view, starting with an inconsistency in its answer to the thought experiment above.

Let’s imagine that 10% of your neurons have been replaced with these silicon neurons. Which of these would your field of vision look like?

If your consciousness were slowly being taken away, would you notice? Would you react? Remember that these new neurons have the exact same cause-and-effect structure as your old ones. With either kind of neuron, if your brain received information that your field of vision looked strange, it would cause you to say “Hey, there’s a bit of my vision missing.” But because the new neurons play the exact same causal role as the old ones, you would do and say exactly what you would have done and said with your old brain. If your old neurons wouldn’t have led you to say “Hey, my field of vision looks weird,” your new neurons won’t either.

So if your field of vision really were dissolving, you’d never say anything about it, and what you said about your vision would have come completely apart from what you actually see. If the silicon neurons cause you to behave in exactly the same way, this strong observation of your consciousness changing should trigger the same behavior the old neurons did, but for some reason they wouldn’t here. It seems like the only way for this to work is for you to somehow not notice your own phenomenal consciousness changing. You’d be completely and permanently wrong about your own experience while your brain went on working exactly as before. This seems to imply some kind of epiphenomenalism I talked about in Part 1, where your consciousness helplessly observes your life but never has any causal influence on it.

Searle accepts that this could happen, but I find it much harder to believe than the alternative, that your field of vision isn’t dissolving at all. (This is basically the philosopher David Chalmers’ “fading qualia” argument). Philosophers really do disagree about the experience part. For example, if experience turned out to be nonphysical (option 2 from earlier), physics being causally closed would mean it couldn’t affect what you say anyway, which is one situation where Searle’s scenario hangs together. But the case is much stronger for thought and understanding. The new neurons do exactly the same things with information as the old ones, and in Part 1 I argued that’s what thinking and understanding come down to, so a brain made of this kind of silicon could think and understand. What matters for these new neurons is the role they play in your broader mental system, not what they’re made of. If they play the same role, they’re the same for your mental activity. I see this as a strong argument in favor of belief #1.

Another way to put this is as a dilemma. Either the special ingredient in carbon makes a difference to what the brain does, or it doesn’t. If it does, then a silicon brain that does everything the carbon brain does has something playing the same role. If it doesn’t, it can’t be what thinking is, because thinking makes a difference to what you do, and nobody could ever tell who had it, including you.

The carbon view as I’ve described it is mostly a folk view. The philosophers and scientists who take substrate seriously today (like Peter Godfrey-Smith, Anil Seth, and Ned Block) don’t think carbon atoms are special in themselves. They believe that the functional biological processes like metabolism might matter for consciousness specifically. The debate among experts seems to be mostly about phenomenal consciousness, and they are often otherwise open to machines thinking and understanding language.

Even if you find the replacement argument convincing, the carbon view can still feel obviously right, because every mind we’ve ever seen has been made of carbon. The next few sections explain where that feeling comes from, and why it doesn’t tell us anything about whether other materials can think. If you already don’t believe the carbon view, you can skip ahead to “Some takeaways from neuroscience.”

Is carbon required for thought?

Evolution and carbon

A lot of people believe the carbon view because of a simple observation: every mind we’ve ever seen is carbon-based, with no exceptions at all. Anything that we’ve ever called a mind, whether it’s in a human or crow or octopus, was in biological matter. This seems like strong evidence!

However, there seems to be a much more obvious reason why minds have only appeared in carbon-based biological matter.

Minds need an enormous amount of very specific, organized complexity. We only know of two processes in the universe that can build very complex things that work:

• Evolution by natural selection

• Deliberate design by people

Nothing else we know of can create anything as complex as human and animal brains. And for almost all of Earth’s history, only the first one existed. On Earth, as far as we know, natural evolution has only ever acted on carbon-based biological matter, so only carbon-based material could be shaped into the level of complexity required for minds. Thus, the only things we see that have minds are carbon-based.

Why has natural evolution only acted on carbon-based material?

Well, stepping back, evolution isn’t a force in the world with goals. It’s just an emergent pattern that shows up on its own, because things that survive and replicate more end up producing more things like them. We can expect the pattern of evolution by natural selection to emerge whenever objects in the world have these three properties:

1. They can replicate themselves.

2. They carry information that gets passed on to their copies, and that information can be modified in the act of replication, leading to variation among the copies. The copying has to be mostly accurate, or the information would get scrambled over the generations, but it can’t be perfectly accurate, or nothing new would ever appear.

3. Some of that change affects how good the objects are at replicating in their specific environment, so some versions end up making more copies of themselves than others, spreading their variants more.

Over time, this process can stumble on mutations that add more and more complexity to the species they happen in. With enough time, this creates features that look like they were the product of design, like eyes, wings, immune systems, or minds, even though no designer was involved. For almost all of Earth’s 4.5-billion-year history, this was the only possible source of the level of complex organization minds require.

The basic dynamics of evolution don’t depend on any specific substance. In a famous 2003 study using a digital evolution program called Avida, scientists made self-copying computer programs that evolved to do complex logical operations by building on simpler abilities they had evolved earlier, in the same way complex features emerge in living things over many generations. Evolution can act on anything that checks all three boxes I listed above.

So why has natural evolution on Earth only acted on carbon-based material? It turns out that it’s mostly because carbon is more useful for the kinds of replicators evolution needed to get started on Earth specifically, and is a good, stable material for more complex structures.

The main reason carbon’s so useful and versatile as a building block for replicators is that it has four outer electrons (valence electrons) available to form bonds with other atoms. As it happens, silicon is the very next element with 4 valence electrons on the periodic table.

This means silicon can form some of the same kinds of molecules as carbon. Silane (SiH₄), for example, is silicon’s version of methane (CH₄), but it has some very different properties (it bursts into flame on its own when it touches air). Silicon is much, much more abundant on Earth than carbon. By the most commonly cited estimate, there are around 600 silicon atoms for every carbon atom in Earth’s crust.

If silicon is so much more abundant than carbon, and can form similar molecules and bonds, why were the first replicators that kicked off the process of evolution carbon-based and not silicon-based?

One reason is that when a carbon atom bonds to another carbon atom, that bond is about 50% stronger than the bond between two silicon atoms. This means silicon is much worse at forming the long chains and rings that are required for larger and more complex structures. Chains of carbon atoms are also very stable in water, whereas silicon chains break apart much more easily in both water and oxygen. That matters a lot because the first replicators (which arose at least 3.5 billion years ago) almost certainly formed in water (the earth was mostly covered in it). The silicon versions of carbon’s building-block molecules (like the silane mentioned above) also tend to be much less stable in general.

Silicon also bonds much more strongly to oxygen than it does to other silicon atoms, so in nature it almost always ends up bonded to oxygen. Almost all the silicon in Earth’s crust is locked up in rock as a result. Silicon dioxide (one silicon atom for every two oxygen atoms) is what we know as quartz, the main ingredient in most sand. If silicon-based animals breathed out silicon dioxide like we breathe out carbon dioxide, they’d breathe out sand.

So it seems like the main reason life is carbon-based is that carbon forms strong bonds with other carbon atoms, those bonds hold up in water, and the first replicators almost certainly arose in water. These facts are interesting, but on their own they don’t tell us anything about whether there’s something unique about carbon-based molecules that makes them important for thinking and understanding. What they tell us is that carbon was the basis of the specific molecules that kicked off the evolution of all the complex life we see today. As far as we know, that happened exactly once. Any new replicators that showed up now would almost certainly be eaten or outcompeted by the life that already exists.

So what evidence do we get from the fact that all minds we’ve ever seen are in carbon-based biological brains? All it tells us is that the universal common ancestor of all life (a single-celled organism that lived more than 3.5 billion years ago, roughly 3 billion years or more before the first simple brains evolved) was made of carbon-based stuff, and it was successful at replicating itself and met the three criteria needed to run the pattern of evolution by natural selection, which was the only process on Earth that could produce the level of complexity minds would need much much later on. Until people started building flying machines, the only objects in the world that could fly were also biological and carbon-based, because again, living things were the only things complex enough to fly. This told us nothing about whether there was something inherent to carbon-based biological material that made it necessary for flight. Like flight, thought and understanding can only happen once some process has organized incredibly complex, specific systems. For billions of years the only process that could do that was evolution, and if I’m right, in the last year or so human design has become a second way to create key high level aspects of minds.

If carbon-based biological matter were required for thought, over and above its ability to play the correct functional roles in systems, this would imply a lot of other strange things about the world. For one, on any planet where evolution acted on another type of replicator (maybe one where the environment was more hospitable to silicon-based life), evolution could never produce thinking animals.

This would be a massive missing tool that would’ve been useful to those silicon-based animals. Thinking has obviously been incredibly useful to animals on Earth, to the point that complex thought seems to have evolved independently multiple times, in lineages that split off from each other before any of them had complex brains. For example, humans and other primates, crows, and octopuses can all solve complex problems that require step-by-step planning, and their evolutionary lines all split from each other long ago.

Octopuses and humans last shared a common ancestor about 600 million years ago, long before either lineage had much of a brain. Crows and humans split off about 320 million years ago, when brains were still small and reptile-like. Our shared ancestor looked something like a small lizard, and birds and mammals each separately went on to evolve brains with about 20 times as many neurons as reptiles of the same body size. This means that on at least three separate occasions, relatively simple brains independently evolved to the point that they could solve complex problems like when an octopus carries coconut shell halves across the sea floor to use as a shelter later, and when a crow pulls up a short stick dangling from a string, uses it to fish a longer stick out of a box, and then uses that to get food out of a hole. The wild differences in the look of the brains of the three species are a result of approaching the same broad ability from wildly different starting points:

It is crazy that intelligence has independently evolved this often.

Notice as an aside that when we say a crow thinks, we’re not saying it has the same structure as a human brain. Crow neurons are arranged very differently from humans’. Most of an octopus’s neurons aren’t even in its brain, they’re in its arms. No one concludes from these deep structural differences that octopuses and crows can’t think or only imitate problem solving. We determine that they think because of what their brains do, not whether they have the exact same structure as human brains. We’ll come back to that later.

But according to the carbon view, this ability, which has been useful enough to animals on Earth that it evolved multiple times, could never evolve on that silicon-based planet. Even though silicon could play all the required physical roles in the animal’s broader system, to the point that animals would behave exactly as if they were thinking, it would somehow “not count” as thinking because it wasn’t carbon.

This view makes the history of life look like a bizarre miracle. The first real brains didn’t evolve until a little over 500 million years ago. For more than 80% of life’s existence, there were no brains. What are the odds that the first replicators happened to be built around the one element that, for some separate reason, is the only one that can support real thought and understanding, or consciousness?

Why would this quality of “necessary for thought” even belong to a specific element? Or one that bonds so easily with other elements? Could there be a universe where only neon could produce real thought, but because neon is a noble gas and doesn’t form stable molecules, no thinking things ever evolved anywhere? The universe would be permanently full of this hidden capacity for thought that nothing could ever actually tap into. Does that concept even make sense? It sounds incoherent to me, which makes it hard to believe that any one element has some inherent property that makes brains made out of it “do real thought” while systems made of other stuff that can perform all the same physical functions don’t. Did we just get incredibly lucky that the thought-element happened to be perfect for replicators?

The carbon view looks pretty strange in the context of evolution. But evolution also gives us clues to why we might hold it so strongly.

To indulge in armchair evolutionary psychology for a moment, it seems like any human in the ancestral environment who would treat rocks or other material as potentially other entities like them, entities with complex mental properties, would probably be at a pretty bad disadvantage. They would waste a lot of resources either trying to cooperate with or fight them. It seems like most animals that can perform complex thought have a pretty strong inbuilt sense of what’s a living thing (to either avoid or fight or eat or cooperate with) vs what’s a nonliving physical object. This sense obviously doesn’t directly track carbon, and can misfire. It looks for things that biological life has, like faces or independent motion, which is also why humans often over-attribute minds to things that do either. This inbuilt sense would’ve been shaped by what helped or hurt animals in survival and reproduction, and in a world where carbon-based life was the only stuff that could develop the kinds of complexity that matter for whether animals react to them as other agents in the world, the strong intuition that only things that look like carbon-based life can have minds would have been very useful, regardless of whether it was true about the universe more broadly. There’s no clear causal path from whether other stuff can perform mental activity in principle to the intuitions that humans in the ancestral environment developed. I suspect a lot of our intuitions about philosophy of mind come from something like this evolutionary pressure to not consider nonliving matter as possibly possessing any complex mental behavior, and the thing that created that evolutionary pressure was ultimately the simple fact that carbon is an especially useful base for replicators, which doesn’t actually relate to whether other matter could in principle perform mental activities if arranged in the correct way at all.

Carbon is itself just a specific arrangement of smaller parts

Another problem for the carbon view is that atoms aren’t the most basic physical things reality is built from. They’re made of protons and neutrons and electrons, and protons and neutrons are themselves made of quarks. A normal carbon atom has 6 protons, neutrons, and electrons, and a normal silicon atom has 14 of each. They’re both made of the exact same stuff. There’s just more of it in the silicon atom.

Everything that makes a carbon atom behave differently from a silicon atom is just an emergent pattern resulting from how many parts there are and how they’re arranged. Both have the same number of outer electrons, which is why they can form similar types of molecules. Silicon forms weaker bonds with other silicon atoms mostly because it has one more layer of electrons below its outer electrons than carbon does, which makes it a bigger atom. Every difference in their chemistry comes from differences in how the same underlying particles behave in each.

This, even more than evolution, is what makes the carbon view look so strange to me. It doesn’t make much sense to say that carbon has some quality over and above the role it plays in larger systems, because carbon itself is just the pattern emerging from the individual roles being played by simpler interacting parts, the same parts that make up every other element. If you arrange the simpler parts differently, they do different things, which we call other elements. Carbon is made up entirely of parts that aren’t carbon. How could a mere role that simpler parts play somehow have a fundamental property beyond the role it plays in a broader system?

About 1% of the carbon atoms in your brain are carbon-13, which has an extra neutron. It chemically behaves almost exactly the same as normal carbon, and your body uses it interchangeably. Does carbon-13 also have the same special thinking property as normal carbon? If it does, it’s a little suspicious that the only other type of thing that has the thinking property also plays the exact same functional role in the broader system. Adding a neutron is fine, but a proton is bad? A neutron is itself made of one up-quark and two down-quarks, and a proton is the reverse. Is adding one more up-quark and taking away a down-quark the thing that thinking is so tied to? If the quality of “real thought” disappears with the extra neutron (which barely changes anything the atom does), then about 1% of the carbon atoms in your brain, scattered randomly throughout it, aren’t being used to really think. Again, seems bizarre. And if adding just 1 neutron doesn’t change the picture, why does adding one proton or electron change it so completely, if not for their effect on how the arrangement behaves in a larger system?

If thinking needed something from carbon atoms over and above what carbon does, it’s difficult to see where that would come from. Everything we know about carbon is completely accounted for by what its smaller parts are doing, and it shares those smaller parts with silicon, just with different amounts and arrangements. Carbon itself is just a specific functional arrangement of smaller parts, so if thinking depends on carbon at all, it depends on a particular arrangement of more fundamental particles, the same particles every other element is made of. So thought still ends up as a specific functional process of matter, and only relies on the type of matter if it can perform that function, partly because every type of matter more complex than quarks or electrons is itself just a function of an arrangement of smaller bits and the roles they play in the system. This circles us back to belief #1: thinking depends on a specific substrate only in the sense that the substrate can or cannot perform the specific causal roles required for thought.

So now we need to figure out if silicon-based structures can play roles that are necessary and sufficient for real thought and understanding. To figure that out we need to first look at neuroscience and then computer science.

Some takeaways from neuroscience

Neuroscience is still deeply uncertain about what happens in our brains when we think and understand. However, we can draw some important lessons from it about what’s happening in brains and whether silicon computers could also do it. These are the key takeaways from the field relevant for my argument:

1. Thinking and understanding happen in the brain through neurons passing information to each other.

2. What matters about those signals is the information they carry, rather than the stuff carrying it.

3. The brain’s information processing can be described with ordinary classical physics, and doesn’t seem to rely on special quantum effects.

These support my core premises 2 and 3:

2. What makes a physical system think is how it processes information, and what it’s made of only matters insofar as it can do that processing.

3. A computer can simulate the brain’s information processing to whatever level of detail matters.

Thinking and understanding each happen in the brain through neurons passing information to each other.

A cornerstone of modern neuroscience is the “neuron doctrine,” the idea that the brain is made up of separate cells (neurons) that each receive signals from other neurons (often thousands of them) at contact points called synapses. Some signals make it more likely that the neuron will fire a brief electrical pulse, and others make it less likely. If the combined signals cross a threshold, the neuron fires, sending a pulse down a long fiber that releases chemicals onto the next neurons, pushing them toward or away from firing, and so on. Your brain has around 86 billion neurons and something like a hundred trillion synapses.

The fact that the brain can be broken up into separate units that each take in inputs and produce outputs means that the entire brain can be described in terms of what its units do and how they’re connected to each other. The true way the brain works is messier, with additional cells and chemicals and neurons wired directly to each other, but ultimately all of it is still physical signaling that can be described the same way.

What matters about those signals is the information they carry, rather than the stuff carrying it.

It appears that whatever makes a pattern of activity in your neurons a thought or perception or memory is the information it carries and what the rest of the brain does with it, rather than the particular physical stuff carrying the information. In the 1920s, the physiologist Edgar Adrian found that individual signals in single nerve fibers are all basically alike. A stronger stimulus (like a harder touch or a brighter light) doesn’t lead to larger pulses (or “spikes”). It just makes them happen more often. So what makes some of these signals sights and others sounds is which neurons they reach and what those neurons do with them.

There are already examples of information being processed by silicon before it reaches the brain, the most famous being cochlear implants, which do the job of the cells in the inner ear that normally turn sound into nerve signals, using a computer chip that sends electrical pulses into the auditory nerve. Many implant users can hold phone conversations without lip reading, so the brain is clearly able to build understanding out of information that silicon has processed.

In 2018, researchers worked with eight epilepsy patients who already had electrodes in their brains for medical reasons. They recorded activity in the hippocampus (a part of the brain that’s key for forming memories) and built a mathematical model of the activity patterns that went along with remembering things correctly. They used a computer to stimulate the same patterns into the patients’ brains during later memory tests, and their scores improved by about 35%. A computer model of part of the brain’s information processing was helping with a cognitive job inside a human brain! This seems a lot like the thing the brain was doing was manipulating information that a computer was able to also manipulate in the same way.

For the most part, neuroscientists connect thought and understanding to how information flows through the brain, rather than to which material is carrying it.

The brain’s information processing can be described with ordinary classical physics. It doesn’t seem to rely on special quantum effects.

Most neuroscientists and physicists think the brain doesn’t make use of quantum effects like superposition, and that the way it processes information can be fully described with classical physics. (Quantum mechanics underlies all chemistry, including the brain’s, but that’s different from the brain using quantum effects to process information.) They think this for two reasons:

1. Classical models of neurons work really well. Every successful simulation of neurons treats them as classical, mechanistic systems.

2. Quantum states are incredibly fragile, and the brain is warm, wet, and very crowded with jostling molecules. Max Tegmark estimated that quantum states in the brain would fall apart within 10^-20 to 10^-13 seconds. Neuron activity itself takes thousandths to tenths of a second, at minimum 10 billion times longer than the quantum effects happen.

Two things about classical physics become really important here:

1. Classical physics is mechanistic. The brain is way too complicated for anyone to actually calculate what it’ll do, but everything that happens in the brain, as a system subject to classical physics, is determined entirely by its starting state, its inputs, and the laws of physics. Some randomness exists in things like ion channels that randomly snap open and shut, or synapses failing to pass a signal on. If you rewound the universe and replayed it, some of those random events might come out differently, but otherwise the brain would give the exact same response to the exact same input if it started in the exact same initial condition.

2. Every classical physics situation in the everyday world can be modeled with differential equations (equations that describe how things change from moment to moment). In the brain, these can be used to describe how quantities like voltage and chemical concentrations change smoothly over time.

So the brain’s processes, including whatever thought and understanding are, are reducible to classical, mechanistic physics (plus some randomness).

It seems like thought and understanding have to be specific information flows in the brain

I don’t really see what else thought and understanding could be if not very very complex flows and processing of information in the brain. If you sit down to think about something, or understand something, what are you actually trying to produce? Usually you’re trying to change patterns in things outside your brain, like your mouth when you speak, your body when you move, or your hands when you write. The goal of thinking doesn’t seem to be to change anything about the physical stuff in your brain. The stuff inside your brain DOES change (synapses get stronger or weaker, and neurons change how easily they fire), but only to better shape the flow of information toward what you’re after. So it seems like whether our brains (or anything else) can think and understand comes down to the specific way information flows through them. This is not to say that anything could think if it could produce the same outputs from the same inputs as my brain. If I type “Hello, World!” and a computer runs the line of code print("Hello, World!"), we produce the same output, but obviously the code isn’t thinking. Thought and understanding depend on the specific ways information is processed inside a system, because that’s what determines whether it can do similar useful things with information in very new contexts, which seems like a clear baseline requirement for real thought. It’s also not to say that all information processing is thinking. A thermostat processes information too. The claim is that thinking is one particular, very rich kind of information processing, and the question for this post is whether things other than brains can do that kind.

If thought and understanding are about the flow of information, then they don’t depend on the specific substance carrying it (as we saw in the information theory section, information is substrate independent). The question is whether a given physical system can perform the causal roles needed for the kind of information flow required for thought and understanding. To figure out whether computers can perform these functions, we need to turn to computer science.

Some takeaways from computer science

In 1936, Alan Turing, often called the father of computer science, wrote a paper on what it means to “compute” something, and what could do it. At the time, “computer” was still a human job title, and he was trying to come up with a definition of a machine that could do all the same things a human computer possibly could. He proposed what came to be known as a “Turing machine,” which has a long strip of tape divided into squares that can each hold a symbol, and a head that reads one square at a time. The machine is always in one of a limited number of “states,” each of which is like one specific line of the instruction sheet. At each moment, based only on the combination of the state it’s in and the specific symbol in front of it, it writes a new symbol, moves one square left or right, and switches to a new state. The tape’s assumed to be infinitely long to allow the machine to get as detailed as possible. The reasons take a while to explain, but Turing suggested that any calculation a human could do by mechanically following step-by-step rules, with a pencil and unlimited paper, could also be done by this machine. This claim is now called the Church-Turing thesis.

Different Turing machines could perform different tasks. Solving a calculus problem is a step-by-step process, so is finding the optimal route to travel to a location. A system is “Turing complete” if it can simulate any specific Turing machine, which means it can carry out any calculation that can be written as step-by-step rules with enough memory and time.

Your laptop is Turing complete, because it can follow basically any set of instructions you give it, whether it’s running a spreadsheet or a video game with complex physics. The only two things that limit it are what you do with it and how much memory it has.

A lot of other things are Turing complete. For example, the trading card game Magic: The Gathering. In 2019, researchers found that you can set up Magic games where the creature tokens stand in for symbols on the tape of Turing machines and can be made to run any computational process. People have also built working computers out of the circuits you can make inside Minecraft.

A simulated computer could also be Turing complete. Emulators are simulated computers running on other computers. Many people use emulators on their laptops to play old Nintendo games that can’t be played on modern computers. The simulated parts of the computer move information around the same way a physical computer’s parts do, for the same reason people can build working circuits inside Minecraft.

Importantly, a Turing complete computer can simulate any process that follows the laws of classical physics to any level of detail, because classical physics can be written entirely as equations that a computer can work through step-by-step. Since the brain’s processes follow classical physics, a machine that’s Turing complete could in principle simulate a brain as precisely as we like. That gets us to premise 3 of my argument: a computer can simulate the brain’s information processing to whatever level of detail matters.

Even if the brain did depend on quantum physics, this could also be simulated, just much, much more slowly. It’s been shown that quantum computers can’t compute anything a Turing machine can’t, they can just do some kinds of computing much faster. The brain being classical mostly matters for how practical it would be to recreate the processes inside.

Would a simulated mind be able to really think and understand?

Many philosophers have debated whether a simulation of a brain would have phenomenal consciousness, whether there would be something it’s like to be the simulated brain. John Searle seemed pretty confident that a simulated brain would not have phenomenal consciousness, while David Chalmers believes it would.

I’m going to sidestep the question of phenomenal consciousness in simulated brains. As I argued in Part 1, I don’t believe that phenomenal consciousness is required for thought or understanding. What’s required is for a system to handle information in particular ways, using very high-level functions we don’t really understand in human brains yet. The question is whether a simulated brain could think and understand. A common line (going back to Searle) is that a simulated brain wouldn’t really think, for the same reason a simulated rainstorm doesn’t get anyone wet.

Importantly, I’m not claiming that AI models are simulated human brains. When you make an LLM, you’re not mapping the human brain and mimicking all the physical stuff happening inside. I’m instead trying to show that it’s possible in principle for a computer process to think, and will get to the details of how AI systems actually work in Part 3. If you’d agree that a simulation of a human brain with enough accurate detail could really think, you agree that computer programs can in principle think, and the question from there is just what computer systems qualify as really thinking. Right now I’m trying to get you to that first step, using an imagined perfectly simulated human brain to do it.

So far I’ve argued for premises 1 through 3. Thinking is something physical systems do, what matters is how a system processes information, and a computer can simulate the brain’s information processing to any level of detail. Together, I think these get us almost all the way to the conclusion. What matters for thinking and understanding is how information is altered by the system and affects other information in the system, and what the system can do with that information once it’s done processing. Whatever processes in the brain manage this information flow can all in principle be replicated by a Turing complete computer. If a simulation at one level of detail leaves out key subtleties in how those processes interact, it can just add more detail. And because the brain is so noisy (ion channels snap open at random, synapses fail), details far below the level of that noise can’t make any systematic difference to how it processes information (they just add to the noise that’s already there), so there has to be some finite level of detail that captures everything relevant to thought. A Turing complete computer can simulate the brain at that level of detail. The simulated brain would do the exact same type of information processing as the real brain, in the same way that a video game run on a good emulator (a simulated computer) plays exactly like it did on the original console. The simulation is simply another substrate that is handling the information. Because thinking and understanding are a specific type of information processing in the brain, and a simulated brain (like a simulated computer) could perform all the same types of information processing, computers can in principle really think and understand.

The last step is probably the most controversial. Is a computer simulation merely another substrate? That’s premise 4 from the start: a detailed enough simulation of an information process is that process, running in a new substrate.

To simulate a process on a computer is to set up the computer’s physical parts so that they carry the same information as the parts of the original process, and change in response to each other in the same pattern (at whatever level of detail you’re simulating). That’s also just a description of a process running in a new substrate. Earlier I defined a substrate as the physical stuff a process happens in. In a computer simulation of a brain, the process is the brain’s information processing, and the physical stuff it’s happening in is the computer’s silicon.

Let’s circle back to the imagined silicon neurons from earlier. Start with your brain after every neuron has been swapped for a silicon one. Now replace a small cluster of those silicon neurons with a single chip that takes the same inputs and produces the same outputs the cluster would have. Then replace bigger and bigger clusters, until one computer is calculating what every part of your brain would do. Every step changes how the information processing is physically carried out, but none of them changes the processing itself. At the end you have a brain simulation. If the gradual neuron replacement kept your thinking intact, a skeptic has to say at which of these steps it disappeared, and why.

In any other case that involves something that’s fundamentally information rather than matter, it’s clear that if you simulate something that handles the information in the exact same way, the exact same pattern of information emerges, and thus “the same thing is happening.” If you run a calculator program on an emulator, or in Minecraft, it really also calculates. Right now, you’re looking at words on a simulated piece of paper (your screen). Are these “real” words? Did I “really” write them? Yes, because words are fundamentally information, and specific symbols that communicate the information, that don’t depend on the specific substrate that hosts them. If the mind is also about information processing, all this same processing could happen in a simulated brain, and thus it would have the same qualities of mind we call real thought and understanding, for the same reason that you’re reading real words right now.

I think a lot of people’s immediate reaction to this is that the simulated brain wouldn’t have phenomenal consciousness in the way we have. Again, I’m leaving that debate aside, and would just ask you to recall the arguments I made in Part 1 that phenomenal consciousness doesn’t seem to be required for real thought and understanding. What matters is the right ways of handling information, which the simulated brain would have.

“Simulation” is itself just a name for another physical process. If we go back to the example of an emulator, a simulated computer, what’s actually happening is that we’ve basically just added an additional layer of programs and code to another program and code, that are all interacting with each other. There’s no deep difference between a simulation of a computer running a game, and running a game that just happens to have a lot of specific extra layers of code to it that it comes with. This is all ultimately just different specific causal relationships between computer code, which all come down to transistors in computer chips reacting to each other in specific ways, and it’s convenient for us to call a part of that code “a simulated computer” because it happens to be able to also perform the functions of a computer with other programs. These are just two separate methods of doing the exact same information processing, in the same way that the same type of boat can be made from different types of wood, or with a second layer of wood. If what matters for thought and understanding is how the brain works with information, not the specific atoms it’s made of, and a simulated brain would do the exact same things with the information, then what it’s doing seems to also be fully and completely “thought” in the exact same way the video game run on an emulator is fully and completely the same game.

Maybe the most famous objection to this is John Searle’s line about how simulated rainstorms don’t get anything wet, and simulated fire doesn’t burn down real neighborhoods, so why would a computer simulation of understanding actually understand anything?

This looks to me a lot like saying that the words you’re reading are just simulated words, so they can’t really convey information in the way a physical book can. If I’m right that thought is a kind of information processing, it’s mixing up what understanding and thought are. The nature of water and fire is fundamentally bound up with specific physical properties resulting from what they’re made of. But information is independent of any one substrate. These words can exist in my head and as electrical signals in my computer and radio waves to my router etc. Many simulated games have both things that we’d consider “real” and “fake” that are obviously differentiated by whether the thing depends on specific physical substance. Fire in Minecraft can’t actually burn anything, it’s fake, but if I build a calculator in Minecraft it can really do arithmetic, so it’s a real calculator. The realness of information processing clearly does not depend on what substrate is hosting it.

So everything comes down to which kind of thing thought is. Searle compared understanding to biological processes like lactation and photosynthesis. Photosynthesis produces sugar, a physical substance, but as I argued earlier, what thinking produces is patterns in your speech, your movements, and your writing, which is a lot more like what the Minecraft calculator produces.

Maybe real understanding requires a specific embodied interaction with the outside world, so it does rely on our physical bodies in a way computers can’t access. I’m very skeptical of this, because our interactions with the world are entirely mediated through systems in our body that convert them to the type of information our brains can handle: electrical and chemical signals in our nervous system. When I have an embodied experience of walking through a forest, the physical wood the trees are made of isn’t directly involved in my mental processing. Instead, what’s happening is that my body has receptors all over it (touch receptors in my skin, cells in my eyes that detect light, cells in my inner ear that respond to sound, etc.). This all seems obviously there to pick up on informational patterns light or sound or the texture of objects is carrying, and then convert it to the type of information patterns that the brain can work with, in the same way that while I can experience a lot of different patterns in the world that I can communicate in my writing, my wifi router can only work with pulses in radio waves, so the information needs to be translated into those before it can deliver them to you. The question then becomes whether a simulated brain could receive the kind of constant informational input that human bodies receive, and that seems like a difference of degree in access points to information rather than a difference in the kind of thing the simulated brain is doing.

Programs aren’t just in the eye of the beholder

A more sophisticated objection to the idea that a computer simulation of a brain could really think is that whether a computer is running any specific program at all is in the eye of the beholder.

In Borges’s short story The Library of Babel, the universe is a library containing every possible book of 410 pages composed from a fixed set of symbols. Its inhabitants search through overwhelming nonsense for books that explain their lives and their world. Any answer that can be written within the library’s format must be there somewhere, alongside countless false answers. But the narrator also suggests that apparent gibberish may carry meaning in an unfamiliar language. Taking this idea further, any book could be made to say anything, provided we were free to invent the language or code in which it was read. Any book could explain everything about your life if read with the correct language.

Similarly, any object, including rocks or walls, can be said to contain any computer program, if someone just used the right tools to read them in the right way. If I had the exact right scanner for a specific rock, where the scanner read the details of the rock’s surface as inputs, and I taught it to translate those signals in specific ways, I could determine that the rock contains full instructions for running the computer game Roller Coaster Tycoon, and play it off the rock like I would a CD. Searle argued that minds seem much more fundamental to the world than this, so computer simulations of minds can’t be real minds, because whether a simulation is doing anything in particular is just a story we tell about it.

The standard response to this, which David Chalmers worked out in detail in a 1996 paper called “Does a Rock Implement Every Finite-State Automaton?”, is that running a program can’t just rely on physical states happening to line up with a program’s steps. It requires the right cause-and-effect relationships, including what would happen if things went differently. A rock only “runs” Roller Coaster Tycoon in the specific sense that someone could run a very specific linear play through of the game from beginning to end. The rock lacks the responsiveness to enable the player to make any decisions or include any cause and effect relationships.

A simulation of a brain could easily respond to new causes with new coherent effects that a rock or wall couldn’t. If you showed its simulated eyes a different picture, its neurons would fire differently in the way yours would. Any argument that it’s still “fake” fails my brain test from earlier, where whether you yourself have a mind is an equally arbitrary matter of perspective.

There are a lot more philosophical arguments about the meaning of understanding and thinking and how it relates to simulated brains, most famously Searle’s Chinese Room. Before getting to those, I need to address a more basic common scientific complaint, that “the brain is not a computer,” and then build an argument against those other philosophical objections based on the fact that, the way they’re usually used, most of them would also rule out any physical system thinking, including the brain.

What do people mean when they say “the brain is not a computer”?

One of the most common ways people push back against what I’m arguing here is to say “but science says the brain isn’t a computer.” There are many popular articles making this claim:

• Robert Epstein’s 2016 Aeon essay “The empty brain,” with a subtitle that says “Your brain does not process information, retrieve knowledge or store memories.” That seems bad for my argument!

• Matthew Cobb’s Guardian article “Why your brain is not a computer,” which was adapted from his book “The Idea of the Brain”

• The neuroscientist Anil Seth has argued that the brain is not a computer, and that the idea that computation is enough for consciousness comes from taking that metaphor too literally.

These articles often get shared to say that AI cannot in principle think or understand. I’ve seen Epstein’s article shared a lot on Twitter recently. However, I think that even if every claim in these articles were true, it wouldn’t change my argument at all, except for two claims. One is that brains don’t process information, which seems obviously false. The other is that the brain is analog in a way computers can’t capture, which is the strongest version of this objection, and I’ll answer it below.

Look back at the five steps of my argument at the top. None of them says the brain is a computer. I’m saying something more like “The game Monopoly can be fully run and played on a computer. All relevant aspects of Monopoly can happen in a computer” rather than “The game Monopoly IS a computer.” Even if the brain is nothing like a computer, computers successfully simulate things that are not at all like computers all the time, like weather, and as I argued above, thought is the type of thing that is really truly thought if it is successfully simulated. Whether a computer can do what the brain does is a completely separate question from whether the brain is a computer, for the same reason that whether my computer can play Roller Coaster Tycoon is separate from whether Roller Coaster Tycoon is itself a computer that can run programs. Large language models aren’t computers either, in the same way Roller Coaster Tycoon isn’t. They’re things computers run.

Part of why this distinction gets lost is that the word “computer” actually has two different meanings that regularly get confused. In everyday speech, it means something like your laptop, that has a very specific architecture involving memory and hand-written code. In computer science, it has a more technical meaning that goes back to Turing, where it means any system that processes information by following a well-defined set of rules or instructions, regardless of its physical makeup. Our brains might be computers in the second sense, and obviously work very differently from the first sense. This is a good paper from 2022 arguing that the whole debate is so semantically confused that it doesn’t seem worth having. This is one reason why I’m interested in constructing an argument that doesn’t hinge on it at all.

Take Epstein’s essay, which has been going around a lot recently. His main point is that the brain doesn’t work like a laptop. It doesn’t store clear, distinct memories that can be easily retrieved, and it doesn’t follow clear internal written rules for how to use words. On most of this, I agree! But his picture of the brain is a much better description of current AI models than of the computers he’s contrasting the brain with.

Take this passage:

As we navigate through the world, we are changed by a variety of experiences. Of special note are experiences of three types: (1) we observe what is happening around us (other people behaving, sounds of music, instructions directed at us, words on pages, images on screens); (2) we are exposed to the pairing of unimportant stimuli (such as sirens) with important stimuli (such as the appearance of police cars); (3) we are punished or rewarded for behaving in certain ways.

We become more effective in our lives if we change in ways that are consistent with these experiences – if we can now recite a poem or sing a song, if we are able to follow the instructions we are given, if we respond to the unimportant stimuli more like we do to the important stimuli, if we refrain from behaving in ways that were punished, if we behave more frequently in ways that were rewarded.

As I’ll discuss in Part 3, AI models don’t store what they learn as separate files in memory that they can easily pull up the way your laptop stores information. The information they “learn” is spread across their weights connecting their “neurons” and it can’t be read off as clearly written rules. Like people, they can still memorize poems word for word with enough training, but this is very different from a computer storing the poem’s text in easily accessible memory. This passage describes current AI models so well that it’s strange to see it shared as evidence that AI can’t think.

The one claim in the essay that would actually hurt my argument is that brains don’t process information at all. Epstein seems to be using “information processing” in a much narrower sense than the formal one. He’s attacking what psychologists call the “information processing” model of the mind, where the phrase means storing, retrieving, and manipulating symbols, a lot like a laptop does. In Shannon’s sense, and information theory in general, information is anything that reduces uncertainty, and Epstein’s own description of memory, where learning a song changes your brain so you can sing it later, is a clear example of processing information. Learning the song reduces uncertainty about what notes to sing when. He also describes a student who can’t draw a dollar bill accurately from memory, as evidence that nothing like a picture of the bill is stored in our brains:

I agree with Epstein that this obviously shows that the mind doesn’t process information in the specific way a laptop does, but it also shows by the standards of information theory more broadly that the student clearly had a lot of information about dollar bills, just not perfect information. They could answer lots of yes or no questions that reduce uncertainty, like “Are dollar bills triangular?” or “Do dollar bills have ‘In God We Trust’ written on them?” His fly ball example is a good one too (outfielders really do follow a simple visual rule instead of calculating where the ball will land), but following a rule like that is still processing information about the ball. None of this affects my argument, which never assumed the brain stores perfect copies of anything.

A lot of people seem to be getting tripped up on this semantic confusion between the strict information processing model of the mind, where the phrase means storing, retrieving, and manipulating symbols like a computer, and the more broad definition of information from information theory. It’s undeniable that the brain processes information in the second way, and I agree with the critics that it doesn’t “process information” in the first much more narrow way. But the confusion between these claims is causing some people to make the bizarre assertion that what happens in brains is somehow not really information and not available to Turing complete systems, neither of which actually follows from the correct use of the term. People are mistakenly assuming that computers can only process information in the more limited, computer-like way of storing and retrieving code. In fact, Turing complete machines can do any form of broader information processing at all with the right programs running on them, and the type of information processing that happens in the human brain is a subcategory of that broader definition. This specific confusion has been a unique disaster for the AI debate.

Anil Seth’s commentary is more interesting and relevant to modern AI, but he’s talking about consciousness, and seems to separate it from questions of machine intelligence like I do. He has asked whether it’s possible to understand something without being conscious of it, and Part 1 was my argument that it is, so if Part 1 is right, his argument is also irrelevant to my claim. Maybe he’d agree? I’m not sure.

One last note on this is that a lot of people will often complain that the mind is always compared to the latest technology at the time. When we were in the Newtonian era, the mind was compared to clockwork, then when electricity and information technology took off it started to get compared to wires, then computers, then AI. I personally think each of these was actually a pretty useful metaphor compared to the common wisdom at the time, maybe with the exception of computers. It was a radical proposition when Hobbes suggested that humans are basically very complex machines in Leviathan. The mechanistic explanation of minds to this day draws a lot of ire and scorn despite being most compatible with our best science. Connections of wires transmitting information again looks like a much better explanation of minds to me than a lot of what was on offer in the 19th century. AI has renewed debates about how much of our own thinking is about prediction and pattern matching and carrying those forward, and again this is leaps and bounds better than the folk theory people carry around and get mad when you poke at it. I’m not really troubled by these metaphors, and insofar as they’re bad (like the computer metaphor reinforcing the idea of clear, easy-to-access concepts and memories stored in the brain), they are bad insofar as they reinforce our folk pre-scientific concepts of minds that they are otherwise helping us to move away from.

But isn’t the brain analog?

One other point that gets brought up a lot is that the brain is analog, in that it operates with a lot of continuous processes, whereas a computer being digital has to work in discrete steps. Anil Seth makes a point like this about consciousness. Maybe if thinking depends on infinitely fine-grained details of continuous physical processes, a digital simulation of it will never be able to model it exactly. It’s like trying to redraw a circle with lines that can only go up down left or right. You could approach the smooth curve of the circle infinitely but never fully get there.

But all real analog systems can only ever make use of a limited amount of precision, there’s always some random noise too. Your neurons can’t respond differently to two voltages that differ from each other by less than the random jiggling of the molecules around them, since that gets lost to noise. Anything below that is invisible to the brain’s information processing, so a computer program only needs to get down to this level of detail to perfectly mimic the information processing in the brain. There must be some finite level of digital detail that can capture everything relevant to thought in the analog systems of our brains.

A lot of arguments that computer processes can’t think are arguments that any physical system can’t think

Some of the most common arguments that AI can’t think seem to unknowingly reduce to “it’s just a physical system,” which would also apply to brains. If an argument applies just as well to your brain, it implies nothing can think or understand. This is the brain test I mentioned earlier.

The classic version of this argument came from the philosopher Leibniz in 1714. He imagined a thinking machine blown up to the size of a mill so you could walk around inside it. All you’d find, he said, are parts pushing on each other, and he took this as proof that perception couldn’t be explained mechanically. (The header art in this series is a jokey reference to Leibniz’s mill.) A lot of modern arguments against AI thinking look like versions of Leibniz’s mill. You can look inside an AI model, find only arithmetic and cause-and-effect relationships, and conclude that it’s not actually thinking. But a brain the size of a mill would also look to us like it wasn’t thinking, since it’d just be ions and proteins bumping into each other. The fact that we can’t see where the thinking happens when we look at the individual parts of a mechanistic system tells us nothing about whether the system thinks. Leibniz’s mill fails the brain test.

Two other common arguments that fail the brain test (assuming what happens in the brain is physical) are the claims that AI models are too mechanistic to think, and that they’re just a version of Searle’s Chinese Room. There’s also my argument in Part 1 that we often require AI to have a kind of introspection that seems physically impossible for humans to have.

When you give an AI model a prompt, every calculation it does is fixed by the prompt and the model’s weights. AI models have a randomness setting called temperature, and if you turn it all the way down to zero, the model always picks the single most likely next word, so the same prompt will give you the same answer almost every time. Because the chips don’t always add numbers up in the same order, tiny rounding differences can sometimes change a word, which acts like a little extra randomness. But that doesn’t seem to add anything to the model’s ability to think.

If the temperature is above zero, the only reason you get different answers is that a random number generator is choosing among different words, weighted by how likely the model thinks each one is. The AI model is really just a domino chain plus dice: a completely straight line from cause to effect, with some randomness thrown in if you’d like.

It seems obvious that dominoes and dice can’t think. A straight single line of dominoes from premises to conclusions can’t think and understand. A straight causal deterministic line from one given input to one specific output isn’t thinking along the way, and adding randomness to this process doesn’t make it more like thinking at all. In fact, it makes it even less like thinking because pure randomness doesn’t tell the system anything about the question it’s answering. That means that the AI model must not really think.

The domino-and-dice picture is in fact a pretty accurate description of how AI models work. Any AI model can be understood as an incredibly complex web of domino chains, plus some randomness, and nothing else. The problem is that this is also a complete description of every physical system, including your brain. As I argued earlier, every physical system is made up of unimaginably vast numbers of processes that are either mechanistic or random, and there’s no third option. Your brain is a physical system, and your thinking can be described by classical physics (plus some randomness). Suppose we asked you a question and recorded your answer, then rewound the universe to exactly the same state, atom for atom, and asked you again. You’d follow the exact same chain of cause and effect and give the same “output,” for the same reason a billiard ball hit at the same speed and direction always ends up in the same spot. The only way your answer could come out differently is if some random events (like an ion channel happening to open) went differently the second time. Your brain is dominoes plus dice too, so this argument fails the brain test.

The argument that AI models are too mechanistic to think therefore implies that no purely physical system can think or understand. I’ve given a lot of reasons in this post and the last one to believe that thinking and understanding rely on purely physical processes in the brain. Either our sense that thinking needs something beyond mechanistic cause and effect plus randomness comes from a vague folk theory about what happens when we think, or AI models are missing some third way of processing information that’s neither mechanistic nor random. The first seems much, much more likely.

Many arguments that AI models can never really think are based on the idea that their mechanistic cause-and-effect processing is basically a form of unknowing information retrieval. People usually back this up with two famous thought experiments:

• Ned Block’s “Blockhead”

• John Searle’s Chinese Room

Blockhead is a computer that simply has a canned response stored for every possible conversation. You say “How are you?” and it just matches this to a pre-written response it’s supposed to give: “I’m well!” This obviously wouldn’t really understand anything. Block meant this thought experiment to show that whether something is intelligent depends on how it produces its answers. So passing a conversation test like Turing’s doesn’t, on its own, show that a machine is intelligent. I agree with that.

The Chinese Room is a bit different. In a 1980 paper, Searle imagined a man who doesn’t speak Chinese locked in a room with a rulebook (a program) for shuffling Chinese symbols. People input questions in Chinese into the room. The man follows the rules, and he passes back answers that are indistinguishable from a native speaker’s, without actually understanding a word. Searle argued therefore that following any step-by-step program, however complicated, can’t by itself be what we mean by understanding what the symbols mean, because programs only work with the shapes of symbols and can’t access their meaning directly. Some people in the AI debate now use the Chinese Room as if the rulebook were just a list of canned answers, which turns it into Blockhead.

I agree that Blockhead doesn’t really understand, and neither would a Chinese Room whose rulebook was just a list of canned answers. But that’s only because they lack the internal causal processes that would let them respond to fundamentally new situations using the meaning of the words. If the rulebook could mimic literally all the causal processes required for thinking and understanding, the man and the rulebook system together would “understand the words” the way we do, even though neither does on its own. It’s the same reason my brain matter and the laws of physics together produce a system called Andy that understands words, even though my individual neurons and the laws of physics don’t understand. Here the room and book are my brain matter, and the man is the laws of physics. I’ll address the comparison to Blockhead first, and then the point the full Chinese Room is making.

My problem is with how the two thought experiments get used in the current AI debate. People correctly point out that AI models can be described as “mere information retrieval,” because some specific answer is waiting at the end of any possible prompt. With the temperature at zero, a given prompt can only go one way, and any deviation from that is due to randomness. So maybe models are like the canned-answer version of the Chinese Room, with the guy inside sometimes spinning a wheel to pick at random between a few similar responses he doesn’t understand at all. But any purely mechanistic system can be described this way. If you let a block slide from rest down a frictionless ramp of height h, it will always reach the bottom at one very specific speed (√(19.6h) meters per second, with h in meters). In this sense, its final speed was “canned” and merely “retrieved” by the input. If you ask someone a question, their brain follows classical physics to arrive at one very specific answer. The only things that can change it are randomness and new input from you or the world while they’re answering. The brain can only respond to any input with systems that are mechanistic or random, and our sense that it’s some secret third thing comes from just how much information it’s handling. It’s hard to even imagine what that secret third thing could be.

Every physical system’s outputs are fixed by its inputs (plus randomness), but that’s different from the outputs being written down somewhere in advance and looked up. Blockhead looks its answers up. Brains and AI models have to work theirs out. There are more possible 30-word prompts than atoms in the observable universe (even if you only used the 10,000 most common English words), so no model could have an answer stored for each one. Block himself noted that a real Blockhead could never be built for this reason. So if “retrieval” means the answer is fixed by the input, brains are retrieval machines too. If it means the answer was stored ahead of time, AI models aren’t retrieval machines at all.

I separately worry that part of the reason the Blockhead and simplified Chinese Room examples are so compelling to people is that most people believe that AI models literally just have a large collection of canned responses. In a Searchlight Institute poll from August of last year, 45% of Americans said ChatGPT looks up an exact answer in a database, and another 21% said it follows a script of prewritten responses:

Only 28% got this right, which might be partly because the options seem kind of poorly written to me. Still…

If your takeaway from the Chinese Room is that anything whose outputs are fixed by its inputs (plus randomness) is by definition not really thinking or understanding, then you don’t believe any physical system can think or understand. That means the mind must hinge on extra nonphysical stuff. My claim in these last two posts has been that thinking and understanding very likely depend on purely physical processes. This makes it look more likely that the intuition that “mere mechanism + randomness” isn’t enough for thought comes from a folk theory that dissolves when you poke at it. And in fact when people try to pin down what these purely rote cause-and-effect processes are lacking, they get very hand-wavy and defer to a strong felt sense that understanding requires something over and above roteness + randomness. As I argued above, that strong felt sense might come from evolutionary pressure that rewarded early humans for only treating carbon-based living things as having minds. That pressure itself comes from carbon forming stable bonds that hold up in water, so carbon-based molecules could work as replicators billions of years before brains existed. This felt sense seems like a bad guide to machine thinking.

Turning now to the actual Chinese Room thought experiment that isn’t just a restatement of Blockhead. Searle meant it as evidence that manipulating symbols can’t on its own produce understanding, so running a computer program can’t either. But “blindly following step-by-step rules that can be written in a book” also describes any physical system that processes information, including the human brain. Searle actually agreed that brains can be described this way. He said we’re machines ourselves, running any number of programs, but that the programs aren’t what make us understand. If the rulebook could somehow contain the rules for everything every physical process in the brain would do given a specific input, I’d argue the system running it could think the way a brain does. Searle considered this case in the same paper. He was responding to the idea that a computer simulating every neuron firing in a Chinese speaker’s brain would understand Chinese, and he imagined the simulation built out of water pipes instead.

In his other thought experiment, a man who doesn’t speak Chinese operates an elaborate system of pipes and valves, with each pipe connection playing the role of a synapse in the Chinese speaker’s brain. He receives inputs and follows instructions telling him which valves to turn on and off. Eventually, the entire system produces answers in Chinese that come out the other end. Searle claims that the man operating the system obviously doesn’t understand Chinese. The pipes, “obviously,” don’t understand, and so Searle thought it was absurd to say the man and the pipes together did. He then concluded that a brain simulation only copies the formal structure of the brain’s neurons and leaves out the brain’s ability to understand. Thus, biological brains can understand, but simulations of brains cannot.

I think the man is a distraction here. In the pipe system, he’s playing the role that the laws of physics play in a brain, moving things along according to fixed rules. Asking whether he understands Chinese is like asking whether the laws of chemistry understand English. It helps to remember that the Chinese Room is Turing complete. The man can follow any instruction, so anything a computer can do, the room can do too. Someone could write an extremely long book telling him exactly what to do to run Roller Coaster Tycoon (humans already write those instructions at each level a computer works at). He wouldn’t need to understand anything about Roller Coaster Tycoon to run it, and it probably wouldn’t even be clear to him what was happening. Nobody would say the game isn’t really running. Searle’s reply to this kind of point was that the man could memorize the whole system and run it in his head, so there’d be nothing in the room but him. But as I’ll get to in a second, that would mean memorizing something like 100 trillion connections and running them at the speed of a brain, which no person could do, and at that point he’d be simulating a second brain in his head, and as I’ve argued before, that simulated brain’s thinking would be identical to a real brain’s thinking, so it could also really think and understand.

In this thought experiment, Searle seems to be leaning on the idea that to really understand a word is to be conscious of it, to have a subjective phenomenal experience in your inner mental theater to use it correctly. He defended this directly elsewhere, with what he called the “Connection Principle,” which says a mental state can only really be about something if it could at least in principle become conscious. I don’t see another reason to say that the pipe system doesn’t understand the words it’s using. I’ve argued in Part 1 that this view of language and understanding as requiring this inner phenomenal theater seems wrong. It doesn’t cohere with physics, or with how words actually behave, or even with our own subjective experience when we use language. It also doesn’t appear “obvious” to me at all that this system wouldn’t understand the words it’s using. To argue for my own view that the pipe system doesn’t need extra-physical phenomenal consciousness to understand the words it’s using, I’ll paint a picture of what it’d actually look like.

There are roughly 100 trillion synapses in the human brain, and since the average neuron fires about once a second or less, a simulation like this would need up to around 100 trillion valve operations per second. This guy managing the pipes would either have to keep up that pace, following unimaginably detailed instructions about which valves to turn depending on what the water is doing, or (operating at a more human speed) each response would take millions of years. Even if each valve and its pipes fit in a box about 4 inches on a side, the whole structure would be a cube almost 3 miles on each side. Suppose he could keep up with the brain’s real speed. You could theoretically remove my brain, hook up my body to signals generated by the outputs of this water system, and I would walk around and interact with the world, and because Searle is assuming that this water system can actually perfectly mimic all the same information processing happening in the human brain, any informational input to my body, whether it be a sight or sound or touch or impulse from my body, would be taken in by this massive system, and it would give out the same information back that my brain would have. I would say and do the exact same things real Andy would, including sitting down to write this long blog post. It’s easy to lose sight of the fact that “having the exact same ability to merely process information” as the human brain would lead to systems that can also perfectly run human bodies to the point that they could type blogs and talk and interact with the world without anyone noticing. This version of me run by the water system would write the same blog posts about data centers and water and go on podcasts to talk about them, no one noticing he was using way more water than all data centers… This watery version of me controlled by the pipes would, I’d say, “understand” the world just as well as I do. Literally all the ways I can use language, it can as well. This seems like a point in favor of my belief that phenomenal consciousness isn’t necessary for understanding.

Similarly, if someone were in the Chinese Room with a sufficiently advanced rulebook, that could simulate all the processes in my brain with trillions of little individual decisions like this, they could perfectly accurately control my brainless body and I’d live out my life exactly as I’m doing now. I’d use words in all the same ways I do now. The fact that the man in the room is merely following instructions without understanding them tells us as much as the fact that the physical laws governing my brain don’t themselves understand what I’m doing, which isn’t much. Sufficiently advanced mere rule following can simulate any form of information processing down to an arbitrary level of detail, so I argue that the full Chinese Room rulebook can think and understand the words it’s using with some sufficient level of detail in the rules the book uses (which would add trillions of steps per second per input).

Searle himself anticipated this reaction in the same paper, and called the entity like the version of me controlled by the pipe system or room “an ingenious mechanical dummy.” This seems ridiculous to me. At this point, Searle’s “ingenious mechanical dummy” can do anything and everything humans can do with words. It could live a life identical to mine. It could write all these blog posts and go about its day. It seems at this point that Searle’s relying so heavily on the idea that “real understanding” depends on phenomenal consciousness that he’s willing to say a system that could do literally everything humans can do with words in any and all contexts doesn’t understand the words. At this point the disagreement itself seems pretty semantic to me. If AI is a Chinese Room, it can do literally everything human minds can do, and just happens to lack some additional thing we call “understanding” that doesn’t have any effect on how we behave.

Searle makes a lot more arguments about why computer programs can’t think, including that they merely follow step-by-step rules and lack the “intentionality” of what the words they use are about. I claim that his argument here cannot be made to work with any conception of the brain as a physical system, because all physical systems are reducible to mechanistic step-by-step cause and effect processes. His man in the Chinese Room is playing the role of the laws of physics. The laws of physics don’t need to be able to understand something for the system as a whole to understand. If no combination of these can think, the brain can’t either. Searle’s Chinese Room often looks to me like an argument for a nonphysical Cartesian theater where the true meanings of words can be grasped by something not merely mechanistic (and thus not merely physical). Searle would deny this. He would often say that humans are biological machines. But his demands for what minds can do seem to require something over and above the physical to make them work, and if you agree with my argument so far, it seems much more likely that this itself is a folk theory that masks the possibility that the processes that happen in our brain can be reduced to more simple physical processes.

If thinking is a kind of information processing, and nothing about carbon makes it special, then Searle’s pipe intuition is relying on exactly the two things this series has argued against. One is a sense that only biological stuff can think, and the other is the idea that understanding a word requires a conscious inner observer experiencing its meaning. This whole series so far has been an implicit argument against the view of language and understanding that Searle puts forward as “obvious,” and I hope that after making it this far you can see why I don’t think it’s obvious at all.

Semantic understanding needs to made of parts that don’t semantically understand

A lot of people seem to want to say both that physics is causally closed and that “mere information processing” in the broader information theory sense (rather than the much more specific “information processing” model of the mind, where brains work a lot like laptops) isn’t enough for true semantic understanding of words, where you really understand what words mean. I’m not sure what other physical processes could play this role. The only options for the building blocks of semantic understanding are rote cause-to-effect domino chains and randomness, and what those do in brains is either move matter around or process information. Again, I worry here that people are making one of two mistakes when they say that the mind is purely physical, but also that understanding needs more than information processing:

• They are mistakenly assuming that computers can only do the more narrow kind of information processing involved in following clear, hand-written rules stored in memory, whereas in fact a computer can in principle do all the information processing the brain does, by simulating the same systems the brain uses, which are very different from computers. This is again like saying that because Roller Coaster Tycoon isn’t itself a computer, it can’t be run on a computer.

• They are smuggling in extra-physical folk theories without actually exhaustively listing the very limited set of basic building blocks that physics provides for any complex system. Physics only offers simple mechanistic, “rule following” patterns, or randomness, and at the level of what a system does, information processing in the general information theory sense is the only option on the table besides literally moving matter around. Semantic understanding needs to be built from individual parts that each have no semantic understanding.

As I said in the section on what minds are made of, once we accept that anything that thinks and understands needs to be made of parts that obviously individually don’t, a lot of the thought experiments about the ridiculousness of pipes or Chinese Rooms or mills really thinking start to look more like someone looking too hard at the fact that the individual parts can’t think, and failing to look at the behavior of the system as a whole, which is the only place where understanding can possibly appear.

Conclusion

To return to my main argument:

• Premise 1: Things that think and understand are reducible to physical processes. There’s no extra nonphysical ingredient.

• Premise 2: What makes a physical system think is how it processes information, and what it’s made of only matters insofar as it can do that processing.

• Premise 3: A computer can simulate the brain’s information processing to whatever level of detail matters.

• Premise 4: A detailed enough simulation of an information process is that process, running in a new substrate.

• Conclusion: a computer process can, in principle, think and understand.

If you accept all 4 premises, you believe that thought and understanding are specific types of information processing that happens in physical systems. Information is independent of specific substrates as long as that substrate can carry the information and perform the cause and effect behavior required to process it. It would be very weird if there were something fundamental to carbon over and above the causal role it plays in larger systems that made it necessary to thought, and the only reason all minds seem to have run on carbon-based biological matter so far is that it was the only stuff evolution could act on to create complexity in the first place, which minds require. The next question is whether silicon can run the same causal processes required for the type of information processing that makes up thought. Silicon computer chips are Turing complete, which implies they can simulate any classical physical process to an arbitrary level of detail. The type of information processing in a human brain would require a lot of detail, but it relies on classical physics and doesn’t rely on infinitely small details, so it’s a finite amount, so all information processing that happens in a physical brain could in principle also happen in a sufficiently detailed and accurate simulation of a brain, and since thought is just the right type of information processing, this simulated brain could also truly think. Thus, computer processes can in principle think and understand.

So if you’re with me, you agree that computer programs can in principle think and understand. The next question that Part 3 will try to answer is whether AI models specifically are specific instances of computer programs thinking and understanding. This is a long stretch from simulated human brains. AI models are very different from human brains. But remember that octopus and crow brains are also radically different from human brains, and yet they also seem to be able to do what we mean by thinking. It’s on me now to argue that AI models have the same internal qualities that together qualify for this, which will require a deep dive into how current AI models work and how they might successfully mimic all the processes that make up thought and understanding, to the point that they are real in the same way calculators built in Minecraft are real calculators.

Introduction

Okay, first of all, I want to be clear that when I say AI models can “think” and “understand” I’m not claiming that they have minds like ours. People use “consciousness” to mean two related but different things, which the philosopher Ned Block calls “phenomenal consciousness” and “access consciousness.” Phenomenal consciousness is the first-person experience of what it’s like to be you. We usually think of this kind of consciousness as something more than information processing. There’s some unique experience that can’t be translated into a pure third-person account of your brain states. Access consciousness is information in your brain being available to lots of parts of your mind at once. This one IS fully describable in the third-person language of information flowing through a physical system. An AI could have access consciousness (sharing information across lots of its internal processes at once) without having phenomenal consciousness. There wouldn’t be “anything it’s like to be” it. Being that AI would “feel” just like being dead. I won’t assume or argue that AI has phenomenal consciousness.

I’ll make three claims, one in each post:

1. Our thinking and understanding mostly don’t depend on consciousness, and where they do, the aspects of (access) consciousness they depend on could in principle be replicated in AI models (This post)

2. Silicon computers could in principle think and understand (Part 2)

3. Current AI models seem to have the qualities that are necessary and sufficient for real thinking and understanding (Part 3)

These claims are controversial, but I’m pretty convinced by the arguments for each. My argument won’t rest on any one controversial belief about the nature of thinking or AI. Instead, I want to show that most leading theories about the mind seem to strongly imply both claims, or at least give them a high enough chance of being true that we need to take them seriously.

Importantly, I won’t argue that AI can do all the thinking we can do, just that what AI does at this point fits my definition of real thinking and understanding. I can’t think in the way Einstein could, but that doesn’t mean I can’t think.

You might think this is all just arguing over semantics, and it doesn’t matter what words we use for AI, because it can do what it can do either way. I think this is wrong, because most people who say AI can’t “really think” go on to infer a lot from that. They conclude it won’t be able to “discover anything new,” or that it’ll be permanently limited in how much it can change the world. If it’s “just linear algebra” it will remain a fun toy. If it can’t think, it certainly can’t “plan,” so it might not be especially dangerous. The most fundamental inference seems to be that AI will never do well when it has to carry a line of reasoning through to a case it hasn’t seen before. These are the situations where only the most general, subtle patterns from past experience can help, and only when a careful conscious person deliberately thinks them through. This, above all, is what a lot of people mean by “thinking,” as opposed to regurgitating information. Getting this question right matters a lot for making sense of where AI is headed.

I’m also worried that as debates about AI have heated up, a lot of people have adopted a rule of thumb that any talk of AI “thinking” is effectively assuming that there’s a ghost in the machine, like assuming a radio has a little band inside it playing the music. This seems like a wild overreaction to me, and one that wouldn’t make sense outside our current cultural moment with AI. I’m pretty convinced that if I took a current model like Astra or Opus 5.5 back to 1960 and let people play around with it, the reaction from serious people would be “Wow! You’ve created machines that can actually think! They understand the words they’re using!” Alan Turing predicted in 1950 that by the end of the century, “the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.” Today, people are much more wary of saying this, partly because they think “anthropomorphizing” AI buys into the hype or is a sign that you’ve developed psychosis. I think that’s an understandable (initial) reaction, but it’s starting to cause people to make serious mistakes. Part of my argument will be that the hard rule against “anthropomorphizing” AI is itself philosophically confused, and potentially anti-scientific, because it often implicitly depends on and enforces pre-scientific folk theories about what’s special about human minds.

It’s bad and dangerous to use anthropomorphic language for AI when it leads people to believe that AI has qualities it doesn’t. If someone comes to feel an AI really cares about them, or that it has a human-like mind, that seems pretty destabilizing, and it’s understandable that people worry about it. But the worry can go too far.

Before machines existed, humans and some animals were the only things that lifted objects. Once we built machines to help, it wasn’t bad to use anthropomorphic language and say a forklift “lifted” something, even though lifting used to be something only conscious, biological animals did. The word “computer” used to be a human job title, but no one today would say it’s anthropomorphizing a laptop to say it “computes.” These are obvious extreme examples. Where’s the line between clear cases like these and cases where it becomes bad to use anthropomorphic language? The obvious answer to me is that the line is where this language implies AI has a lot of abilities it doesn’t have. But the same line also shows where it’s bad NOT to use anthropomorphic language for AI, which is when avoiding it implies that AI lacks abilities it actually has. Ultimately, what makes something human-like is a specific combination of lots of simpler properties that aren’t human-like on their own. Anthropomorphic language is useful because it helps us simplify, understand, and predict the behavior of our fellow incredibly complex systems we call humans. It doesn’t pick out some fundamental unifying aspect of the properties of our minds that walls them all off from anything else in the universe having them.

So deciding when to anthropomorphize AI depends on what abilities it actually has, but also on what abilities our own minds have, and I think most people’s picture of how their own minds work is deeply mistaken. I’ve called this picture “folk-Cartesianism” in past posts. This view is that a lot of aspects of the mind come in a single inseparable package, held together (and walled off from machines) by the fact that all of them require a conscious, first-person perspective observing and managing them. This view imagines the mind as an internal movie theater, with a little version of you inside watching the movie of your life. The philosopher Daniel Dennett called this idea the “Cartesian theater” after Descartes, who often wrote about the mind as an unchanging observer (the “I” in “I think therefore I am”) watching its own experiences and thoughts directly.

The folk theory of mind says that when you “think,” you’re consciously observing individual concepts and moving them around the screen in your mind. You react to them and decide the direction of your thought. A key part of thinking (in this view) is powerful, direct introspection, where you turn your mind’s eye back on your own thought process and observe it directly. Your thoughts and inner experience are clear and directly accessible to you. You can observe them as if they were all written out on a piece of paper you were reading.

The folk theory says that if something can’t do this (if it can’t look at its inner mental screen and see its own thought process laid out clearly), then its “thinking” is basically only a domino chain running from premise to conclusion, like a rote computer program chugging along with no idea what it’s doing. That can be useful, but isn’t thought.

I believe that this picture is a big reason so many people object to the idea that AI “thinks.” If you hold it, “AI can think” smuggles in two drastically wrong (according to the critics) ideas at once. The first is that AI has first-person conscious experience. The second (which follows from the first) is that whatever AI does that looks like thinking is just as good as real human thought, when without that inner glow of consciousness it would have to be missing most of them.

In this post, I’ll argue that this folk theory is deeply wrong, for a few overlapping reasons. In Part 2 I’ll address a lot of popular philosophy (and folk) theories about why computers cannot think and understand, and give my reasons for why I think they’re wrong. In Part 3, I’ll go through what current AI can do. I’ll argue that current AI does enough of what we mean by thinking and understanding that the burden should now be on anyone who says it doesn’t think to say what’s still missing. And after this post, I hope you’ll agree that the list of what’s missing doesn’t include a Cartesian theater (a conscious observer inside the AI with direct, easy access to its own thought processes and control over them), because I don’t think humans have one either. I’ll knock down human thought a bit and raise up AI thought a bit, to the point that they might come close to meeting in the middle.

To be clear, I don’t mean to say here that AI can think all the thoughts humans can, for the same reason that when I say I can think, I don’t mean that I can do everything that any human mind can do. But a core part of how I think about the world is that most of our beliefs about our own minds and thought are useful but wildly imperfect folk theories and heuristics that get at reality about as much as saying “the rock wants to move toward the ground.” I think it’s a gigantic mistake to assume we have total access to what’s going on in our minds, and to let folk beliefs about our minds’ specialness override any debate about them.

How the human mind is different from our conception of it

Some examples of simple thought and understanding that don’t require consciousness

I’ll start with some simple, first-person tests you can run on your own thinking, to show that it’s not entirely like an inner theater. If your conscious mind does all your thinking and has full access to it, it should be pretty easy to watch it happening.

First, think of a city, any city at all.

Do you have one? Why did you pick that one? Do you know?

When I do this, I don’t consciously search through a mental list of every city I know and pick one. Instead, a city just appears, or several appear and I pick one of them. This seems like a pretty clear sign that some part of your brain understood what “city” meant well enough to search your inner catalog of examples without your consciousness overseeing it.

Now we’ll do another, and this time you should pay really close attention to the process in your head.

Pick an ice cream flavor.

Candidates appear, but again it’s not clear where they’re coming from.

Now finish this sentence: “The early bird catches the...”

I don’t think you had any mental control over whether you thought “worm” there. It appeared before you made any conscious decision, and I think it would be very hard to decide not to think of “worm.”

Think about the last time a word was on the tip of your tongue. You might be trying to remember an actor’s name, and can recall their face, the movies they were in, even the letter their name starts with. Then the answer suddenly appears hours later while you’re doing the dishes. Some mental process was humming away without your conscious mind knowing, and was suddenly able to produce a result and deliver it to your conscious mind.

All of these are pretty obvious evidence that retrieving information (which itself depends on understanding a huge amount of context about what words mean and how they relate to each other) can happen without our conscious minds observing it or making any decisions about it.

Folk theories of the mind often imagine language as playing a pretty secondary role to thought. When you speak, the folk theory imagines that you have some clear, pre-linguistic picture in your head of exactly what you want to say, and choose the words that will best communicate that picture to another mind. The full idea is clear before you speak, and language is just a useful bridge between minds, rather than something that our mental pictures depend on.

I’ll challenge this folk idea of language in a lot of ways in these posts, but one immediate problem with it is that the words we say often require a shocking amount of specific reasoning and understanding that doesn’t exist in our conscious minds before we say them. To see this, you can choose a topic you know a lot about, set a timer for 3 minutes, and then start speaking about it as much as you can. As you talk, try to pay really close attention to what’s happening in your conscious mind, and what decisions you’re actually making before the words come out of your mouth. You might be surprised to find that you don’t really know how a sentence will end when you start it, and there’s very little conscious planning happening at all. And yet you speak coherently, and can even learn from the things you hear yourself say. E. M. Forster tells a story about an old lady who asked, “How can I tell what I think till I see what I say?” This seems to imply that a lot of the “thought” required to plan and produce a complex paragraph happens outside your conscious awareness. And because producing it takes a deep understanding of the words and how they fit together into coherent English, as well as the concepts and how to best explain them via carefully structured sentences, a lot of that “understanding” is happening outside your conscious experience too. The fact that this works is one reason why writing can often be useful for learning. Writing can reveal full thoughts we’ve had that we might not have been consciously aware of.

One famous example of complex thought happening outside consciousness comes from the mathematician Henri Poincaré. He’d spent about 2 weeks working hard on a new kind of function. He took a break to go on a geology trip and stopped thinking about the problem. He says:

At the moment when I put my foot on the step the idea came to me, without anything in my former thoughts seeming to have paved the way for it, that the transformations I had used to define the Fuchsian functions were identical with those of non-Euclidean geometry. I did not verify the idea; I should not have had time, as, upon taking my seat in the omnibus, I went on with a conversation already commenced, but I felt a perfect certainty.

When he got back, he checked it, and it was correct! He concluded that sudden flashes of insight like these are “a manifest sign of long, unconscious prior work.”

In all of these examples, your conscious mind only gets access to the output of thinking. It doesn’t observe the processes that produced it. Scientists disagree about how far this goes. The neuropsychologist Karl Lashley had an extreme take in the 1950s, when he wrote “No activity of mind is ever conscious.” The psychologist George Miller later said, more mildly, that it’s “the result of thinking, not the process of thinking” that shows up in consciousness on its own.

So the folk idea that all thought and understanding require consciousness seems wrong. The question is what role consciousness plays in higher level thought and understanding.

A lot of your understanding happens outside of consciousness

So far my examples have mostly been loose associations (like thinking of a city) or drawing on things you already know (like talking about a topic). Maybe a lot of understanding and thought is by definition more complex than this, and requires conscious experience.

But we also understand much more complex things that we’re almost never conscious of, and never consciously learned. For example, English speakers put adjectives in a very consistent order. “A big red balloon” sounds normal, but something sounds strange about “A red big balloon.” The pattern adjectives follow is roughly opinion, then size, age, shape, color, origin, material, and purpose. I could describe Washington DC as a pleasant small old square gray city, and that sounds fine, but saying it’s a gray square old small pleasant city sounds strange and unnatural. You understand and use this pattern every time you speak, but you might never have been conscious of it, even when you “learned” it from years of hearing English. I would say I understand how to use English well, and this pattern is a key part of using English well, yet I went a long time not being conscious of this specific rule.

You also understand which words ought to follow others, well enough that your brain reacts to a strange word in a sentence within a fraction of a second, without you consciously deciding to check whether it makes sense. Neuroscientists have recorded people’s brain activity as they read sentences one word at a time. Some sentences ended with words that didn’t fit, like “He spread the warm bread with socks.” Strange words like “socks” trigger a distinct electrical response in the brain that peaks about 400 milliseconds after the word appears. Later experiments found that words that make complete sense also produce this same response, and the electrical response gets bigger the less expected the word is. So as we read, our brains seem to be constantly tracking how predictable each next word is. One explanation is that our brains use context to anticipate likely words. This seems like a core part of how we understand what we read. It’s subconscious (we’re not consciously weighing every word that could come next), and it looks surprisingly similar to how modern AI “predicts the next word.” This also looks to me like a deep understanding of how lots of possible words relate to the situation at hand, all happening subconsciousnly.

Some researchers wanted to see if there were any signs of deeper similarities between how our brains predict words and how AI does. AI models also assign a likelihood to every possible next word (that’s how they write), so we can see when a word shows up that the model considers very unlikely, like spreading bread with “socks.” Several research groups have found that these likelihoods inside language models reliably predict how strongly people’s brains react to more or less expected words. And the models that are best at predicting the next word tend to be the best at predicting the brain activity of people reading. So AI seems to successfully mimic at least this part of our subconscious understanding.

Our introspection and memory of our conscious thought are fallible

So we’ve seen there are types of thought and understanding that don’t occur in our conscious minds, which implies they don’t require consciousness. But the folk picture also runs into trouble with the thinking that does happen in our conscious minds. Our very recent mental past is surprisingly opaque to us. Even our honest explanations of our own recent conscious decisions can often be false.

The most extreme example of our beliefs about our own minds being false comes from split-brain patients, who have had the main bundle of fibers connecting the two halves of their brain cut to treat severe epilepsy. Researchers can show these patients a picture that only one half of their brain sees. In most people, only the left half of the brain can generate speech. In a famous study, the neuroscientist Michael Gazzaniga showed a picture of a chicken claw to the left side of a patient’s brain, and a picture of a snowy scene to the right side. The patient was then asked to pick related pictures from a set. The patient’s right hand (connected to the left side of his brain, which had seen the claw) chose a chicken, and his left hand chose a snow shovel. When he was asked why (remember, this is the left side of his brain talking), he gave an answer he honestly believed. He explained that the chicken obviously went with the claw, and “you need a shovel to clean out a chicken shed.”

So the speaking part of him had no idea why his other hand picked the shovel, but it came up with a compelling story, after the fact, for why he’d consciously chosen it. Gazzaniga came to believe that we all have a system like this in our brain, which he calls the “interpreter.” Its job is to come up with after-the-fact justifications for what we do, which we experience as honest beliefs, and to weave them into a coherent story about us, whether or not it has access to the real reasons behind our decisions. If this is right, the reasons behind even our very recent conscious decisions are more hidden from us than they seem, and consciousness plays less of a role in our higher-level decisions than we’d expect.

This hints at a broader claim I want to make, that introspection is just another mental process. It takes in uncertain information, processes it, and gives an output that’s as reliable or unreliable as our beliefs about the outside world. Ultimately, our thoughts are the result of activity in our brain. There isn’t some ethereal realm we exist in where we can observe our thoughts directly. Our beliefs about our own inner lives are fallible stories about the past, made more coherent by the (often self-serving) narratives our brains offer up, which we honestly come to believe. The idea that introspection is some transcendent act of turning the mind’s eye back on itself and seeing its own processes completely clearly is pretty suspicious.

We’ve seen similar evidence for something like an “interpreter” in people with intact brains too. In a 2005 study, people were shown pairs of photos of women’s faces and asked which one they found more attractive. In some trials, the researchers used sleight of hand to swap the photos, so people were handed the face they’d rejected and asked why they’d chosen it. The swaps were only caught in the moment 13% of the time, and only a quarter of the swaps were noticed at any point in the process. Instead, people very confidently and honestly explained why they had “chosen” this face, often indicating features that only the swapped face had. For example, one man said he had chosen the “more attractive woman” because she was wearing earrings, but the woman he had actually chosen wasn’t wearing any. The woman he had been handed after and was looking at was wearing them. 84% of the people who didn’t notice a swap said they thought they would notice one.

So people can honestly come to believe fake narratives their brains offer up about very recent conscious decisions. Dennett believed that this process describes our whole minds. He didn’t think there’s actually a single place in the brain where everything comes together for a viewer to see. He believed the self is basically just the broad story all these individual narratives and justifications add up to, which he called a “center of narrative gravity.” We tell stories about ourselves, but “for the most part we don’t spin them; they spin us.”

This view of introspection makes sense if you think about what actually happens when we introspect. Whenever we think at all, we’re feeding information about our own past behavior back into our cognitive processes.

The output of mental activity can then be used by other mental activity.

All of our mental activity is fallible, and takes in uncertain information about what’s happened before. Introspection specifically takes in past (fallible) evidence we have about our own behavior and computes a (fallible) output.

What doesn’t seem possible is a folk theory of introspection that imagines us as floating observers with a god’s-eye view of everything happening in our own minds:

Ultimately what’s available to the mind is the results of past computations and mental processes. Some of those could involve reporting back to the mind a lot of the individual steps taken. Introspection in my view is like examining any other aspect of the world. This more natural kind of introspection seems easily available to AI, which could also just collect information about what its past processes did and act on it. It doesn’t need a floating eye in the middle of its “experience” looking back at its own processes.

Some people point out that AI models can only move information forward through their layers, and take this as a sign that they can’t do what we do when we introspect. To me, this sounds kind of like saying that human minds can’t introspect because all the processes inside are moving forward in time. I think our folk theory often gives us the mistaken idea that we can directly peer into the recent past of our own mind and examine what’s there. What’s actually happening is that we’re using current mental processes to retrieve information about past ones. AI models can do the same thing, retrieving the results of earlier processes while answering a question (and we know they do this regularly). Once you think about introspection this way, it’s a little hard to see why current AI models couldn’t do something similar.

So it’s looking bad for the ‘little version of you in a theater’ view of the mind. The process that produces your thoughts (which takes understanding) is hidden from your conscious mind, and the process that explains your thoughts to you might be a separate “interpreter” without reliable access to the first one.

What about slow, careful thinking?

Of course, most of my examples so far are quick, automatic kinds of thinking. Slow careful reasoning, like working through a philosophy argument or doing math, looks like a much stronger candidate for where the conscious version of you takes over.

Even here, lots of individual steps happen subconsciously. If you ask me what 7 times 8 is, 56 just appears in my head. Maybe slow thinking works by summoning these opaque individual answers into our attention, and then examining them in the light of consciousness to build a deeper understanding.

Slower reasoning is not our default. It takes more energy and often feels unpleasant (this was the big theme of my last post on EA). In the famous simple problem:

“A bat and a ball cost $1.10 together, and the bat costs a dollar more than the ball. How much does the ball cost?”

The math looks so simple that most people don’t even think to slow down, and only switch into careful reasoning after they hear the ball isn’t $0.10 and get confused.

What actually determines when you enter this slow thinking? Is it a conscious decision? You can definitely choose to think more slowly and deliberately about a problem, but often the switch happens subconsciously. We don’t start reasoning slowly until something “snaps us awake,” like coming off autopilot. A lot of the time, it’s not clear to my conscious mind what aspect of me causes that slower reasoning to kick off.

But once we’re in slow reasoning mode, this does seem like where consciousness matters most for our thinking. It’s hard to picture a machine doing our most important kind of slow, deliberate thinking (pulling in information from lots of contexts at once and carrying very general patterns into new areas) without something like consciousness. So I now need to say what consciousness is (or might be) and what role it seems to play in our most careful thinking.

What phenomenal consciousness is

I’ll rely on Ned Block’s distinction from the introduction, between phenomenal consciousness (the first-person, what-it’s-like experience Thomas Nagel writes about in “What Is It Like to Be a Bat?”) and access consciousness (information being broadly available to lots of processes in your brain).

I am personally pretty convinced that phenomenal consciousness is not required for thinking or understanding. Access consciousness is required, but I think the way it works in humans implies current AIs probably have something close enough to it to allow for high-level thought.

Most of this section will be on phenomenal consciousness, and the next will be on access consciousness.

I think most people see phenomenal consciousness as the clear ultimate barrier between human minds and machines. No matter how advanced machines get, they will never ascend above information processing and have the first-person subjective experience we have of the world. For example, a machine could look at this square, and detect the wavelength of the light it’s giving off:

But it could never have the first-person experience of the redness of the red.

When you look at this square, I could write a third-person description of what’s happening in your brain. I could record the activity of every neuron, the wavelength of light, the associations you’re drawing between the color and past information your brain’s received about apples or sunsets. But no amount of third-person information about your brain, even the position of every particle and every electrical signal, could ever give me the first-person experience of being you seeing the red, the redness of the red over and above the information carried by the light.

This is what the philosopher David Chalmers called the “hard problem” of consciousness, the strange fact that consciousness alone seems to be inaccessible to third-person observation. Nothing else in the universe works like this. Everything else exists somewhere in space and time, follows regular physical laws we can write down as math equations, and can be measured by anyone with the right instruments. More complex “things in the world” like the national debt can ultimately be reduced to the behavior or expected behavior of systems made up entirely of things that have these basic qualities. First-person conscious experience seems to break all these rules. People could dig around in your brain as much as they wanted and find the neurons that respond to red, but it doesn’t seem like they’d ever find the redness.

This seems pretty hard to deny. Unfortunately, another fact about the world also seems really hard to deny, and it contradicts the first one. Physics seems to be “causally closed,” meaning the only things that affect physical objects are other physical things described by the laws of physics.

One of the main reasons to believe this is the conservation of energy, which says that when energy leaves one object, it always goes to other objects. When one type of energy disappears from an object without going to another object, it’s just turning into another type of energy. For example, when a rock falls, its gravitational potential energy goes down, and its kinetic energy (the energy it has as a result of moving) goes up. If energy is never created or destroyed, then no energy can be added to the world from some mysterious realm beyond physics. If nonphysical things were affecting the physical world, objects would gain or lose energy or momentum out of nowhere, breaking these laws. If a ghost that exists outside physical reality picked up a rock, the rock would gain energy that came from nowhere. Classical physics couldn’t work if violations like this were happening all the time, so physicists came to assume they weren’t happening at all.

We now know that classical physics was way too simplistic, and its version of these laws IS regularly violated. Mass and energy aren’t conserved separately. Mass can turn into energy, and energy into mass. Physicists now believe in the conservation of mass-energy. You can turn about a gram of mass, roughly the weight of a dollar bill, into the energy released in the Trinity test (the first atomic bomb), but you can’t turn it into the amount of energy in the sun. There are still strict rules for these conversions, set by E = mc² and the broader conservation law.

On the standard textbook reading of quantum mechanics, there’s also fundamental randomness in the world that isn’t the result of our not having enough information. If you rewound the universe, started it from the same initial conditions, and played it back, different things could happen. This is different from the everyday probabilities we deal with. When I roll dice, they’re so completely governed by classical physics that if you knew their exact position and velocity, their distance from the table, and exactly how they’d bounce off it, you could predict with perfect certainty what numbers they’d land on. If you rewound the roll and rolled them in exactly the same way, they’d land on the same numbers every time (unless quantum effects showed up at the everyday scale, which is extremely unlikely but not impossible). Our everyday uncertainty just comes from missing information. Quantum mechanics in comparison appears to be truly probabilistic.

Some people mistakenly think that quantum randomness opens the door for nonphysical entities to affect the world. The idea is that something like consciousness, existing beyond the laws of physics, could take an event with a 60% chance of happening and “choose” that it happens. From the outside, this wouldn’t look like a violation of physics. But this deeply misunderstands what quantum probabilities are. For the rest of quantum mechanics to work, a 60% quantum probability has to reflect the true odds of the event. The odds themselves are part of physical law. An outside entity meddling with them would basically be changing the 60% to 100%, which violates physical law in the same way as the ghost lifting the rock. Among other problems, the ability to change these probabilities would mean we could use entangled particles to send messages faster than the speed of light, breaking another core rule of modern physics. So quantum mechanics is actually just as walled off from supernatural effects as classical physics was.

Physics is (I’m biased) our very most successful field of science. It has broken down the nature of ultimate reality and successfully predicted the behavior of distant stars and black holes. Giving up causal closure would break a lot of what makes it work.

So, all that to say, physics looks really really causally closed to me. That leaves us with a problem, because conscious experience doesn’t seem to behave like anything physical. All this means that of these three obvious-seeming statements, one has to be false:

1. Physics is causally closed. Everything that happens in the physical world is fully caused by other physical things (or, where there’s quantum randomness, has its odds set by them).

2. Phenomenal conscious experience isn’t reducible to physics.

3. Phenomenal conscious experience affects what we do, including what we say about it.

Which one you cut matters a lot for how consciousness affects thought, and so for whether machines can think. I’d say my main problem with human existence itself is that cutting any of these looks like complete garbage to me.

The decision to cut 3 is called “epiphenomenalism.” This is the idea that consciousness is not reducible to physics, but physics is causally closed, so consciousness doesn’t actually have any effect on the physical world. Our conscious experience is like a passive observer in a theater helplessly watching our lives unfold.

This can sound intuitive at first, but I think it looks completely crazy the more you consider it.

This view implies that whenever you say “I’m not just processing information, I actually experience the redness of red,” your experience of the redness of red played no part in making you say it. Your physical brain, your speech, and your writing (and anyone else’s) have never been affected at all by the fact that you have this special experience. It’s just a pure coincidence that our physical bodies and brains, which are basically machines without this phenomenal experience on their own, happened to start writing about this thing that has never once affected them but definitely does exist beyond the physical world. You may have heard of “philosophical zombies” (or p-zombies), an idea that fits naturally with this view. A p-zombie is physically identical to a normal person but doesn’t have phenomenal consciousness. There isn’t anything it’s like to be them. An epiphenomenalist believes an entire world could exist exactly like ours, with everyone doing everything they normally do, except that everyone’s a p-zombie. That’s easy for an epiphenomenalist to imagine, because our own world is basically that plus some extra stuff beyond physics, caused by the physical world but never causing anything in it.

I find this so strange that I can’t entertain it. Smarter people than me do entertain it, so take my reaction with a grain of salt. But after 15 years of wrestling with the idea it remains the theory of phenomenal consciousness I give the lowest odds of being true.

As a well-behaved physicist, I’m also very hesitant to cut the first option. There are lots of other problems with cutting it. Evolution gives us a lot of reason to think we’d see ourselves and other people as monumentally important compared to the rest of the world, so it’s a little suspicious that the one place our most successful scientific theory breaks down is inside our own heads.

I can see myself believing this if I were religious. If you are religious, and your belief that the mind transcends physical reality is why you don’t believe machines can think, my only ask is that you say that directly in conversations about it. I often find that debates about minds and machines can meander in meaningless details for hours before one person flags “Oh by the way, the main reason I’ve been denying your position is my religious beliefs, which I’m only mentioning now.” That clarifies where the actual disagreement is.

If you’re not religious, there are a ton of additional problems with cutting Option 1. Evolution by natural selection is a purely physical process. How did merely physical animals at some point start accessing, affecting, and being affected by some ethereal realm or substance beyond physics? Why are they the only entities in the universe that can access this? If you cut the first option, you’re not religious, AND that’s why you don’t believe AI can think, I think the burden of proof is squarely on you to explain how your view fits with a broader scientific picture of the world.

If you do cut the first option, an interesting question is how much of our thinking and understanding happens outside the physical world. Do we have thoughts that happen outside of our brains? If so, why does severe brain damage also impair specific kinds of thinking? Whatever role the nonphysical part of consciousness plays, our slow, deliberate, higher-level thinking still depends massively on the physical brain working in specific ways. That leaves open the possibility that machines without the inner spark of phenomenal consciousness could think this way too.

The last option to cut is 2, where we say that the sense that consciousness is somehow above physics is some kind of complex illusion. This is the one I lean toward, but only because the other two look so much worse. There’s a lot that troubles me about this view, most of all:

Come ooooon! It really really really really seems like there’s something happening here that isn’t just information processing. There’s a “way it is like to be me” that can’t be articulated with sufficient physics equations. Aaaaah!

But phenomenal consciousness is incredibly strange. There are ways it doesn’t cohere at all with our folk theories of it. It’s strange enough that I can at least entertain the idea that it’s a complex misunderstanding happening in an information-processing machine called my brain. To build your intuitions for how this could be possible, I’d very strongly recommend Dennett’s paper Quining Qualia, one of the most fun and accessible papers in philosophy of mind. Another good essay is Joe Carlsmith’s Grokking Illusionism.

This view, that phenomenal consciousness is reducible to physical processes, has many flavors, like illusionism. However weird you find this idea, epiphenomenalism is just as weird, and it adds a bizarre extra entity that doesn’t behave anything like how I experience consciousness. For example, whatever we mean by consciousness, I personally mean the thing that causes me to see that red box and type the words “Aaaaah!” But epiphenomenalism tells us my typing that was not at all caused by the redness of the red.

There are also philosophers who, when faced with this trilemma, throw up their hands and announce that our concepts of the world are simply not built to understand consciousness, and the answer here might be stranger than we can conceive.

How does any of this matter for whether AI could at some point think? Well, which story we believe tells us something about how phenomenal consciousness interacts with our thinking.

If you believe in epiphenomenalism (that phenomenal consciousness exists but has no causal power), then our having phenomenal consciousness has never affected the world at all. We could lack it and the world would look exactly the same. And humans can think and understand things. So on this view, whether AI could think and understand doesn’t depend at all on whether it has phenomenal consciousness, because our having it doesn’t matter to our thinking either.

If you believe consciousness is nonphysical and does affect the physical world, I’ve said why I think the burden of proof is on you, and even then most of our deliberate thinking still seems to run on the physical brain.

If you believe (like me) that phenomenal consciousness is probably entirely reducible to physical interactions, then whatever role it plays in thought could in principle be replicated by other physical processes. This is necessary but not sufficient for showing that AI could have it. Consciousness could be a special kind of physical process that depends on the substrate (the material the mind runs on). This view is currently defended most prominently by the neuroscientist Anil Seth. But Seth (like many others who hold this view) believes intelligence and consciousness can come apart, so machines could “think” and “understand” without being conscious. The question for people who hold this view becomes whether machines can do the correct type of physical things that human brains do, not whether there’s some extra-physical spark inside the machine.

If I consciously notice a mistake, and that lets me correct an argument or line of thought, something has changed in how my brain is processing the argument. Relevant information has been successfully introduced. We can look into what’s changed, and how it’s improved my thinking, seemingly entirely via information processing, without ever explaining what it felt like to notice it. If phenomenal consciousness is not reducible to access consciousness, this makes it look like one specific way information is introduced to my mental system, and anything it introduces that can’t be fully reduced to information (the redness of the red etc.) doesn’t seem to play a direct role in thought.

The fact that “I am experiencing the redness of red,” can be reducible to information the brain can use (I can write it out as a sentence that you can read and make sense of) but the phenomenal content remains out of reach to summary by pure information. But this same phenomenal experience also becomes instantaneously unavailable to our own minds the second our perception changes. If I look away from the red object, it seems like the only way I can use that past phenomenal experience is via the information I’ve drawn from it that I can record. “I saw a red object, it has the same wavelength of light as other red objects. Also, I saw a deep redness of red. Aaaaah!” It seems like thought and understanding cannot depend on what we’re phenomenally experiencing at any one time. I can form thoughts about the color red without seeing it in the moment. And when we’re not experiencing something directly, it seems like the only access we have to it is via the third-person information it provided (our past selves are effectively other people here). So unless there are types of thinking that we can only perform as we’re having the phenomenal experience in the moment, it seems to me like the only parts of consciousness that can affect thinking are reducible to access consciousness. That means that for thinking about whether AI models can think, we need to turn to theories of access consciousness. Whether AI has phenomenal consciousness thus looks to me to be pretty unrelated to whether it can think.

Access consciousness’s role in thinking

Consciousness is a controversial topic. A recent attempt to map all theories of consciousness lists over 200. As someone who’s far from an expert myself, I can’t confidently argue for any one. But only a few theories get most of the serious academic attention, and they all contribute to my case that AI can think. I’m going to summarize what each says about the role of consciousness in thought, and how it compares to the folk “movie theater” picture of the mind. In every one, processes we’re not conscious of do the actual work of thinking. Consciousness is at best a way of coordinating those processes, or something separate from them that’s helplessly along for the ride, just justifying their results. A few of the most prominent theories imply that everything we’d call thinking could happen with no consciousness at all.

Global workspace theory

Global workspace theory is probably the most popular scientific theory of consciousness right now. It describes the brain as full of specialized processes all running at the same time, almost all of them subconsciously. Some processes do things like recognize faces, or enforce rules of grammar, or keep you physically balanced. Each one is good at its own narrow job in the broader system, and they mostly cannot communicate with each other directly. But they share a general “workspace” that can only hold a little at a time, so different pieces of information compete to get into it. Whatever wins gets broadcast to all or most of the processes in the brain at once, so the whole brain can use it. According to this theory, being conscious of something is just the information being broadcast.

The theory’s creator, Bernard Baars, ironically used a theater metaphor to describe it. The workspace is the stage, attention is a spotlight on the stage showing what’s on it, and the audience is the giant crowd of subconscious processes in your brain. In this picture there’s no self, and no single you in the audience watching the whole thing. Baars himself said “You don’t have a little self sitting in the theatre.”

Some neuroscientists (most prominently Stanislas Dehaene) have tried to translate this into a theory about physical brain tissue, where the workspace is a network of neurons with long-distance connections. When you become conscious of something, activity suddenly spreads across that network.

If the theory is right, individual mental processes should each have information the others don’t, and we only become conscious of it when it’s broadcast to lots of them at once.

The first part of this claim is pretty self-evident. One of my favorite tests for this is to think of a word at least 5 letters long, and then (without looking!) try to picture where each letter is on a standard QWERTY keyboard. I personally find this is very hard. Next, place your hands on a hard surface like a table, and mime typing out the word as if you had a keyboard below you. When I do this, my fingers show me exactly where each letter is. The muscle-memory part of my brain had this information all along, and wasn’t sharing it with the part that forms visual memories.

Scientists have come up with ways to try to test this theory. In one 2009 study, researchers flashed numbers on a screen too quickly for people to consciously see them. People could still do slightly better than chance at simple operations on the mystery number, like naming it, saying whether it was bigger than 5, or subtracting 2 from it. But they failed at chaining these operations together (like subtracting 2 and then saying whether the result was bigger than 5). This suggests individual subconscious processes picked up bits of information about the number, but couldn’t combine what they had, because the number never got broadcast to the whole brain. A similar pattern will appear in AI models in Part 3.

This theory stands out for giving consciousness a clear, active role. But even here consciousness is more of a spotlight than a director of what’s happening on the stage. It only needs access consciousness, with no transcendent viewer guiding things from above. This also doesn’t rely on the specific nature of neurons, because a machine could just as easily broadcast information to lots of individual processes.

Higher-order theories

Higher-order theories of consciousness say that a mental state is conscious if your brain is also representing itself as being in that state. A red apple might be in your field of vision without you being conscious of it. You only become conscious of it when another part of your brain forms the belief that you’re seeing the red apple. The philosopher David Rosenthal is the best-known defender of higher-order theories.

Blindsight, a bizarre condition, seems to fit this theory well. Some patients with damage to their primary visual cortex report that they can’t see in part of their visual field, but do significantly better than chance when asked to point to things in front of them or guess basic shapes. A higher-order theorist might explain this as their brains still processing some visual information without the higher-order representation required for conscious seeing.

These higher-order stories the brain tells can easily be inaccurate. Worse, they don’t seem to play much role at all in most of what we normally consider our conscious thought and decision-making, like reasoning or planning. Rosenthal argues that reasoning and planning are done by other mental processes whether or not they’re represented in consciousness, and their becoming conscious “adds no significant function.” So under this view, thought and understanding do not seem to depend on consciousness at all.

Attention schema theory

Attention schema theory (AST) describes the brain making lots of important but subconscious decisions about where to spend its limited processing power. Giving a process more of that power is what it means to give it “attention.” If you’re trying to follow one person in a crowded room full of other conversations, your brain can give much more processing power to their words. Michael Graziano, who came up with AST, points out that your brain keeps a simplified model of your body to control your movements (reaching for a cup means making a lot of assumptions about where your hand is). He proposes that it also keeps a simplified model of its own attention, to control where that attention goes. Among other things, it would be bad to let anything that shows up in your senses grab a lot of processing. Your conscious awareness is this simplified model your brain runs to manage itself. Here consciousness plays a useful role, giving other brain processes information they can use to manage how much processing goes to each task.

Introspection under attention schema theory is also a simplified model of what’s happening in our brains. Again, our consciousness doesn’t have direct clear access to what’s happening in our own heads. Your sense of yourself as an indivisible observing entity is actually the brain telling a useful simplified story about itself.

Graziano thinks that AST implies that a machine with a similar attention model of itself could form the same conviction that it has subjective awareness. So this is another theory where access consciousness plays a useful role in the brain that could be replicated in machines.

Predictive processing

Predicting our incredibly complex environment was central to our ancestors’ survival, as it is for other complex animals. Predictive processing approaches make this central to explaining conscious perception and how the brain works. Here, your brain is constantly predicting sensory input using internal models shaped by its experience. Prediction concerns both what is causing your present sensory input and what future input may arrive. The brain compares these predictions to incoming sensory signals and updates its internal models to better match them. On this account, your conscious perception is your brain’s best interpretation of sensory signals. It’s the brain’s best predictive guess about the world. In this theory, you don’t draw raw information into your conscious mind and then interpret it. Your conscious experience is the interpretation of the raw data itself. A lot of our everyday experiences can be explained this way. The reason you usually can’t tickle yourself may be that your brain predicts the sensory consequences of your own movements and dampens its response. Looking into a hollow mask from the back can create the illusion that it’s a face bulging out at you instead, partly because our brains strongly expect faces to protrude outward rather than cave inward.

This is remarkably similar at a broad level to the basic idea behind modern language models, where by “merely predicting the next word” models develop complex and subtle internal representations during training that enable them to write coherently about the world. I’ll flag here though that Anil Seth is a prominent supporter of predictive processing as an explanation for what happens in the brain, and he’s also skeptical that current approaches to AI will produce consciousness. Among other things, he suspects that consciousness is too tied up with the actual biological processes that keep us alive for computation alone to reproduce it. He accepts that machines can be very intelligent and separates consciousness from intelligence. He also entertains the possibility that machines could understand without being conscious, though he remains cautious about whether current language models do. My own take is that this theory sounds strikingly similar at a broad level to descriptions of large language models, to the point that while I accept that they might not be conscious, taking predictive processing seriously as an account of perception would make me more open to thinking that current AI models can think and understand.

Integrated information theory

I’ve always found integrated information theory (IIT) strange, but enough smart people are into it that it deserves a mention, though I’ll note that in 2023 over 100 researchers signed a letter calling it pseudoscience.

IIT’s starting observations include that experience is unified (you experience the parts of your visual field together, as one experience), and specific (you have one exact experience out of countless possibilities). What physical system could have both of these properties? Maybe something with parts that influence each other in such a way that the system as a whole is irreducible. Dividing it into independent parts changes the cause-and-effect structure of the whole thing.

As an example, the system underlying the human visual field can’t, on this view, be like an idealized camera’s sensor array. The array is made up of individual sensors that don’t notice or react when others are on or off. You could divide that array in half without changing how each half works. The brain instead contains dense loops of mutual influence. IIT tries to quantify integrated information in a system’s cause-and-effect structure with a score (called “phi”), which, according to the theory, measures how conscious a system is. Consciousness, on this view, is specific, irreducible cause-and-effect structure within a system.

One interesting fact that lends some credence to this view is the fact that the cerebellum has about 4 times as many neurons as the cerebral cortex, but damages to it seem to not affect consciousness nearly as much as the cerebral cortex. The cerebellum has a lot of relatively independent processing units, which according to IIT give it low phi.

This has a lot of weird implications, that make me think it’s probably not correct. The computer scientist Scott Aaronson pointed out that IIT’s method of calculating phi could make simple logic-gate arrangements that don’t do anything interesting or useful in the world more conscious than humans. The creator of IIT does accept that a sufficiently large grid of logic gates could be conscious even while it’s inactive. That seems pretty bizarre, but consciousness is bizarre, so here we are.

My read is that IIT is specifically about phenomenal consciousness. I’m not sure what role access consciousness plays in the theory, and I can infer that it doesn’t play much at all. As an example, a one-way network without any loops or dependencies has zero phi. And any network with loops can, in principle, be rebuilt as a one-way network that produces identical outputs. This seems to mean that the theory implies that anything and everything done by a conscious entity could also be done by an entity with zero consciousness. A recent paper using IIT as its framework seems to agree that consciousness and all abilities of the brain come apart, and so AI can do everything the human brain does without being conscious. Thus, IIT does not rule out the possibility of thought and understanding in AI systems.

These all seem to imply that the aspects of access conscious important for thought could exist in machines

In all of these theories, most of the work of thinking happens outside consciousness, and where consciousness does help, it’s by sharing information or managing where processing power goes. If that’s consciousness’s whole role in thought, it seems like something we could easily imitate in AI. None of this requires the AI to have phenomenal consciousness, only (sometimes) access consciousness. And if access consciousness is mainly about managing the flow of information (sharing it across a system, and deciding how much processing power different parts of the mind get), that sounds like something a machine can do. So even if you’re very skeptical that machines could ever be conscious, that tells you very little about whether they can think.

Any part of your mind that understands something is made up entirely of parts that don’t understand

This one seems too obvious to need saying, but it helps keep me grounded in the debate, and a lot of people talk as if it isn’t true.

Unless thought and understanding are supernatural and happen outside of physics, any system that understands or thinks is ultimately made up of fundamental particles and forces. These on their own don’t understand or think. Thinking is an emergent property of arranging them in specific ways to perform specific functions. At a higher scale, individual neurons also don’t “think” or “understand.” They are building blocks for a specific system that thinks and understands. All that human neurons do is receive basic chemical signals, change their electrical activity, and release some chemicals. Any system that understands will be made up of parts that “only” do simple things. That’s a basic part of living in physical reality.

It’s surprising how often people will say other systems can’t think or understand because they are made up of individual parts that don’t understand. People will say that an AI is “just doing matrix multiplication,” as if the fact that none of those individual mathematical operations understands anything means the whole system cannot understand anything either.

The role of access consciousness in thought, like anything else in the world, also needs to be completely explained by individual processes that are not conscious. If thinking requires some higher-level awareness, that awareness also has to be explainable in the language of physics (again, unless you believe it happens in a separate nonphysical world that interacts with ours). Dennett once observed (in his book Brainstorms) that explaining an intelligent system means breaking it into smaller, dumber systems, then breaking those into even dumber ones, until you get down to parts so simple that they can be “replaced by a machine.” I think this is the only way minds can ever be explained.

Language, meaning, and consciousness

The folk picture also leads people astray about what it means to understand language. The Cartesian theater picture of language is that to understand a word, you need a clear, first-person experience of what the word refers to. Anything without this experience doesn’t really understand the word. According to this theory, when I say “apple,” you understand me because you can summon a first-person image of an apple, or at least have experienced one before. AI might have a huge collection of associations with “apple,” but without an inner Cartesian theater to experience what the word refers to, it can never truly understand it. So (the theory goes) AI models only manipulate words, instead of using and understanding them the way we do.

Again, I’ll start with some first-person experiments you can run on yourself. Think about the word “dog.” Maybe a picture of a dog appears in your mind. This passes the folk test of understanding. Now think about the word “unless.” What appears in your mind when you do that? You obviously understand “unless” perfectly well. But it’s hard to see what clear inner experience could contain the meaning of “unless.” “Unless” changes the relationships between other things you could imagine, and mental pictures don’t seem much use for a word like that.

So maybe we can understand relational words without mental pictures, but simple nouns do require mental pictures. Already, this seems like a weird arbitrary distinction. If you can fully understand a word like “unless” without a mental picture attached, this suggests mental pictures aren’t that fundamental to understanding. Another problem with this view is that some people have aphantasia and can’t form visual images at all. These people can go most of their lives without even noticing that their mental experience is weird or different in any way. If understanding words required mental images, these people would regularly use language incorrectly, or not really understand the words they use. That seems obviously wrong.

You might object that these people have still seen the things they talk about. They’ve still seen dogs even if they can’t form pictures of them. But there are obvious counterexamples. Do blind people not understand the language they use? Do I not understand the word “Beijing” because I’ve never been there? At this point, I worry that when people say “understand” what they mean is “know everything there is to know about” something. I don’t know everything there is to know about biology, and I can’t form a clear, complete picture of it in my mind, but I can use the word, and I feel like I deeply understand what it means. This understanding is, I find, partly inaccessible to my consciousness. There are a lot of details about what “biology” means that I can’t hold in my conscious mind all at once.

So it seems like phenomenal conscious experience can give us new information about something. It can show me what it’s like to experience the red of a sunset. But a blind person who can’t experience this obviously still “understands” the word “red.” They can use it coherently and know how red relates to lots of different things in the world. They’re just missing one specific aspect of it: the phenomenal first-person experience of it. This seems more like me using the word “Beijing” without having been there than me using the word “Buchladen,” which I don’t know the meaning of at all. Phenomenal consciousness does not seem to be required to completely understand the meaning of the words we use.

This gets at a confusing way we use “understanding” to mean two different things. It can sometimes mean a deep first-person knowledge of what an experience is like. If someone told me that I don’t understand what it’s like to live in Beijing, I’d agree. There’s a lot about the day-to-day experience of living there that I don’t know. But if someone said that I don’t understand the sentence “what it’s like to live in Beijing” that’s obviously wrong. I know what the words are referring to and how to use them. In conversations about AI, these two definitions are sometimes blurred. When I say “AI understands the words I’m saying” I worry that what people think I mean is that AI has some first-person subjective experience where it deeply relates to me. All I actually mean is that AI has enough information about the words, and their relationship to all the other language and images it can process, that it can work with the information in them about as well as I can.

My own view is that understanding a word is a matter of what it lets you do with the word. Consider what it means to understand “fragile.” Someone who really understands “fragile” knows that dropping a fragile object is risky, and that whether something is fragile often depends on what it’s made of. They can use the concept in new situations. To me this looks like having a lot of specific, hard-to-articulate information clustered around a concept like “fragile” (including the logical relationships it implies, like that fragile things are hard to ship), which often can’t be boiled down to a single clear idea we can hold in our heads. Our conscious experience is just one way of getting closer to the right cluster of information and relationships, and it isn’t what holds that cluster together.

One role language plays in thought is letting us understand things we’ve never encountered by connecting them to things we already understand. I can explain a tool you’ve never seen by describing what it does and what it’s made of (all things you already understand), and you can understand the new tool without a clear mental picture of it or any first-hand experience of it.

A problem in the philosophy of language often brought up in discussions about AI is the “grounding problem.” Our language can’t just be a web of relationships between words. It has to be grounded in the reality it describes, so the relationships between words need to be determined by the relationships that actually exist in the world.

This is an interesting problem, but the folk theory often leads people to believe that any solution to the grounding problem requires a first-person conscious observer seeing for themselves “what the language is about.” They need to have the phenomenal experience of the world and connect that to the words they use. If we drop the folk theory, our observations become basically a way of collecting a lot of information about the world at once. As long as information from the world shapes what language says about it, well enough that predictions made in language match what’s actually happening, that seems completely adequate for solving the grounding problem. We don’t need floating observers from outside physical reality giving words meaning based on subjective experiences that information processing can never reach.

The grounding problem does mean that information processing alone isn’t enough for language. A being could have a perfectly coherent internal language that does not at all relate to anything in the world. The main question for whether something “understands” its words is whether they’ve been shaped enough by information from the outside world to reliably describe and predict it. I’ll argue in Part 3 that AI has several ways of getting this information, mainly during training.

So once we drop the folk theory, understanding language means being able to use words correctly by other speakers’ standards, make a huge number of subtle, correct connections between each word and other words, use words shaped by the outside world to correctly describe and predict what happens in it, apply them in unfamiliar situations, and correct mistakes. I think phenomenal consciousness, if it exists, is basically one way humans acquire the information that structures our language, not the thing that gives our words meaning. If something has all of these properties, I would say it understands words for the same reason that humans understand them.

Conclusion

Here’s a summary of the picture I’ve tried to paint here:

• A lot of the work of thinking happens before we become aware of the results. Whatever story we tell about thought and understanding needs to include the fact that a lot of it is capable of happening outside of our conscious attention.

• Our inner world is often opaque to us. It is easy to quickly develop incorrect beliefs about our very recent conscious experience and decision-making.

• Introspection is just another fallible mental activity that draws on information from the world and past mental activity, like anything else. We don’t have some inner god’s eye view of our mind where we can clearly see everything about our own thought process. Perfect self-knowledge can’t be a requirement for AI thinking, because humans can think without it.

• Careful reasoning depends on specific complex kinds of coordination, like holding onto intermediate results, bringing the relevant information together, understanding the semantic content of our words, noticing conflicts, directing attention to different places, and revising mistakes. These are substantial, but none of them currently seem inaccessible to AI models. Without a Cartesian theater guiding them from above, this looks much more accessible to machines.

• It seems like whether AI has phenomenal consciousness does not matter for whether it can think. What matters is whether it has the specific qualities of access consciousness that facilitate thinking. The best theories of access consciousness all seem to imply this is possible in principle. Having an experience and being able to work with all the information that experience would provide seem like different things, and thought and understanding seem to exclusively depend on the second.

• We don’t actually have to settle every debate and disagreement about consciousness before taking machine thinking and understanding seriously, because many of the most popular accounts of both phenomenal and access consciousness imply that AI could think and understand with the right kind of information processing.

• The parts of a system do not need to understand anything for the whole system to understand. Individual neurons don’t understand what’s happening. The argument that a system cannot understand because it’s made of fundamental building blocks that don’t understand is confused. Everything physical is made of these building blocks, so any story of systems that understand has to build them ultimately entirely of things that don’t understand and could be “replaced by machines.”

• Understanding words requires knowing what follows from using them, which is structured by how they relate to the outside world, and their broad logical and semantic connections to all other language. It does not require clear coherent phenomenal conscious mental images.

Once we poke at the folk conception of our minds, the picture that comes out is a mind with abilities spread across many interacting process. Some receive information, or find patterns, or catch mistakes, or generate possibilities to investigate, or follow logical steps from premise to conclusion, or broadcast information to lots of other parts. Our stories we tell about our own minds are products of these imperfect piles of capabilities working together in complex ways, aided by what we’d call access consciousness, which itself appears to be a pile of specific information processing abilities. It’s amazing what these all can do together.

This all I think radically changes what we should expect from machine thought. When investigating whether a machine can think, this gives us way more specific questions to ask, like whether it can represent the world with models, whether it has information about the subtle semantic content of the words it uses, whether it can monitor the output of its past processes and look for any problems or contradictions, whether it can follow a trail of logic from premises to conclusions. Asking “but is it human?” after all this makes less sense to me. It seems like asking whether a submarine is a fish after establishing whether it can dive, navigate, and cross an ocean. These are all individual qualities that make up but don’t completely summarize both fish and submarines, and confusing them as being inseparably tied together in either is obviously silly. We have strong evolutionary reasons to treat the human mind as special, but once we poke at what we actually know these abilities also look like amazing but separable abilities and functions that could with the right technology be replicated in machines.

I haven’t yet given you any reason to think that current AI can do any of this, that’ll be in Part 2 and 3. I hope I’ve shown why “the AI doesn’t have conscious experience” can’t end the discussion about AI thought. By itself, the claim (if it means phenomenal consciousness) gives us very little reason to think that a system cannot do most or even all of what we mean by thinking. I believe that the main question facing us now with AI is whether AI will be able to do all the types of thinking human minds can do, or even valuable forms of thinking we’ve never accessed, and what the world looks like when they can. The difference looks way more like one of degree rather than kind to me.

I worry that people who want to enforce strict bans on “anthropomorphic” language being used to describe AI are often implicitly enforcing the folk conception of how our minds work. They do not want to pick apart the mind’s cognitive abilities and list which AI has and does not have, because for them the human mind seems so obviously special and transcendent that it’s somewhat sacrilegious to imply it could even be divided up into these individual abilities, cutting them off from the unifying light of phenomenal consciousness. This enforcement of a ban on anthropomorphic language is often done in the name of cold hard science up against superstitious suggestible people who have mistakenly identified a ghost in the machine. In reality, I’m worried the people enforcing it have superstitiously discovered a ghost in themselves that isn’t there, and have closed themselves off to a serious attempt to move beyond our own self-concepts and demystify the surprisingly inhuman processes and forces that underly our own minds to the point that some of those processes could be considered to have already been achieved in machines.

How this could all be wrong

I’ve painted a picture here that’s very sympathetic to physicalism, the idea that everything (including consciousness) is physical. Physicalism could be wrong, and that’s the main way I think my story could fail. In polls of professional philosophers of mind, physicalism is more popular but far from a consensus in the field.

It might be that phenomenal consciousness has nonphysical aspects that can’t be reduced to access consciousness, that these aspects somehow depend on the material the mind runs on (brain matter vs. computer chips), and that thinking and understanding depend on them way more than I’m giving them credit for. I think the odds of this are low, but not too low, so I’m open to the idea that machine thinking will be permanently limited by lacking this nonphysical ingredient. But my default assumption is still that this is all wrong, and that machines can think.

The other way I could be wrong is if the material or structure of brains lets them do things with information that computers, given their design, never can. That would mean computers are more limited in how well they can mimic access consciousness. This is interesting, but it doesn’t seem likely to me that computers will be so limited they can’t mimic most of the important stuff in thinking. I think the case that they aren’t gets much stronger once we look at what current AI can actually do, and why, which is where Part 3 will start. Before that, we’ll need to explore the ongoing philosophy debates around whether computers and nonbiological matter in general could possibly think, and that’s where Part 2 will pick up.

The effective altruist story of the world

The core story effective altruism tells about the world, which was already there in the drowning child thought experiment that a lot of the movement grew from, is that the ways we go about doing good, whether through our donations or our careers, are often strongly determined by what we believe will get us social status or otherwise help us get ahead. There’s good reason to think our higher-level thinking itself evolved to help us win social games, so when we set out to find “how to do good,” a lot of what we’re actually doing is picking up on subtle social vibes around different causes, trying to suss out what the “good” (often meaning high-status) people work on and believe in.

This is not inherently bad or useless. Status systems communicate a lot of useful information. Society often gives status to things because they really are useful, just not in ways that can be clearly articulated to every person affected by the push and pull of what we give status to. So social status behaves a lot like a market, with the behavior of billions of people together determining how much status different things get, without any central planner making the final decision. A lot of our inclination to do good is really an inclination to respond to these market incentives. And that’s good! It keeps society running, helps us live together, and often helps us respond to subtle human needs that can’t be clearly articulated through direct argument. When I say status is like a market, I mean both that markets are phenomenally efficient and that status isn’t always bad. The status we give our friends is not meaningless evolutionary noise, just like the money a local baker receives for a cupcake isn’t the full story of what happened in the act of making it.

But like any market, this one has deep failures and missed opportunities. Our sense of how to do good was built for the small bands of early humans we evolved in, who couldn’t meaningfully affect the world far away from them. We now live in a hyper-globalized world of over 8 billion people, and can affect almost any of them by donating to the right places. We also have unbelievable sci-fi control over the world, to the point that we could bathe the entire planet in nuclear fire if we wanted to. Among other things, we now farm hundreds of billions of animals every year. Despite our strong evolved intuition that the world will mostly stay the way it was when we grew up, the last 200 years have seen constant, previously unimaginable technological change, and the near future might bring new technologies that upend our safety at least as much as nuclear weapons did a mere 80 years ago. In this environment, our evolved intuitions around goodness and status are much blunter instruments than they used to be for figuring out where we could actually do the most good. To rely on them completely because they feel right and normal and good, and to never be willing to override what immediately feels virtuous or what your peers (or society more broadly) are encouraging you to do, seems at the very least like an unserious way of living, a way of not fully thinking through the situation we’re in. It seems like a form of playing pretend. EA is one of many ways I try to think more seriously about ethics and complicate my immediate sense of what’s good.

A metaphor I like for two types of thinking is serial vs. parallel processing. In serial processing, you follow logic from premises to conclusions, step by step. In parallel processing, you “vibe it out.” You pick up on subtle, inarticulable aspects of the world and respond to them. When you do a math or physics problem, you’re thinking serially. When you pick up on a social cue or sense you’re in danger, you’re thinking in parallel. Most of our thinking is actually an incredibly complex mix of both. Importantly, we didn’t really evolve to do consistent, thorough serial thinking without having it easily thrown off by the vibes of parallel processing. Thinking step by step is much more cognitively demanding. As Dennett says in Consciousness Explained:

Conscious human minds are more-or-less serial virtual machines implemented—inefficiently—on the parallel hardware that evolution has provided for us.

In the evolutionary environment, serial thinking about morality probably wasn’t very useful. Social cohesion was a matter of life and death, and relied on members of the group not randomly getting some idea in their heads that seemed logical and right but didn’t fit what the group needed from them. Our desire to look virtuous to our peers was heavily selected for, regardless of its effects on the wider world, which we had little ability to affect anyway. How would evolution even select for what “had good effects on the world” far beyond us?

Of course, our moral intuitions have also evolved alongside society. Modern people’s moral intuitions are wildly different from those of people thousands of years ago, and it’s not a coincidence that they now line up with a lot of very complex ways of behaving that happen to make a complex society function remarkably well. Our general respect for and good treatment of strangers, and the corresponding ability to trade with and benefit from people very different from us who live far away and well outside our immediate networks, is a relatively new and miraculous development in the history of humanity. But society has changed, and keeps changing, so much that even our newer moral intuitions often can’t tell apart the very different levels of impact we can have on the world. They’re still too blunt for our hyper-globalized, hyper-technological situation.

So to circle back to effective altruism, this is the core story it tells about the world:

Our thinking about ethics and about what we should do is often heavily structured around what will give us social status. This isn’t necessarily bad, because social status is like a complex market, which can both communicate a lot of useful information and incentivize a lot of pro-social behavior. But this market is likely to have gigantic failures, places where it drastically misjudges where we could do the most good, because our moral intuitions ultimately come from a very different situation, where we lived in small, close-knit bands in a world we mostly couldn’t affect, rather than in a world of 8 billion people where each of us can affect lives on the other side of the planet with a simple donation. While our moral intuitions and status systems have evolved to keep up with our changing situation, the world has changed so fast in the last few hundred years that there are likely massive gaps between our moral sense of what we can do and what we can actually do. There are lots of other serious belief systems and ways of investigating the world that have been phenomenally successful at finding truth (including what looks like moral truth), and we should defer to them (people into EA are quite deferential to scientific and philosophical consensus where it exists). But the new world we’re in is so strange and vast, and people are so averse to thinking step by step about doing good, that other moral market failures likely persist. We should try our best to find them, compare them, and act on the best ones.

Getting more concrete

It’s rare for people to actually try to think step by step about what they’re doing overall.

A lot of our core beliefs about ourselves and the world are surprisingly surface-level. One of the most humbling experiences is sitting down with a blank Google Doc and trying to type out a detailed explanation of some belief that’s important to you and guides a lot of your life, or what you’re ultimately trying to do with your career, or your life plan in general, or why you donate to the charities you do. Things you believed you “think about a lot” might turn out to be a few slogans you repeat in your head over and over while you’re at the grocery store. Our own minds are pretty opaque to us. We often think we’re doing deep, premise-to-conclusion serial thinking about the problems we care about, when in fact we’re just basking in the warm glow of status we get from turning simple slogans over in our heads. A lot of my own “serious thinking about economics” looks more like me offhandedly thinking “Markets communicate information. Hayek! I’m so smart” and feeling great about it while I wait in line at Trader Joe’s. My most popular post was basically a long meditation on how we do this when learning about the world, and how to avoid it.

This doesn’t make sense if you think of the world as a sequence of rational, thought-out decisions by actors carefully weighing every trade-off. It does make sense if you think of human society as mostly guided by markets for social status, where our behavior and thinking are strongly pulled toward whatever makes us feel we’re doing or believing the right thing, and careful serial thinking about what we’re doing is much more demanding and unpleasant. When we do think step by step, we regularly bump up against our own failures or lack of knowledge or seriousness, all things our brains are hard-wired to avoid.

But doing it also often yields a surprising amount of useful insight.

There are many ways of thinking serially about the world (my favorites being physics and analytic philosophy) that have been remarkably successful at overriding our longstanding moral and factual beliefs and getting us to better behavior or deeper moral truth. The specific goal of EA is to scout out the most important of these that are still left to find, the ones that seem robustly good after wrestling with all the reasons we have to be uncertain, and that merely require us to think step by step about how to live coherently with what we value in this new sci-fi world.

This is a good time to come back to the drowning child argument for giving much more to global charity. Here’s the argument as Peter Singer originally wrote it:

I begin with the assumption that suffering and death from lack of food, shelter, and medical care are bad. I think most people will agree about this, although one may reach the same view by different routes. I shall not argue for this view. People can hold all sorts of eccentric positions, and perhaps from some of them it would not follow that death by starvation is in itself bad. It is difficult, perhaps impossible, to refute such positions, and so for brevity I will henceforth take this assumption as accepted. Those who disagree need read no further.

My next point is this: if it is in our power to prevent something bad from happening, without thereby sacrificing anything of comparable moral importance, we ought, morally, to do it. By “without sacrificing anything of comparable moral importance” I mean without causing anything else comparably bad to happen, or doing something that is wrong in itself, or failing to promote some moral good, comparable in significance to the bad thing that we can prevent. This principle seems almost as uncontroversial as the last one. It requires us only to prevent what is bad, and to promote what is good, and it requires this of us only when we can do it without sacrificing anything that is, from the moral point of view, comparably important. I could even, as far as the application of my argument to the Bengal emergency is concerned, qualify the point so as to make it: if it is in our power to prevent something very bad from happening, without thereby sacrificing anything morally significant, we ought, morally, to do it. An application of this principle would be as follows: if I am walking past a shallow pond and see a child drowning in it, I ought to wade in and pull the child out. This will mean getting my clothes muddy, but this is insignificant, while the death of the child would presumably be a very bad thing.

The uncontroversial appearance of the principle just stated is deceptive. If it were acted upon, even in its qualified form, our lives, our society, and our world would be fundamentally changed. For the principle takes, firstly, no account of proximity or distance. It makes no moral difference whether the person I can help is a neighbor’s child ten yards from me or a Bengali whose name I shall never know, ten thousand miles away. Secondly, the principle makes no distinction between cases in which I am the only person who could possibly do anything and cases in which I am just one among millions in the same position.

I do not think I need to say much in defense of the refusal to take proximity and distance into account. The fact that a person is physically near to us, so that we have personal contact with him, may make it more likely that we shall assist him, but this does not show that we ought to help him rather than another who happens to be further away. If we accept any principle of impartiality, universalizability, equality, or whatever, we cannot discriminate against someone merely because he is far away from us (or we are far away from him). Admittedly, it is possible that we are in a better position to judge what needs to be done to help a person near to us than one far away, and perhaps also to provide the assistance we judge to be necessary. If this were the case, it would be a reason for helping those near to us first. This may once have been a justification for being more concerned with the poor in one’s town than with famine victims in India. Unfortunately for those who like to keep their moral responsibilities limited, instant communication and swift transportation have changed the situation. From the moral point of view, the development of the world into a “global village” has made an important, though still unrecognized, difference to our moral situation. Expert observers and supervisors, sent out by famine relief organizations or permanently stationed in famine-prone areas, can direct our aid to a refugee in Bengal almost as effectively as we could get it to someone in our own block. There would seem, therefore, to be no possible justification for discriminating on geographical grounds.

From Famine, Affluence, and Morality

This thought experiment is, among other things, a way to snap you awake into the hyper-globalized world we’re in, out of the still very strong moral illusion that we live in small hunter-gatherer bands and can only affect the people right in front of us. In reality, there are enormous numbers of invisible levers floating in front of each of us that we can pull at any time, each with some effect on other people’s lives, and some with unimaginably larger effects than others. These levers go undetected by the status games around us, and at times even by the more complex and nuanced ethical systems that have arisen to keep pace with modernity, because the world has just moved too fast for most people to intuitively sense how much real impact they can have now.

One of these levers is donating to specific malaria charities, which can reliably prevent one death from malaria for roughly every $5,000 donated.

This fact, that each of us could save a real person’s life for a meaningful but still small fraction of our annual income (the median American household makes $87,000 per year), seems at first like one interesting moral claim among many, but once you think through its implications, a lot of our behavior looks kind of absurd. For example, the ongoing Russian invasion of Ukraine has been an unimaginable catastrophe, with around 600,000 people killed since the war began. I don’t want to cheapen what’s happened, and to be clear, I’m not saying “the money spent on this war should have been donated to charity instead.” Instead, consider that the budget of the school district I taught in (Fairfax County, admittedly one of the largest in the country) is about $4 billion per year. 600,000 x $5,000 = $3 billion. So politicians in Fairfax County, Virginia could get together and say “Hey all, we’re going to double the taxes you pay into the school system for exactly one year, which will be a lot, and donate all of it to anti-malaria and similarly effective charities, but by doing this our one county will prevent an entire Ukraine war’s worth of deaths in a single year, just spread around the world.” If a single American county had the opportunity to prevent a major war’s worth of deaths with one policy change for one year, that would be major news! We wouldn’t treat it as one interesting decision among many. “Oh, this one random county could’ve prevented a whole war’s worth of deaths, but decided not to. They want to focus on infrastructure!” That would be pretty shocking.

And yet we all do have this collective opportunity, and it mostly goes undiscussed. When I bring it up, people often treat it as one interesting fact among many, go on with their days, and get morally fired up about things that don’t seem nearly as consequential, and that I don’t think even they would believe are nearly as consequential if they spent some time writing out how those things compare to malaria. But people often just never think to do these comparisons at all. If they took the time to seriously consider how they use their resources, I think they would often come to different conclusions. My claim here isn’t that malaria is the one big cause every smart person needs to care about, only that it’s evidence that many people won’t even take the first step in seeking out ethical market failures unless really pushed. Malaria looks like a cause that would get a lot, lot, lot more attention if people actually sat down and thought step by step about what they’re doing with their charitable donations. It wouldn’t fade into the background as one interesting problem among many. For a lot of people, donating to fight malaria would seem much more urgent than most other things they could donate to, for the same reason it would seem drastically important for a single county to prevent a major war if it could.

There are of course problems with this thought experiment. Malaria kills around 600,000 people every year, so that’s the very maximum malaria charities could possibly prevent. And malaria charities hit diminishing returns. It gets harder to prevent each additional death once the easier cases are covered, so the cost per life would rise well before we got anywhere near that many people saved in a year. But there are a lot of other ways to spend $5,000 that save or come close to saving a life (malaria is just one of the causes GiveWell directs money to). Once malaria crosses some cost threshold, you can divert money to the next cheapest way of saving a life, and a single county in Virginia can still prevent a major war’s worth of deaths in a year.

Individuals, and large groups of individuals, could make moves like this whenever they’d like, each preventing at least one unnecessary death per year by donating to the right places, and collectively preventing major wars’ worth of them. Yet it often doesn’t get talked about at all. When people first hear the basic argument for “effective” giving, that we should direct our donations wherever they save the most lives, their reaction is often “That’s so obvious, why would you even need a special name for that?” It may seem obvious, but almost no one does it, anywhere. Walk around where you live and, as you pass people or homes, ask yourself “What are the odds this person has actually sat down to work out where their donations would go the farthest in saving lives or otherwise doing good? Have they compared the options available to them?” For the vast majority of people I’ve spoken to, the answer seems to be no, even though a few hours spent comparing the options would probably let the same amount of money change vastly more real people’s lives. Why not? Why doesn’t this happen?

There are a lot of reasons, but it’s useful to keep in mind that most people aren’t invested in thinking step by step about their donations, rigorously following premises to conclusions without first sussing out how those conclusions fit into their social sense of what will make them seem important. Thinking step by step is often arduous, we didn’t evolve to do much of it, and we often don’t even do it when planning our own careers!

If you talk to people about donating to malaria bed nets, they’ll often quickly exit the act of thinking through the problem with you, and let their parallel-processing sense that “this isn’t actually one of the cool things to care about right now” take over. They’ll come up with strange justifications you know they wouldn’t later endorse. They’ll repeat simple slogans they haven’t thought much about. Some of my more left-wing friends have told me bed nets are bad because “we need systematic change” instead, but then don’t go on to criticize similar one-off donations to people in need when they’re local.

This doesn’t always happen, to be clear. People also give thoughtful answers about why it makes sense to donate elsewhere, or not to donate at all. I’m not saying that people’s beliefs about malaria nets are a sign they’re ideologically asleep. What does strike me is that so many people, who spend so much of their lives guided by specific modern moral systems, treat the fact that we could collectively prevent major wars’ worth of deaths about the same as any other random fact about the world, and won’t follow along with thinking through what it implies about how we should behave, or will come up with on-the-spot justifications for not donating to effective causes that they probably wouldn’t endorse with a little more thought. Again, this suggests there could be massive, simple ethical market failures created by our status systems that most big ideologies today miss, and that could be found by just applying serial thinking, of writing out a Google Doc of what we actually know about the options available to us. EA is about trying to find those market failures with serial thinking, and to work out which look most promising to work on and which a lot of different moral views converge on once the facts are in front of them (or in slogan form, “using reason and evidence to do the most good we can with our time and resources”).

Another key part of seeking out ethical market failures is that they should look reliably good from a lot of different ethical perspectives. The idea that you ought to prevent someone from dying of malaria when the cost to you is a few minutes of thinking carefully about your donations seems like a very strong reason to do it whether you’re a utilitarian or a deontologist, a Christian or a Muslim. This isn’t to say there’s some objective morality written into the universe that we believe we can find, only that we want to take actions that look good under a lot of different possible moral systems, and it’s surprisingly easy to use serial thinking to find actions that look “robustly” better than others, meaning clearly good across a wide range of ethical assumptions.

So to recap: the core of EA is the idea that simply applying step-by-step thinking to the ways you do good (which, again, is rare, because we mostly didn’t evolve to do it and are often socially incentivized against it) can yield new options that look so robustly better for our values that letting them fade into the background of everything else we do looks ridiculous, and that people should just take this step and do things like simple cost-benefit analysis of the charities they donate to. When we do, we often find that the best things we could do are hidden from us, can have thousands of times the impact we care about (like saving lives) compared to what we were doing before, and look surprisingly compelling from a lot of different basic moral perspectives. So, as just some guy in DC, I can reliably prevent someone from dying of malaria every year because I’m careful about comparing where to donate. If I weren’t, it’d be extremely easy for my charitable giving to never prevent a single death.

If you want a sense of how this serial thinking looks in practice, a good place to start is the 80,000 Hours problem profiles, which guide you step by step through what they see as the most important problems in the world to work on. Reading a lot of writing like this in the 2010s, which tried to walk rational people through the full comprehensive deal with complex global problems without relying on social passwords or going along with the coolest things to work on at the time, and which was one of the few places online that actually tried to put numbers on all these different interventions, had a big influence on how I write. I credit a lot of my own blog’s success to the style I picked up from the blogs I was lurking on back then.

The main cause areas of EA (global health, animal welfare, AI safety, biosecurity) grow out of this intuition. Each is based on a plausible idea that doesn’t get much social status, so it doesn’t get considered much, and that also has the potential to be massively, robustly important:

• People in rich countries like the United States can save the lives of people in poor countries with modest donations to the right charities, which often drastically outperform local charities in lives saved per dollar.

• Animals aren’t just automatons, and may have complex forms of happiness and suffering that matter morally.

• Humanity could last a very long time, so if we go extinct or civilization collapses, this seems like a uniquely large loss.

• AI might gain most of the economically relevant abilities of the human mind sometime this century, and the consequences could be insane. (This one has gone mainstream.)

Each of these is complicated, and brilliant people disagree with them. But there are also (especially for animals) very strong social biases against even considering them in the first place, which leaves open the possibility of a massive market failure. At best, a lot of people react with “Oh wow, neat idea” and keep these in their pockets as talking points to bring up every now and then. A big goal of effective altruist thinking is to not do that, to not collect neat ideas and fold them into your current status game, but to behave as if they’re really real, as if they reflect something actually happening in the world that ought to shake you into behaving at least somewhat differently, and to act the way your future or past self would want you to if you suddenly discovered you really lived in a world like this. To then reason through what else we can know and do about the problem, and try our best to act on it under uncertainty instead of letting it become another neat conversational move.

Behaving as if the world exists beyond our status games

I worked at a summer camp when I was a teenager. Once, a camper crossing a bridge suddenly gripped the railing and started breathing heavily. A counselor asked what was wrong, and the kid turned to him and said “This is all REAL.”

“What? Yeah.”

“No, really, like us being here right now, and the bridge, and the world. It’s all REAL.”

I think this is a relatively common experience. A lot of the time, the world can feel somewhat fake. We go through the motions of our lives and a lot of what we see starts to feel like a dream, or like some kind of rehearsal while we wait for the life where things are real to start. It can take effort to remember that the faces we pass every day have lives just as complex as ours, never mind that the rest of the world is actually happening too, with billions of people in it. Sometimes the sense that our lives are real, that this is all really happening, suddenly floods us, or comes to us gradually as we live more in line with what we actually think is valuable.

For a lot of people moved by the basic EA story of the world, I think a key shared intuition is that thinking step by step about how to do good is a way of letting the world beyond our status games actually reach us, of taking it seriously as really existing out there beyond our little games, and of trying our best to work out how to live most coherently with our values in a world far bigger than the part of it we can see. Of letting the full unimaginable complexity and depth of the world beyond our friends briefly flood us, and try to act and respond seriously after being so overwhelmed. To not laugh it off because it doesn’t perfectly align with the social status detectors in our brains, and instead to follow the real facts about it to whatever conclusions they support about how we ought to behave, to do the best we can with the actual state of the world beyond our little corner of it. To put numbers on things, to compare the costs of saving abstract people far away, is to treat those people as really existing, whether or not we ever hear the specific story that would make us feel for them. Someone just as real as a close friend could get to live out their life because of your decision to donate to a malaria charity.

Another motivation is to behave like adults, like how our past selves would have wanted adults to behave. The idea “you should carefully compare the charities you donate to to figure out which will have the best effect on the world” seems so obvious that you’d expect most adults to do it, and yet these “adults” mostly don’t exist. The world is reliably more nuanced, but also more goofy, than you immediately expect, and its full goofiness can often be revealed by just following a chain of thought like “are people actually taking the incredibly easy opportunities to impact the world in line with their values?”

I also find that a lot of people who are into effective altruism, myself included, have at some point felt burned by a moral flare-up we were part of. Things that gave us so much meaning, that seemed so important in the moment, that turned out to be a game we were playing with ourselves and the people around us, that wouldn’t have survived much serial thinking at all. They maybe wouldn’t have withstood even a single Google Doc page of clear premise-to-conclusion thinking about what we were trying to do. As you get older, you start to look out for what you won’t regret, for what will let you feel at home with yourself later on. There’s a deep appeal in using serial thinking not just to access the world, but to live coherently with what your past and future self would’ve hoped you’d do. To gain some distance from any one specific cause you care about, and to invest more of your emotions into the act of adjusting, over time, toward what you actually think is worthwhile. You want to live sincerely.

Over the past 10 years, the world has felt more and more prone to mass movements and moral panics. While EA has generated a lot of weird behavior itself, it has also in some ways been a useful ideological haven from the constant demands to get maximally fired up about the thing of the moment, which usually dissipates into nothing shortly after. Some people have developed strange defense mechanisms against being captured by these panics, leaning hard into nihilism or performative cruelty to show they won’t be mind-hacked. I think this gives way too much power to the sources of the mind-hacking, and I’d rather calmly and technocratically assess the claims from an emotional distance.

This worldview only goes so far

Obviously, this story is also incredibly dangerous. Any story that tells you that you can easily ascend beyond your local status games should be treated with extreme suspicion, because humans are much more social and status-oriented than this simple story implies, and what’s waiting for you beyond your immediate status games can be much worse. Those games exist in part to protect us from new and worse social dynamics that can warp our thinking, be exploited by charming sociopaths, or lead us to believe the normal ethical rules constraining everyday people don’t apply to us anymore. While I’ve loved, and been excited to represent, large parts of the network of people who identify with EA, it doesn’t surprise me at all that loose associations of people organized around the idea that we should investigate the ways we’re taught to do good and try to transcend them through reason have at times led to everything from stultifying groupthink to outright cataclysm. Nothing written into this worldview prevents people who believe it from committing horrible evil, or from being led astray by the strange, invisible new status systems that exist in these peripheral social spheres. Holding views that normal society doesn’t reward can itself turn into a status symbol, and into a status market of its own. EA at its worst can also fall into what Dan Williams calls “naive realism,” where we believe we’ve simply ascended to the brute ethical truth of the world and can easily perceive it.

So whenever I write about what I think is so special about effective altruism, I’m not going to hype up the community. I think the community has collectively saved at minimum tens of thousands of lives and done incredible good for animals, and I increasingly feel proud of a lot of our work in AI safety, but I also see horrible decisions continuing to be made that harm people into the idea, and the world more broadly.

I’ve always approached EA with a pretty heavy epistemic conservatism, and a wariness of abstract human reason. The piece of writing that had the single biggest effect on the way I think was Charles Sanders Peirce’s Some Consequences of Four Incapacities, which notes:

Philosophy ought to imitate the successful sciences in its methods, so far as to proceed only from tangible premisses which can be subjected to careful scrutiny, and to trust rather to the multitude and variety of its arguments than to the conclusiveness of any one. Its reasoning should not form a chain which is no stronger than its weakest link, but a cable whose fibers may be ever so slender, provided they are sufficiently numerous and intimately connected.

I can’t follow a lot of people in EA when their thinking gets too abstract. But I don’t think it’s possible to engage deeply with EA arguments and come away honestly dismissive of the idea that EA has stumbled on some profound ethical problems that deserve way more help than they’re getting, or of the more general point that there’s disturbingly little serial thinking in the world about how to do good (or even how to pick a career). One of my favorite ethics papers is Evan Williams’s “The Possibility of an Ongoing Moral Catastrophe,” which lays out the case that, just given human history, we are very likely in the middle of unseen, gigantic moral catastrophes right now, exactly the kind we’re often stunned that past people lived alongside without trying to solve or even thinking about much. EA is one specific tool for finding these and trying to act on them, and it needs to be surrounded by a lot of outside moral and epistemic rules to keep you from drifting into the more dangerous dynamics of believing you’ve ascended above mere human social games. Here, my personal rules for not going ideologically crazy have come in handy.

It seems obvious to me that a central tragedy of the human condition is that two things are true at once: we are almost certainly living with and tolerating horrific ethical catastrophes that we would act on if we could see them clearly, and attempting to rise above our social situation to identify and solve those invisible catastrophes is itself unspeakably dangerous, carrying the threat of ideological extremism to the point of cults, groupthink, or a flattening of our intellectual and emotional engagement with the world. I think the only way to be morally serious is to try to occupy the emotionally difficult middle ground between these extremes, to allow ourselves to be deeply troubled by counterintuitive moral arguments, and to act on them when the evidence and reasoning are overwhelming, even if that violates our social sense of what the cool good people do. But also to stay intensely wary of, and somewhat apart from, the social and intellectual dynamics we land in when we follow these arguments.

I’ve seen effective altruism as an amazing and dangerous button to press. It scrambled my brain a bit and complicated most of the ethical narratives I used to take as given, and of all the clusters of ideas that could have done that to me, I feel especially proud of the decisions I’ve made as a result of this one, and of the balance I’ve struck with the story it tells. Among other things, I’m sure I wouldn’t have had the success I’ve had with this blog without the tools for thinking I picked up from EA writing. Even my data center stuff came out of the basic observation that applying some simple serial thinking and math to a widely discussed problem (like whether it’s an environmental disaster to use ChatGPT) can, if done well, take you far past the public debate, because way more people are just vibing it out and repeating slogans than you might think.

More broadly, the donations I’ve made, the brilliant people I’ve helped get careers in DC policy areas that I still think are some of the most important in the world, and even some of how I try to talk to friends directly and serially instead of running on social autopilot, all trace clearly back to the ways the EA story of the world disrupted my background sense of status and importance. But I’m also happy I didn’t chase status within EA scenes in ways that would’ve warped my thinking and made me much more confident about AI, animals, or global health than is warranted. And I’ve seen disastrous decisions made in the name of EA, and emotionally stunting social dynamics among true believers, which have reinforced my own epistemic conservatism and my wariness of straying far from my otherwise pretty ordinary ethics. Right now this middle ground, where EA is one important idea but deathly serious idea among many, feels like the most authentic way I can use it. I can’t use it to structure my life or give me a deep sense of meaning, but I can tap into it for a few very broad stories about the world, and after wrestling with the arguments for a long time, occasionally see a massive problem coming a decade early. And I definitely can’t turn away from the basic arguments, go back to my cozy human-scale world, and pretend I never saw them.

My goal in writing more about EA is not to make anyone like us more as people, trust us with power, or put on a lightbulb shirt and show up to an EA conference. Instead, I’d like the aspects of EA that I think are so valuable, and that have shaped my thinking for the last 15 years, to be more available in the general intellectual buffet that smart people choose from to interpret the world. I’d like people to encounter, be challenged by, and use the very best mental tools I’ve personally pulled out of the sprawling intellectual ecosystem EA has spawned, without feeling that they need to give any status to the people (including me) who happened to get into this cluster of ideas in the first two decades of its existence. I think the EA cluster of ideas could have a much bigger positive effect if it were used by a much larger number of people. The specific scene that happened to get into these ideas early is mostly a coincidence, though it also carries deathly important lessons about both the promise and the danger of believing you can use reason and evidence to override the status games around you and do the most good.

In a more legible form (already an imperfect metaphor), this says that all ordinary matter is some combination of these particles (which are really ripples in underlying fields), acting on each other through fundamental forces:

If you put them together in juuuust the right way, you eventually get Antarctica, evolution by natural selection, or tables. These things are our imperfect maps and metaphors for the physical reality underneath, but we often come to believe they’re foundational entities in the world, as if the lamp I see in my room shares some fundamental lampness with all other lamps, and at the heart of reality there are neatly sorted categories like “lamp” or “continent” or “human.” The category of “lamp” is useful, but it would be a mistake to think that lamps are a fundamental part of reality, and that there will be 100% clean and predictable ways all lamps behave that I can infer from any one of them.

A frustration I regularly have in debates about AI is when people act as if the words we use about it are getting at fundamental categories of reality itself, even when they’re clearly especially imperfect, pragmatic metaphors we use to predict our human-scale world. In these moments I kind of want to grab the other person and announce to them that the concepts we use to think about the world in general are just very imperfect social conventions we use to try our best to find these basic but fallible patterns in the incomprehensible complexity lurking beyond our perception. This is obvious in most other circumstances, but it somehow often gets lost in debates about the future of AI.

One example that drives me crazy is the statement that AI is “just a tool.” People seem to infer from this that AI is and always will be completely inert, like a hammer, and that it’s the person using it or designing it who’s responsible for anything bad that happens. This just assumes away the possibility that AI could do things the user doesn’t want, or that the designer didn’t expect and had no way of foreseeing. How do people arrive at this conclusion? By studying how AI actually works? It looks to me that instead they mostly get there because AI, like tools, is something created by humans to be used by humans to solve problems, or that it’s not a literal biological animal. I don’t see anything in how AI models are trained that implies they’ll always behave the way we want, like a hammer does, but this metaphor seems to convince a lot of people that they will. Why?

When people say this, I worry that they believe they’ve accessed some real part of fundamental reality called “tools” and discerned that AI has this same tool-ness as hammers. It seems obvious to me that the word “tool” is a useful but wildly imperfect social game we’re playing to try to make sense of reality. There’s no fundamental tool-ness at the heart of reality. Anything from a rock to a nuclear weapon can, in the right circumstances, be called a “tool.” In some sense a vaccine is a “tool,” but some vaccines contain live pathogens that, in rare cases, behave in ways nobody wants. Does the fact that the vaccine is a “tool” tell us much about how the pathogen inside it will behave? Or that no one could ever have a bad reaction to it unless the person who made or administered it intended that to happen? It seems like the fact that the vaccine shares some properties with other tools doesn’t automatically tell us anything about other unrelated properties it might have. I need some clear additional reason for the fact that a vaccine is a “tool” to imply that it shares any one property with other tools. I can’t just say “Well it’s a tool, so therefore like a hammer it will only cause problems if the person using it intends it to.” That’s obviously silly! Yet in conversations about AI, moves like this regularly fly.

I would think that “AI is just a tool” is meant as a stepping stone to a real debate about whether it’s controllable the way other tools are, but instead most people who say it seem to want to frame the entire conversation around any and all evidence that AI has some inherent property of tool-ness that other things in the universe share. If it’s created by people, that gets counted as evidence that it has this tool-ness, and therefore evidence that it will be inert and controllable. I don’t believe this kind of property actually exists, so this step can’t be taken. It’s all just atoms and the void! So I don’t believe that a fact like “it was created by people” can tell us much about whether AI has some other, unrelated property, like “it will always be easy for the user to control,” just because the completely different objects we call “tools” happen to have both. Drawing this inference seems about as reasonable as saying that because candlesticks aren’t made the way weapons are, a candlestick can never be a weapon. It assumes there’s some part of fundamental reality that literally always connects how a thing was made to what it can do, and I just don’t see the connection.

This has also come up for me in a lot of conversations about when it’s right or wrong to “anthropomorphize” AI. I agree that because AI writes as if it’s human, it’s often tempting, and wrong, to ascribe qualities of the human mind to it that it doesn’t currently have. But critics of anthropomorphizing AI often go way farther and imply that “human” is itself a fundamental category of reality that a system either belongs to or doesn’t, and that because AI isn’t human, it will never be useful to describe it in anthropomorphic terms. This seems completely mistaken to me. I don’t think “human” is a fundamental category of reality either. What we experience as human is the very specific result of incredibly complex systems of biology, neural wiring, and social and economic interaction, each of which is itself made up of simpler nonhuman systems working together in nonhuman ways to give rise to entities like us. To say that we ourselves are human is itself a useful simplification of the complex nonhuman processes that work together to make us up. When I attribute intent to another person, I’m not actually saying “There is some fundamental property of the universe called ‘intent’ that this person is experiencing.” What I’m actually saying is “The complex patterns in this person’s brain and behavior line up in such a way that saying they ‘intend’ to do something helps me predict what they’ll do and experience.” Everything we consider human is a pattern that emerges from simpler nonhuman processes, and the words we use to describe ourselves are imperfect metaphors that help us imperfectly predict and react to those more complex processes in ourselves and others.

Our minds don’t actually work like our folk theories of psychology suggest, and yet our folk language about them still often yields good predictions. Because anthropomorphic language is ultimately a useful fiction imposed on a much more complex process, there are times when it might also be a useful fiction to impose on AI. I think saying an AI “filled out a form” is a useful description of what it did. It often, but not always, yields the same true predictions about the AI’s behavior as it would about a person’s. Similarly, the most recent models check a lot of the boxes in my own definition of what it means to “think.” I don’t think AIs have first-person experience, but they seem to form world models, move step by step through chains of reasoning, and manipulate words in ways that seem impossible without granting that they basically “understand” them. Is it wrong to say that AIs “think”? I think this debate should focus entirely on the pragmatic question of how saying so would affect our predictions about AI. It’s bad to say AI can “think” if it communicates to the other person that the AI is very humanlike, has first-person experience like we do, or has complex emotions and intentions like ours. But it seems useful to say AI can “think” if it communicates that AIs can work with language the way we can and draw reasoned inferences from that language based on world models. In this case, it seems good to anthropomorphize AI for the same reason it’s good to anthropomorphize people: both give us good predictions about the world. In figuring out when it’s right or wrong to use “anthropomorphic” language, it seems useless to frame the question around some abstract fundamental category called “humanness,” existing above and beyond the physical world, that AIs either are completely or not at all tapping into. I don’t think this is something that actually exists, and so trying to figure out whether AI can access it or not won’t tell us anything about what they’re currently like or will be like in the future.

We aren’t able to think without simplified concepts and metaphors. But we have a responsibility to recognize that basically all our concepts are simplified and imperfect. Figuring out the near-term future of AI requires us to work out the specifics of when our imperfect metaphors are useful or not. This debate is not helped at all by conversational games that imply that the speaker has accessed some fundamental category in the universe that AI belongs to along with other entities, so it must always be exactly like those other entities, and any property they have, it must have too. The same game gets played in reverse, where AI is declared to be outside some category like “human,” so it can’t share any property with the things inside it. Neither move tells us anything, because the categories aren’t real. There’s no tool-ness at the bottom of reality that guarantees AI will stay as controllable as a hammer, and there’s no humanness that AI is locked out of that settles whether it can think. Words like “tool,” “human,” and “think” are shorthand we invented to get by in a world far more complicated than we can perceive, and the only real question about any of them is whether applying the word to AI helps us predict what AI will actually do. If it does, we should use it, and if it doesn’t, we should drop it. Whether AI carries within it some fundamental nature of tool-ness or human-ness is a terrible way of approaching a question about it, because like everything else, AI is ultimately just atoms and the void. Or more precisely, the universal wave function.

My basic argument here will be that at its best, the data center backlash is a good opportunity to invite lots more people to think more seriously about AI, but it doesn’t seem likely to be useful for safety on its own, and there are lots of reasons people in AI safety should not (and to my knowledge are not) promote the goofy statistics driving a lot of it.

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Contents

• What is AI safety?

• My initial motivations

• I’m unsure, but suspect the extreme data center backlash is neutral for AI safety, and I see a lot of ways it could be harmful

  • The data center backlash seems to be mostly about data centers, not AI

  • Extreme environmental reaction to data centers often reinforces the “AI is fake and useless” view, which prevents people from thinking seriously about current and future capabilities

  • Anything short of a national moratorium and strong export controls doesn’t seem likely to slow AI progress much

  • The data center backlash might cause voters to reward and prioritize the wrong moves against AI companies

  • Navigating through advanced AI safely would probably require voters not thinking of data centers as demons

  • Conclusion

• Effective altruist AI safety as a place where we won’t abjectly bullshit you

  • Societal resilience to speculative future risks requires strong healthy epistemic communities of debate and disagreement

  • We’re already making extreme, wild claims about AI and need to otherwise respect the epistemic commons to have them taken seriously

  • The broader societal backlash to AI is often based on obvious simple lies because people are having a visceral emotional reaction. AI safety needs to distinguish itself from this backlash

  • The arguments we give to the public will affect who gets involved

  • A lot of other spaces discussing AI seem interested in using bullshit stats about data centers to fire up their base for unrelated political wins. Effective altruists and the broader AI safety community can set ourselves apart by simply not doing that

• Some recommended media on AI safety

What is AI safety?

I buy the basic effective altruist story of why AI is a uniquely big deal:

1. There is nothing fundamentally magical about the human brain. Our intelligence was produced by an algorithm (evolution) interacting with the world. There’s no reason in principle that machines couldn’t also eventually have what we mean by intelligence. Talking about the possibility of future machines that can “outthink” us should feel about as normal as talking about machines that can outlift us. We have a strong internal bias against thinking this is possible, maybe for evolutionary reasons to avoid thinking about objects as members of our social groups. But by any reasonable definition, humans are themselves intelligent biological machines, which already proves that machines can in principle be intelligent. We are not heavenly observers looking into the physical world from outside.

2. Machines that can do most or even all of what the human mind can do seem possible (and even likely) sometime in my lifetime. This is commonly called artificial general intelligence, or AGI. The definition of AGI is incredibly fuzzy, but a concept can be fuzzy and still point to something cataclysmically important. Most people have a fuzzy idea of the Industrial Revolution. Before the Industrial Revolution, it would have been reasonable to theorize about some point where more and more complex machines could accomplish the physical labor of many thousands of people at once. People could have theorized about the abstract concept of machines that could do the work of farmers or horses. They could even have theorized about when these machines’ effects on the world would start to compound, with each new efficiency spilling over into the others and speeding up technological progress, until we entered a period of change so strange and rapid that someone could have lived to see both the start of the Civil War and the dropping of the atomic bomb. People living before the Industrial Revolution might not have been able to pin down exactly when something like it would happen, what would qualify, or what the long-term effects would be, but that fuzziness doesn’t make the concept useless, or any less the main story on Earth over the past 250 years. That 80-year timespan from the Civil War to the atomic bomb is about the same as the time between when the world’s first programmable electronic general-purpose computer (the ENIAC) was revealed to the public and when the US government ordered Anthropic to cut off access to Fable and Mythos over the models’ cybersecurity capabilities. The current AI paradigm may not get us to AGI, but it’s becoming hard for me to see how we don’t get there with another 80 years of AI progress. We got from the ENIAC to here in that time.

3. AGI could be wild, because human intelligence has been the main force shaping the planet for the past 10,000 years or so, as we’ve found ways to collaborate with each other in more and more complex ways. Some might point out that it’s not necessarily the raw intellect of individuals, but rather the collective intelligence of society that has been the real player here. Sure, I believe that too, but AIs are also able to communicate with each other and interact with the world. They could form collectives and markets, or even mimic whole societies, and access and aggregate the same amount of dispersed knowledge that human society does now. We’re all so used to societies built out of complex systems of communication between human minds that it’s easy to forget how truly strange and new their effects are. I mean really, look at this factory!

   

   AAAHHHHH!!!! (but neat). The main question about advanced AI is what would happen if we suddenly and massively scaled up the amount of general intelligence in the world, given that this same force has already reshaped the planet into something much more alien (yet also much more human) than early humans could ever have imagined. Would we be able to control it, and what would the long-term result be? Pontificating about this might be about as useful as hunter-gatherers pontificating about what the agricultural revolution would be like, or medieval societies pontificating about the Industrial Revolution. But whether or not you think we have any mental handholds to grab onto, it does seem likely that we’re heading toward a future where massive numbers of generally intelligent agents are widely available, and that this could make the world at least as weird and alien to us as our current lives would look to a medieval peasant. Maybe much more? Regardless of whether it’s even possible to think about what would happen, I’d like to at least think about whether we can think about it, to try to look for the handholds. Holden Karnofsky’s Most Important Century blog series does a good job of getting across these intuitions.

4. When I try to think through the biggest risks of AGI, a few obvious candidates come up. Each one is either almost certain, or impossible, or somewhere in between, depending on facts about science, philosophy, and economics that smart people all seem to disagree with each other about:

   1. Advanced AI tools could be used maliciously by humans. Maybe having swarms of hundreds of thousands of genius agents is like having access to nuclear weapons.

   2. Society could come to be governed more and more by AI processes that humans can’t follow. As AI moves further ahead of human intelligence, it starts to resemble the other major forces structuring our lives, such as the market, that we can get hints about but can’t fully predict or understand. Eventually this might amount to a gradual AI takeover that leaves us at the mercy of systems we barely understand.

   3. AI could enable extreme power concentration and lock in a stable totalitarian global government.

   4. The AIs themselves could develop their own goals and override ours, potentially rearranging the world in such a way that we die. Just as humans don’t factor the wellbeing of ants into construction projects, AIs with the collective intelligence of thousands of human societies working together might just not see us as especially worth protecting.

   Are these risks real or fake? Can we know? Can we do anything about them, and if so, what? Figuring out the answers to these three questions and then acting accordingly is the main project of AI safety from an effective altruist perspective.

Maybe this is all hype? Maybe this is all an elaborate social game? Could be, but if you’re willing to entertain the idea that mimicking the human mind is both possible in principle and reasonably likely within the next 100 years, it’s difficult not to think that crazy things could happen that we should entertain and think through now. A lot of people who believe this may have their thinking warped by their own social and economic incentives, but that doesn’t mean the right move is to wall your mind off from the question and focus exclusively on the here and now so as not to be turned weird by it yourself. You might disagree with every major case for serious AI risk, or for advanced AI capabilities in the first place, but we’re getting to the point where it’s probably time to calmly step back from whatever counts as the cool opinion in your immediate circles and spend some time working out what you actually concretely think.

So how does the data center buildout relate to this? If I’m worried about this unique cluster of risks from advanced AI, why have I spent time pushing back against misconceptions about data centers? Maybe I should just nod along with the crowds angry at them. After following the debate for a year and a half, I see a lot of reasons not to.

My initial motivations

When I wrote my first post about AI’s environmental impacts I wasn’t expecting much of a response. My main thoughts on the debate at the time were:

• A lot of adults seemed really confused about how tiny an amount of energy “10 Google searches” actually is compared to everything else they do. They were comparing an AI prompt to one of the least energy-intensive things we do and fixating on the relative difference, rather than asking how much the prompt actually adds to their total emissions. I’d been following climate debates since college and this seemed like a terrible way to think about where to cut, and I wanted to articulate why.

• I was disappointed by the environmental politics implicit in these conversations. I said so here in the original post.

• I was surprised by how many people I was meeting who hadn’t actually played around with chatbots yet, and their reason was almost always that they were “so bad for the environment.” This felt akin to people ignoring the early internet for similarly confused reasons (which were also being given at the time). It seemed sad that they were missing out on how good the tools already were, and it also seemed bad for AI safety, because the easiest argument for taking risks from advanced AI seriously is just watching how quickly the models are improving, and people who won’t touch them never see that.

So making the case felt like a win-win-win for my personal climate politics, my interest in AI as it already existed, and broad public education about future threats from AI. The case was just that worrying about 10 Google searches’ worth of energy for the sake of the climate is like stopping your microwave a few seconds early for the sake of the climate, and everything I’ve learned since has only reinforced that. See my full post on that for all the arguments I was making at the time. Above all else, it felt like living in a strange dream reality to have grown adults speak so grimly to me about the equivalent of 10 Google searches, and my initial writing was partly an attempt to wake up from that.

For a while I was nodding along to what a lot of the people I was reading were saying about data centers. My initial explanation for how they could be bad while individual prompts were fine was extremely simple: data centers concentrate huge numbers of individually tiny prompts in one place, so they could plausibly have a big impact on the places they were built. I only realized a few months later that the discourse about data centers was at least as confused as the discourse about individual prompts (probably more so), and got kind of obsessed with how universally strange the media coverage of data centers was. It was somewhat shocking to realize that almost every popular story about them had some incredibly simple twist that completely undermined what most people were taking away from it. For example, take what might be the most influential headline ever written about data centers:

The story is about the construction of a data center, not its normal “guzzling” of water. The subtitle refers to a separate story in the second half of the piece about how Arizona’s going through a drought, which has forced the state to slow home building, and oh hey, data centers are being built there too, so maybe, possibly, that’s related to the home building? We never get any numbers to check for ourselves.

It seems basically impossible for an intelligent person in a hurry to read this headline and subheading and come away believing anything other than that the data center’s normal operational water use (“guzzling”) is what made the taps run dry. There are headlines like this for almost every problem people associate with data centers. I felt like I was living in a dream where I was one of the only people poking at them and getting any attention for it, while most educated adults were getting lulled into a make-believe picture, stitched together from confusing headlines and isolated statistics, of what I expected to become the most consequential and fastest-growing kind of building of my lifetime. Growing an audience who agreed and could pass these arguments on felt like a way of waking up from the dream together.

But given my broader worry about AI risk, I wasn’t sure how to think about the actual value of my newfound role commenting on data centers for my niche audience. Before I stumbled on the Empire of AI error, I had actually decided to pause data center writing for a bit, partly because I wasn’t sure whether what I was doing was good for the world at all, or potentially drastically bad. After thinking about it a lot more, I remain uncertain but have leaned much more toward thinking that the data center backlash is neutral to somewhat negative for AI safety, and that there are a lot of reasons for AI safety people (or at least one of them) to plant a flag and say “We will not be one of the groups abjectly bullshitting you about simple statistics in order to trick you into worrying about AI.” The rest of this post is my explanation for why.

I’m unsure, but suspect the extreme data center backlash is neutral for AI safety, and I see a lot of ways it could be harmful

I’m pretty deeply unsure about the overall effect the data center backlash is having on AI safety, but I see some clear ways it could harm it on net. To be clear, this could all be drastically wrong, and I stand ready to revise any of my takes if I see good arguments against them.

The data center backlash seems to be mostly about data centers, not AI

I think the polling we have so far strongly implies that most people getting angry about data centers are motivated by the object-level claims being made about their environmental and economic impacts. See my post here for a much more in-depth argument:

Importantly, when given the option to write out as much as they’d like about why they personally oppose data centers, very few people choose to list anything that could be called part of the AI safety worldview:

This doesn’t at all mean that they’re not potential allies, it just means that the data center backlash does not look to me like a groundswell of public demand for AI-safety type stuff. It doesn’t seem like someone’s beliefs about data centers can tell us much about what they think about risks from advanced AI, and there’s no reason to think the backlash will naturally trend toward people making better decisions for safety more broadly.

Extreme environmental reaction to data centers often reinforces the “AI is fake and useless” view, which prevents people from thinking seriously about current and future capabilities

A big dividing line in AI discourse is whether AI is dangerous because it’s very capable, or dangerous because it’s very stupid. The “AI is dangerous because it’s stupid” view is maybe best represented by On the Dangers of Stochastic Parrots, the most influential AI ethics paper ever written. Many critics on this side correctly point out that people worried about extinction risks from AI and uncritical AI boosters share the same underlying view, that AI is already pretty capable and could become much more so, and that this shared assumption actually puts them both on one side of a broader debate about whether AI is or ever will be that capable in the first place.

I agree that this broader divide exists, and have three thoughts about it:

• AI models today are more capable than a lot of their critics believe. The original criticisms in Stochastic Parrots don’t seem to apply to current models (see here for a list of what’s changed), and my impression is that the authors have resorted to adding more epicycles to their theory of why AIs in the current paradigm are not and never will be especially capable or even useful. That’s looking less and less tenable in 2026. It would be really good if people in the AI debate actually understood what the models can do right now.

• The future of AI is pretty uncertain and I don’t feel equipped to judge things like whether the current paradigm of AI will scale to AGI, or whether we’ll need many more breakthroughs that could take decades. Because smart, well-informed people seem to radically disagree on this, the most important thing is to keep the debate rigorous and open so that we learn more and the stronger arguments rise to the top. This isn’t perfect, but it’s the best we have. Beliefs that are false and that also keep people from engaging seriously with this debate in the first place seem pretty harmful.

• There’s a lot of social reward right now for locking into overconfident beliefs about AI capabilities. I could definitely be rewarded in a lot of AI risk spaces for expressing more confident beliefs than I actually have. At the same time, I see a lot of people get a LOT of social reward for refusing to even consider that AI might already be capable (“this tech is stupid and if you think otherwise everyone will say you’re an idiot”), or to seriously enter the debate about how it might become more capable in the future and whether that would be dangerous. In my experience this is often downstream of people becoming convinced that individual AI prompts are terrible for the environment. It’s hard to believe both that spending 10 Google searches’ worth of energy on a prompt is incredibly wasteful and that the same prompt is doing something shockingly powerful and different from a normal search. I’m worried that a lot of the weird environmental misconceptions about data centers really lock in this mindset of “I cannot ever entertain that these models might be powerful” because that gives some status to these otherwise evil stupid buildings. I want people to just consider the arguments and form their own conclusions, and for that to happen their opinions about AI can’t just immediately calcify into “it’s always bullshit and dumb” based on background social pressure and confused ideas about data centers rather than reasoned thought. Politely pushing back on the environmental misinformation going around seems like one way to help with that.

Anything short of a national moratorium and strong export controls doesn’t seem likely to slow AI progress much

There’s a gigantic amount of capital behind data centers, and data centers used for training mostly just need power and empty land. America has a lot of both, even if most states end up banning data centers. All data center buildings in 2028 will together take up as much space as Disney World. The most important bottleneck is power, but even here data centers rejected from one county usually have others competing for them. It seems like the result of most state-level data center opposition will be just moving where data centers are built.

My impression is that the big AI companies mostly don’t bother fighting local opposition, they just go somewhere else. They don’t seem to spend much as a portion of their revenue on countering the data center backlash in general, which I think tells us something about how worried they are about it.

Even state-level moratoria might not do much. Arvind Narayanan estimates that a state banning data centers for a year probably delays AI progress by about 5 to 10 hours, and that’s assuming none of the blocked data centers get built anywhere else, which is pretty unrealistic.

Frontier training (where most of the dangerous capabilities show up) happens at maybe a dozen specific data center campuses. A restriction on one of those would matter more, but to my knowledge no data center block has hit one of them yet.

There’s also a big difference between slowing AI progress and reducing AI usage, and people who are part of the data center backlash may not see it or react to it. Restrictions on new data centers mostly seem to harm deployment rather than capabilities. They make AI slower, more expensive, and less available to everyday people, but leave the training runs I’m actually worried about mostly unaffected. That seems like a bad trade from a safety perspective! The models keep improving while the public’s hands-on sense of what they can do falls further behind.

The data center backlash might cause voters to reward and prioritize the wrong moves against AI companies

Suppose that there are two candidates. One wants heavy regulation on the AI industry itself to avoid catastrophic risks. The other wants to ban data centers. Which will voters see as the “toughest” on AI? I’m worried that right now the answer’s clearly the second. In a lot of political conversations about AI, a lot of people are starting to frame data center moratoria as the “tough” position against the companies, and regulating AI models themselves as something more friendly to big tech. This seems like a pretty bad disaster for getting public approval for governing advanced AI.

This one I really don’t know about. But I’ve seen more and more political commentary where candidates who want data center moratoria are framed as the “most tough” on AI that they can be. If you don’t believe that state-level moratoria will do much good at all for safety, this seems like a dangerous distraction.

This could be a simple both-and situation, where the public gets data center moratoria and serious model regulation. I’m worried that voters just don’t actually think enough about AI for that to be guaranteed to happen, and as long as a politician checks the “Doing something about AI” box that might be seen as enough, and a data center moratorium on its own checks that box. A politician who’s passed or even just advocated for a moratorium has satisfied the public demand and collected the credit, and the next person proposing frontier model regulation is now proposing a second AI bill that might be seen as unnecessary. Public attention on an issue is a scarce resource, and more of it being spent on the data center backlash might start to compete with good legislation.

Navigating through advanced AI safely would probably require voters not thinking of data centers as demons

I’m pretty unsure about the specifics of AI risk, and therefore unsure about what would make things go well, but it seems unlikely at this point that managing AI well will be helped by the average person thinking about data centers as radiating evil. In very extreme AI safety scenarios like AI 2040, data centers eventually manage the vast majority of all economic activity on Earth. Is this good? I don’t know. It’s one of many visions of where the risks and benefits of AI are and how to react. But in this world, everyday people will need to know what’s actually going on with data centers, and having a country reacting to them as if they’re demons seems like it’ll mostly override reasoned sober decision-making.

It might be that it’s preferable to have the data centers we want to eventually govern all in one country (or maybe that’s bad because of power concentration? Oh no). The data center backlash pushing out more data centers to other countries instead of America increases the amount that AI companies can do and train outside the US’s reach. This could be bad, or good? But making that decision again requires citizens with clear-eyed views about the actual trade-offs.

It’s in some ways very lucky for the governance of AI that frontier models currently require a small number of gigantic physical campuses with a huge heat footprint. It’s good to know where this is happening, and massive data centers make that somewhat easier. Any pressure to scatter the computing into smaller buildings (or blast it into space) makes it somewhat harder to know where frontier training runs are happening.

Conclusion

So it looks to me like the data center backlash is mostly just shuffling around where inference data centers specifically are built, and isn’t affecting the data centers used for training, which are often built pretty far from where lots of angry locals live who would block them (with the exception of xAI’s Colossus in Memphis). Even state-mlevel data center moratoria maybe only slow AI progress by hours. The backlash gives political reward to politicians who focus more on “governing AI” via what looks like a purely symbolic move against the AI companies that leaves their most dangerous capabilities completely untouched, and rewards the idea that AI is such a waste because it’s useless and stupid rather than dangerous and powerful. It removes people’s ability to think seriously about the trade-offs of running data centers in their area, which will be pretty important if we manage to get to the point where we’ve avoided the risks and are capturing the benefits of advanced AI.

The main simple way I could be really wrong here is if this all builds to a national moratorium with strict export controls to limit China’s ability to catch up. That strikes me as within the window of possibly good governance of AI. I don’t know. But unless it leads to that, we get all these negatives and basically none of the potential positives.

And separately, the backlash is just fundamentally mistaken and wrong. People aren’t mistaken to worry about data centers and AI, but they are blatantly, consistently mistaken about how data centers actually affect local communities, and I’m more convinced than ever that if they just knew the full economic trade-offs of the buildings themselves, the full list of costs and benefits on either side, they would usually choose to allow data centers to be built. More broadly, I see the general politics driving the backlash as disturbingly illiberal. As Alex Tabarrok notes, they’re a loss for the open access order. I would like liberalism to survive a potential intelligence explosion.

I think the data center backlash leaves a lot of room for AI safety people to build bridges with everyday people. I’m not saying here that we should just shun them. But I also think we should talk to them (and everyone) as if they were intelligent friends we respected who we thought were getting one question wrong but really wanted to get on board with a broader idea. I think treating people respectfully often involves saying what our own beliefs are clearly. This next section will be more on how I think these basic ethics of discourse are important to building a community of real thinking on AI safety.

Effective altruist AI safety as a place where we won’t abjectly bullshit you

I’ve been very happy to plant a flag for what I see as the great aspects of EA thinking on a specific topic, as part of broader behavior I want others to replicate. To be clear, I think EA mostly already does all this very well. I’m writing all this as “this is what I want to authentically show the world that EA is already doing” rather than “I wish people in EA did this, because they’re currently not.” I haven’t had anyone in EA suggest lying about data center water use or scold me for telling the truth. We’re not the ones pushing this stuff, and I have high confidence that we’ll stay that way.

Societal resilience to speculative future risks requires strong healthy epistemic communities of debate and disagreement

Suppose you were living in the 1930s. One day the neutron is discovered, and you infer from this that nuclear weapons are possible and could appear within a few decades. You want to understand where the actual risks are, what to do about them, and how to build institutions that could avoid them. What do you do?

One move that doesn’t seem promising is deciding that everyone needs to believe your very specific account of exactly how nuclear weapons will be dangerous, or of how countries will behave once they have them, when you aren’t completely confident that your answer is the one all reasonable people would agree with once they saw the argument. If you start using emotional or rhetorical tricks to win over people who wouldn’t have assented to what you were saying if you’d treated them like rational friends you respected, you’re going to quickly discover that a lot of other people have plenty of legitimate reasons to use their own emotional and rhetorical tricks to override your team with their beliefs. The debate about this incredibly dangerous new technology would slowly wall itself off into angry islands of belief that don’t communicate with each other, and new people would sort onto these islands based on a roulette wheel of the contentious beliefs a rational person could hold given limited evidence. Society would become brittle and inflexible, and by the time the threat appeared, people would be too invested in their own team to adjust quickly and work together based on how things were actually playing out.

So the basic ethics of discourse become monumentally important as part of the collective project of moving society toward a place where it can respond well to a gigantic new risk. It seems very likely to me that future AI systems could pose gigantic risks to civilization, and also very likely that I, as just some guy thinking about it, have an unbelievable number of blind spots, overconfidence I don’t notice, and social incentives that warp my thinking in bad ways. The only way to deal with this is to directly welcome any and all reasonable people into debates about AI safety, at a high level where we aren’t slinging cheap rhetorical tricks at each other. Preserving this fragile state requires treating people who disagree with almost everything we believe, and who are engaging seriously in the debate, as respected and equal participants. Trying to lure them in with ideas and statistics we know are wrong, like the claim that data centers are also destroying local water supplies, or with overconfidence about our own specific risk case, basically tricks them into positions in the debate they might not otherwise have assented to. This is a way of burning the commons. It was good that debates about nuclear weapons happened across academia and government, with wild disagreements, rather than each side trying to override the debate and just vying for power. Managing the nuclear era involved a lot of complex, counterintuitive moves that couldn’t have been reduced to simple slogans. It was good that serious thinkers were able to plant flags of high epistemic integrity in that debate early on.

We’re already making extreme, wild claims about AI and need to otherwise respect the epistemic commons to have them taken seriously

I think the core ideas of AI safety are both surprisingly intuitive (it seems likely that we’ll make significant AI progress over the next 80 years, and if you sit with the idea of replicating human intelligence in machines and really internalize it, the wild implications are easy to see) and very aversive to think about (it all sounds so weird and sci-fi, like a trick big tech is playing on you). The basic argument is that AI could get so powerful within our lifetime that we need to start getting our footing now on what that would actually imply and how society would manage it, instead of just sitting back, thinking “wow, wild,” and feeling like we’ve done our part. For an intelligent person to actually receive that argument and think it through, it has to come with a lot of careful acknowledgement of how incredibly complex and uncertain the problem is. The space of possible reasonable opinions on AI risk is so varied, and so full of rival views that would each be incredibly consequential if true, that it seems easy for anyone to adopt whichever beliefs are most convenient for their immediate social status games, and then lord them over everyone else because those beliefs are so consequential. People correctly recognize that extreme beliefs are often social crutches for overriding normal social rules, and unfortunately every possible belief about AI risk is somewhat extreme, because all possible views about humanity’s future are wild. Because we’re trying to figure out and debate our extreme beliefs together, it seems especially important to be clear that we won’t use them to justify breaking unrelated social rules, which includes not lying about relatively simple questions about AI. Debates between people who hold rival extreme beliefs about the future are only really possible if neither side is using those beliefs to justify burning the simple epistemic bridges between them.

A lot of the broader intellectual culture seems incredibly averse to thinking about even the near-term future. Basically everything outside our immediate field of vision gets treated as a convenient distraction from the real, immediate problems of the world. I think this hyper-distrust of thinking through what the future could actually be like has become intellectually incapacitating for a lot of otherwise very smart people. Hyper-skepticism that treats every truth claim as really just a move in some secret status game locks us into a situation where nobody has any incentive to care about what will actually have good effects on the world, and everyone just tries to get to the top of their own immediate status system. That’s not a healthy place for an intellectual culture to be as AI becomes more capable. I agree that a lot of skepticism is warranted, and I’m pretty cynical myself about all of our motivations when we go out purportedly seeking truth. But there’s a difference between being skeptical of people’s motives and refusing to think about the future at all.

To preserve even the possibility of articulating and refining our views about what the near-term future with AI could be like, we need to be really, really careful to show over and over that we’re not letting those views license us to break other social rules. Breaking them sends too strong a signal that we’re either holding the views for exactly that reason, or that we’ve shut ourselves off from rationally adjusting them when other people argue with us, since we’d be demonstrating that we think rival views are so worthless they need to be steamrolled with lies and misleading statistics. This is the core reason I’m happy that effective altruist AI safety scenes have (to my knowledge) not indulged in lying or hyping up clearly misleading claims about data centers and the environment, and why I’m happy to be both a face of effective altruist AI safety and someone pushing back against what I see as basically a minor mass moral panic over unrelated problems with AI that don’t hold up to scrutiny.

The broader societal backlash to AI is often based on obvious simple lies because people are having a visceral emotional reaction. AI safety needs to distinguish itself from this backlash

It’s becoming more and more socially fashionable to hate AI, often for confused reasons, like the mistaken idea that each prompt uses a bottle of water. Negative reactions to AI are understandable, but there’s a growing gulf between people who understand the technology and people who are clearly running on rumors they picked up on social media, and who are ready to believe those rumors because they already see AI (maybe correctly) as a bad guy for other reasons. It seems very easy for serious people to lump risks from advanced AI into this same bucket of fake beliefs, and to assume that it comes from the same general negative emotional reaction and isn’t any more substantive. I think AI safety is clearly separate from these confused ideas, and we need to find ways to make that clear. One way to do that is to distinguish ourselves by pushing back on the bogus ideas.

The single most painful sentence I heard last year was at a discussion about EA and AI with people who mostly didn’t know much about either. A panelist asked, “So is EA just hopping on the AI bandwagon now that more people are worried?” I started worrying about AI in 2016 and put up with a lot of sneers and scorn for it, as did most people I know in AI safety, and hearing that made it feel like all of that had been for nothing, and that most people now saw us as just one more part of the vague angry backlash to ChatGPT specifically. Because most people still don’t know much about EA or AI safety, it seems useful to plant a flag and say something like: “We agree that some general worries about AI, like its long-term effects on jobs and economic power, are real and important. But we’re not here to nod along with just anything that’s anti-AI. We have a specific, coherent argument for why AI could be dangerous, and we didn’t get memed into it because worrying about AI recently became high-status.”

The arguments we give to the public will affect who gets involved

I think if you want to build a complex movement of people working together to figure out what’s true, it’s going to need to not rely on memes the people in it know to be false for community building. The AI safety debate shouldn’t be diluted by very lazy thinking, and I think indulging and making knowingly lazy arguments with bad statistics will attract people who are being somewhat lazy in how they think about AI, especially if they’re so ideologically brittle they’re unwilling to question the statistics. This would be bad for our core coalition. AI safety is entirely about judgment under radical uncertainty and being willing to follow clear arguments and evidence where they lead. Relying on goofy ideas for growth seems like it’ll massively dilute the quality of thinking.

Again, I wanna be clear here that I don’t mean we should shun people originally taken in by the bad statistics about water, that’d be ridiculous. People’s minds can change, and we’ve all been taken in by goofy ideas at some point. We just shouldn’t use the bad statistics and lies as an active outreach strategy. I will say that to my knowledge no AI safety group is actually doing this, and most I know will patiently explain to people why the water stuff specifically is a little off if asked. I’ve never been asked by anyone high up in AI safety or EA to shut up about the stats to slow down AI.

A lot of other spaces discussing AI seem interested in using bullshit stats about data centers to fire up their base for unrelated political wins. Effective altruists and the broader AI safety community can set ourselves apart by simply not doing that

I’ve lost a lot of patience for sitting through lectures or monologues by people who seem to be speaking in fake social memes that don’t actually relate to anything real. I’d rather be around people and scenes that don’t blatantly bullshit me. And luckily there are a lot of these in the world, with certain scenes within EA being one of many.

For example, I think specific 80,000 Hours problem profiles are some of the very best writing on the topics they cover, because they’re trying to give you a complete, no-bullshit overview (as they see it) of a massive global problem that they actually want you to take concrete action on. To me this is one of the best things about good writing influenced by effective altruist ideas. It’s basically designed to be the rapid but comprehensive rundown you’d want to give a smart friend on a topic you want people to act on.

Of course none of this means that those of us in effective altruist or AI safety communities can’t unknowingly bullshit ourselves or other people. The human tendency to truly start believing things based on the status we subconsciously expect them to confer is incredibly powerful. In many ways it’s the original sin of human thought (partly because our higher-level thinking likely evolved to win social games with each other, so when we acquire fake but socially useful beliefs we’re doing exactly what our higher cognitive capacities exist to do). It could be that all the beliefs in this post are themselves downstream of my desire to make the data center backlash fit conveniently with my AI safety beliefs. Effective altruist spaces can be insular, and at some point we can’t fully guard against the chance that we’re acquiring beliefs this way too. But separately, I almost never encounter in EA communication the kind of knowing bullshit that seems to permeate so much of the broader discourse on AI, where people seem to know that data centers make useful political enemies regardless of whether the specific claims about them are true. I think there are a lot of people like me out there kind of wandering in the desert, desperate for direct, honest, high-level debates about this technology with such mind-shattering but uncertain potential, who don’t want to see the tech immediately devolve into a cudgel used to win completely unrelated political and social games without any regard for the truth. There are plenty of scenes having those debates, but a scene where people uncritically share lots of wild, goofy claims about data centers is a pretty bad sign. It goes a long way when a scene simply makes it clear, “Hey, we’re going to be careful about what we say about AI, and we’re going to express our honest (and sometimes very extreme) opinions about the situation as we actually see it. We’re not going to get you hyped up about every little AI thing just because we’re having some general emotional reaction to it and want to lash out. We’ve been thinking about this for years or decades and have landed at this cluster of positions in the debate. Here’s what we’ve got, take it or leave it as you react honestly to the arguments and evidence.”

Some recommended media on AI safety

• The 80,000 Hours series on specific AI risks:

  • How AI could create the world’s biggest problems

  • Loss of control

  • Extreme power concentration

  • Their full list of main problem profiles

  • Their podcast in general

• Skeptical takes from 1a3orn, especially On Those Undefeatable Arguments for AI Doom

• Cold Takes, on the weird historical situation we find ourselves in

• Joe Carlsmith’s Is Power Seeking AI an Existential Risk?

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People from basically all political sides are interested in owning the data center backlash, and are all saying it’s being caused by what they’ve always been on about. It looks too powerful and intense to be about data center buildings themselves, so it must secretly be about something else, and commenters tend to assume that secret other motivation conveniently lines up with how they themselves think about politics and AI. People who are anti big-tech will say it’s a backlash to big tech. Some say it’s a broad rejection of economic growth as a measurement of human wellbeing.

My fellow AI safety enthusiasts will sometimes say it’s a reaction to rapid unsafe progress in AI that threatens to destabilize society or destroy the world. People in the “AI is bad because it’s bullshit and fake” camp will say it’s a reaction to AI flooding the world with slop, and Americans recognizing we’re investing in something stupid.

I follow a general rule that anything narratively satisfying is suspicious, and also that everyone’s thinking is less structured and coherent than we like to imagine. For me, it would be so much more satisfying if this bizarre massive uprising based on ideas that often collapse under mild scrutiny were all secretly about my own worry that AI could be incredibly dangerous incredibly soon. The idea that this is all happening because people did actually just get fired up about often goofy interpretations of isolated large numbers is much more disappointing, but reality is often disappointing. You must try your best to dwell in anti-paranoia when you can.

I think any movement that wants to either stop the backlash or capitalize on it will be making a mistake if they assume secret motives are driving it. However, because people’s beliefs are often opaque even to themselves, it seems possible for politicians to capitalize on the backlash and meme people into thinking this was “always about” some other unrelated specific thing, so if you’re trying to do that, this seems like a fruitful time to twist the narrative, but I don’t think you should go out assuming it’s actually true. I’ll argue here that based on the limited info we have, it looks a lot like the data center backlash is not about AI safety or job loss or AI being a scam or social media or big tech having too much power or anything else like this, it’s about data centers and the claims people believe about them.

Of course, the data center backlash being all about object-level claims and facts about how data centers interact with the places they’re built would be very convenient for me personally, so take it with a grain of salt. Maybe I’m no better than all those other commenters, but I think my case here is pretty solid. I’ll go through other explanations offered. This will be more speculative than a lot of my posts, I could be getting this one pretty wrong.

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It’s probably not about big tech

It’s tricky to disentangle worry about big tech from worry about AI. To try to separate them, I’m going to consider the hypothesis that Americans are lashing out against data centers because they don’t like the tech companies building them in general, and want to find a way to limit their power over society, and data center opposition is a tool they think will have some effect. This hypothesis seems to imply that when the average person hears “A data center is being built by AMAZON” it’s Amazon that really sticks out to them as the bad thing, and they start to formulate how to stop the data center. The data center is a coincidental building that could as well be something else.

What would we expect to see if this hypothesis were true? A few things:

• Americans would dislike the tech companies building the data centers about as much as they dislike the data centers. If opposing a data center is just a tool to harm a tech company, you’d think about as many people would hate the tech company as the number trying to oppose data centers to harm the company.

• Americans would also oppose other things being built by and for the same companies building data centers.

Neither of these seem to be the case.

Americans love big tech, as long as you ask about specific companies

If you poll people on how much they like “big tech” you’re going to get negative responses, but I think any vague powerful sounding entity like this is also going to poll negatively. If instead you replace them with the big four hyperscaler companies building out the biggest AI data centers, Americans are mostly pretty positive on them. The four companies building the biggest data centers right now are Google (technically Alphabet), Amazon, Microsoft, and Meta.

Amazon is one of the institutions Americans view most favorably, competing with “the military”

Even Facebook is +13% in approval, with just as many people very favorable to it as very unfavorable. More Americans are favorable to Facebook than NATO or the European Union. A more recent poll from The Argument, which didn’t include Meta, also found strong approval for the other three hyperscaler companies actually building data centers.

So the specific story that voters are looking to “get back” at the hyperscaler companies for what they’ve done to society using the only lever they know how to use looks wrong. I would think that if you’re so motivated to punish big tech companies that you want to block their attempts to build things near you, you would also at least say you “strongly disapprove” of the companies on a survey, yet the number of people who strongly disapprove of Google is 5%, Amazon 8%, and Microsoft 7%. Meta’s the outlier at 22%, but is still +13% compared to data centers’ -60%. So far this doesn’t look like a promising explanation.

When Americans are asked about tech companies in the abstract, they think they need “more regulation”

but I’m not really sure what this tells us. If I asked anyone on the street “do you think major companies are regulated too much or too little?” I’m willing to bet they’d default to too little regardless of what they otherwise believe about specific companies. A lot of the polling about very general industries like this leaves me worried that if you ran polls on any industry calling it “big __________” it would also get mostly negative approval. I suspect Americans have a negative view of “big ag” but a positive view of “farmers” and (outside of Monsanto, because they mostly only hear about it in negative stories) neutral to positive views on a lot of food companies. How do you feel about Archer-Daniels-Midland?

The fact that the hyperscalers each get net positive favorability I think at least complicates the story that the data center backlash is just a concealed way of lashing out at them.

The other thing that complicates the story is that this doesn’t seem to be happening with other buildings associated with tech companies.

Americans are kind of fine with other buildings associated with big tech

If someone felt Amazon had too much power in society and wanted to stop them by putting up blocks to large buildings Amazon was trying to build, what specific type of building would be most likely to be blocked? Obviously Amazon warehouses/fulfillment centers. And yet these are some of the very most popular buildings in America.

If you want to “strike back at the power Amazon has over our lives” I don’t see a better way of doing that than opposing a distribution center. This directly harms Amazon’s ability to affect your community’s buying habits.

It’s interesting that the chip manufacturing plant polls worse than the traditional manufacturing factory, with an additional 11% of people opposing it, but also a lot more people being unsure. Maybe that additional 11% do actually oppose AI specifically or big tech in the abstract? It’s also interesting that an AI data center does meaningfully worse than a traditional data center, which also polls much worse than other types of buildings. It seems like this could be some combination of people disliking AI specifically, or people (correctly) assessing that the really wildly large new data centers going up are mostly for AI, that these are often orders of magnitude larger than traditional data centers, and therefore that they’re uniquely dangerous for environmental reasons. I’ll explore this more in the next section. Either way, data centers seem to be uniquely unpopular compared to other buildings associated with the same tech companies building them, though we could really use more polling on this. The way they stand out seems to fly in the face of the idea that they’re just a way to get back at tech companies in the abstract. Why are Americans then not also reacting to the buildout of other similar buildings? One response could be “they benefit from Amazon warehouses in a way they don’t from data centers” but it seems a little weird to imagine that Americans are lashing out against tech companies’ influencing more of their lives unless the tech company is doing something that will specifically make its services much more directly present in their lives.

So I come away from this data pretty uncertain, but much more skeptical about takes like this specifically:

It’s probably not mainly about AI

I’ve scrolled by a lot of very confident takes that the reason people are reacting so negatively to data centers is that they can sense that they’re the tip of the spear of some potentially horrific alien future with advanced AI. That’s how I feel about them personally!

But polling really doesn’t seem to imply this is mainly a reaction to AI. It’s obviously true that a lot of people are motivated by their beliefs about AI, but this doesn’t look like the main story to me. Whenever Americans are given the option to say “Among other issues with data centers, I also separately either think AI is a useless waste or a dangerous technology and that’s influencing how I think” they mostly opt not to say that. I think this is partly due to the average person just not actually thinking about AI that much, and separately having seen tons of news stories about data centers harming local communities.

Polling implies AI’s playing a small part in the backlash

Here’s some polling on why Americans say they oppose data centers. This from Fox News in July asked voters what their “primary reason” was for opposing data centers:

This seems to be a clear story of voters worried about the immediate impacts of the buildings themselves, mostly not AI more broadly. But maybe this poll excludes people’s worry about AI? Just because it’s not their first concern doesn’t mean it’s not playing a part.

An earlier Gallup poll in May asked respondents to just write out why they were opposed to data centers however they wanted. Gallup then organized them into lots of different general worries. Here are the results:

Something unfortunate here is that as far as I can tell, Gallup didn’t give us a way of clarifying how much overlap there is between “negative views of AI” and “specific AI concerns” and how they classified them, so people’s concern about AI could be as high as 27% or as low as 14% (if the entire 13% of people with specific AI concerns were also categorized as having “negative views of AI”). Still, when people opposed to data centers were given the option to clearly say why, at least 3/4ths didn’t mention AI at all. This leads me to think the backlash isn’t mainly about AI, at least not consciously.

Most recently, a survey was done that found that when voters learned that a data center would recycle the same water for 10 years instead of drawing new water, their support jumped by 31 points (!!!!!!!). Voters say they’re worried about water, and when they learn water won’t be a big deal they stop worrying. This comment points out that the survey results are deceptively framed in the document I shared, and that the 31 points actually mean “31% of people become more supporting than they were before” which could mean anything. I retract this point.

Americans have mixed opinions of AI

To be clear, Americans seem to have skeptical though mixed views on AI.

The polling we have on American attitudes toward AI isn’t great right now in general so I don’t feel like I can draw much from it. It would make sense to me that a big part of the data center backlash is that voters don’t feel like they’re benefited from what’s happening inside data centers and therefore are more inclined to criticize them.

But the polling we do have doesn’t show the kinds of dramatic swings you’d expect to explain the sudden and intense data center backlash. The percentage of people who are “more concerned than excited” about AI has stayed the same since about the release of ChatGPT.

Young people’s concern has significantly risen, but older people’s has stayed relatively flat in the past 4 years.

This is interesting, but I don’t think it fits with the sudden massive drop we’ve seen in data center attitudes:

Meanwhile, the share of people who “haven’t heard much about AI” has also stayed about the same, at half the population.

All this combined with the survey data on Americans’ stated reasons for opposing data centers makes me think that attitudes toward AI are probably a meaningful factor for a minority of people opposed to data centers, but I don’t think the backlash overall is driven by people’s thoughts on AI.

It’s probably not China

Is this actually all a Chinese psyop? I really don’t think so. I have a long separate post here looking at the evidence we have, and I come away thinking the data center backlash is basically entirely homegrown.

I think the backlash can be mostly explained by years of media coverage of object-level local environmental and economic harms

For the past few years, basically all news outlets everyday Americans trust were saying over and over that there were these strange new buildings plopping down without the locals knowing and using a disturbing amount of water and energy. Here’s a representative sample of lots of the most popular news articles on data centers between 2023 and 2025:

• Artificial intelligence technology behind ChatGPT was built in Iowa — with a lot of water - AP

• Google and Microsoft report growing emissions as they double-down on AI - NPR

• Tech giants face emissions reality - Axios

• Google falling short of important climate target, cites electricity needs of AI - AP

• A bottle of water per email: the hidden environmental costs of using AI chatbots - Washington Post

• Three Mile Island nuclear plant will reopen to power Microsoft data centers - NPR

• Ireland embraced data centers that the AI boom needs. Now they’re consuming too much of its energy - AP

• Data center report shows high cost of Virginia’s high-tech future - WVTF/Radio IQ

• ‘How come I can’t breathe?’: Musk’s data company draws a backlash in Memphis - E&E News / POLITICO (to be clear, this one’s one of the biggest real problems of any data center built in the last few years).

• We did the math on AI’s energy footprint. Here’s the story you haven’t heard - MIT Technology Review (I wrote about this one being pretty goofy here)

• Power Hungry: AI and our energy future - MIT Technology Review series hub

• Their Water Taps Ran Dry When Meta Built Next Door - New York Times

• AI Data Centers Are Sending Power Bills Soaring - Bloomberg (this is the most misleading article that I haven’t had time to comment on yet)

• Your utility bills keep going up. Here’s everyone you can blame — AI data centers included” - Fortune. The article itself argues that data centers are getting blamed too much, though you wouldn’t know it from just the title.

• How the AI-driven data center boom is leading to skyrocketing energy bills for many Americans” — CBS News. PolitiFact later found this to have mischaracterized Bloomberg’s 267% wholesale figure from earlier as a consumer bill increase.

• Data centers now account for half of all new U.S. electricity use, just as Americans start to sour on AI - Fortune

• AOC Confronts Trump Official With Effects Of Data Centers On Local Water Supplies - HuffPost

• AI data centers use far more water than most tech giants report - Wall Street Journal

• The Impending, Inescapable Deluge of A.I. - New York Times interactive

Some of these are legitimate stories and others are strange isolated statistics or just flat out fake (the idea that data centers raise electricity bills by 267% is this zombie stat that won’t die, the number referred to a specific electric wholesale price at a node near a nuclear plant in New York that had recently closed down, not residential bills, and via this game of telephone we now have politicians like Elizabeth Warren repeating the misconception).

A lot of media coverage has gone on to just share people’s broad concerns without seeing whether they stand up to scrutiny. Many reporters now seem interested in seeing “what everyday people have to say” without clarifying what they’re getting right or wrong. Take this recent clip about the backlash from ABC:

Very very few media organizations are both reporting on locals’ reactions (which note in this video are entirely about the environment) and then contextualizing them with the actual amount of land, water, or power the data center would use, and how that compares to the bigger picture in the places they’re built. Because there’s so little incentive to correct this stuff, it goes uncorrected, and millions of viewers at a time see upset sympathetic locals in news stories that leave them thinking all their concerns are probably correct.

All of this has also been amplified a ton on social media, which created lots of common wisdom among everyday people about data centers that they didn’t have much reason to question. I think these ideas are kind of “locked in” now because they’re perceived to be the expert consensus on data centers, so people are more resistant to questioning them for the same reason they’re (rightly) resistant to specific anti-vax arguments.

With no real reason to each individually do some massive deep dive to try to figure out what was actually going on or put the number in context, people naturally just accepted that these were bad for local communities, and this became the default wisdom among educated people. Then the data center buildout accelerated, and suddenly more people found that their communities were also considering data centers. Why would this be happening if all educated people agreed that these were terrible? This was probably pretty annoying and confusing to a lot of people, who have now seen so much consistent negative coverage of data centers that saying they’re fine seems almost like a conspiracy theory. This all just seems like a much more satisfying explanation to me! It fits the limited polling data and evidence we have.

I’m left uncertain, but I lean in the direction that the data center backlash is mostly about object-level concerns about data centers, and the main role AI plays is that people’s opinions of it are too mixed to justify looking into whether their negative beliefs about these large strange buildings are correct. I’ll eventually get around to finishing my history of the data center panic, but I’d recommend that first linked post if you’d like to see more of my thoughts on how this all started.

One last thing to consider is that data centers are so unpopular now that it might just be fun to join in on the hate. A recent NYT opinion column talked about the communion people feel with each other in this fight. “People like hanging out with each other to attack a perceived enemy” on its own might go a long way in explaining the most intense reactions we see. The column itself gives exactly one sentence passively acknowledging why people are mad in its overview of how much this brings people together.

Some people will bizarrely imply that saying this is all about object-level worry “ignores what voters are actually mad about” or doesn’t take them seriously. I think it’s important here to just look at what voters are actually saying, and take that seriously, rather than trying to work out their inner psychology and secret beliefs (that always so conveniently line up with the person writing about them), and that this is the more democratic and fair way of engaging in data center discourse. I can then make my own case that, with nothing but respect to the voters, they are often mistaken about the facts they share. This is I think a much more respectful, direct, and democratic way of engaging compared to imagining that they all secretly agree with me about the very immediate dangers AI could pose. Somehow in the last year, the dialogue completely flipped from “elites are ignoring the incredible environmental harms of data centers” to “elites are ignoring that voters secretly aren’t activated by these environmental harms at all” and I didn’t get the memo.

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Part of the reason this image is so amazing to me is how easy it is to mistake it for a computer chip.

Among other things it’s a reminder to me that outside of my little neighborhood in Washington DC, the world is unimaginably large and strange, and humanity has a shocking, overwhelming ability to shape things at the macroscopic and microscopic level to optimize the flow of material or information using these incredibly abstract methods of thought mostly alien to me (that I’ve only approached while playing Factorio), and that my little bubble of friendships and calm cafes and nice little offices in brutalist buildings exists on the thin surface of a deep global system more vast and sprawling and strange than my meager human categories can grasp. It leaves me much more open to thinking that the near future might be incredibly weird and alien, because the present already is.

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Gwangyang Steel Works is the world’s largest or second largest steel mill, depending on how you count. It has enough capacity to produce 10% of the world’s car production, about 22 million tonnes per year.

I’m worried that if we reported on and thought about Gwangyang Steel Works the way we currently report on and think about data centers, the coverage would look something like this:

Gwangyang Steel Works is an immense, imposing, ugly, and alienating building complex that guzzles gigantic amounts of water. It consumed about 9.8 billion gallons in 2024 (about half of the total water consumed onsite at all American data centers combined in the same year) and is one of the largest emitters in South Korea, responsible for about 30 million metric tonnes of CO2e in 2022, about 4% of the entire country’s emissions (and the low end of a recent estimate of all emissions from all global AI usage combined in 2025). It also adds a lot of local air pollution, emitting about 30,000 tonnes a year of dust, sulfur oxides, nitrogen oxides, and other pollutants, the highest total among the country’s large industrial sites. Local leaders say the Steel Works brings lots of benefits and is necessary as the world uses more steel, but a lot of steel is used for things like cars, which themselves have a gigantic climate cost. The plant has capacity for 10% of all steel going to cars, and if you include the emissions of those cars in its full cost, this makes it one of the most environmentally destructive buildings in the world.

All of these facts are individually correct, but leave the reader with just a small sliver of the full picture of the ethical, environmental, and economic trade-offs of the plant, to the point that I wouldn’t trust someone who knew only these facts to make the right call about whether to build the plant in the first place.

The ethics of attributing the emissions of the cars made with its steel to the plant seem pretty confused. Should local governments in South Korea decide that because a factory is making something very generally useful that can be purchased by any other industry, and one of those industries makes cars, and cars emit a lot, they should therefore ban the factory from being built? This would create global chaos as local bureaucrats and activists tried in vain to predict the broad trends of society, and to impose rules based on their likely arbitrary and confused conclusions. Steel production is also vital for renewable energy sources, so in deciding to block this plant, local governments and activists may unknowingly also slow the green energy transition. In general, broad rules (like a global carbon tax) that punish emissions while leaving the information flows of the economy otherwise free to operate, or industrial policy aimed at speeding up the learning curves of emerging green industries like solar, or investments in modernizing the grid, seem much more promising than attempts to stop complex global industry because some of what it produces will be used in products that emit.

Another framing my pretend coverage misses is that the world has billions of people in it who benefit from steel in complex ways, and concentrating the production of steel to meet this unimaginably large demand in an unimaginably large building complex likely reduces its environmental cost as opposed to spreading it across a lot of small steel mills, none of which would likely receive as much scrutiny. It’s very easy in lazy environmental writing to punish buildings for concentrating in a single visible spot something that no one would have an issue with if it were dispersed among the hundreds of millions of consumers it’s serving. Scale and centralization often allow incredible levels of optimization and efficiency that could never be achieved if everyone were given their own backyard steel furnace, but centralization also allows people to see the full environmental cost that used to be invisible, and to condemn it in a way they wouldn’t if it were dispersed and faded into the background. A lot of the ways people think and write about the environmental effects of specific buildings seem to punish and condemn scale without considering the efficiencies that centralization allows.

A lot of writing about data centers seems to imply that all large industries need a glowing, positive, almost loving relationship with the local communities in which they’re built, but I think most of the best and successful industries operate completely invisibly, set off from the communities they’re built in and the people who buy their products. It’s likely that right now you’re within a mile of some piece of steel produced by this mill, or maybe you’ve used a product with steel that came from it, but you never knew it existed. The mill hummed in the background unnoticed. The locals of Gwangyang obviously know the plant exists (it’s a popular tourist destination), but many other industries don’t ever bother to create these powerful relationships with local communities and instead just operate mostly unnoticed. If you were to draw a circle with a radius of 50 miles around you, do you think you’d actually be able to name the top 10 most lucrative large industries within it? Does it tell you anything about their relative virtue or importance if you can’t?

This is all to say that the default way many educated people think about the environmental effects of individual industrial buildings, which they believe is a morally serious and far-seeing way of questioning authority and fighting for the environment, often looks to me surprisingly childish and unserious. It often fails to reckon with the incredibly complex trade-offs of serving a world of billions of people with wildly different demands, and instead reduces individual buildings to heroes or villains, detecting their villainy by compiling a bunch of individually alarming isolated statistics that any industry at a similar scale would also come with. This can look numerate and informed, but I think it’s actually a clumsy and lazy failure to fully internalize the deep complexity of the world, and the trade-offs and systematic thinking this complexity demands of us.

To make what I’m saying here a little more concrete, I’d be interested in asking data center opponents: “Would you allow the Gwangyang Steel Works to be built? Why or why not? What specific environmental rules are you applying here? How do you think about the full trade-offs involved?” Then I’d want to see whatever rules they give applied to data centers. I’m worried that one of the rules people often seem to be operating under is “We can’t have any new emissions at all,” which would effectively ban all new industry until the climate crisis is solved. It would seal the current global system in amber and leave us with no way to adjust or progress to more promising technology over time, at a moment when we also need radical change to adapt to a changing climate. This rule would ban the steel works, and would have banned almost every industry we currently use. Seems like a very bad rule! The data center debate is awash in strange rules like this that appear to have been memed into existence only recently but are now often presented as if they’re obvious truths that all reasonable people believe. These rules are easy to nod along to when applied in a vacuum to a strange new industry like data centers, but mostly fall apart and appear ridiculous when applied to almost any traditional industry, like steel manufacturing. The green energy transition is going to require way more steel, and we can’t follow the rule “no new emitting steel mills until the green energy transition is complete” in the meantime. I suspect a lot of rules like this emerged from the internet in the last 15 years as people developed new general moral outlooks, often informed by degrowth and localist ideas, that went mostly unnoticed and unquestioned because American energy usage overall didn’t grow much.

Populists on the left and right now talk a lot about local communities being abandoned by industry while also holding a cluster of beliefs that wouldn’t allow any industry in the first place.

If I tried to write rules for which new industries that emit should be allowed to emerge during the green energy transition, they would look something like this:

1. Their local externalities are limited to the point that people nearby mostly don’t notice any meaningful effects of them being there.

2. If possible, the industry should be fully electrified, because this leaves it primed to eventually receive 100% green power and avoids the hard engineering challenge of greening primary energy usage, which is the main reason the transition is so difficult.

3. The industry shouldn’t be punished for being centralized and optimized. If we wouldn’t have a problem with the industry’s energy usage and emissions if they were caused by dispersed users running the same processes in their own homes, we also shouldn’t condemn it for centralizing and optimizing these processes in one location. I say a lot more about this one here.

4. If possible, the industry should help fund upgrades to the grid, or ideally be broadly useful for generating and delivering green energy in other ways.

AI data centers often easily pass 1, and pass 2 and 3. Their net effect on 4 is an open question. More broadly, outside of extreme examples, I’m generally skeptical of blanket bans on specific industries to solve climate change. The general policy solutions to climate change specifically as I see them are carbon taxes, industrial policy for renewable energy, society-wide regulations and standards, and massive investments in the grid. Compiling scary lists in a vacuum of the total environmental costs of specific buildings, which always sound large for anything centralized at all, will often just introduce ethical and economic confusion and leave us not thinking like adults about the incredible complexity and trade-offs of the green energy transition.

In general, I’m worried that because we’re often only exposed to the thin human surface of society in our little apartments and coffee shops and cars and offices, we don’t notice how almost everything that goes into making our little lives human and gentle flows from the operation of incredibly complex systems operating at inhuman scale, like the steel mill. We have a general attitude that inhumanly large, complex, systems that can serve many different value systems are bad, and it’s the tiny, local, simple systems that serve our specific values that always need to win. This attitude is incredibly dangerous to pluralist society, because a lot of what makes something “inhuman” is often just the fact that it’s so general that it can serve many contentious value systems at once, not just ours. I have a lot more thoughts on that in this post on liberalism and the environment.

Data centers are the first new inhumanly large industry that has been generally visible to us for a while, and I’m worried a lot of the debate about them includes this backwards story where the big things sap our humanity and the little things restore it. In reality, the little things are often where the broad goods we’ve received from the big abstract processes are spent and fill our lives with meaning, not where they’re generated, and the mindset that “small is often better” threatens to rob the world of many of the things that make it most human, and allow us to live alongside each other while wanting wildly different things out of life. To help break out of this mindset, it might be helpful to look at Andreas Gursky photographs or watch Koyaanisqatsi1 or Tie Xi Qu or play Factorio or listen to Sunn O))) or this EconTalk episode about making potato chips or think about markets a lot or read Vaclav Smil. I don’t know, you can also find your own path to this “big complex systems wow” mindset, but it’s important that you do. Decisions about global industries require people willing to entertain the complexities of the global system, and what I see as the standard educated “with it” opinion of the data center buildout is very far from doing that.

More and more, I’m seeing people say that this backlash can’t be entirely homegrown. We should obviously consider this. There’s precedent for foreign interference in our discourse, and other countries have plenty of reasons to try to slow the spread of data centers.

There have been a few high-profile reports and claims purporting to show foreign involvement, and lots of people are sharing them as if they’re smoking gun evidence that China’s had significant impact. I’m worried that a lot of people are reading those headlines and nodding along without poking at the claims. When you do, they mostly collapse into a few specific examples of tiny organizations who happen to be based in China and other countries running some ineffective bot farms and behaving in ways that make them seem unlikely to be directed by the CCP, some negative coverage of data centers from Chinese state media, and a CCP-aligned billionaire funding networks of goofy goober fringe groups in the US like CodePink, or groups that overlap in leadership with the Party for Socialism and Liberation (PSL) which are now focusing a bit more on data centers, and in one or two places successfully blocked them with the help of lots of angry locals. All of these examples seem to be completely dwarfed by the organic local backlash driven by American media coverage, social media, and ordinary land-use politics. There have also been a lot of media headlines and graphics like this, that seem to imply that we know China’s played a big role in the data center backlash:

My goal here is to give you an overview of what we know about foreign influence on the data center backlash and explain why I think its effect has so far been pretty negligible, and why I think both the media and government coverage of China’s purported influence has been goofy.

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Contents

• My immediate reaction as someone who’s followed data center politics for a while

• The evidence that China’s influencing the data center backlash is pretty weak

  • The American Energy Institute report

  • The Bitcoin Policy Institute reports

  • OpenAI’s report

  • Kevin O’Leary’s claims

  • The Alethea report

  • This thin evidence is being inflated in policy discussions

• Evidence against

• Conclusion

My immediate reaction as someone who’s followed data center politics for a while

When I hear that this backlash “can’t be homegrown” I wonder if the speaker went to college in the 2010s like I did. If I went back in time to 2011, and asked my past self: “If a massive new industry appears with significant emissions, associated with billionaires and new strange polarizing technology, and it comes in these massive imposing boxes that draw as much power as whole cities, and its users are all dispersed and invisible to the local residents around it, how will your classmates react?” I would have predicted that they would have on average been furious. Degrowth and localist ideas were already in the water, and had become the default for “this is what all the good people believe.” I was already running up regularly against the established wisdom recently invented in the cauldron of 2010s internet leftism, to the point that saying anything generally positive about corporations or markets was seen as “not getting the memo.” And now my generation runs society!

The buildout is actually the first time American electricity consumption has seen sustained growth since I was in college.

I suspect that in this time, general anti-building and anti-energy use sentiment has grown unnoticed, because there wasn’t much new building or energy use for it to react to and make itself visible. As I personally became significantly more liberal (as opposed to illiberal left), pro-market, and appreciative of the importance of economic growth, I also saw the default political attitudes of my peers slip away from me more and more into a general cluster of beliefs about climate and politics that, if I were asked, I could have easily predicted would have caused them to resist the data center buildout. Flat energy use in the 2010s left dry tinder ready to catch fire as soon as any hint of growth appeared. It’s to the point now that a lot of educated people seem to believe that there’s this agreed upon rule in climate that we can’t have any new industry at all until the full green energy transition is complete.

All this made me pretty temperamentally skeptical of the idea that the data center panic is too intense and targeted to be an organic result of local American politics. American politics is pretty wild right now! I had also been following the panic from the start, and knew that many of the most popular talking points and ways of thinking had come from specific overconfident or clumsy reporting from established US papers, and spread quickly because their claims were shocking and alarming when read in isolation, and also sometimes exciting to talk about on social media (it’s very easy to buy a water bottle to hold in front of a camera on TikTok and compellingly repeat a fake statistic). Unless the Washington Post or New York Times or Bloomberg or More Perfect Union are taking marching orders from China, I can always trace the panic to a decidedly American source, as well as the networks of people elevating it.

But obviously these are just my own intuitions. And there are compelling reasons to be worried. China and other countries definitely have a national interest in slowing the American data center buildout. In China’s case because it’s become more interested in AI and its relation to national power over time. Other countries may just want to cash in on the data center buildout, and the more American data centers they can block, the more the hyperscalers will turn to them instead as a place to build. And we know that foreign influence campaigns have amplified past political panics. I’m not temperamentally inclined to trust the CCP, and I’m aware of how strange the data center debate has been, so I went into this with an open mind. And yet…

The evidence that China’s influencing the data center backlash is weak

Most of the early reports on Chinese and other foreign influence in the US data center debate offered very weak evidence for any significant effect. Yet they’ve often been shared as if they’re smoking guns showing that China played a major part in the backlash. Looking at their details instead suggests that some foreign actors have definitely shown interest in the data center panic, but we have no clear evidence that they’ve played a meaningful role.

The American Energy Institute report

In April, the American Energy Institute published a report called “Foreign-Funded Opposition to U.S. Data Center Expansion.” It found that 12 US organizations opposing data centers had received more than $39 million from foreign donors.

That is interesting, but mostly about individual foreign private philanthropy from Europe. The donors are wealthy people and foundations in Switzerland, the UK, and Denmark. The most politically controversial is Hansjörg Wyss, a Swiss citizen who has lived in Wilson, Wyoming, for decades and funds Democratic-aligned advocacy groups and US environmental stuff.

Wyss has been somewhat shifty with his donating, sending hundreds of millions of dollars into US advocacy groups, including via the Berger Action Fund, a 501(c)(4) that is not generally required to publicly disclose its contributors.

The other donors in the report are Chris Hohn (a British hedge-fund manager who gives mainly through the Children’s Investment Fund Foundation), the British Quadrature Climate Foundation, Denmark’s KR Foundation, and Geneva’s Oak Foundation. None has a meaningful connection to China, with a tenuous exception for Oak that I’ll talk about below.

The AEI report itself doesn’t claim that this money involves China at all, but the report’s been regularly twisted to imply that it does. For example, a popular early write-up of the findings was this Center Square article syndicated in dozens of local papers. It opens like this:

A new American Energy Institute report shows that foreign billionaires have provided funding upwards of $39 million to the anti-AI data center movement in the United States, with experts saying AI infrastructure must be advanced in order to assert American tech dominance over communist China.

Founder, CEO and Chairman of State Armor as well as expert on Chinese influence in America Michael Lucci told The Center Square: “Either the United States or communist China will dominate artificial intelligence, and therefore, likely dominate the leading technologies of the 21st-century.”

“China is actively executing strategies to win this competition,” Lucci said.

“The world we leave behind for our children and grandchildren will be a very dark one if a communist regime has technological dominance over the United States of America,” Lucci said.

“We will enter a dark ages as communist China exports their governance and technological models to countries currently aligned with the United States,” Lucci said.

Lucci said that foreign money funding the anti-AI data center movement is “classic political warfare for the detriment of our sovereignty and ultimately our way of life.”

This leaves a strong impression that the report itself is about Chinese influence, but it’s not!

The $39 million number that gets shared a lot from this report isn’t all going to data center stuff, it’s the total amount given to climate orgs and progressive causes in general. Some of those orgs have then gone on to oppose data centers, often for climate reasons.

So framing this as “$39 million to the anti-AI data center movement in the United States” is silly. It’s $39 million to specific climate orgs from billionaires worried about the climate. The report doesn’t make it clear what time periods these donations happened over. If you look at their sources, most of these donations happened over a 10 year period or longer. So for most of the time these donations were being made, the data center buildout wasn’t even happening.

The report opens with an implication that the anti-data center letter from 230 environmental orgs that inspired Bernie Sanders’ data center moratorium was influenced by these donations:

A network of activist groups receiving significant foreign funding is waging a coordinated campaign against the development of critical AI infrastructure across the United States. According to tax filings, annual reports, and grant databases, 12 organizations actively opposing U.S. data center development have collectively received more than $39 million from foreign donors — including billionaires and foundations based in Switzerland, the United Kingdom, and Denmark.

In December 2025, more than 230 leftwing activist organizations — many affiliated with the groups highlighted in this report — signed a letter demanding Congress impose a national moratorium on the approval and construction of new data centers, calling the industry "one of the biggest environmental and societal threats of our generation." Local chapters have since organized protests, lobbying days, and public pressure campaigns in states from Louisiana to Michigan.

These are not isolated campaigns, they are a part of a repeat network of organizations funded by the same donor ecosystem. At least one organization identified in this report has also been included in prior analyses of coordinated energy policy advocacy networks. Previous research by the American Energy Institute has documented overlapping funding sources, shared partnerships, and aligned policy objectives among environmental groups engaged in opposition to domestic energy and infrastructure development.

This is a confusing framing of the numbers here. What happened here was foreign donors worried about climate donate to climate stuff over a decade, then some of those orgs go on to sign a letter signed by 230 orgs in total.

Here’s another goofy example of how this got reported on, from the Washington Free Beacon:

They put this in their China category despite all these guys being from Europe! These “groups pushing for a data center moratorium” are general climate groups, they didn’t just appear to fight data centers. The second half of the article pontificates about China’s role with a few examples of Chinese state media writing anti-data center articles.

The Bitcoin Policy Institute reports

The Bitcoin Policy Institute (BPI) published the first of two reports on Chinese influence in the US data center debate on May 18. I don’t think the reports demonstrate significant foreign influence, beyond showing that specific fringe US groups like CodePink and the Party for Socialism and Liberation have focused more attention on data centers. And I think the infographic they made is getting shared uncritically as strong evidence that China’s playing a significant role in the debate, but when you poke at it this reading mostly falls apart.

First Report: Foreign Influence in the Campaign against American AI

This first report documents connections between the CCP and a US media and nonprofit network, and summarizes its case with this graphic:

The same European billionaires from the American Energy Institute report are here:

They’re donating to lots of climate causes and believe data centers are bad for the climate. They’re mostly reporting all their donations (with the exception of Wyss’s donation to the c4). The report frames them as donating billions of dollars into orgs which are now fighting data centers, but what this means is they’ve given a lot to US climate groups in general, and now some of those climate groups are anti-data center for climate reasons. The report notes:

Multiple foreign-tied charitable vehicles, including those of Swiss billionaire Hansjörg Wyss and British billionaire Alan Parker’s Oak Foundation, have funneled more than $2 billion into US 501(c)(3) and 501(c)(4) advocacy infrastructure. A significant portion of that money now flows directly into the organizations driving the anti-data-center campaign.

This is a reference to a number from this Americans for Public Trust report which at first found around $2 billion and in a revised report later bumped that to more than $2.65 billion in total grants from six foreign-based or foreign-donor-linked sources to U.S.-based orgs, mostly over 10 year periods. This is the same report AEI drew from above, with the same organizations like Wyss and the Oak Foundation. This money went to hundreds of advocacy orgs, that focus on things like climate, conservation, social justice, health, and other stuff. After 10 years of donating, some of these groups (like other US progressive and climate-focused groups) opposed data centers. We don’t know how much of this money was meant for anti-data center advocacy specifically, but it seems likely that it was pretty small, and importantly all coming from European billionaires and orgs, not China.

BPI doesn’t explicitly claim that these foreign billionaires are working for China, but still draws a line connecting them to China like this:

What’s “Oak Funding for BRI Projects?” It’s not the CCP influencing the Oak Foundation, it’s the Oak Foundation funding some Chinese Belt and Road Initiative (BRI) projects (China’s global infrastructure project). Why would they fund this? Well, they made a one-time $1 million grant to ClimateWorks in 2018 “to support the greening of the Belt and Road Initiative.” They also supported related work by the World Wide Fund for Nature, and BPI says they funded the Beijing-based Global Environmental Institute (GEI). The report describes these grants as evidence of “direct policy and financial ties to the People’s Republic of China.” That’s technically true but an incredibly silly framing once you know the details.

The Belt and Road Initiative builds, among other things, ports, railways, highways, power stations, and telecommunications, and is operating in 150 countries making up 75% of the world’s population and half of all global GDP. Opinions on BRI vary a lot, but the reason why a climate group would want to pay to make the massive infrastructure project greener is incredibly obvious. Throughout the 2010s, China was the largest public financier of coal power in the developing world. Lots of Western climate philanthropies funded research and advocacy to try to get Chinese banks and ministries to stop. The ClimateWorks report BPI mentions as funded by Oak, Decarbonizing the Belt and Road, finds that if BRI countries kept growing along carbon-intensive paths, they could make up 2/3rds of global emissions by 2050 and push the world past 2 degrees of warming even if everyone else hit their targets. This seems like a good study for a group focused on climate to fund!

The report also mentions the fact that Kristian Parker, the head of Oak’s environment program, is also on the ClimateWorks board. I know a bit about climate philanthropy and understand this to be pretty common.

The ClimateWorks grant was $1 million in 2018. I can’t find Oak’s total spending that year, but their environment program gave out $65 million in 2019. This all happened before the data center buildout. I think it’s goofy that the BPI drew this line connecting the CCP to Oak at all in a graphic about data centers.

What about the Beijing-based org GEI? This is a Chinese environmental nonprofit. They work with the Chinese government on making foreign investments greener. If you want to influence the government of China to build less coal power, it seems necessary to use an intermediary in China itself, which will by definition have to work with the government. Again, this seems like a necessary piece of one of the most effective climate philanthropy moves in the last few decades. I’m not seeing anything suspicious here.

This global campaign to green the Belt and Road Initiative may have worked! In 2021, Xi announced that China “will not build new coal-fired power projects abroad” and researchers tracking Chinese energy finance have found that new coal funding by the government actually did stop. This seems like one of the very best possible things a climate-focused group could spend money on! BPI includes Oak’s explanation that the giving was meant to reduce the Belt and Road’s projected emissions, but calls the ties “troubling” anyway. I don’t think they’re troubling at all.

It seems like none of the people or organizations on the right side of the chart are actually trying to influence the data center debate for national security reasons, or are working on behalf of foreign adversaries. And most of their donating happened for general climate or progressive reasons, and some of the climate orgs they funded became anti-data center. They don’t seem to be interested in “slowing AI development” in the way the chart implies. It doesn’t seem like these people are motivated by the pace of AI progress at all, the general climate and progressive orgs they’ve donated to just think the physical infrastructure is so wasteful that it should be blocked. I disagree, but they don’t seem to fit what the chart’s implying, so I’ll remove them.

Let’s keep moving through this chart. Who are the guys on the left?

These are Yi Zeng and Xue Lan, the two Chinese AI-governance researchers Bernie Sanders invited to a panel on existential risk from AI on April 29th. The report describes the event as “the culmination of an influence campaign.”

Both men are widely respected outside of China for their AI safety and governance work. Zeng was on the UN Secretary-General’s AI Advisory body for a year, he addressed the UN Security Council’s first-ever session on AI in 2023 to warn about existential risk, and made the Time100 AI list in 2023. He served on the expert advisory panel of the international AI safety report led by Yoshua Bengio. Xue is the dean of Schwarzman College at Tsinghua (the college that hosts Schwarzman Scholars), did his PhD at Carnegie Mellon, and co-authored the 2024 Science paper “Managing Extreme AI Risks amid Rapid Progress” with Bengio, Geoffrey Hinton, Stuart Russell, and Andrew Yao. He chairs the Ministry of Science and Technology’s New Generation AI Governance Expert Committee and is a Counsellor of the State Council.

Many researchers in AI safety and governance, despite being wary of China, believe that some basic coordination with the CCP will be necessary to avoid catastrophic risks, in the same way we coordinate over other global risks. You can disagree with this, but it’s ridiculous to imply that anyone who believes that some kind of cooperation over what they see as incredibly dangerous new technology would be good must be bought off by China.

What else does the report offer?

Both men do at times seem to parrot or at least share the opinions of the CCP. Zeng wrote pieces for China Daily using language associated with official Chinese foreign-policy messaging. Xue has argued that US chip controls can be counterproductive. The report uses Xue’s opposition to US export controls as evidence of alignment with the CCP. But Sanders’ Artificial Intelligence Data Center Moratorium Act would prohibit exports of advanced computing hardware to countries that don’t have equivalent AI safeguards, which would include China. If Xue’s opposition to export controls is evidence that Sanders’ panel and bill is the result of a foreign influence campaign, the bill’s contents seem like much stronger evidence in the other direction. More broadly, the report lists as evidence that they’re mouthpieces for the CCP their appearances in Chinese state media like CGTN, China Daily, and Global Times. But all major Chinese media are state-owned or party-supervised, and two of the country’s most prominent academics will obviously appear in them.

These are two of the most internationally known Chinese scholars on AI governance. If you want to understand the Chinese perspective on AI risk, these are the people you go to! If I were running a panel on global AI governance, I’d want to invite these guys, and I don’t need to be funded by China to say that. Both men regularly appear in AI safety and governance conversations. China would not have needed to fund anything to make a panel focused on global AI governance interested in having them join. Their presence alone isn’t evidence at all that China’s run a successful influence campaign here. The panel wasn’t even about data centers. It was about existential risk, unemployment, and Sanders’ idea of an international AI treaty modeled on Cold War nuclear agreements, which Max Tegmark (one of the panelists) has been pushing for a while.

The report doesn’t even explain how Sanders’ selected these guys or why this is the “culmination of an influence campaign” besides the fact that these are Chinese researchers who sometimes share the opinions of the CCP, and a very tenuous connection between China and the anti-data center letter US environmental groups signed.

Sanders’ Artificial Intelligence Data Center Moratorium Act was introduced on March 25, 2026, but he had first demanded a moratorium in December 2025, four months earlier, after a large group of US environmental groups wrote a letter demanding a national moratorium, as shown in the graphic.

The timing is consistent with the environmental letter inspiring Sanders’s moratorium push and with his later emphasis on AI safety and governance. The report’s already implied that this coalition letter was influenced by foreign billionaires with some kind of tie to China, but we’ve already seen that that doesn’t hold up. This was a coalition of normal US environmental groups mad about the environmental impact of data centers. You can look at the letter with the full list of orgs here. I found two orgs who have a credible tie to the CCP-aligned Singham group (discussed below):

• The Institute for Policy Studies, whose 2015 annual report thanks its donor community and includes Neville Roy Singham in the individual donor list. IPS’s fiscal 2024 IRS filing lists Jodie Evans as a trustee. Evans is Singham’s wife and cofounder of CodePink.

• The Progressive Democrats of America, whose current advisory board page lists Medea Benjamin, a CodePink cofounder. A 2009 PDA release also named Jodie Evans among its board members.

So out of 231 organizations, I found 2 that had loose ties to a CCP-aligned network. Doesn’t look much like a successful CCP influence operation to me.

The report says that Sanders’ data center moratorium was introduced 107 days after the coalition letter, and concludes that “That kind of efficiency typically distinguishes coordinated advocacy infrastructure from spontaneous grassroots opposition.” Does it? 3 and a half months is a long time.

So to recap, Sanders was originally motivated by US environmental groups, only 2 of 231 seem to have any ties to China at all, to propose a data center moratorium, then talked a lot with the AI safety community, got more worried about extinction risk, promoted Max Tegmark’s specific view of global AI governance, and invited the two most prominent and respected Chinese AI governance researchers to a panel on said global governance. This doesn’t look like a foreign influence campaign to me at all.

So I’ll knock all these off the chart as well. What’s left?

We’ve got the Singham Network, and Chinese State Media. I agree with the report that both of these have shown interest in slowing the data center buildout and have tried to influence American attitudes toward data centers, so the question is how successful each has been.

First of all, the Singham network is in fact incredibly skeevy, CCP-aligned, and capable of influencing some US discourse. It is a coordinated group of nonprofits funded by Neville Roy Singham, a Marxist billionaire who lives in Shanghai and is married to CodePink co-founder Jodie Evans. A 2023 New York Times investigation found US and international organizations producing content closely aligned with the CCP on major geopolitical issues, sharing staff and office space, and cross-posting one another’s material. Singham works from a Shanghai office shared with a Chinese media firm whose mission uses the CCP’s same slogan for overseas propaganda, and he attended a party workshop on promoting the CCP internationally shortly before the Times story ran.

From 2017 to 2023, the Singham network moved about $278 million into six US nonprofits: including more than $167 million to the People’s Support Foundation, more than $68 million to the Justice and Education Fund, and more than $22 million to The People’s Forum. This is currently being investigated by the government.

So this is a real case of a powerful CCP-aligned actor trying to influence American thinking. The question is how influential the Singham Group’s been in the data center debate. Here’s what the BPI finds:

• Tricontinental published a dossier criticizing Big Tech in 2021 and a paper in March 2026 arguing China is overcoming US export controls.

• Code Pink published a piece in January 2026 attacking Meta’s data center projects in Louisiana and Wyoming and framing them as part of a new Cold War on China.

• Peoples Dispatch ran a piece attacking American AI firms by name.

• Liberation News (the paper of the Party for Socialism and Liberation, more on them further down) has been covering data centers and electricity bills since at least July 2025.

The report describes this as the network seeding anti-data center talking points, but almost all of the data-center-specific material came out after people were already fired up and didn’t seem to add new talking points. The five year old dossier is just Tricontinental commentary arguing that the United States should let advanced chips flow to China. This is unrelated advocacy for Chinese access to compute for a small audience, and doesn’t suggest any kind of local NIMBY opposition to data centers. The only media here focused on data centers shows up in mid-2025 or later. By then, local opposition had already blocked or delayed $64 billion in data center projects, and electricity bills purportedly linked to data centers were a major American news story. There’s no new information here that the groups seeded, and I haven’t seen evidence that they inflamed the debate much. It doesn’t look like anything here got much readership.

The report notes that Chinese state media is warning Americans about data center energy costs while subsidizing the energy costs of its own data centers. I agree that the CCP seems to clearly not mean what they say about the US data center buildout, but I need to see more evidence that this all has actually had an effect.

I’ve seen a lot of people share this graphic as solid evidence that China’s playing a big role in the data center backlash, but when you poke at almost any part of it, this reading seems to fall apart.

Second Report: The Singham Ground Game

BPI published this second report in June. I think this is a clear improvement on the first, but is still far from showing that China’s played a meaningful role at all in the data center backlash.

This second report looks at 21 local anti-data center campaigns across 14 states where the Party for Socialism and Liberation (PSL) showed up to fight data centers via door knocking, petitions, protests, and showing up to city council chambers. Two things about PSL:

• Their leadership heavily overlaps with the executives at Singham’s nonprofits.

• They also attempt to join basically every popular mainstream protest to recruit new members. As a veteran of protests in DC myself, basically every large one I went to had a PSL presence. When I showed up at a march after Roe was overturned I remember them running around yelling about how we need to abolish the Supreme Court. I felt like maybe getting into an epic debate with them about how this would have also ended Roe, but decided it wasn’t worth it. I did once had a longer conversation with a member who alternated between telling me about the Hands Off North Korea movement and about how almost every night he had a dream about peeing his pants. Anyway…

The report finds that these campaigns were involved with 10 local moratoria, 1 permanent ban, and 4 rejected or withdrawn data centers, adding up to $23.6 billion in delayed or blocked investments. The report also repeats that “the American opposition to data centers is real and mostly homegrown.”

My general impression of PSL is that they’re mostly ineffectual and just ride along on other protests, so the question is whether they were actually responsible for any of these shutdowns. It looks like there’s one clear case where they were. In Prince George’s County Maryland (right near me!) the local NPR station described the PSL as leading the county’s data center fight, and talked about how the chapter trained local residents to canvass and ran a petition that got 20,000 signatures. They turned out crowds large enough that the county executive paused data center permitting.

Most of the other cases don’t manage to show that PSL caused the shutdown or delay. In DeForest, Wisconsin, the lead organizers were a normal local residents’ group, PSL “provided support,” and the data center was blocked after local opposition and a Wisconsin Watch investigation exposed details of the NDAs. Tucson’s $3.6 billion Project Blue was rejected by the city but moved to unincorporated Pima County land, where it is proceeding, and BPI counts it as delayed in part by PSL.

On top of all this, the report doesn’t even find that Singham actually funds the PSL. There are lots of linked people, but we don’t know which way the causality runs. PSL was founded in 2004 as a split from the Workers World Party, way before Singham himself started funding his network. The report relies on the public statements of James “Fergie” Chambers, a disaffected Marxist millionaire who used to organize alongside Singham. He says The People’s Forum and BreakThrough News are wholly controlled by the PSL. So this claim seems to be that Singham’s money is flowing into a Marxist scene that already existed independently. It’s possible that without Singham’s money flowing into these other orgs, PSL might be similarly active. I don’t know. Again, this definitely seems like the clearest case of bad behavior from a CCP-aligned group. Its scale still appears small. The Maryland campaign is the clearest example, although BPI also documents substantial PSL organizing in Charlotte. It’s unclear how much either campaign changed the outcomes.

But meanwhile, Gallup found in March that 71% of Americans would oppose a data center in their own area, 48% say they would “strongly oppose” one. The PSL in Maryland could have only mustered this opposition by drawing on a pre-existing very negative public sentiment that doesn’t seem to have been influenced by the Singham network.

OpenAI’s report

On June 10, OpenAI published the report “PRC-linked influence operations are targeting AI debates in the US” which describes how and why they banned two clusters of ChatGPT accounts that seemed to be operating out of China (the operators prompted the model in Chinese and connected through VPNs, since OpenAI doesn’t allow access from China). The first cluster, which OpenAI nicknamed the “Data Center Bandwagon” campaign, generated short English-language comments and images claiming that AI data centers were pushing up electricity prices for ordinary American families, and fake accounts posted them on Twitter. The second, “Tech and Tariffs,” generated comments and political cartoons attacking US tariffs, and was connected to a network of fake Twitter accounts pushing false claims that ChatGPT user data had been breached.

OpenAI concluded that the operators were likely connected to a private Chinese company that conducts public-opinion work for provincial government clients. So this may have originated in China, but we don’t have solid evidence that China’s central government directed it. The disinformation researcher Darren Linvill made a point here that if the Chinese government were serious about covertly shaping the data center debate, they probably wouldn’t run an operation like this using OpenAI’s own product.

Most of the posts received little or no engagement, and OpenAI found “no evidence of meaningful breakout beyond its own activity.” The report therefore shows likely China-origin operators testing and amplifying existing narratives, with no observed meaningful reach.

So twitter bots originating in China shared comic strips about electricity prices based on American reporting that the public was already following. The bots didn’t get meaningful attention. They were designed to amplify a pre-existing panic, and failed. They behaved in ways that don’t seem consistent with a state-led operation, among other things using OpenAI models. That’s what we know from this report.

This report has generated a lot of goofy headlines, like this from The Hill:

Sure, people likely based in China were behind one specific, extremely small anti-data-center campaign in the US. But readers might understand this headline as saying OpenAI found that the CCP itself was behind the campaign, or that the full anti-data center movement was coming from China.

Kevin O’Leary’s claims

After Kevin O’Leary’s proposed 9 GW data center in Utah met with intense opposition, he announced that his team had discovered evidence that China was behind a lot of it. He claimed that his team had traced much of the opposition back to the Alliance for a Better Utah, a progressive advocacy group, and that the group’s IRS Form 990 filings showed ties to Chinese-linked funding channels connected through an organization called Arabella. He said that there was a spike in misinformation about the data center on Instagram and X stemming from IP addresses connected to these organizations. On Fox News he suggested that this is China because there’s only one adversary who would want to stop America from building its grid and its compute capacity. In a later Fox appearance he implied that specific people were working with China, including Alliance for a Better Utah founder Josh Kanter, Gabrielle Finlayson of Elevate Strategies, and Taylor Knuth. He called locals “proxies for the Chinese government,” and said data scientists had established the link.

I can’t find any evidence anywhere that these organizations or people are linked to China. And it turns out O’Leary can’t either. On June 25, O’Leary posted a statement saying he has “no evidence” that Alliance for a Better Utah, Elevate Strategies, Finlayson, Knuth, or Kanter are funded by China or the Chinese Communist Party, and Fox News later issued an on-air correction. Everyone he listed besides Knuth went on to sue him for defamation.

He managed to dump gasoline on the fire of public opposition by telling local activists that they were funded by China, and confuse the debate by creating lots of news stories implying China might be involved without any real evidence.

The Alethea report

This report is the most serious evidence of foreign interference I’ve seen. The New York Times reported on it on July 9th.

The NYT says that between January and June, Alethea found about 700 mentions of data centers in Chinese, Russian, and Iranian state media, a small fraction of each country’s AI coverage. They found Russia-linked Storm-1516 publishing two fake stories about Firebird’s Armenian data centers, and the pro-Kremlin Rybar network separately pushed anti-data center stories. The Chinese and Iranian examples in Alethea’s public report were mostly overt state-media coverage, not covert operations.

The report did not show that any of this activity materially changed American opinion. Much of the Chinese material was triumphalist propaganda apparently written for domestic readers. Alethea also found what looked like engagement farming around anti-data-center slop posted by Facebook pages anonymously run from Bangladesh. Russia appeared to be using data centers more aggressively as a wedge issue, while the evidence involving China was mostly overtly state-media. Again, I’m left without a strong case that China’s having significant influence here.

And that’s basically what we know about Chinese influence on the data center debate!

This thin evidence is being inflated in policy discussions

Trump suggested China's playing a big role in the data center backlash recently, saying "A big part of it is China" and claiming the communities giving developers a hard time are “China-oriented” and that Beijing is spending heavily to promote not building because the US is winning the AI race.

Secretary of the Interior Doug Burgum has made similar claims:

• On Mornings with Maria, he called the buildout an arms race with China and that some opposition is “geo-targeted” propaganda.

• At an industry event called “Harnessing American Power,” he told the crowd the data center opposition wasn’t organic and local and that some of it was foreign-sourced dark money.

• On Fox Business in late May he said the debate was “bombarded with foreign-directed propaganda”, and called it “another attack on the U.S. and our ability to be competitive.”

Members of Congress have seemed to overreact to the evidence we have. The House Energy and Commerce leaders wrote to FBI Director Kash Patel and PCAST co-chairs David Sacks and Michael Kratsios saying there was “evidence that strongly suggests foreign influence campaigns,” demanding a briefing by June 18, and the evidence was mainly BPI and Power the Future reports (Power the Future’s report is on the European billionaires funding progressive groups, not China). The letter said the possibility that CCP-backed entities are influencing data center decisions shows how serious a fight the US is in. I don’t think these reports demonstrate that level of seriousness. I think investigation into possible influence campaigns makes complete sense, but it doesn’t make sense to imply that we have strong evidence of meaningful campaigns.

Tom Cotton wrote another letter to Acting AG Blanche that alarming reports indicate “a network of foreign actors, led by the Chinese Communist Party” is manipulating US policy and opinion on data centers, and his letter uses the $2 billion number from BPI (which, remember, is a decade of publicly disclosed general climate philanthropy from Europeans, and some of the groups funded signed an anti-data center letter). Here’s the full text of the letter:

Dear Mr. Blanche,

I write requesting the Department of Justice (DOJ) investigate foreign influence efforts targeting the buildout of American AI infrastructure. Alarming reports indicate that a network of foreign actors, led by the Chinese Communist Party (CCP), is attempting to manipulate U.S. policy and public opinion on data centers. Maintaining America’s AI advantage is vital to American economic strength, diplomacy, national security, and military power. Americans certainly have valid concerns about potential rising energy costs and strains on natural resources related to data centers, and we should implement common sense regulations and legislation to protect communities from adverse effects. But we can’t allow any effort by foreign adversaries to extort these fears and undermine our technological development.

A recent report uncovers a multi-year campaign waged across multiple vectors of foreign influence. Among them is a network of U.S. nonprofits funded by Neville Roy Singham, a Shanghai-based American expatriate under U.S.-government scrutiny for his ties to the Chinese Communists. For years, Singham’s network has been producing content opposing the development of American AI infrastructure, and various investigations have found that the Chinese government is the network’s ultimate paymaster. All while the Chinese government promotes its own AI ambitions at home, where sentiments are overwhelmingly pro-technology.

Foreign-funded charitable entities tied to this campaign have reportedly funneled more than $2 billion into U.S. advocacy organizations, with a significant portion going to groups that are driving the campaign against U.S. data centers and influencing policymakers. Just one month ago, Senator Bernie Sanders hosted a 75-minute panel on “the existential threat of AI” where two of the four panelists were Chinese government affiliates. This included a professor from Tsinghua University, a crown jewel of China’s Military-Civil Fusion strategy and a host to multiple defense research laboratories.

Several of these matters are already under congressional review. Yet no entity in the network has been charged under the Foreign Agents Registration Act (FARA). I therefore request that the DOJ launch a full investigation into these matters.

Sincerely,

Tom Cotton
United States Senator

“This campaign” is doing a lot of work here. Cotton appears to mean the broader very general campaign against American data centers that includes all opposition to them, but the wording can easily be read as connecting the $2 billion directly to Singham’s network or China. BPI’s $2 billion number is entirely about European donors sending lots of money to lots of US progressive and climate orgs over a 10 year period, some of whom went on to oppose data centers. The way this is used in the letter strongly implies this money is either coming from China or a significant fraction of it was meant to be for anti-data center stuff, or that it somehow influenced Sanders’ decision to host Chinese panelists. In fact it’s all unrelated giving from European progressives to American progressive orgs.

Also, I think I’ve established above that the evidence doesn’t suggest Sanders’s panel resulted from Chinese influence.

I think it’d be good for the government to look more into foreign influence in the data center debate, so I’m not opposed to Cotton’s overall interest here. But the evidence he’s working from is weak, and the letter (and reporting on it) strongly imply that we have much better evidence than we do that China’s influencing the debate.

One good result of these reports is that they’re prompting more government scrutiny of the Singham network. My worry is just that their actual effect on the debate is being inflated.

Evidence against

People who study foreign influence campaigns professionally haven’t said much about data centers so far. Darren Linvill’s Clemson Media Forensics Hub, which tracks foreign influence campaigns, says “We haven’t found much.” China’s bot armies aren’t talking much about this topic, and Chinese state media appears more intent on promoting China’s own data centers. Graham Webster of Stanford told WIRED the BPI’s examples don’t resemble documented Chinese influence campaigns, and Graphika analyst Dina Sadek said that research shows domestic US actors are leading the online anti-data center reaction.

I have to say, for most talking points people use against data centers, it’s always very easy for me to trace the game of telephone to a confused American source. Take people’s professed biggest issue with data centers: water. Google searches for “AI water” spike for the first time in May 2025.

Early that month, Bloomberg published this:

Followed shortly after by this from the New York Times:

Entirely anecdotal, but in conversations I’ve had about data centers and water, someone has almost always brought up one of these two articles.

We have some recent examples of China trying to influence US politics, and they were mostly duds. For example, in 2022 China ran fake accounts pretending to be Texans to oppose rare earth processing facilities in Texas and Oklahoma, claiming they’d cause radioactive contamination, cancer, and birth defects. An assessment by Mandiant (a cybersecurity firm) found that this wasn’t really effective and got pretty limited engagement. Maybe all the most effective Chinese psyops remain secret to us, but I can’t find any that we know about that had a meaningful impact on American thinking.

I often hear people suggest that TikTok might be boosting anti-data center videos for propaganda reasons. Maybe? I don’t know, anti-data center stuff is ridiculously popular across all social media platforms, and anti-data center creators don’t seem to do any worse on Instagram.

Some people bring up that a lot of the most popular anti-data center posts on websites like Twitter seem to come from foreign bots. Here’s an example:

True, but there’s no pattern in where these are coming from, and it seems more likely that the people managing the bots know that data center stuff is likely to get a lot of attention right now and are doing it for money.

One other interesting pattern here is that while data centers have received a lot of absolute pushback, they might not relatively receive more pushback than other building. About 30% of proposed solar and wind projects are also cancelled, because of a mix of community opposition, zoning, and interconnection problems. I’d like to do a deeper dive on this comparison, but would like it even more if someone who knows more about it beats me to it! I’ve recently been following opposition to solar farms in very liberal parts of Massachusetts near where I grew up. Even people who hate climate change often hate new buildings more.

I’m far from an expert on our understanding of foreign influence campaigns more broadly, but summaries I read of them consistently leave me skeptical that they have as much influence as is often attributed to them. To play a meaningful part in the data center backlash, a Chinese influence campaign would need to be much more successful than the ones I’m aware of.

I’m separately writing a series on the history of the data center panic here. I’m more convinced by the story I describe in Part 1 than the idea that China’s involved. I think it was basically predictable that Americans would feel negatively about big new buildings they don’t understand associated with the tech industry, badly reported on by rushed journalists looking for clicks and being built at a rate and scale most of us have never seen before.

Conclusion

There’s still a lot we don’t know about the data center panic, and maybe some new evidence will emerge that China or other countries have somehow had a surprising amount of influence over American attitudes toward data centers. But so far the evidence that’s been produced hasn’t given us any sign of significant Chinese influence. So far the big upside of all this has been more scrutiny on the Singham network and their buddies, though I suspect the main victims of groups like the PSL are the members of the PSL. I personally just feel like I know a lot of my generation and Gen Z’s attitudes well enough to have predicted that there would be mass opposition to data centers (or any significant amount of new building or energy use on a contentious product), and the opposition seems surprisingly aligned to opposition to other building at the same scale, so my gut says this is overwhelmingly homegrown. I’m open to evidence to the contrary, but I haven’t seen it yet.

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In a lot of my writing on the environment, I find myself implicitly standing up for political liberalism over what I see as attempts to use the environment as an illiberal cudgel against the writers’ identified enemies. Reading Vaclav Smil and David MacKay when I was young was incredibly useful for thinking about the current AI data center debate, but I think actually less useful than reading Rawls, and Joseph Heath’s summaries of Rawls. That’s because most of my disagreements with public commentary on AI and the environment are not actually factual disagreements (I often completely agree with the same underlying facts about the numbers involved as everyone else). They’re normative disagreements about what the facts imply we should do. For example, I agree with all the factual claims in this UN University report on AI and the environment, but I think its framing of those facts is completely off the wall. Here are six especially strange framings from the report:

GPT-4’s training carbon footprint of 25,000 tonnes of CO₂e would require the sequestration capacity of 420,000 tree seedlings grown for 10 years, or about equal to the number of trees in 105 Hyde Parks in London.

The land footprints of training GPT-4 and GPT-5 are estimated at roughly 0.9 km² (126 football fields) and 1.5 km² (210 football fields), respectively.

A typical ChatGPT-style text query is about 200 times more energy-intensive than text classification (such as spam filtering).

Video generation represents the most energy-intensive frontier with high-resolution long clips on large models drawing more than 415 Wh per clip, drawing as much electricity as 200,000 spam classifications.

The water footprint associated with training GPT-4 was about 600 million liters, enough to meet the minimum annual domestic water needs of 81,000 people in Sub-Saharan Africa, or to fill 237 Olympic-sized pools.

The land footprint of 2025 data centers’ electricity demand was 6,900 km², nearly 4.5 times the size of Greater London.

My complaint is that each of these leaves the reader less capable of comparing AI to other parts of their everyday life. None of these leaves someone with any way of understanding how AI compares to other industries, how it fits into the environmental picture it actually operates in. Each number is selected to be maximally alarming, and no industry would look acceptable if the authors applied the same comparisons. I don’t know why it would ever be useful to compare a chatbot to a spam filter. If my friend drove a few miles to film a video, I wouldn’t say “You know, for the energy you used to drive over to film that video, you could have filtered 1.5 million spam emails.” They’d give me a funny look. Household water use is a small fraction of anyone’s total water footprint, so any industry that uses meaningful amounts of water will always use as much as tens of thousands of households do, whether those households are in Sub-Saharan Africa or anywhere else. A back-of-the-envelope calculation suggests the water we use irrigating Christmas trees in America matches the household water use of 1.5 million people in Sub-Saharan Africa. Does this tell us anything about Christmas trees? Does it give us any useful normative information at all? The whole intro to the report is full of comparisons like this, I’d recommend reading it yourself.

Underlying wild comparisons like these is a philosophy I think is fundamentally illiberal and increasingly mainstream in environmental thinking. At the extreme end, some environmentalists argue explicitly that political liberalism is itself responsible for the climate crisis, a claim that usually relies on heterodox economic theories. I don’t want to get into that right now. I’ll instead assume that whether you’re a capitalist or a socialist, you’re sympathetic to the broad liberal goal of building societal structures that let people with radically different values and religions live and prosper together. It’s this general liberalism that I worry some environmentalism is being used to attack, and I’ll argue here for the opposite: approaching environmentalism through a broadly liberal lens.

This is the core of illiberal environmentalism as I see it: We the good people broadly know what is good and what is bad, how scarce resources should be spent to maximize people’s wellbeing, and which contentious ethical values are right and wrong. Because we’re in an environmental crisis, our main goal should be to make sure no resources are spent on the bad things, since any environmental cost paid for something bad is by definition pure waste, which we just can’t afford right now. We have no responsibility to mediate the complex, contradictory desires of the billions of people alive right now, or to think about how to trade off their desires for radically different goods and ways of living. Instead, the goal is to attack the bad guys, the people spending scarce resources on things we already consider bad for other reasons. All good people know what’s bad, or will once they’re awakened from the lies of the bad people.

To get across why environmental illiberalism is bad, I like to imagine a group of Catholic environmentalists who get together to figure out the most promising ways to help the environment. After a few days of research, they publish a report claiming to have found a powerful new way to help the climate: shutting down the places of worship of false religions. For the Catholics, every pound of CO2 or drop of water used to operate a Protestant church or a mosque or a synagogue or a Buddhist temple is wasted on something that’s actively sending people to hell and making society worse in the meantime. The value of all these rival places of worship is at best zero, and likely extremely negative. So there’s a huge opportunity to cut enormous amounts of CO2 and water use “without harming anyone,” while in fact making the practitioners of the false religions much better off by guiding them to the light of Catholicism.

If you sit down and try to explain why the Catholic environmentalists are wrong, the rules you come up with will either be some version of liberal environmentalism, or just a claim that your own religious beliefs are the true ones and should run society instead. After 20 years in the internet atheist trenches I don’t have much hope for converting the world to Quinean Naturalism, so my next best move is to participate in the subtle ethical and social dances we do to let each other pursue rival religious beliefs. My answer to the Catholic environmentalists is that their plan breaks a background social contract: we don’t use technocratic questions about society-level collective action problems as cudgels for imposing controversial religious beliefs on each other. If they break the rule, everyone else might defect too, and over time people will become less free to pursue their visions of religious truth, which harms Catholicism in the long run. This doesn’t require some deep shared belief about what’s good in the world. It just requires acknowledging that society rests on a delicate balance of mutual tolerance, and that any ethic we use to change society has to respect that balance, or it falls apart and leaves Catholics (and everyone else) worse off and unfree. Our ability to solve climate change will also collapse, as claims about climate become mere vessels to express unrelated value disagreements, and our ability to act on climate is stalled until everyone comes to a final agreement on all other major ethical disputes.

I always start with religious freedom, because it’s an extreme case most people agree on. Religious freedom is important, and the rights of religious minorities shouldn’t be squashed under the guise of solving climate change. But whether someone uses AI is obviously far less fundamental to their freedom than which religion they’re allowed to practice, so I need to take a few more steps.

I want to expand from religion to say that climate should not be turned into a cudgel to push any controversial ethical beliefs that aren’t about climate change itself. Our rules for solving climate would ideally respect all contentious beliefs about ethical questions in other areas.

For example, I think that industrial animal agriculture is among the very worst things that human society has ever done. Animal lives on factory farms are often extremely bad, there are an unbelievable number of animals in factory farms, and the suffering of those animals I think ethically matters enormously. In my ideal world there would be a global ban on industrial animal agriculture, which obviously under some interpretations is deeply illiberal, though I think this can be justified if we consider animals themselves as subjects of political liberal societies.

Given that I think factory farming is such a catastrophe, how should that affect how I talk about its climate impacts? Any time someone wanted a chicken sandwich, I could point out that it has a climate cost, and that since the chicken suffered so much, all of that cost went to something truly horrible. On that logic, the sandwich’s climate impact is much worse than almost anything else in the person’s life. The enjoyment they get from driving isn’t offset by an animal’s torture, so it’s better to spend the emissions on the car than on the sandwich. Would this be a reasonable way of talking about the climate?

I don’t think it would. To arrive at that claim, I’m measuring how much CO2 is wasted according to the contentious value I assign to different products. It looks something like “Harm = CO2 emitted / (Goodness of thing),” where goodness is my own contentious ethical assessment. But lots of other people don’t agree with my assessment of the goodness or badness of a chicken sandwich. And since my claim about the sandwich’s environmental harm mostly hinges on whether it’s evil for other reasons (producing chicken emits only a moderate amount of CO2e), I’m really using climate to smuggle in a completely separate contentious value. This is a shifty way of breaking the liberal compromise.

I’m choosing meat partly to show that liberals themselves don’t have to be neutral about their own values. I think meat is a moral disaster. But to live among billions of people whose values I wouldn’t want to live under, I have to do the complex social dance and play the game of value neutrality when talking about other problems in society.

A third and final category that I’d like to remain untouched is rival tastes. I find specific things a lot of people like boring or dumb, they feel the same about things I like. I shouldn’t be able to use climate as a cudgel for my taste. While I didn’t like the recent Wicked movies, I don’t think the emissions they caused were “wasted” and wouldn’t argue to stop going to see Wicked for climate reasons.

How then should we make moral claims about the climate? Because climate change is a massive problem, and we can’t just live and let live. The answer is that our climate rules need to be compatible with different value systems. We can make general claims about how people should sacrifice some of what they want to reduce their emissions, without making contentious claims about the value of the specific things they want, or about which of the things they cut are good or bad. If I’m talking to a meat eater, it’s fair game to say “Hey, it has a moderately positive climate impact to swap chicken for tofu,” because that’s just a correct, neutral statement about which emits more. It’s illegitimate to say animal agriculture is a unique problem for the climate because the animals are suffering. Similarly, Catholics have every right to ask Protestants to put solar panels on their church property, but they don’t have a right to tell them to cut services to keep people from driving there in the first place, on the grounds that the services provide no value anyway. For an environmental liberal, the equation for the social badness of emissions looks more like “Harm = CO2 emitted / (how much the thing lets people pursue their own values, or how much someone personally values it).” A little clunky, but I think it’s a better way of thinking about climate change in a world with wild value differences. This is also why I’m such a fan of charts that break down where the emissions in people’s lives show up. To let different people pursue different things, instead of saying “You should not do x evil thing,” we should say “You and everyone else are obligated to cut your personal emissions down to this amount. How you do that and what you cut is up to you, but this is what we need to do to hit our climate goals.” The second statement lets people with radically different values coordinate on solving climate change. The first one doesn’t.

This brings me back to the UNU report, and why I think its way of talking about the environment is illiberal. The report’s move is to choose confusing, disorienting comparisons that vilify a new industry without giving readers any way to weigh it against the other things in their own lives. You’re left not knowing, for example, that the water cost of the non-AI American internet equals the household water use of about 900 million people in Sub-Saharan Africa, and that this pales next to things like American beef, which withdraws as much water as 2.2 billion hypothetical people in Sub-Saharan Africa. Basically all American industries look bad under this metric, and this gives you no reasonable way of choosing between them. What the report is doing is identifying AI as a bad guy for unrelated reasons, then piling on alarming statistics to reinforce its badness, without the full environmental context that would let you say “Oh, now I know how AI fits into my day. I can structure my choices around what’s valuable to me and what emits the most.” They’re making the value judgment for you, and leaving you without a way of choosing for yourself how to make the sacrifices necessary to reduce your emissions. It’s incapacitating the reader, leaving them with less ability to make informed decisions about what activities they’d like to keep while reducing their emissions.

This may sound uncharitable, or even a little conspiratorial, so I want to step back and clarify that I don’t think the authors consciously decided to be illiberal or to impose their values on their readers. What I think is happening is that a lot of people haven’t internalized the fact of reasonable pluralism: people have wild disagreements about what’s good and bad, and there is no secret Manichean split between good and bad views that all rational people would sort into if left free. In environmentalism, people are often taught, more via background social norms than anything explicit, that the world contains good things all decent people agree on, and bad things only power-seeking evil people enjoy. People rarely even discuss the idea that environmentalism should leave value questions untouched and instead pull levers that cut everyone’s environmental harm while maximizing their ability to pursue their own goods. Instead they learn that there are bad things in society, like AI, and that because they’re bad they’re inherently wasteful. The specific amounts of emissions and water become decorative statistics, a way of articulating badness. They don’t give people who like these things the context to see how much they actually contribute to harm, and therefore to decide whether to keep using them and trade that off against cutting other things. Thus environmental language that uses large, contextless numbers to confuse readers seems subconsciously designed to control the behavior of other people based on unrelated value judgments.

Further, I think that a lot of people enter into social games with each other, where they’re mutually rewarded for attacking bad guys and for supporting good guys. This is deeply natural and human. Liberalism is an unnatural social practice that needs to be relearned and reinforced in comparison. But it leaves people with a strong reason to sneer at demands for putting very specific numbers on things, on comparing problems concretely to each other, and for giving listeners the information required to decide for themselves what values they want to pursue. For a lot of people, this sounds a lot like a naive blind trust that people will all come to the good values, or that we don’t need to attack our mutual bad guy and instead need to let people decide for themselves whether the obvious bad guys are in fact bad. This closes people off to making specific comparisons between industries and environmental impacts, and leaves them suspicious of people like me trying to actually make those full comparisons available. Thus, my own writing is often met less with “You’re getting the numbers themselves wrong” and more “Oh, you didn’t get the memo, these comparisons don’t matter because AI is a bad guy, why are you trying to defang our criticism anyway?” A few months ago I was giving a talk and got a question that basically amounted to “What nefarious effective altruist reasons do you have for making us okay with AI?” The person asking didn’t seem to have any disagreement with my specific numbers themselves, instead the question was more “Why are you disempowering us from attacking a bad guy?” My counter is that I’m empowering everyday people to just see where their environmental impacts actually are and how they can cut them the most. If AI’s a big part of them, or if they don’t like AI for other reasons, it becomes a more promising thing to cut. But I think the person asking the question hadn’t fully considered this idea that I might just not be an agent of the evil team and instead just get frustrated when people are incapacitated in their decision making about the environment by strange presentations of very simple numbers. I don’t think they or the authors of the UNU report received some top-down directive to promote illiberalism, instead they’re just succumbing to the natural human tendency toward Manichaeism over pluralism.

Liberal environmentalism may sound wishy-washy, or too lenient on bad guys, but it’s actually much more useful for solving the climate crisis than illiberalism, because a core problem with the crisis is that most of the things that cause emissions are incredibly useful to us. They’re not clear villains all good people want to destroy. If you look at a breakdown of exactly where global emissions come from, the most striking thing is how obviously useful almost all of it is.

Liberal environmentalism also has more respect for the problem of climate change itself, because it’s more explicitly opposed to using climate as just a tool to win unrelated fights. How many times have you heard someone use the language of a climate emergency, and then instead of talking about the most promising ways of cutting CO2, pivot to their personal political enemies or unrelated problems in the world that happen to have some climate cost?

This also explains why liberals are often more excited about carbon taxes than illiberals. Carbon taxes neutrally price carbon regardless of what’s emitting it. This is in many ways a much more powerful method for changing people’s behavior around climate than targeting specific industries. It reaches into literally everything we do and modifies our behavior as much as prices do normally (a lot), while conveniently leaving the social value of what those emissions were spent on open to interpretation. If gas becomes a dollar more expensive, it’s equally more expensive to drive to either a church or a mosque or a UU service.

My simple application of liberal environmentalism to AI has been: “A lot of people want to use AI, and a lot of other people hate it. The people who hate it shouldn’t use it, and the people who love it should be allowed to make it part of their emissions budget, as long as they still make their overall cuts and AI doesn’t push them over the per-person threshold.” For chatbots specifically, pushing people over that threshold is nearly impossible, since their emissions are so low.

There are some criticisms of AI that I think completely respect the rules of liberal environmentalism, such as “AI uses more energy than the alternative to get the same quality stuff. Since you can get exactly what you want using fewer resources, you’re obligated not to use AI.” This is a legitimate form of criticism: it asks people to pursue the same values with fewer resources. The problem is that I think the background factual premise is just false. For almost any AI tool, running my laptop for as long as it would take me to produce the same thing by hand uses way more energy than just having the AI make it. The average AI image, for example, uses roughly 0.3-1 Wh, about 20 to 70 seconds of a laptop drawing 50 W. If you want a royalty-free image of exactly the thing you’d otherwise generate, you have roughly a minute to find or make one before the search itself uses more energy than the AI would have. If, like me, you want anime images of the Montague Bookmill, this might be hard.

People in the AI debate will often say we don’t “need” AI. Requiring people to use emissions on only what we “need” and not what we “want” pushes the conversation into a place where solving climate requires that we commit to contentious ethical claims about what’s necessary in life, again what looks like an illiberal move. In wealthy countries like America, most of what we spend our time pursuing are really “wants” rather than needs. Anything above rice and beans, a roof over your head, water to drink, and basic healthcare, is a contentious “want” for living your life in a specific way. But the people who say we don’t “need” AI don’t usually mean we need to reduce our lives to this bare bones minimum. Any move that says that climate requires us to cut our lives down to only what we “need” seems more like a way of reifying the author’s personal wants as needs. Thus again using climate more as a cudgel to smuggle contentious values. Make a list of all the things you spent your time doing today. How many of them are “needs”? I don’t think people “need” to see Wicked, but that doesn’t mean I get to use the climate to say the movie shouldn’t be released.

Illiberalism shows up in plenty of other areas of life. It’s the human default. I worry that when people lurch away from a full numerate picture of the world, it’s less that they don’t understand the numbers and more that their minds are already made up: there are specific bad guys to destroy, and statistics are only useful as weapons against them. Anyone giving other people ways of comparing and contrasting the full situation is suspicious, they’re providing avenues to be okay with the bad thing. On the environment at least, our ethics should let people with radically different beliefs and tastes live together. Enough people get real value from AI products that the decorative statistics critics hurl at them look to me like unjustified attempts to override a difference in taste among reasonable people. We owe each other the full numerate picture of what’s actually happening in the world, mainly because we shouldn’t deceive people into adopting our own contentious value systems. Going forward, I’d like environmental spaces to refocus on the project of building a pluralist world without carbon emissions, and, along the way, to respect the subtle societal and ethical structures that maintain that pluralism.

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But AI images and music have turned out to be pretty emotionally resonant with me at a level I didn’t expect, for another reason. They let me curate very specific vibes that matter to me and would be hard to assemble anywhere else. Curation isn't creation, but it's a separate and (I think) equally valuable outlet. A way to get at intuitions that are hard to put into words but feel important to communicate.

My basic experience of creating an AI image is like finding a neat rock. I’m not responsible for the shape of the rock at all, but I might be interested in putting it into an arrangement with other rocks and showing people my specific taste in neat rocks. Maybe I get some points for knowing where to look for the rock, for knowing broadly what types of rocks appear where. Playing with an AI model is like searching its high dimensional vector space for neat rocks, with a taste for specific rocks I can only find in specific places.

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It’s also like making a music playlist. When I put a playlist together, I obviously know I didn’t create the songs themselves, but I’m hoping that by associating with them, and combining and contrasting them in specific ways, I can get across some general sense about the world I think is important. Typing a very specific style prompt into Suno and then playing around with the result feels pretty similar to creating a playlist. I’m venturing out into the vector space of the model to try to find extremely specific sounds I value a lot to add to it. I’ve had way more success here than I expected. The first 2 minutes of this collection of shoegaze songs Suno made for me is a very specific sound I value a lot and want to associate with, especially the big explosion at 50 seconds. It gets across a very specific inarticulable sense of openness to the world I had when I was a teenager. I feel like if I could just glue this sense together with specific images and smells and memories and vibes, people would really “get” me.

No AI music has reached the same level of emotional resonance for me as my favorite bands. I like the sound of the shoegaze songs I made a lot, but the best human-made albums in the genre are in another stratosphere, and I don’t expect AI to approach this quality soon. Suno’s already showing too many signs of being too optimized for more general mid taste. Still, comparative advantage exists. The best human artists remain in a completely separate league from basically all AI music for me, but I don’t always want to listen to the best stuff. Sometimes I want a very specific sound I haven’t heard before in a genre I like, and the best shoegaze album was so good it kind of singlehandedly destroyed the genre, so there’s less human made shoegaze to pick from than I’d like. AI gives me a way of reaching out into the vector space of all possible mid shoegaze to say “This… this is what I’m into, this ‘gets’ me” and the experience of compiling said mid shoegaze in one place is rewarding in the same way making a playlist is rewarding. For even less explored genres I love like dungeon synth, AI can fill the gaps I feel as I explore the very limited but great set of what humans have actually produced in the genre. None of this feels like I’m creating the music, but it does feel satisfying to put these things I’ve found together in specific ways, to know where to look to find things I like, and to use my past great experiences with music to try to express my taste with the new sounds I find.

Last year I posted a big collection of AI images I’d made that I liked. Again, I didn’t feel artistic responsibility for these images. I didn’t feel like my creativity had gone into them. And yet I did feel like I’d been creative in how I used them to express my taste, in the same way making a playlist can involve creativity, and the post resonated, where multiple readers reached out to talk about how our tastes were way more similar than they had expected. This was a fun experience of feeling a bit like discovering far off people secretly in the same tribe, who were moved in the same subtle ways by similar experiences. It was fun to find this together, and the way I found it was purely via what I consider curation, not creation, and that was really valuable on its own!

I really love finding ways other people have curated what they like and exploring them. The sense that other far away people have resonated with specific art or vibes or other media makes the world feel like it has incredible depth. The world can often feel lonely and flat. Maybe the most flat-feeling image I’ve seen is the famous Breezewood PA picture:

The polar opposite of this image for me is a good curated list of things specific people like, either on their personal sites or Goodreads or Letterboxd or music app. These always feel like the world has so much depth, so many points of surprising contact between what we experience alone. I’d made a long list on my personal site of media I like mainly to just imitate other bloggers who made dense explorable curated lists of media they love. I could spend hours poking through these.

The greatest curator on the internet is Piero Scaruffi, who over 30 years has reviewed basically all music on his website that looks basically identical to when he started it in the 90s. It’s my favorite example of curation elevated to a monumental complex achievement:

Scaruffi’s a cognitive scientist who’s focused on AI a lot and among other things also has what I think is the funniest subtitle of any book on AI:

I mutter “Life in the coming age of incredibly stupid machines” once a week, though more and more as cope.

Scaruffi seems to have reviewed most other culture as well, and also publishes long dense essays on things like the history of knowledge, and curates other lists like his favorite waterfalls.

I’d recommend poking around his website for a while, maybe starting with his reviews of his favorite albums.

Scaruffi has a short (and self serving) post on the “Meaning of Art”:

This flips the status of creation and curation: most creation is really just imitation, raw material for curators to build statements about the world.

This seems a little extreme, but consider how many albums you listen to at least in part to be the type of person who listens to them. How often when you listen to music do you feel somewhat ascended from your everyday life because you’re part of the secret community of people who listen to it, the people who know the canon of truly great music? How often do you feel like listening to specific new music will ensure you’re one of the elect?

The curation of rival canons of great music really exploded as the internet took off, and canons reacting to each other to distinguish themselves, and to rise above all others, got faster and faster. For example, if you were on the 4chan music board /mu/ in the early 2010s, you probably liked a lot of these albums:

I know I did! I worry that when I talked about shoegaze earlier, I added a link to Loveless partly to say “Of course I’m allowed to listen to AI shoegaze, because I know about and am deferential to the truly great shoegaze canon, I’m not some pleb.”

But the existence of these canons encouraged rival canons to pop up, where you could not only feel more “in the know” and ascended compared to most people, but even more in the know by people who thought they were in the know. Take this other list someone on 4chan made, and especially the note at the bottom.

Yerself is Steam is a great album, but in my opinion it’s clearly not nearly as good as Loveless, which the author probably considers /mu/core and “radio music.” This is basically a hostile act of curation, using the art in the way Scaruffi describes to say something about the values and ideology of the critic, separating the critic from rival canons. This becomes more and more far removed from the art itself and is instead part of a drawn out contest of curation.

Once you start paying attention to what curators are trying to do, and how often any specific art is more of a stepping stone to say “I am with the cool good authentic community of people, by listening to my curated canon you will ascend to them too” it’s hard to look away. So many Pitchfork reviews were much more about the type of people listening than the music itself. Look at their 10/10 review of Kid A, how much of it is referencing other key parts of the canon, or the interesting location of the reviewer. Or their 0/10 review of Bachman Turner Overdrive’s greatest hits, which very specifically says “If you like this music, you are not one of the elect”:

Those who still cling to BTO-- and you know who you are-- are usually dorky minivan types who think rock and roll is about having your friends over for beer and barbeque. Let me say on behalf of the world's young people that all you people truly suck. Even in your retro phase there's nothing remotely charming or rocking about you. Here's hoping that next T-Bone you eat leaves you choking on your own excess. Die! Die! Die!

Seems a little much…

This is all to say that curation on its own can be a monumental and impressive task, that it’s incredibly fraught and bound up with our self conception and rival systems of status, but also at its best can be a unique satisfying bridge between people, all separate from any act of creation of the art itself. The idea that AI radically expands the space of curation and opens up ways for individual people to be much more specific with the images and sounds they collect and display to the world as “what they’re about” is under discussed in the conversation about AI and art. Most AI art conversations I see online are still reduced to whether AI art “involves creativity.” For me it involves as much creativity as assembling a playlist. Whether that’s like artistic creativity seems less interesting to me than what it means about the future of how people share their taste with each other, and use it to either find or compete with each other. How much of the good feelings we get when we find others who like the same art is from authentic shared experience vs just the camaraderie of identifying other members of our ascended rival tribes is a pretty foundational question about human life I don’t have a clear answer to. I worry that a lot of people in the AI and art debate fail to appreciate how interesting curation is as a separate concept, and the heights of achievement and depths of emotional turmoil it can reach.

I’ll close with a few AI images I made recently:

Some Massachusetts triple decker art:

Nostalgic photographs:

Geodesic domes:

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I’ve noticed that in lots of conversations about current AI models and the general AI buildout, it’s becoming more and more popular to say that AI isn’t happening "in a democratic way. There are times when I strongly agree. I’m very worried about the ways AI could enable the concentration of power, or impose huge (including existential) risks that citizens never signed up for and would hit the brakes on if they could.

But there’s a specific way of arguing that AI isn’t “democratic” that’s gaining traction, and that I find ominous and antithetical to what makes a good democratic society. It’s usually dressed in the language of progressive politics, but underneath, I think it carries authoritarian, homogenizing and anti-democratic implications.

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Pluralist vs homogenizing democracy

There are two visions of democracy hiding behind our talk about it, one I think is good and the other I think is bad, and you can probably tell which is which by the labels I give them.

The goal of pluralist democracy is to give people with wildly different beliefs about the good life and what’s really valuable control and veto points over how they’re governed, without letting them steamroll each other into all living under one contentious ethical or religious vision of the world.

Most liberal democracies like the US manage to balance pluralism with majority rule. Despite being a majority Christian country, and that majority having voting power over who represents them, the US government is not able to impose Christianity on everyone else. People have a lot of democratic control over contentious issues of government, but they often can’t use this to steamroll other people into very specific visions of the good life. Christians are restrained by governing norms from forcing non-Christians from attending church.

The goal of homogenizing democracy is to discover the true, good ethical values and beliefs about the world, with the faith that the majority either publicly or secretly believes them, and then to structure all society around those true, good ethical beliefs, and push ideological minorities into going along.

Here’s a more ominous way to frame it: “There is an authentic community of rooted, powerless, good people, who know what’s good and right in life and agree on it. They’re up against unrooted powerful people who don’t know what’s good in life and want to override it for their own gain. The good people are in the majority. Democracy means discovering these good values all good people hold, and then representing and acting on it, steamrolling the bad powerful people into going along with these good values.”

I read this as ominous because it’s an especially useful cudgel for forcing people into your specific vision of ethics and the good life. If you invalidate another value system by saying it’s not what all the good, powerless people believe, this gives you more permission to steamroll the people who hold that value system in the name of resisting power. Many Christian nationalists will frame themselves as representing the good, simple, rooted majority beset by powerful cabals who don’t understand their values, and use this to argue that underneath these differences is only a raw struggle for power that the good people need to win. Thus to them, seizing the government to impose Christianity on religious minorities is actually a form of democracy. It’s simply letting the values of the good majority be represented in how the country is governed. Stuff like this is why I worry about accepting a narrative that lets homogenizing democracy sneak in unexamined. Democracy at its best weighs the pluralistic desires of radically different people. At its worst it excludes those desires from the debate entirely, on the grounds that anyone who disagrees with the majority is illegitimate and not worth making concessions for.

And it can creep in under the guise of noble-sounding rules, like “a town should have control over how its resources are used.” For example, consider the claim that a small town should have complete democratic control over what gets built there. This sounds nice in the abstract, but is actually a way for homogenizing democracy to slip in. Imagine what happens if 80% of the town is Catholic, and 10% is Muslim. The Muslim population gets together and says they would like to buy some of the town’s land to build a mosque. Will this mosque get built if it’s voted on by the town? Well, 80% of the town believes that the mosque is part of a false religion that sends people to hell. It seems unlikely that the mosque will get built. By giving citizens democratic control over everything that gets built in their town, we’ve allowed them to steamroll pluralist values and homogenize the area into the contentious beliefs of the majority of people who happen to live there. This is bad!

Some people imply that in a “truly” democratic society, all value disagreement would ultimately be dissolved through deliberation. Communities would come together and deliberate until they discovered the most virtuous or liberatory way to live. I think deliberation like this is a great model for specific decisions the government makes, but it shouldn’t be allowed to seep as far into everyday life, because taken to the extreme it implies everyone must eventually be made to agree on every contentious value.

So while I think that at its best, liberal democracy is the transcendent crowning achievement of human history, talk about democracy can also be used to justify authoritarian homogenizing impulses and needs to be carefully examined. When people invoke democracy, they’re usually smuggling in general rules about how society should be governed, and those rules’ political implications need to be drawn out. The specific model of democracy where the goal is always to discover the good values that need to be imposed on everyone, that all good people either publicly or secretly believe (or would believe if they could be led to the light), and that anyone who disagrees with is not worth considering in deliberation, is a recipe for destroying the delicate balance that allows people with wildly different beliefs to live together and change their values freely.

People in democratic societies have strong reasons to try to frame their opponents’ opinions as outside the sphere of legitimate democratic consideration. An effective way to do this is to frame their opponents as representing “the opinions of the powerful”meaning illegitimate, out-of-touch views trying to undermine the good people. This is often a way of trying to banish different views from the process of democratic deliberation. I’m worried that this framing comes up a lot in the AI debate, used by people who think they’re behaving as the defenders of democracy, but are often actually stifling authentic difficult democratic deliberation between radically different values.

Which visions of democratic AI are pluralist vs homogenizing?

I think AI poses a unique technological threat to pluralist democracy. Society currently exists in a delicate, kind of miraculous balance where most people are able to add a lot of economic value, and have lots of ways to defect from important systems if their political power is threatened. We are deeply interdependent. AI could, under some models, erode our economic interdependence and leave powerful people with less incentive to respect the will of everyday voters. It’s very possible that democracy is a lucky passing phase in human history, built around a specific economic regime that’s existed since the Industrial Revolution that might go away if we enter a new and different mode of economic production. See here for more.

AI also poses lots of other risks, either indirect risks that people will use it to create dangerous powerful new weapons, or more speculative direct risks of AI itself overriding what we want. Just like I think everyday people should have democratic control over whether their neighbors have access to nuclear weapons, I think people have a democratic right to expect the general flow of technological progress to not descend into a specific basin where society enters an incredibly unstable equilibrium of extremely powerful easily accessible weapons.

The dynamics of automation with AI also threaten to concentrate wealth and power in the labs themselves. OpenAI and Anthropic are now some of the fastest growing companies in history. Many of their members have become billionaires, and some local political fights are becoming proxy battles between them over rival visions of AI safety and governance policy. If you expect AI capabilities to continue to progress, this poses a danger of a unique form of corporate power consolidation we haven’t seen in America in a long time, if ever. This also threatens political pluralism, and people have a right to expect that their representatives respond to this new threat. How? I don’t really know, but I’m willing to entertain lots of different answers from libertarians or socialists or anyone in between. I worry our learned political impulses might be as useful in the new era of AI as Aristotle would have been in the Industrial Revolution.

Lots of concerns like this I think are legitimate threats to pluralist democracy, and people have the right to democratically demand forms of intervention that I wouldn’t normally consider for more normal technology. More broadly, a new era of intense automation will demand democratic deliberation between people with radically different values on the shape of the new world that they want to live in together.

I think AI will over the next ten years demand an “all hands on deck” response from political philosophers, economists, and everyday people, and authentic pluralist democratic deliberation needs to happen.

While I’ve been happy with some aspects of how the left’s thinking has evolved on this, there are specific strands of intensely anti-democratic thinking that seem to be attempting to take over the debate and define what democratic AI even means. Here I worry a lot that people are using the language of democracy to push for homogenization and authoritarianism.

A clear example comes up a lot in the data center buildout. A very common line among people claiming to represent democracy in the debate is to say “No one is asking for these! They only benefit billionaires!” I think if I were able to separate these people from the debate, take them out for some nice tea somewhere, have a long conversation with them about something else, and then politely ask “Just being real, do you think there are a decent number of everyday people who think about AI in a different way than you whose demand for more is incentivizing the data center buildout?” I think they would say yes, but that these people are mistaken. We could go on and have a nice conversation from there. I worry that the initial claim that “no one is asking for this!” is basically a way of pushing the vast numbers of people who are in fact asking for this out of the difficult process of deliberation that should happen with any new technology.

There are a lot of people who want to use AI. Chatbots are consistently the most downloaded apps. Half of all web developers polled say they use AI daily. About a quarter of Americans say they use chatbots daily.

More people say AI helps than hurts most things they use it for.

However, Americans also believe AI is more likely to make society worse.

So what do we do with this? How should we think about this from a democratic perspective?

If a quarter of people like a product, two quarters hate it, and one quarter is indifferent, should that product be allowed to exist?

Homogenizing democracy would say no. The plurality has spoken. People with different opinions from the plurality should be steamrolled.

This is obviously wrong, yet I see people imply basically this in lots of debates about data centers. People will often say that people using AI who like it are both a small minority, and so out of touch with what’s good and right that their AI use can’t be allowed to use any resources at all. It can’t be allowed a drop of water or an acre of land or to emit any CO2. When I argue that people who like chatbots need to have their own demands represented in the market and in democratic deliberation, I’m often met with a knowing sneer, like “Oh, this guy hasn’t gotten the memo. These people using AI aren’t really ‘the people.’ They’re alien outsiders and don’t get access to the sphere of deliberation. Real democracy means making sure these people are completely disempowered and are never able to use any community’s resources.” To anyone who actually thinks about political philosophy at all, this is a bizarre and sad mangling of the democratic impulse. It’s an intense assertion of anti-democratic power for one specific faction in the debate. For any other unpopular product, we’d recognize how ridiculous this is.

One way to steamroll behavior you don’t like is to argue that it imposes lots of externalities on other people, so it’s actually an attack on their own freedom. Despite data centers not really imposing more externalities on locals than any other normal industry (from what I can tell), a huge media bubble has developed where wild stories are told about data centers forcing whole towns to evacuate, of infrasound ruining people’s lives, of water polluted and taps running dry, most of which evaporate the moment you poke at them. People seem to have a strong desire for these stories to be true. There was a recent New York Times opinion column on how the data center backlash may be a “winning issue for Democrats,” framed almost entirely around the sense of community people feel in anti-data center protests rather than any solid issues with them. I worry a lot of this is a sublimated authoritarian lurch to control the behavior of people doing something you don’t like. Stories about data centers being bad feels good because it hands you a cudgel you can use on people you think are out-of-touch losers. There’s a lot happening here, but I don’t see this as a good sign for authentic democratic deliberation about the future of AI. It seems much more like an impulse to homogenize everyone into a specific way of thinking about AI that they wouldn’t assent to on their own.

This also shows up in very confident claims that AI cannot, by definition, ever produce anything of value at all. At some point this looks a lot more like ignoring the vast numbers of intelligent people saying they’re getting something of value by using it. Critics don’t have to agree with them, but the confident metaphysical claims about the inherent value of AI output start to look more like a lurch to stifle debate by casting people who disagree out beyond the realm of legitimate conversation.

Separate from this debate, another instance of the homogenizing impulse is the idea that individual models themselves should be made to more democratically represent the opinions of the majority. There’s a lot of talk about “for whom” chatbots are designed, implying that they have lurking biases that would be fixed by making them more democratic. I don’t doubt that chatbots (like anything) carry lots of blindspots and misunderstandings of the experience of people very different from the people who designed them and who wrote the data they trained on, but I don’t at all think democracy specifically will make this problem better, and this implication that private products should all represent the democratic will of the good people I think is another way of smuggling in the homogenizing impulse.

Current AI models would become way more conservative if their values were voted on

In my 20’s I went through a huge Civil War phase. Anyone who does the deep dive comes away kind of shocked at the state of public discourse about the Confederacy. While public understanding has improved, I think a lot of people still really don’t know just how bad slavery was or how central of a motivation it was for the South in the war. This is reflected in public polling. More Americans have a positive than negative opinion of Jefferson Davis, for example:

Maybe this is an artifact of most people just not knowing who he was, or confusing him with Thomas Jefferson. Regardless, this is a pretty clear case where I’m very happy that the main AI models deviate from the winning view of Jefferson Davis among the public.

Claude:

ChatGPT:

Gemini:

Maybe someone could program an AI model that more accurately represents the views of the median American. This AI would be unsure about evolution, and in general much more conservative across the board than the average person demanding that AI’s democratically represent the beliefs everyday people hold.

Someone who holds a simplistic, homogenizing view of democracy often needs to invent a story where everyday people all secretly agree with them. If you imagine that the good rooted majority of people all know your values are correct, this gives you grounds to steamroll the few out of touch people who disagree with you. Because they’re out of touch, their divergent views have no claim to political respect or protection.

But the views of everyday people are complicated and often way out of line with progressive visions of the world. There are many values I share with my fellow coastal elite city dwellers that I’m happy to see reflected in AI models. These values show up less outside of cities, and even less in other countries. They are often not the views of either the American or global majority.

I think a lot about this exchange from this 80,000 Hours interview with Mustafa Suleyman, the co-founder of DeepMind:

Rob Wiblin: Yeah. While you were involved with DeepMind and Google, you tried to get a broader range of people involved in decision making on AI, at least inasmuch as it affected broader society. But in the book you describe how those efforts more or less came to naught. How high a priority is solving that problem relative to the other challenges that you talk about in the book?

Mustafa Suleyman: It’s a good question. I honestly spent a huge amount of my time over the 10 years that I was at DeepMind trying to put more external oversight as a core function of governance in the way that we build these technologies. And it was a pretty painful exercise. Naturally, power doesn’t want that. And although I think Google is sort of well-intentioned, it still functions as a kind of traditional bureaucracy.

Unfortunately, when we set up the Google ethics board, it was really in a climate when cancel culture was at its absolute peak. And our view was that we would basically have these nine independent members that, although they didn’t have legal powers to block a technology or to investigate beyond their scope, and they were dependent on what we, as Google DeepMind, showed them, it still was a significant step to providing external oversight on sensitive technologies that we were developing.

But I think some people on Twitter and elsewhere felt that because we had appointed a conservative, the president of the Heritage Foundation, and she had made some transphobic and homophobic remarks in the past, quite serious ones, that meant that she should be cancelled, and she should be withdrawn from the board. And so within a few days of announcing it, people started campaigning on university campuses to force other people to step down from the board, because their presence on the board was complicit and implied that they condoned her views and stuff like this.

And I just think that was a complete travesty, and really upsetting because we’d spent two years trying to get this board going, and it was a first step towards real outside scrutiny over very sensitive technologies that were being developed. And unfortunately, it all ended within a week, as three members of the nine stood down, and then eventually she stood down, and then we lost half the board in a week and it was just completely untenable. And then the company turned around and were like, “Why are we messing around with this? This is a waste of time.”

Rob Wiblin: “What a pain in the butt.”

Mustafa Suleyman: “Why would we bother? What a pain in the ass.”

Rob Wiblin: It was a very striking story to me, reading that in the book. People complain, I think correctly, that decisions of enormous global importance, historical importance, are potentially going to be made inside these AI labs — and the kinds of people who work at these labs are a very small fraction of the people in the world in terms of their political views, the values that they have, the things that they’ve studied, the kind of information that they happen to know. So it would be good if we could get a wider cross-section of the human population involved in scrutinising or having some input on these.

But to share power with the general public in the Global South, or even just outside big cities in the US or UK, will inevitably involve giving influence to people with views that I imagine are very offensive to Google staff — probably more offensive than Kay Coles James, who had to resign from the board for having more conservative, traditional views on gender. So based on that experience, it just seems like it’s very unlikely to happen, when we might just be flat out trying to get acceptance for having more input from a broader cross-section of educated people in the UK into Google. Like, that’s going to be the most that people will tolerate.

Mustafa Suleyman: Yeah, totally. This is part of the problem, right? I mean, 40% of people in the US believe that trans rights are moving too quickly; 30% believe that abortion should be made illegal; 30-odd% are against gay marriage.

Rob Wiblin: And then think globally, right?

Mustafa Suleyman: Right, exactly, then think globally. So I think we have to just learn to sit down with people who we fundamentally disagree with. That goes for China and the Taliban and all these people who hold these views — because if we can’t do that, then we’ve really got no chance of actually hearing one another out and changing one another’s views and making progress. I think in the last two or three years, I feel like we’ve really taken a few backward steps in that direction, and it’s super problematic because it just demonizes the other and then we just end up hating on one another.

And it’s very frustrating for us, because we put in a huge amount of effort to make that happen. Before that, when we were acquired, we made it a condition of the acquisition that we have an ethics and safety board. That in itself was a first step towards this kind of broader public effort. Then after the ethics and safety board, we actually tried to spin DeepMind out as a global interest company: one that was legally governed by the requirement to consider all of the stakeholders when making decisions. So it was a company limited by guarantee. And then the charter definition had an ethics and safety mission for AGI development; we actually had the ability to spend vast amounts of our income on scientific and social mission.

So it was a really creative and experimental structure. But when Alphabet saw what happened with that board, they basically just got cold feet. That was the bottom line. They saw what happened there and they were just like, “This is totally nuts. The same thing’s going to happen for your global interest company. Why do that?” And then eventually we pulled DeepMind into Google, and in a way DeepMind was never independent — and isn’t independent now; obviously now it’s completely part of Google.

Rob Wiblin: Yeah. The core of trying to address representativeness is that you will be ceding power to people who don’t share your values, and if people are not willing to make that compromise, then it’s not going to happen.

I’ll just flat out say that I have zero interest in sitting with what the Taliban has to say about what values current AI models should reflect. The Taliban can just not use the big chatbots, or build their own more in line with their values. I don’t feel any sense of democratic injustice at all that their beliefs are not influencing the behavior of a private product. The only worlds where the opinions of the Taliban become relevant at all for AI are the really wild speculative situations where AI comes to determine basically everything that happens on Earth. I do see some risk of that happening, but few of the people worried that AI doesn’t represent everyone on Earth equally seem to share this particular worry. If AI rises to run all society, I will circle back with the Taliban.

I’m sure the AI models get plenty of important facts wrong about the lives and experiences of people in very different situations from their designers. But the solution here doesn’t at all look like making AI more representative of the average person in the world, because the average person also carries horrible biases against lots of other people. One way to fix this could maybe be selecting specific expert representatives of different under-represented communities or ways of living and having those experts impose specific ways of responding on the AI. Maybe different worldviews could make their own LLMs, each representing those specific views. But I worry that making these private products “more democratic and representative of everyday people” when they already hold more liberal views than most people do would mostly just lead to more wrong or hateful models.

Lurking under this idea that AI models themselves should be “more democratic” is sometimes the idea that all products in society should reflect the correct opinions of the good majority, and never reflect the opinions of the bad minority. It’s again a form of homogenization. I think a better solution here would be for radically different ideological communities to each have powerful AI models of their own, none claiming to represent the democratic will of all good people, something impossible in a complex pluralist world. But I also am just not especially bothered that AI models don’t reflect a lot of the very conservative values of everyday people that I think are just incorrect. I’m not eager for CreationistGPT.

Conclusion

I think authentic democratic deliberation about AI is going to be deathly important over the next ten years, as AI threatens to concentrate economic power and unleash new risks on the world that we’re not ready for. I don’t want this debate to lead to anyone using the banner of democracy to override actual democratic deliberation. We need to acknowledge that there’s already both a lot of demand for and a lot of hatred of AI, and welcome both as full participants in the debate about the future. We should be wary of anyone declaring that barring huge numbers of people from the debate is a form of “democratic resistance to AI.” This threatens to mostly burn the commons of the debate and alienate people from more subtle ways of resisting and democratically governing the unique risks AI poses.

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

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

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

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

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

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

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An example

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

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

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

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

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

• Reduce regional NOx air pollution by 4%.8

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

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

Carbon

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

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

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

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

Water

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

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

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

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

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

Land

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

Electricity bills

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

NOx air pollution

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

Adding them up

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

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

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

Other ways this helps

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Early environmental critics of AI

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

Right?

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

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

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

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

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

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

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

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

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

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

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

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

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

• MIT Tech Review

• Cory Doctorow in Boing Boing

• The Columbia Climate School

• The Guardian

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

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

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

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

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

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

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

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

4. Green AI

5. Tackling Climate Change with Machine Learning

6. Quantifying the Carbon Emissions of Machine Learning

7. Sustainable AI: Environmental Implications, Challenges and Opportunities

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

9. Carbon Emissions and Large Neural Network Training

10. Estimation of energy consumption in machine learning

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

12. Green Algorithms: Quantifying the Carbon Footprint of Computation

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

14. Aligning artificial intelligence with climate change mitigation

15. The carbon impact of artificial intelligence

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

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

18. Carbon Aware Computing for Datacenters

19. Anatomy of an AI System

20. Measuring the Carbon Intensity of AI in Cloud Instances

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

Stochastic Parrots ends its section on the environment like this:

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

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

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

Deep history

In 1999, computers were using more and more energy.

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

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

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

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

The left and right become more anti-tech

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The decline of science journalism

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

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

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

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

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

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

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

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

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

Conclusion

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

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

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

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

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

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

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

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

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

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

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

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Contents

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

• Particles that make PM2.5 and ozone

• Harms from short-term exposure

• Anthropogenic vs natural air pollution

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

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

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

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

• Comparisons between different pollution sources are often counter-intuitive

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

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

• Quantifying the public health cost of air pollution

• How air pollution is governed in the US

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

• What does the future look like?

• Takeaways

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

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

PM2.5

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

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

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

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

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

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

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

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

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

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

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

Ozone

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

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

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

Direct carcinogens

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

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

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

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

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

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

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

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

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

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

Particles that make PM2.5 and ozone

NOx

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

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

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

There are six key things to know about NOx.

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

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

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

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

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

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

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

SO2

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

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

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

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

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

Harms from short-term exposure

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

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

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

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

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

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

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

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

Anthropogenic vs natural air pollution

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

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

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

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

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

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

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

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

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

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

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

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

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

US air has in general become significantly cleaner.

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

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

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

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

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

Why is California such an outlier?

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

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

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

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

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

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

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

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

How air pollution compares in deaths and DALYs to other problems

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Comparisons between different pollution sources are often counter-intuitive

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

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

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

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

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

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

Which would be worse for your health?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

PM2.5

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

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

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

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

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

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

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

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

Ozone

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

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

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

Quantifying the public health cost of air pollution

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

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

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

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

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

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

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

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

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

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

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

Surface area and population

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

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

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

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

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

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

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

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

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

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

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

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

How air pollution is governed in the US

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

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

• Something to control general ambient air quality.

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

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

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

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

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

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

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

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

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

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

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

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

Inequity in exposure to air pollution, and failures of governance

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

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

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

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

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

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

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

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

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

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

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

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

• Highways were deliberately built through them.

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

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

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

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

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

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

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

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

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

What does the future look like?

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

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

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

Though NOx intensity has also declined a lot.

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

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

PM2.5

Agriculture

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

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

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

Industrial

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

Transportation

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

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

Residential

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

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

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

Electricity

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

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

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

NOx

Highway and off-highway vehicles

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

Stationary fuel combustion

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

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

Industrial processes

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

Other

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

Climate change

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

Takeaways

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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