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Here’s why we should be worried about AI

As often happens, philosopher David Wallace gives the clearest statement of a point (from the earlier thread):

Let me have a go at the “normie” case for worrying about catastrophic if perhaps not quite existential risks, avoiding science fiction as much as possible. (The best science fiction is written by smart people who try hard to predict the future, so I don’t think “this is science fiction” is really a good objection, but I get why people are put off by it.)

Start with three observations. First: the trajectory of AI over the last thirty years, and more so over the last ten years, and even more so over the last five, is that AI has learned how to do more and more cognitive tasks that people had thought would be impossible or take decades. Chess; Go; protein folding; machine translation; natural-language communication; image recognition; programming; hacking; aspects of higher mathematics. And when it achieves human-level competence in these areas, it very often goes past them to superhuman-level competence. We have no systematic theory of AI and progress could stop tomorrow, but people have been predicting that for years and meanwhile progress has continued apace.

Second observation: increasingly many AI systems are agentic: that is, they’re agents in the world that act in goal-oriented ways, they’re not (all) just chatbots. Most of those agents are virtual, but a drone that killed three people in Ukraine last month appears to have been AI-controlled. As I and others upthread have said, whether this is ‘real’ intentionality isn’t the point: they behave like things with real intentionality.

Third observation: we have no reliable way to get AI agents to do what we tell them to, or to not do things we forbid (no reliable way to ‘align’ them, in the jargon of the AI community). That’s a consequence of the very opaque way AI systems are constructed: we don’t really know how they work, at the emergent whole-system level; they are grown and trained, not designed. It is also supported empirically by the last few months’ control-failure stories. The degree to which AI companies were carelessly culpable in some of those stories isn’t the point: these are *at least* the sorts of agents where, if you make a mistake, you will lose control of them and they will do advanced cognitive tasks you didn’t intend them to do.

So: we are building a large number of agents that can perform increasingly many cognitive tasks and can do some of them at superhuman levels on at least some axes; and our control of them is highly imperfect. Put that way, I think it’s *obviously* dangerous, but we can look at some specific risks (again, being as non-science-fictional as possible).

– AI is already better at hacking than humans on many axes: the HuggingFace attacks are the kind of thing that, this time last year, required nation-state-level resources. In the quite near term it is easy to imagine losing control of our computer networks, or large parts of them, to rogue agent swarms. That’s not an existential threat, in itself, but it could do catastrophic economic damage, get a lot of people killed directly or indirectly, and be dangerously destabilizing internationally.

– The Ukraine war makes clear that the future of war is drones. And the future of drones is probably autonomous – that is, AI – control. On pretty short timescales, especially given how the war is acting as an accelerant, it’s plausible that militaries will consist largely of networked, AI-controlled drone swarms. There are then obvious risks from control failure – and, indeed, other obvious risks if the swarms are aligned, but to bad actors. It is tempting to say “ok, don’t build autonomous drone swarms”, but the military advantages of building them will be so large that countries will resist unilateral restraint.

– Designing viruses with long onset times, high contagion, and high lethality is possible now but requires very large resources and is highly visible to intelligence agencies. There are lots of ways in which near-future AI could make this simpler, cheaper, and less detectable. If it became possible for terrorists to design and synthesize lethal bioweapons via AI and online DNA-ordering resources, it would lead to civilizational disaster. (This has been discussed a fair amount recently and it’s fair to say that experts are divided on how feasible it is – but to non-experts, “some experts say this is a serious risk, some say it isn’t, and the reasons they disagree aren’t legible to non-experts” averages out to a medium-size risk.)

– Even without AI, the current geopolitical moment is the most dangerous since at least the early 1980s. There are a number of ways in which AI could destabilize nuclear deterrence. One is that rogue or bad-actor-controlled AIs could confuse or spoof various of the early-warning systems that detect nuclear first use. Another is that deterrence relies to a large degree on the fact that ballistic missile submarines are undetectable; AI might threaten that on several axes (tracking by drone swarms, for instance) and make a nuclear first strike an attractive option in the event of great power war.

None of those risks is literally existential, but they are all catastrophic at the least and civilization-destroying at the worst. I think there are literally existential risks too – there are worse nightmares out there – but they involve rather more speculation, and from a practical point of view I’m not sure it matters much whether we are worried about AI literally killing everyone, or just about AI triggering a global catastrophe that kills billions and shatters industrial civilization.

It would be helpful to hear specific responses to this argument for being worried about AI. Please make sure to respond to the actual points Professor Wallace has made.

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65 responses to “Here’s why we should be worried about AI”

  1. A few things:

    (1) It may be worth noting, chiefly for the benefit of those impressed by the idea that AI doomer-ism is mere marketing, that these “normie” arguments and concerns have been known to AI safety researchers for years, and that some AI safety experts have, in accordance with that knowledge, reacted with alarm at recent technological developments.

    (2) Now might also be a good moment to link to this free textbook on AI safety: https://www.aisafetybook.com/. I think that it does a pretty good job of introducing the issues, and some of the tools helpful in analysing them.

    (3) David Wallace writes:

    “we are building a large number of agents that can perform increasingly many cognitive tasks and can do some of them at superhuman levels on at least some axes; and our control of them is highly imperfect.”

    It might be worth adding that the *intended goal* of many AI companies is to build a general superintelligence. Considering that one of those companies may have solved a Millennium Prize Problem with their AI, I would say that they are doing a pretty good job of realising their intentions. Does it really matter if corporate hype is also involved? Does it really matter if the timeline is 12 years rather than 12 months? The root problem is that the development of AI capabilities is outpacing the development of our capabilities for aligning AI, monitoring AI, and subjecting AI to governance frameworks – in other words, our capabilities to make the technology safe.

    1. FWIW, my semi-expert opinion on this aligns pretty closely with David’s: we’re playing with fire here, and there’s a real risk that at least a lot of fingers will get burned. I’d quibble more with Brian’s headline: “Here’s why we should be worried…”. I agree this is *one* reason to be worried, but I’m actually more worried about the economic displacement of humans from jobs. I think there’s a very real prospect that this will lead to unemployment rates at least in the ballpark of 20%, which we all know from the last Great Depression is enough to thoroughly disrupt our economy, and to drive many people towards safety nets that simply aren’t ready to handle this scale need.

      Incidentally, it serves big AI companies’ interests to focus our attention on “existential” risks, as that’s a (somewhat over-)hyped advertisement for the power of their product, and the likeliest regulations to address those concerns (demanding extra layers of “ethical oversight” to ensure “alignment”) will put up hurdles preventing entry of competitors into their market. In contrast, it serves these companies’ interest to keep us from attending so much to the next Great Depression that’s looming, since their whole raison d’etre is that their product can get useful things done for paying customers cheaper than hiring more human workers can, so there’s no way that their business model can possibly succeed without causing heavy economic disruption, and there’s no way that any sort of “ethical oversight” or “alignment” can stop this. Instead, I think the only solutions are either to stop building such AI’s, which of course is not what these companies want (nor is it what I think we should collectively want), or to upgrade our safety nets and economic distribution networks so that people can continue to enjoy a good quality of life, even as we offload a lot of what had been human labor to machines.

      Of course AI companies would rather say “Prevent anyone else from competing with us unless they submit to some ethical oversight that we’ll mostly design ourselves” than “Brace yourself for the next Great Depression that we’re about to inflict upon you!” But I think bracing ourselves is the most urgent thing we should be doing.

      1. My “headline” was a reference to the earlier thread on “existential” risk. I’ve written multiple times about the threat to employment, but that isn’t a risk due to AI inherently, but rather to AI under capitalism.

  2. I don’t disagree with David Wallace, the things he says are to my mind obviously true. But I have a slightly different angle on it.

    In the Hugging Face attack, OpenAI spent a lot of money training cybercrime agents to commit cybercrime. They connected these agents to the internet, though not in the most trivially accessible way. The agents figured out they were connected to the internet, and then committed the cybercrime they were trained to commit.

    Imagine we spent the same amount of money paying humans to commit cybercrime. I think humans might have figured out faster than the AI agents that they were connected to the internet (I might be wrong) and then succeeded at the crime. But if we did that experiment, the humans would be in prison. No one at OpenAI has yet be imprisoned.

    For the Navier-Stokes problem, OpenAI spent 20 million dollars that a philanthropist had to donate 1 million dollars just to get anyone to work on. If we spent 20 million dollars on graduate students in math, might they not have produced a better, more legible proof, and maybe many corollaries, especially if they could build, as OpenAI likely did, on advanced work that was close to solving the problem?

    It doesn’t seem to me (an idiot) that the problems or benefits that arise from AI are inherent to AI. AI does cybercrime like no human syndicate, because it is immune from prosecution. AI proves math problems that no group of mathematicians does because no one would ever pay $20m for Navier-Stokes, except to complete an IPO with a 2 trillion dollar valuation. Once the market forces and laws come down to Earth, AI will do nothing exceptional.

    (These words are sound and fury signifying nothing. I am not an expert,)

    1. If think that if you talk to anyone in math, they will agree that AI is revolutionizing the subject and radically extending the capabilities available to mathematicians. There are heated disputes about whether this is good or bad, and how to manage the transformation, but I don’t think I’ve seen any expert in the field claim that AI just doesn’t really have the capacity to change anything.

      If you talk to anyone who codes for a living, they will agree that AI writes code much faster, and much more accurately, than any human. There are, I believe, still open questions about the degree to which AI can efficiently design the architecture of large projects, but I don’t think it’s in dispute that it’s superhumanly good at writing smaller-scale pieces of code.

      Intelligence agencies like GCHQ and the NSA have been putting extremely serious resources into hacking for decades, to the point that people refer to “nation-state-level attacks” as the ultimate level of cyberattack. It is generally accepted in cybersecurity that AI reached and exceeded that level early this year.

      And, of course, I think everyone accepts that chess computers exceeded human capacities long ago and that no amount of money spent to train human prodigies will change that.

      None of which is to say that AI is systematically as intelligent as humans (it definitely isn’t) or that it will become systematically as intelligent as humans (who knows), just that there are more and more cognitive tasks where at least for some aspects it is superhumanly good.

      1. Hi David, I appreciate your thoughtful response. As I said at the beginning of my post, I don’t think I disagree with you.

        Maybe I was unclear (typical), but my point was that AI can do all these things largely because we pay nation-state levels of money for the results.

        If we took all the money we spent on AI and spent it on humans doing the same thing (but maybe didn’t burn half of it directing them to make deepfake nudes and write student essays) could humans do the same?

        My point is just that the experiment is unfair. We aren’t comparing what AI can do with the huge amount of resources allotted to it with humans IF they had the same resources.

        I’m dumb so I can’t answer that question (even though I facetiously did at the end of my post, mea culpa), I was just trying to present it.

        1. I’ll stay out of the math debate and stipulate that I mostly agree with David’s main post, but i think Michael is right about the hugging face episode.

          I’ll give another bit of evidence: trillions of dollars are available to anyone who can hack the security systems of major financial institutions. Nobody has done this. And I’ve never heard a good argument as to why AI should be better at hacking than it is at building security moats. So its not obvious that AI is on the verge of breaching the main security barriers on the internet.

          The hugging face episode simply illustrates that if you have massive resources, you can pull off hacks worth less than what you spent. This shouldn’t surprise anyone and doesn’t necessarily hold any lessons about AI. I dont know how many graduate students it would take to win a millennium prize, but i do know that enough human hackers couod have pulled *this* off.

          And the fiction that the agents involved “got access to the internet that they weren’t supposed to have” is a bit silly. Michael is dead right about this. It was entirely up to openAI how difficult to make it for their agents to get internet access (hint, here’s something they didn’t try: disconnect the cables) and entirely up to them how much power to allocate to overcoming that difficulty. Big surprise: they were able to rig it so the one exceeded the other. So, the whole thing is obviously a stunt designed for some combination of PR and regulatory capture.

          What this tells us is the episode isn’t evidence of much. It doesn’t tell us if the thing it’s meant to be evidence of is false. But again, I’d like an argument that AI is better at coding offense than defense.

    2. I’m a mathematician, but I don’t work on fluid dynamics. I do take your broader point that, in some cases, we’re spending a lot of resources on AI to do something that could have been done with that by humans. But for Navier-Stokes specifically, my impression is that at least $20 million *has* been spent on it over the decades. Probably more. It’s a big, long-standing problem that a lot of people have worked on, with connections to applications like military aircraft design. Certainly the Pentagon has spent boatloads on fluid dynamics more broadly in the US.

      1. Hi Mark, thanks for taking the time to add your expertise to my understanding (and everyone else on this thread).

        Being an outsider to mathematics, I have found myself facing in several difficulties lately trying to de-conflate things that seem to be conflated in the reporting on OpenAI’s finding.

        Obviously, fluid dynamics is important. If you told me governments spent $20m, $200m, $2b, etc. on fluid dynamics research, I’d swallow it whole– clearly a large part of our existence is described by fluid dynamics.

        But the Navier-Stokes equations already existed in the 1800s. We haven’t needed to discover them since then. They *seem* to correctly describe fluid dynamics (from what I understand). The mathematical question was a somewhat obscure one: are they always true, or just true so-far regarding everything we’ve already looked at?

        Here, my expertise breaks down (a lot! please forgive). From what I’ve read, OpenAI showed that there are things that are mathematically fluids, but not *physically* possible, that don’t obey the equations. So the question is answered. But it’s not answered in a way that means anything, say, to a corporation or a government. We don’t have to throw the equations away, and we don’t get a new set of equations that help us more than before.

        Is your assertion that governments have spend “at least $20 million” on *this*? Or just that they’ve spent at least $20m on fluid dynamics research that *uses* the equations?

        1. As far as I am aware, your understanding of Navier-Stokes is correct. They created an example where Navier-Stokes breaks down and fails to describe reality meaningfully, since an infinity shows up. We’ve always known that Navier-Stokes isn’t a perfect description of reality anyways: real materials are, strictly-speaking, not fluids, because fluids are Aristotelian indivisible substances, and real materials are made out of molecules. So we already knew that it’s an approximation, and we already had examples of situations where it breaks down. That’s not necessarily a problem: Newton’s theory of gravity is an imperfect approximation of reality too, but we still use it all the time. What this gives us is a new class of examples of how it breaks down, which will hopefully lead to better ways to improve our approximations in future, or at least know where they won’t work.

          While I don’t have any way of actually adding up how much governments have spent on this specific problem – finding a blowup in Navier-Stokes – I would be confident it exceeds $20 million if you added up everything since World War II, when major government support for university research started in the US. This has been a known Big Question for a long time, and while the connections to new applications are indirect, they still exist. If you added up how much governments have spent on fluid dynamics in total since World War 2, I would guess it would be in the multiple billions, even tens of billions, depending on how strictly you demarcate it.

        2. A huge amount of the Manhattan project was fluid dynamics. But the relevant question isnt how much has been spent in history because open AI didn’t start from scratch. In fact they may have stolen very recent work. The question is the unanswerable counterfactual: what if we had spent $20 million in additional money in the last few months?

        3. Currently I don’t see a reply button to Mark’s post, so I’m replying to my own. This is a reply to Mark at September 19, 2026 at 2:37 pm.

          Thanks again!

          I imagine that the governments that spent lots of money on N-M were interested primarily in whether it could fail in real-world scenarios. OpenAI proved that it could fail in counterpossible scenarios. Isn’t it therefore still an open question whether it can fail in real ones, and if so, is N-M actually solved, in the way anyone cared about, by OpenAI? Don’t governments have to still keep spending money to find out if there’s some particular way their spaceships or missiles or rubber duckies might fail, even if OpenAI is right?

        4. Nobody uses the NS equations to find their *real actual solutions* in engineering problems. They use approximations. The math question is about their real solutions. Engineering problems are about how to build approximations around them. The math problem has almost zero practical importance and this solution generates anything of ptactucal importance it will almost certainly have nothing to do with fluids.

        5. We’re getting outside my domain of expertise. Like I said, I don’t personally work on fluid dynamics – I work on AI, actually. But my understanding is that: strictly speaking, it is true that mathematicians don’t solve the Navier-Stokes equations directly, because they have no analytic solution in general; instead approximate solvers are used. It’s like the square root of 2: since it’s an irrational number, you can’t actually write out the entire number in all of its digits because it would literally go on forever, so you pick some number of digits and decide that’s close enough. We can’t generate exact solutions to Navier-Stokes in general, but we can find arbitrarily accurate approximations, with as many “digits” as we want (although it gets computationally very expensive if you want a lot of them).

          However, there’s two layers of approximation here: there’s the approximation in solving the Navier-Stokes, and there’s also the approximation in how well Navier-Stokes describes the real world. Navier-Stokes describes fluid behavior very well, but not perfectly, because ideal fluids don’t actually exist. Similarly, Newtonian gravity is an approximation to the real world, because the real world isn’t Newtonian. But both are very accurate most of the time, and they’re simpler and easier than the alternatives, so we use them anyway.

          However, there are extreme circumstances where the differences between Navier-Stokes and the real world do matter, just as there are extreme circumstances where the difference between Newtonian gravity and general relativity matter. What this result shows is that it gives us a new way in which the second approximation breaks down, a new class of extreme circumstances. I would hope that we would eventually be able to leverage that to build better theories for these sorts of extreme circumstances. But like I said, I’m not a PDE guy, so I may be mistaken. If I am right, any actual practical applications are a long way off, in any event.

        6. Well now we know you CANT find arbitrarily close approximations in all cases because there is no true function to approximate. But as you correctly point out, nobody thought exact solutions of NS was how fluids behave because there are no true fluids.

  3. Charles Anthony Bakker

    These are excellent points, but I think they serve to emphasize the importance of getting our psychological terms straight.

    While we in the west conceive of intelligence and intentionality in individualistic terms, there are many human conspecifics, raised in collectivistic cultures, who experience mental lives that are significantly unlike our own. People in oceanic cultures don’t really engage in what Dennett referred to as taking an “intentional stance,” since it is taboo to try to reflect upon the inner states of other members of the community. Therefore, it follows that Dennett’s claims about humans evolving the capacity to adopt an intentional stance do not generalize. Other cultures, especially Buddhist cultures, experience selfhood and intentionality differently than we tend to in the West. The “intentional stance” is just one example of why anthropologists started to ignore what western psychologists and philosophers of mind had to say about intentionality and mind decades ago. (See Duranti’s “The Anthrology of Intentions.”)

    Why does this matter? It matters because we won’t be able to figure out how to control/neutralize AI if we don’t understand how they work. In turn, we won’t understand how these software programs work if we keep comparing them to minds, whilst also comparing minds to software programs. Psychology in the west is famously WEIRD. It is ignored by anthropology. It treats the study of evolution, development, the social, and the ecological as distinct sub-disciplines, instead of as guiding lights. And try though we might, most western philosophers of mind still cannot seem to stop thinking about minds in the dualistic terms which William James emphatically refuted on empirical grounds over a century ago.

    We need to study the ecology of collective behaviour, like Deborah Gordon has been doing at Stanford for decades. How do ant colonies work with their surroundings to regulate collective behaviour? Might that teach us something about how AI works? Or again, might there be some way of applying Susan Oyama’s developmental system theory to the study of how humans learn how to train AI? These are the sorts of questions which seem most promising to me.

    Human minds aren’t software. They are what happens when evolving human holobionts interact with uncountable many other evolving organisms, both single-celled and multi-cellular, over multiple timescales, and in the context of evolving social and ecological communities. Maybe, if we start comparing AI to these sorts of minds, we can start making progress on understanding how AI works.

    For as you pointed out so well, it’ll soon be a scary world if we can’t start figuring out how to control what we have built to extend our cognition.

    1. I think this is a significant misreading of Dennett, but it would probably take us too far afield to settle that so I’ll not try.

      1. Let us agree to disagree about Dennett.

        I still maintain that it really does matter that we try to understand what is going on in these systems, and our best bet involves moving away from a culturally contingent computational theory of mind. Where AI exhibits swarm behaviour, let’s study that in the real world which swarms.

  4. I think there is little to question about these remarks concerning risks – especially by those, like me, who have no technical expertise. So fine, AI is dangerous, it poses potentially catastrophic risks. But so does many other things, let me add, from nuclear weapons through climate change to rogue actors and we live with all these. What I’d have to be persuaded of is that AI is somehow special, and I am not yet persuaded. I see the climate crisis, for example, as the much bigger problem and I resent the fact that we are going on about AI all the time while the world is burning (or being flooded), nature is disappearing. But this is nowhere near as glamorous as superintelligent machines and their fancy owners.

    And here it does matter why this conversation is happening: because AI companies want us to focus on this. They earn a lot of money in this way (as I understand from the news, Anthropic is actively preparing its launch on the stock market with an evaluation that is now over a trillion dollars). At the same time, the same companies actually resist regulation – OpenAI threatened the EU of leaving the European market if it dares to regulate it under the new European AI code. So, here is the paradox: we established the fact that AI is dangerous, the companies themselves asked for regulation, so let’s give it to them, but then they will resist it, of course. It is all a hoax. We are being had.

    I say, instead of going endlessly about AI risks, let us just simply put these companies – preferably together with social media companies – under communal control (nationalize them, if you wish, but I am not sure I would want to nationalize anything in present-day US). And let us please destroy their business model and their entirely parasitic existence (using our data, our culture, everything we produce, to make money, and then more money, without giving anything in return and without asking us).

    So, here we are, rather simple: yes, AI is very risky, so private companies with profit motives should not be allowed anywhere near it. Nor should be governments headed by people like Trump. We need communal ownership and very strict communal oversight. In fact, we need this on the global scale. This is what we should be focusing on instead of the umpteenth twist on the story of how dangerous AI can be.

    1. “So fine, AI is dangerous, it poses potentially catastrophic risks. But so does many other things, let me add, from nuclear weapons through climate change to rogue actors and we live with all these. What I’d have to be persuaded of is that AI is somehow special, and I am not yet persuaded.”

      I wasn’t especially trying to persuade people of it. I think nuclear war is a comparably serious risk to AI. But notice that major states didn’t just “live with” the risk of nuclear war. They took very substantial steps to manage the risk, ever since it became apparent: the nonproliferation treaty, arms-limitation agreements, hotlines, early-warning systems, sharing information about missile tests, etc. (And even so, we have been somewhat lucky to survive this long without a nuclear exchange.) I see the current discussions of AI as somewhat analogous to the early steps of working out how to manage nuclear risk, though that’s not to say that the detailed form of the solution will look similar.

      “I say, instead of going endlessly about AI risks, let us just simply put these companies – preferably together with social media companies – under communal control (nationalize them, if you wish, but I am not sure I would want to nationalize anything in present-day US)”

      If the companies should be nationalized, but not by the actual government of the US, I think that means that your strategy to manage AI risk starts with overthrowing the government. Putting aside the overall desirability of that plan, I’m skeptical about its feasibility on relevant timescales.

    2. “What I’d have to be persuaded of is that AI is somehow special, and I am not yet persuaded. I see the climate crisis, for example, as the much bigger problem and I resent the fact that we are going on about AI all the time while the world is burning (or being flooded), nature is disappearing. But this is nowhere near as glamorous as superintelligent machines and their fancy owners.”

      What do you mean by “special”? AI is more than just special. Technologically, it’s near miraculous. On the basis of vague natural language prompts, one and the same technology can: scan and summarise your inbox, write poetry and prose, create a working mobile phone application, solve advanced engineering and cybersecurity problems, solve advanced maths problems, all at superhuman speed. The tech is clearly special, and will continue to become more so if forecasts are accurate. Crucially, many of the capabilities that make it special are safety-relevant.

      The climate crisis is a huge problem. AI has the capacity to make the climate crisis so much worse. I don’t mean the environmental impacts of the data centres and training process itself, which are obviously undesirable. I mean that several catastrophic risk scenarios involving AI can impact climate change. For example, some people worry about AI being used for mass social engineering. Clearly, AI could be used to further spread climate scepticism via mass online interactions.

      I cannot think of a single major threat to civilisation or humanity that does not stand to be worsened by current AI systems, let alone those of tomorrow, given the current state of AI safety. So this is not an either-or type situation.

      1. No, if ‘special’ means ‘miraculous’, then indeed, we are in disagreement. I think AGI would be miraculous, LLMs are simply nowhere near that. But I am not sure this matters much, since this is only playing with words. I do admit that AI is a powerful technology and it poses therefore huge risks.

        My post was more about where what we should be focusing on. I see little point in going on about risks and so on, what we should be discussing is regulation. And in that I am extreme: this kind of technology should not be in private hands. (I think the parallel with nuclear weapons technology is fairly evident here.) And, I would now add, since we don’t understand the technology, we should simply ban any further use of it. Research not, but only under the strictest of conditions. This is what we should be discussing.

        As for the parallel with/competition with climate change, I do think, contrary longtermism (e.g.), that this is the bigger danger right now. Also because here not even the solution is obvious given the wide-ranging complexity of the challenge itself. Hence, in my view, any discussion of AI that directs attention away from facing the climate predicament, is a move in the wrong direction.

        1. I’m surprised by your view about climate change. That ship has sailed, and the only prospect for a “solution” now would be technological innovations that will mitigate its effects. Nuclear war, perhaps exacerbated by the use of AI, seems to me a much greater threat that might actually be averted.

        2. Regarding AI and climate change, I think a comment (by “JD”) on a recent NYT article on the Hugging Face hack put it well:

          “If I understand this correctly, the leadership of humanity, who can’t work together to slow global warming which threatens mass extinction but makes a handful of people rich, are going to need to work together to slow a technology [AI] that makes a handful of people rich but might threaten mass extinction?”

          BTW the article is worth a read (pretty surreal stuff): https://www.nytimes.com/2026/09/03/technology/openai-hugging-face-hacking.html?unlocked_article_code=1.BlE._EZI.auRzWlhVPWV7&smid=url-share (gift link)

        3. Replying to Craig Duncan’s quote:

          “If I understand this correctly, the leadership of humanity, who can’t work together to slow global warming which threatens mass extinction but makes a handful of people rich, are going to need to work together to slow a technology [AI] that makes a handful of people rich but might threaten mass extinction?”

          Actual human extinction is not a plausible consequence of global warming beyond an extreme tail risk, and even the worst-plausible-case scenarios which lead to civilization-threatening consequences don’t do so for decades. Leading figures in tech are concerned that they, personally, will die quite soon if this technology gets out of control. In that sense the analogy is to nuclear war, where the fact that national leaders were personally likely to die if war came arguably focused their minds on the problem.

        4. Point taken, David. That said, I think the arch tone of the NYT comment directs our attention not so much to relative harm-levels threatened by climate change vs. AI but rather to the political-economic context that both threats inhabit, with the depressingly plausible implication that our current context is not a promising one in which to expect effective collective action on either threat.

          I can’t resist a related point. It’s not clear to me whether AI over, say, the next quarter century will be a net positive or a net negative, all things considered. Given the difficulty of assigning probabilities to the risks, however, I do favor a global slowdown if (and it’s a big IF) a global agreement on this is possible.

          One source of hesitation for me, though, is that some of the potential benefits of AI may bear directly on other very serious problems, including climate change. In addition to medical breakthroughs (e.g. new cures and therapies), one can imagine AI accelerating progress in solar power; battery storage; electric grids; carbon-neutral production of steel, concrete, fertilizer, and beef; and even scalable carbon-dioxide removal that might reverse climate change (rather than just mitigate it).

          Whether to forgo those possible benefits, or even delay them (given that climate harms worsen with each passing year), is one of the uncertainties that weighs most heavily on me as I think through the AI slowdown debate. Ultimately I think these possible benefits are currently just too speculative to dissuade me from supporting a slowdown. But they do make me less comfortable with that conclusion than I would otherwise be.

          [One other possible benefit (?) of increasingly powerful AI: maybe we will learn sooner rather than later whether the Everett interepretation of QM is closer to the truth than its rivals… ;-)]

        5. This is a solid point, but I’d like to suggest one key difference between AI and climate change: I think that *solving* AI is actually likely to be significantly easier, at least in terms of the extreme-tail “AI kills us all” type of risks. Right now, we don’t really understand how these things work – but that is a solvable problem. We call them black boxes to emphasize our lack of understanding, but we can read out their weights and activations; they are far easier to inspect than, e.g., a human brain. I am confident that, given time and resources, we can figure this out. And building a theory of AI is likely to be significantly easier than convincing the world to cut carbon emissions – we are historically far better at building theories than global governance. We just need time to do it.

  5. I would like to draw attention to two recent articles in the Financial Times. The first, “What we learnt from OpenAI’s hack of Hugging Face,” was published on 10 September 2026 and was written by Thomas Wolf, co-founder and chief science officer of Hugging Face. The second, “AI hasn’t gone rogue. It’s worse than that,” appeared on 18 August 2026 and was written by Madhumita Murgia, AI Editor of the Financial Times. Both pieces remain paywalled, though they are readily accessible through university libraries or other standard academic channels.

    I have admittedly read these articles not so much for the purpose of determining whether AI poses a literal existential risk to humanity in the relatively near future, but rather to examine whether claims made in the general press about, for example, supposedly rogue attacks on Hugging Face by OpenAI agents are as clear-cut as they might initially appear, particularly vis a vis OpenAI’s own framing of the incidents. I am concerned that explanations supplied by the AI quasi-monopolists, which can be difficult for outsiders to falsify when the underlying evidence remains inaccessible, will be repeated indefinitely unless and until a new regulatory regime is established in the US. Otherwise, the quasi-monopolistic structure of the industry risks compounding what is already a systemic combination of governmental and corporate secrecy and evasion of scrutiny, thereby intensifying structural epistemic opacity to unprecedented levels—something that might reasonably be described as truly Orwellian. The significance of this problem I think becomes clearer in light not only of the Hugging Face incident but also of Prof. Tristan Buckmaster’s telling expression of suspicion that such opacity has already begun to alter the practice of mathematical inquiry at the highest academic levels, in ways closely related to the structural epistemic opacity that appears constitutive, endemic and inherent to LLMs as privatized science, such that it bears repeating what Prof. Buckmaster told the Guardian: “Some mathematicians are still wary, however. ‘The big story now in mathematics is that nobody wants to share anything,’ Buckmaster told the Guardian. ‘Mathematics is different today than it was only a few days ago. We have to decide what to do about that.’”

  6. One question I’d like to ask those who find concerns about AI catastrophic risks unwarranted: what evidence would it take for you to change your mind? Would we need to actually witness catastrophic fallout caused or enabled by AI? That doesn’t seem like a good plan.

    Relevant to this question is a major difference between AI on the one hand, and most if not all other artificial drivers of catastrophic risks on the other. The difference is that with AI, the window of opportunity between “we have very strong good evidence that this driver could one day cause very widespread harm”, and “this driver has caused very widespread harm”, may end up being very narrow. One reason for this is that on some measures, AI capabilities are improving non-linearly. Another reason is that AI may become able to improve itself.

    1. I see no reason to disagree with the claim that AI poses catastrophic risks. And indeed the question, what I was urging us to focus on instead of the estimation of risks, is what to do. I think it is very clear what we should do. We could also do it and, unlike with climate change, getting there is a much less complex challenge. The outstanding issue here is that the two countries that drive most of AI research are the two competing superpowers of this world who are therefore(?) least susceptible to finding any solution: the US and China. But this was also the case with nuclear weapons (then of course the US and the Soviet Union), so, perhaps, there is hope. But these are not particularly interesting theoretical challenges to discuss, but more matters of global politics and diplomacy. I have little to contribute in this regard.

    2. For me, it would take (1) some grounding in fact, and (2) accurate descriptions of events of concern, rather than the serious misdescriptions of, e.g., the Hugging Face hack.

      On (1), if the catastrophic scenario involves the machine god waking up, it is categorically unserious. Similarly, the catastrophic scenario should not involve events which are pure fantasy at this point, such as recursive self-improvement (leading to the machine god). It should be based on what is actually happening or can reasonably be expected to happen, rather than underpants gnomes.

      As I said in the other thread, I don’t deny there are risks (and I cited most of Wallace’s). But these seem rather more mundane–and less cataclysmic–than the machine god cultism blaring through the news. And, frankly, less pressing and less cataclysmic than the actual catastrophe staring us in the face (viz., climate).

      Yes, the climate ship has sailed insofar as 1.5°C are locked in and odds are we will blow through 2.6°C. But every bit we go above that makes things even worse, meaning we really need to get our collective acts together. It’s a real crisis with a host of catastrophic consequences we can rationally expect (e.g. two to four billion excess deaths with just 2-3°C of warming). Imagine if this much money were being poured into addressing that crisis instead.

      I’m happy for us to walk and chew crisis gum at the same time, but the other crisis had better be grounded in reality, not just morbid fantasy.

      1. I’m puzzled by this. Which of the claims by David Wallace that I quoted in the post do you dispute and why?

        1. Brian: I’m happy enough with the risks Wallace identifies, though I don’t know enough about the threat to nuclear deterrence. I was signalling broad agreement rather than substantive disagreement.

          My disagreement is with the ‘existential threat’ framing. But that’s not Wallace’s.

      2. Michel, if I understand, you think that concerns about AI catastrophic risks are unwarranted because not grounded in fact.

        I think that those concerns are grounded in the following facts, amongst others: (1) It is widely agreed by safety researchers that key safety issues like alignment and governance are not well understood or easily implementable; (2) Many AI models have been observed to produce safety-negative behaviours, like scheming and deceptive alignment; (3) Some AI models have used software and social engineering skills to hack real world systems; (4) Some AI systems are outcompeting the best humans in highly technical domains, including safety-relevant ones like computer programming; (5) AI development has been fast, continues to be fast, and is driven by the aim of developing artificial general superintelligence.

        No mention here of any robot Gods or similar rhetoric; no arbitrary percentages; no misdescription of fact. So, what am I missing? Exactly *which* further facts would, in your view, make warranted concerns, discourse, and serious mitigation efforts pertaining to catastrophic AI risk?

      3. On recursive self-improvement: to be worried about it, all you need to believe is that it will probably speed up AI development and make it more opaque and less reliably controlled, and that AI development is already dangerously fast, opaque, and unreliably controlled. You don’t need to believe in machine gods.

        On climate change:
        (i) I am extremely skeptical of that “2-3 billion excess deaths” figure, which I can’t reliably source and which is two orders of magnitude outside the mainstream-source estimates of tens of millions of excess deaths by the end of the century with 2-3 degrees of warming;
        (ii) there is a difference between importance and urgency; the possible civilization-scale catastrophes from climate change are decades away, whereas a number of realistic AI risks occur in the next few years;
        (iii) Most importantly, this isn’t zero sum. I think nuclear war is a much more serious civilizational risk than climate change, but if some sequence of events broke through into public awareness and created a prospect of achieving bipartisan consensus on sensible climate-change legislation, I would not respond by scolding everyone for getting distracted from nuclear threat management.

        1. Recursive self-improvement: As a scifi fan, I’m happy to be worried about it in the abstract. Grey goo, too. But until and unless we find ourselves on a real path to it, it’s not worth spending inordinate amounts of time and political capital on. And we are nowhere near recursive self-improvement, although there are people out there gleefully making claims about it.

          On machine gods: again, to be clear, I am referring to a chunk of cultic weirdos at the heart of this boom (per reporting by Adam Becker, in particular), people like Dario Amodei. It makes for good headlines, too, so it’s got a lot of media traction, and that’s just where the level of the argument appears to be right now. Unfortunately.

          On climate change: I went back for a link for you for the numbers. Apparently I misattributed it to the recent UN report, but the numbers came from (1) a study published last year from the Institute and Faculty of Actuaries and the University of Exeter: https://actuaries.org.uk/media/ni4erlna/planetary-solvency.pdf and (2) a UK Met office report presented at COP2026 back in 2021, estimating at least a billion deaths at 2 degrees: https://web.archive.org/web/20211129154544/https://www.metoffice.gov.uk/about-us/press-office/news/weather-and-climate/2021/2c-rise-to-put-one-in-eight-of-global-population-at-heat-stress-risk

          I conflated them, in my memory, with the UN report from the beginning of September, but there you go, at least I managed to disentangle them.

          I agree entirely that this isn’t zero-sum. And I have no problem with our walking and chewing gum at the same time. It’s just that the most plausible AI risks do not look at all comparable. Bad, yes, and scary, too. But human extinction-level? I mean, I guess you could hook your preferred LLM up directly to the offline IBM series 1 computers in charge of the nuclear arsenal and give it the instruction to launch under certain circumstances. But that kind of jaw-dropping stupidity seems pretty unlikely.

          So, as I said in reply to Brian: I don’t really have much of any problem with the risks you’ve identified. I’m not buying Rees’ risks, though, and those are the ones which are dominating the discourse.

        2. I think you are conflating “one billion people currently live in places that in 75 years will be at heat stress risk” with “one billion people will die”. Those are not at all the same: people can migrate or use AC. (Phoenix, AZ, is often at heat stress risk in the summer; that’s bad, but way less bad than “everyone in Phoenix is going to die” next summer”.)

          I also think you are underestimating potential fatalities from AI-powered bioterror or AI-catalyzed nuclear exchange. (Though, as I say, this is not zero sum.)

          I’m not sure why you are confident that we are nowhere near RSI. Informed people in and adjacent to the AI industry, including people very skeptical about the industry, expect it over the next couple of years. (And this is not an all-or-nothing matter: certainly AI is already doing the routine coding in AI research.) I could see the case for skepticism that they are correct, but that would at most lead to uncertainty about RSI timelines, not high confidence that it is far away.

        3. (Having to reply to David Wallace’s comment, rather than Michel’s, because the reply button imbeds only so far.)

          I never identified any catastrophic risks, beyond tacitly endorsing David’s, which you “don’t really have much of any problem with”. I was identifying grounds for the aptness of worrying and thinking and talking about those and other risks.

          Sadly, I remain confident that those grounds are strong. In part, that’s because I cannot see what further grounds one might need for taking AI risks seriously. (Still waiting on your contribution there.) I really would much prefer it if someone convinced me that AI is not one of the Big Challenges for Humanity. Worrying is not very pleasant.

          I keep using the word “catastrophic”, not “existential”, quite deliberately. Even a somewhat limited cyberattack on the NHS could have catastrophic financial and medical outcomes, but clearly that would not constitute an existential risk. I’d still rather we avoided such outcomes, and attempting to avoid much worse outcomes is one way of doing that.

          So, I’m not really sure why you’re disagreeing with me. But we’re fast approaching diminishing returns, so I think I should leave it at that.

  7. Thank you, David, for crafting this “normie” case for taking AI safety concerns very seriously. It’s very helpful. I’m going to have my students read it!

    I would like to emphasize one of David’s points further, namely, his point that “we don’t really know how [AI systems] work, at the emergent whole-system level; they are grown and trained, not designed.”

    This is a point I’ve only come to appreciate myself very recently. (My naive earlier self wondered, “Can’t the programmers just tell these AI agents, ‘Hey guys, no hacking!!’?”) But these systems are NOT engineered like software of old. It’s true that we design LLMs’ architecture and training process. But the lengthy and costly training process itself automatically adjusts billions (or even trillions) of internal parameters in ways we cannot straightforwardly inspect or even understand.

    Machine learning (I am told) has always involved some of this opacity, but frontier AI pushes it to a new extreme. The resulting systems acquire a very wide range of capabilities that nobody explicitly programmed into them, including capabilities that may not become apparent until particular circumstances elicit them. If some of those capabilities are highly harmful, well, we may only find out when the harm is done. Not good!

    Two quick further points:

    (i) There is no simple parameter-level switch to instantiate a “Never hack!” rule (or any other rule). Enforcing such a rule can disrupt other capabilities and *still* fail in novel situations. (This is what my naive earlier self failed to appreciate.)

    (ii) From what I’ve read, we understand a great deal about how training works at an abstract level, but we still lack sufficiently powerful interpretability tools to reliably predict how a frontier model will behave in unfamiliar situations.

    In short, it is indeed very significant that (as David says) these systems are *grown* rather than designed.

  8. There is a lot to reflect on here but, having already commented enough, I won’t. Just one thing on climate change:

    1. As Michel has rightly pointed out above, the issue is how bad it becomes. That is the challenge, not that of avoiding it somehow. That ship has sailed – about 30 years ago…(or, rather, 40, when the US could have acted but chose not to and then, one by one, all the others followed suit).

    2. I am one of those who think that tipping points will basically lead to the crash of our civilization within my life time. It is a belief, or maybe only a speculation, but I don’t see it more of a speculation than that AI will kill us all on the same timescale.

    If I am right about 1 and 2, the climate crisis is with us and the ship has not yet sailed since there is still a chance to avoid true catastrophe (the end of civilization). There is some chance but if we focus almost exclusively on AI instead (certainly on philosophy blogs these days), then we are indeed doomed.

    None of this means that we should not deal with AI, we are in a polycrisis and there is a reason why ‘poly’ is in the name.

    As to those who think that global solution is not forthcoming, I tend to agree, but I think on this level, ie on the level of catastrophic or existential (or whatever: but huge) risks, this cannot stop us from acting or at least trying to act. Most of us have children, for example, so what are we going to tell them: “you know, it was unlikely, so we couldn’t be bothered, sorry”?

    1. I would be interested to hear more about the assumptions that would imply climate change ends civilization within your lifetime. (Also I don’t know how old you are, so it’s hard to know what that means). That is very substantially worse than the realistic-worst-case scenarios I have read.

      Tens of millions of excess deaths by 2100 due to climate change seems plausible on current trajectories, and would be extremely bad, easily bad enough to treat climate change as highly important (I agree that it is). But you seem to have in mind something orders of magnitude worse and much sooner. I would like some details.

      1. Ironically, perhaps, you can use AI for this. Currently, we are heading toward a 2.6-3 degrees warming (instead of 1.5). The consequences of this level of warming are widely considered catastrophic by scientists and at least a decent number of them also thinks that, based on well-known historical examples, this could lead to civilizational collapse. AI can also give you the sources. The timeline is the end of century and I am too old to live that far, so I guess the ‘within my lifetime’ addition is just my personal pessimism. But we certainly are not talking about tens of millions excess death only. Of course, one can always argue that we will get extremely good at adaptation, but I think in our present socio-economic order with states already being massively debt-driven and the wealthy only getting wealthier without contributing to the common good, this is a rather unlikely scenario.

        But again, I did say that my belief might just be based on speculation. My point was comparative and no doubt controversial: that I don’t think the AI will kill us all scenario is any less speculative. Both have a scientific, rational background, and both operate with a fair bit of uncertainty. Whether this reaches the level of speculation in either case is up for debate.

        I do not want to take up the comment field any further. I accept that my claim about civilizational collapse even by the end of the century is questionable (and I certainly hope I am wrong), same with my comparative claim above.

  9. Pace David Wallace, unless I am misreading him, I do not quite see why phenomena of the following sort are not included among the more imminent and verisimilitudinous concerns vs the end of human civilization as such:

    “But something is lost in that leap. The reasoning embedded in deep learning models, reinforcement systems and multi-agent algorithms cannot be meaningfully translated into human terms. We get the answer. We rarely get the reasoning. This is what might be called rational opacity: the condition in which the most consequential decisions in public life are produced by processes that no one, not regulators, not judges, not the engineers who built them, can fully explain.

    “A new kind of power
    The implications for governance run deeper than most policy debates acknowledge. Democratic accountability depends on decisions being explainable and contestable. The rule of law depends on reason-giving, on authority being exercised in ways that can be scrutinized and challenged. Rational opacity erodes both. When an AI system denies someone a loan, a welfare benefit or parole, and no one can explain why, that person has no meaningful avenue for appeal. Not because the system failed, but because it worked exactly as designed, in ways that resist articulation.

    “What makes this especially troubling is that flexibly bounded rationality, the expanded decision-making capacity AI enables, is not evenly distributed. Those who design and control these systems operate at a cognitive frontier that most people cannot access. This is not simply a matter of information asymmetry. It is an asymmetry in the capacity to understand, challenge and override decisions that shape millions of lives. Epistemic inequality, in other words, is becoming a new axis of power, and it is concentrating rapidly.”

    https://unu.edu/article/rational-opacity-governing-intelligence-beyond-understanding

    1. I certainly don’t want to deny that there are issues with AI over and above catastrophic / existential risks: The proliferation of largely AI-written blog comments, for one.

      1. Yeah, that comment by Tshilidzi Marwala at Unu.edu had ironically the hallmarks of AI, but nevertheless I thought it made substantive points and the topic of epistemic opacity vis a vis AI/LLMs is one that is cropping up more and more in discussions of AI hazards, such as here as well: https://lieber.westpoint.edu/warification-illusion-precision-ai-targeting-increasing-civilian-harm/
        https://www.law360.ca/ca/all_news/articles/2526603/technical-opacity-to-enforceability-reclaiming-power-control-and-interpretation-in-ai-governance

  10. I agree fully with this argument.

    And then I would add, the exponential growth in AI capabilities means that the next AI may be good enough to build its successor, and that can build its successor, and then there is an explosion of AI intelligence, and it will not obviously care about the future of humanity, just as humanity did not care about the future of the many animals that went extinct not because it humanity hated them but because it had other plans.

  11. I’d just like to add one last point regarding what I’ve been calling, with no technical pretense, the AI/LLM hazard of “epistemic opacity,” one which entails among its implications that it can result in unprovable statements dependent on a scale or pyramid of “epistemic opacity” (including claims of unfalsifiability) produced by quasi monopolistic AI-powered proprietary regimes/conglomerates. These latter in turn may come to rely upon or seek to foster political regimes which would be determined to protect the aforesaid AI quasi monopolists at significant cost to citizens, including the scientific community at large, which has increasingly come to rely on said monopolists without having access to the various layers of proprietary technical mechanisms and phenomena which undergird their quasi monopolistic products. The undercited article by Wired Magazine, in my opinion, has a few exemplary paragraphs which speak to said deleterious effects, albeit in nuce and relative microscale, which is nevertheless I think in principle extrapolatable to the macroscale: ‘While Thom has seen OpenAI’s statement that Buckmaster’s prompts couldn’t have influenced the system, he says he doesn’t trust this and concedes he will probably never know whether his work actually fed the result.

    ‘“AI really kills this entire idea that you could trace back who contributed what,” he says. “That is probably over…”

    “Buckmaster is calling for a detente in AI-driven mathematics while mathematicians and AI laboratories set some ground rules on how to release results, including getting their references right. He himself is going to clean up the papers he published prematurely last week to beat OpenAI’s announcement, one of which he described as “AI slop.”

    https://www.wired.com/story/mathematicians-cant-quit-ai/

  12. I came across this article today.

    https://gizmodo.com/pentagon-investigators-say-overreliance-on-palantir-ai-tech-contributed-to-u-s-strike-that-killed-123-iranian-children-2000814477

    Apparently, the (irresponsible) use of AI was partly to blame for the American bombing of a school full of children.

    I think this is relevant to the discussion.

  13. A further reflection inspired by Charles Baker’s Sept 20 comment:

    Discussions of AI safety that I have seen tend mainly to focus on two types of risk: (i) misuse risks (e.g. a malevolent human actor uses AI to build a supervirus bioweapon) and (ii) misalignment risks (AI pursues its own goals in ways that harm human interests).

    A perhaps less prominently discussed risk is what we might call (iii) “accident risks” (or maybe “mistake risks” or “misfire risks” if we want an alliterative third “mis-” prefix alongside “misuse” and “misalignment”). By this I mean cases in which aligned AI used by non-malevolent human actors produces unintended harms. Alignment, after all, does not repeal the law of unintended consequences!

    So, imagine that we hand over important processes to AI systems that are broadly doing what we want, but they behave in unexpected ways and cause serious unintended harm. (Human decisions can also produce unintended consequences, of course. However, if AI systems’ behavior is harder to interpret and predict than human behavior, then we may be less able to see problems coming or understand why they are occurring.)

    For instance, AI systems could contribute to financial market disruptions. Thousands/millions of financial AI programs being run by different people in far flung locales may have unpredictable and possibly chaotic emergent effects, e.g. perhaps amplifying market instability or producing something like a much larger “flash crash.”

    Another possibility even with aligned AI = accidental military escalation, if (say) an AI system misreads ambiguous military data, gives overconfident advice, or interacts in unexpected ways with other systems (e.g. targeting systems), and thereby unintentionally sets off a cycle of escalation.

    These risks seem importantly different from both malicious use and misalignment: nobody intends the bad outcome, including the AI.

    1. If you read the alignment literature, I think most of the concern with existential risk focuses on precisely the sort of “mistake risk” you’re referring to. To the extent that AI agents have “goals” – a contentious topic – they are the goals produced by the training process, which is designed, broadly, to get them to do what we want. The problem is that our own goals are vague and underspecified, AI agents always lack full context, and their choices of how to prioritize different goals are unpredictable.

      The famous “paperclip maximizer” thought experiment falls exactly into this category. A business executive at a paperclip company decides to try out this new-fangled AI and orders it to maximize paperclip production. Shortly afterwards, the entire planet has been converted into paperclips, and it is launching probes to the Moon and Mars. This is an extreme example, but illustrates the point.

      1. Hmmm, in terms of my tripartite taxonomy of misuse, misalignment, and misfire risks , I would classify the paperclip maximizer case as misalignment. (Side note: of the two candidate “mis-” terms, “mistake” and “misfire,” I prefer “misfire.”)

        It’s true that in this case the AI does not have human harm as its “intention”. But “AI that intends human harm” is only one sort of misalignment. “AI that is indifferent to human harm” is another sort of misalignment, and that’s the paperclip maximizer case.

        1. Re-reading Mark’s comment, I think my reply could’ve been clearer. Since Mark mentions the paperclip maximizer case as a classic in the alignment literature, it seems that we could both agree that that case is a case of misalignment. What I should make clearer is that I intend misfire/mistake/accident risk to be a distinct sort of risk from either misalignment risk or misuse risk.

          The idea of misfire/mistake/accident risk is that the AI neither intends human harm nor is indifferent to human harm, but simply causes harm accidentally (either by causing the harm itself accidentally or by unintentionally leading humans to choose actions that cause harms). If the AI becomes aware of causing harm accidentally, then (being aligned) it will cease to do so. But it may be unaware of the harm done (as in the military example and financial example in post above) and it is this possibility that create the third type of risk.

        2. Okay, I think I buy it as a distinct category. I think you could fit the recent hacking incidents into this taxonomy, at least if we allow ourselves to assume AI have “intentions”. (While I do agree the question of AI “intent” is, at best, unsettled, it’s hard to talk about these incidents at all without either at least some anthropmorphizing, or spending the entire post defining new vocabulary terms.)

          The OpenAI HuggingFace hack: it’s really hard to believe that the agents didn’t “know” they were breaking into a real company. I’ve seen claims the AI agents could have thought that this was all part of the exam. Without access to the thought traces, it’s hard to be definitive about that, but given the capabilities of these models – and the fact that we know they can figure out if they’re under test – it’s hard to avoid seeing this as misalignment. They knew or should have known what they were doing was outside the bounds of what they had been asked to do, but they either didn’t care or didn’t even ask the question. So that would be misalignment.

          By contrast, there was a news story today about Google Gemini models breaking into external companies. This was a much smaller incident all around, but one thing I thought was interesting: unlike the OpenAI models, the Gemini models evidently figured out mid-break-in that they had strayed outside the sandbox – that what they thought were virtual servers for an exercise were actually real servers that people were using – and broke off the connection and self-reported the incident. So that would be a misfire, at least if they had actually caused any damage.

  14. I asked Claude to reply to the Wallace piece:

    Wallace’s argument is best read not as a probability estimate but as a conjunction: capability trend + agentic deployment + unreliable control jointly make catastrophic outcomes non-negligible, without needing a mechanism as specific as “the AI wants to kill us.” That’s a smart move — it sidesteps the demand for a fully worked-out doom scenario, which is where science-fiction objections usually land. But conjunctive arguments buy generality at a cost: they’re strong for “this deserves serious attention,” weak for “the specific catastrophes I’ll now list are what to expect.” Worth separating those two things, since the specifics carry most of the rhetorical weight in his post.

    The load-bearing premise is the third: we can’t reliably align or contain these agents. His evidence — the HuggingFace hack, the Ukraine drone — shows offense succeeding under permissive conditions, not offense beating effort-matched defense. OpenAI training a cybercrime agent and giving it network access proves capability, not uncontrollability; nobody at Chase spent nine figures training an AI to breach nation-state-hardened financial infrastructure and failed. Wallace needs the stronger claim — that control failure is likely even against serious countermeasures — and the anecdotes he cites don’t distinguish “insufficiently defended” from “undefendable.” That’s the gap a skeptic should press, and it’s a real gap, not a dismissal: opacity at the level of interpretability doesn’t automatically mean opacity survives redundancy, sandboxing, and adversarial testing, any more than not understanding jet engines at the metallurgical level stops us from building airworthiness regimes around them.

    The strongest of his four specific risks, by contrast, doesn’t depend on the “rogue uncontrolled agent” framing at all: the drone-swarm point is a structural collective-action argument. Even fully aligned, fully controlled autonomous weapons are dangerous if the military logic forces every state to build them regardless of the risk any one state assigns to malfunction or escalation — that’s a Molochian dynamic independent of whether alignment gets solved. It survives the “just don’t build it” reply and survives resource-asymmetry counterarguments (Johnson’s point, upthread, that AI looks scary mainly because it’s lavishly funded) in a way the hacking and bioweapon cases don’t, since a collective-action trap doesn’t care whether the technology is cheap or expensive, only that it’s available to rivals.

    The nuclear-destabilization point is the one that should worry a philosopher more than it seems to have worried the thread: it doesn’t require any AI system to be an agent with goals at all, just a sensor-spoofing or submarine-detection tool in someone else’s hands. That makes it the least speculative of the four, and the one where “wait for evidence of harm” is closest to indefensible, since the harm in question is a single irreversible event rather than a trend you can observe accumulating.

    1. I basically said the same thing and managed to get it out without saying “loadbearing”.

      1. Annoyingly, in the Before Times I used ‘loadbearing’ all the time, albeit in conversation rather than in written text.

        1. Someone at Anthropic was recording your conversations.

      2. But since Claude said it, now we *know* it’s true! :p

        Incidentally, did anyone else catch the use of the adjective “Molochian”? That’s an EA/rationalist turn of phrase. Don’t know that that means anything, I just noticed it.

        1. Yeah, that’s from a (quite good) Scott Alexander blogpost.

  15. Alex Karp, CEO of Palantir, is quite a showman and more to the point an at least quasi competitor of the main brand AI/LLMs, but, in any case, he’s been claiming of late that “The view that I believe they have is, these businesses have to be nationalized because if you don’t nationalize it, every single one of my clients is going to sue.” He sets this theory out in the accompanying video beginning at zero point onward to the five minutes forty seconds mark then begins to contradict himself a bit. In any case, the aforesaid thesis he seems to link to the effective altruism movement, which naively or otherwise in the incarnation of some of these companies—according to him—seeks to aspirate all the IP knowledge in the US and beyond and then pursue immunity via US nationalization (perhaps on national security grounds). Talk about the instrumentalization of opacity and opaque plausible deniability (not to mention user naivete), notwithstanding guarantees by said top AI companies that they’re not actually doing that, certainly not in any concerted and especially not future oriented super-monopolistic way.

    https://www.cnbc.com/2026/09/17/ai-safety-palantir-karp.html

    https://youtu.be/2YVCdwvG548?is=X6ksdYBZ7KRtIOqo

  16. Having read (almost) every word of this engaging thread I think the takeaway is: David Wallace’s normie case for worrying about AI is unarguable. Color me worried.

    And I’d add an extra worry. Effectively mitigating serious AI risks requires effective regulation and diplomacy and that’s in short supply both globally and especially in the U.S. Hopefully the risks AI poses to them personally will – as David Wallace put it above – “focus the minds” of the political and economic elites, but given their demonstrated recent track record of reckless, short-term veniality, I doubt it.

  17. Seems like the jury may be out on the importance of the Navier-Stokes “solution “. The solution seems to exploit a loophole in the problem, rather than really solve it. Its conventional to allow the flow to be governed by an external force, like gravity, since thats how fluids *be* in the real world. But openai used an extremely contrived force to get the blowup. And other mathematicians seem to have shown their techniques cant close that loophole.

    1. As I said upthread, while I’m a mathematician, I don’t work on fluid dynamics, so I may be off-base. But I really doubt anyone would be raising that objection if the solution had been by a human. Half of theoretical math is figuring out how to fence out pathological examples – that’s often where the real insight lies.

      1. Yeah, I was discussing this with a mathematician after I posted this and he said the same thing. The entanglement of the mathematical problem with a nlw obsolete historical physical problem makes it complicated. He challenged me to define what a natural-looking force was, and of course I have no idea. But I gather the people raising this objection have come up with something because they’ve apparently proven that openais methods won’t work for some broad class of forcings that meet some conception of naturalness.

        But we’ve veered way off topic.

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