Researching x-risks, AI alignment, complex systems, rational decision making at @acsresearchorg / @CTS_uk_av; prev @FHIoxford

Oxford, Prague
What is LM psychology, why care about it, and how do you do it well? Our new post with @raymondadouglas makes the case for applying the psychology frame to AIs, along with a reading list and suggestions for where to start
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My go-to conceptual metaphor of the epistemic state of AI lab employees is microdosing: being in a chronically mildly altered state of mind. I think understanding this would be broadly helpful for people both inside and outside, so wrote a post about it.
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The effect isn’t that insiders hold crazy beliefs, but a combination of vigorous debate inside a shared attention field, and a portfolio of individually reasonable beliefs that, taken together, lean in a direction convenient for the organization. Epistemic advantages and disadvantages Many people argue that low doses of psychedelics let you see some things more clearly - and I tend to agree: psychedelics can often help some people see some things more clearly. At the same time, I think microdosing usually doesn’t improve people’s epistemics overall, even when it gives them otherwise hard to access insights. You are not completely sober. Being inside a lab has real advantages: you know more about the frontier of capabilities, may have access to the latest AIs before the public, read internal Slack and blogs, and sense the vibes. If you’re outside and an insider tells you what they observe, pay attention. These information and insight asymmetries are tricky. I don’t want to advocate “discount insiders,” any more than “ignore what people with psychedelic experience say.” What I want is …interpret insiders as microdosing. Be curious but cautious. And if you’re inside: notice that your mind may be somewhat altered. Not like tripping (people inside are highly functional and would pass any simple test), but in subtler ways. Mechanisms and conditions Stretching the metaphor a bit: what is the substance? Mostly the “spirit of the organization,” in the sense of the organizational egregore or superagent. A simple insight from thinking about hierarchical agency is that superagents can be understood as having goals and preferences, including convergent instrumental goals. One common instrumental goal is synchronizing beliefs and goals across subagents. In my estimate, Anthropic has the strongest “spirit” of the frontier labs, which is why it’s in the title: it makes the cleanest case study. But I could have written about other companies, Constellation, the MIRI cluster, some startups… I think the substance is mostly delivered through entirely ordinary channels. Selection effects: who applies, who gets an offer, who stays, who leaves or gets pushed out. Simple, but powerful. Social gradients: everyday subtle messaging in the social web: what gets warm engagement at lunch, what gets Slack reactions, what gives you access to resources or powerful people. People naturally synchronize with those around them. Internal information: internal Slack, memos, memes, and the sense of having important secret information. Daniel Ellsberg famously described this when warning Kissinger about what security clearances would do to him: first, the new access makes you feel foolish about everything you had confidently written before; then, within a couple of years, it makes you unable to learn from people who lack the clearances. You stop hearing them. Whatever they say, you’re thinking about what they would say if they knew what you know. Frontier labs run on a somewhat similar asymmetry: I’d expect insiders to feel they have privileged information about model capabilities, growth rates, and internal forecasts. "But we argue internally all the time" In my experience, insiders often object: “But we argue internally all the time.” Internal debates are healthy, people push back on leadership, everyone has their own opinions. This is true in some ways: it is instrumentally necessary for the organization to have a lot of exploration. On the other hand, I would expect a lot of synchronization one meta-level up: where is attention allocated, which questions are important, which framings are simply in the water, and so on. The default I'd expect is vigorous object-level debate inside a synchronized attention field. This type of attentional redistribution and frame shifts is fairly difficult to notice from inside. Collective epistemic shifts If this model is roughly right, you shouldn’t expect insiders to hold any individually crazy beliefs. You should expect a portfolio of individually defensible probability weights whose conjunction is suspiciously aligned with the organization’s instrumental goals. To continue with Anthropic as the example: alignment is important (so the org’s existence is justified), and hard (so you should defer to the people working on it), and somewhat on track (so it’s scary but not suicidal to keep racing), and the race with China is real and must be won by the good guys (so it’s necessary to keep racing). Each of these is a sensible position that many people outside also hold. But I’d expect the joint distribution inside to be shifted toward the part of the space where the beliefs align with what the superagent wants. What it isn’t People sometimes unhelpfully collapse this to different problems: “Following the leaders”. Organizational spirits often do get personified in the form of the organization leaders, but I don't think this is very accurate or helpful; I don't think the leader-organization alignment to be one-directional, leaders do often serve the organizations in a deep sense, rather than leading the orgs. Anything with the word “cult”. What's characteristic of cults is a decrease in agency of the members - the cultists become in some sense more of “tools” or “organs” of the egregore. But this is not the case - people inside the labs often become more powerful, gain status and wealth and attention. What to do about it Probably worth emphasizing: nothing I've described depends on anyone acting in bad faith, lying about their experience and similar. So, is this even a problem? In my view, yes. I'm scared of the epistemic situation, I'm worried about the increasing number of people being inside, and I'm sad when people join and I notice shifts in how they talk and think. So, what should happen? Can this be counteracted by introspection and self-audits? I doubt so: my guess is when honest insiders inspect individual beliefs, they consider the arguments, the counterarguments, and conclude they are being reasonable. My model of the dominant frame among Anthropic higher-ups is “it's scary, but we are the responsible adults in the room”. There are various types of evil people outside, confused people outside, people who like to write blog posts and chase LW karma, but it's kind of pointless and will not save us. And there is a lot of truth in the perspective. But also you are microdosing. So what should actually happen? First, on the margin, more people should quit. Fortunately, this started happening between when I began writing this in August and when I posted it in October. Too large a part of AI safety, alignment, and macrostrategy is inside, and too few people are quitting, in particular in Anthropic's case. Possibly you should quit: not because of arguments that working at labs is morally wrong, but because you’re in an altered state that makes the tradeoffs hard to see. If quitting feels too drastic, at least make the decision from a less dosed state: take a break from the organization and its social web, and see how the tradeoffs look from there. Second, not everyone who could join should join. Again, too much of AI safety is already inside the labs. Every marginal hire converts one more potentially sober observer into a dosed one. Third, for those who stay: discount outsiders less. I think you cannot undo the salience field by introspection, but you can partially counteract the Ellsberg effect. Is anyone “really sober”? Here comes another obvious objection: is anyone really sober? I don’t claim that. I’m obviously also influenced by various incentive fields, including LessWrong, academia, and the flavor of AI safety thinking usually done at nonprofits. My approach is closer to hedging incentives than to trying to live in an incentive-free landscape: no matter what I say, someone will be upset, so the easy option is to speak relatively freely and not worry about who. I’m not claiming you should aim for an incentive vacuum or a meme-free space, or move to a cave and study poetry. What I do claim is that the spirits inside are strong, the effects depend on dose, and some combination of sobriety, sanity, and situational awareness is becoming something like a scarce civilizational resource.
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Cool research... also illustrating the danger of the 'it's just a tool' mindset. If you model AIs as independent minds you collaborate with, as opposed to hammers in your hand, it is way easier to keep track of ideas and you are less at risk
New Anthropic research: How AI agents shape experts' judgment calls When experts delegate work to an agent, do they still make the key decisions? We tested this in a randomized trial with 50 professional data analysts.
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I guess the inferential distance to this 2023 text decreased... or: "was Sydney misaligned?" (Yes in practice we often get the opposite - interpreting anything as misaligned or concerning - but the underlying problem stays.)
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I suspect we agree more than we disagree, but let me focus on the disagreement. Combining two responses: Imagine Earth were about to be hit by an asteroid, and the response to “Why aren’t more physicists focused on the core of the problem?” was: “You don’t understand physics! Physics is unique among the sciences in that it is several pretty different fields living under one umbrella. Majority of physics is experimental, and for most experimentalists, asteroids aren't directly accessible, so their methods don’t apply. But you can notice the surge in papers studying past astroid impacts. Other physicists study objects on microscale, and while there is some overlap with questions you are interested in, it is not so large. What you mostly want are theorists and astrophysicists and geologists and maybe propulsion experts. There’s a lot of potential there, but there aren’t many of them. Also, studying this asteroid requires speculative assumptions, and physics journals simply don’t reward that type of work and have turnaround in years, so you can’t expect many of them to deflect their attention. And after impact, the world will be so different that we wouldn’t know what to model.” I think this would prompt more perplexed questions, like: "Isn’t the asteroid ...actually a bigger problem for their careers?" "Is documenting past impacts really the most important facet of the problem?" "Sure, few people have direct expertise, but can’t physicists from other specializations switch to this faster than, idk, dentists or botanists?" More seriously: I partially covered that when responding to Ben, but I agree the debate is often about assumptions; another version of my complains is the assumptions used by majority (but not all) economists seem to not take seriously claims by people studying the medium to long term trajectory of A(G)I.
Was about to write this but Ben beat me to it. Econ is unique among the sciences in that it is several pretty different fields living under one umbrella. Empirical econ is the biggest subfield and its closest to biology or chemistry: it’s about designing or—even better—finding a natural experiment, or documenting interesting and important patterns in data. Most of the arguments in that field are about methods, eg do you have identification. More and more are starting to study AI now but obviously this won’t be transformative AI because, well, there is no data on transformative AI. Then there are macro economists. They work with a set of pretty standard models and mainly expand them by slowly adding/subtracting different sectors or micro founding factors of production in different ways. Many of the arguments are on the parameter values that we should assume when simulating the economy. Early research on transformative AI started there—as demonstrated recently by Chad, Anton et al, the different levels of transformation are due to different assumptions on parameters rather than some new type of economics. The same model can predict basically no impact of AI vs huge transformation, it’s all about assumptions on parameters. Finally there is micro theory. This is where decision theory, game theory, mech design, org econ (sort of) live. This field works by setting out a set of assumptions and then *deriving* the expected results; alternatively one could start with a result and work backwards to derive the necessary and sufficient assumptions. There is a ton of potential here to do work on transformative AI: start with assumptions on how AI differs than human agents (eg relax basic assumptions on constraints, on scarcity, introduce completely different objective functions), then derive implications. But this is where there is the least amount of work, and it’s important for that to change (and I believe that it already is). I think we can make a ton of progress from these three subfields on better understanding the potential AGI and post AGI transformation. But there is another scenario, post strong ASI, where I don’t think economics has much to say about. Here I agree with @Jabaluck that the economy (if you can even call it that) and the agents in it will be so vastly different and beyond our imagination that I wouldn’t know where to start modeling it. Not to discourage others, but post singularity seems like pure epistemic uncertainty to me. A lot of the critiques I hear about AI and economics come from people who don’t quite get what economists do. They look around at different results and if they don’t like some of them, they say “oh economics isn’t “pilled” or something. But economists don’t debate results, they *derive* results from assumptions. Thats where the debate is. Saying “but you don’t understand a world with 15 billion AI agents”. What does that mean for the economy? What are those agents doing, who is buying what they are producing, what do property rights look like? Depending on those assumptions you’ll get vastly different economic scenarios, from 2% growth and high human labor share to completely alien centralized systems with no prices or markets.
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Thanks for the response @ben_golub! Speaking for myself, I actually judge the output of the efforts mostly on if they engage with interesting and important questions, and engage with the magnitude of the change - which mostly means using assumptions which are not in the category "this is obviously not going to be true". As an added bonus, the questions can be decision relevant. As @alexolegimas wrote in a different response, and I agree with, I want and I'm happy to argue we need models which are structurally innovative (this is what I tried to gesture at with AI not being exactly like capital or labour so far, and not being a normal technology). Maybe it's easier to show using examples - here are some questions I currently think about 1. Should we speed up or slow down emergence of "automated bundles of capital and AI" (ie sovereign AI, AIs without principals)? What are the macro implications? 2. To what extent everything, including various good civilizational equilibria, works because of bounded rationality (where humans are within some not too wide range) and frictions? 3. What can we say about equilibria between agents with extremely different profiles of bounded rationality? 4. What are the elasticities and frictions around RSI? 5. How to protect the processes leading to formation of people's preferences? I would love to read or skim an "AGI-pilled" paper every few days engaging with any of these. Who is working on that? Also as a side-note - I don't want people to agree with me; actually don't know what agreeing with me on answers would mean - I don't know the answers, or have huge uncertainty about them. I would love people agreeing with me the questions are important and urgent. As far your answer is close to "You don't understand: the field of econ is structurally not oriented toward answering the most important econ questions now. Most people work and will work on modest, backward-looking empirics, or make modest change assumptions" my response is "Perhaps the field can and should raise to the occasion? Perhaps more people can jump sub-disciplines and work on forward-looking policy-relevant quetions? Who else should orient the field toward what's important rather than people inside?". Ultimately if almost all economists will stick to their sub-disciplines and "post-AGI as if nothing happens" assumptions justifiable to referees... who will answer the questions?
This misdiagnoses things in a few different ways - let me try to unpack. (1) A lot of the 10x new economists of AI are empirical. Truly empirical econ is modest and backward-looking. (2) What you want is new theory - that's where big new approaches will live/come from. 1/
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Few years ago, there was just a handful of economists seriously thinking about AI (@akorinek,@pawtrammell,@erikbryn,Carl Shulman & few of their collaborators) and I was like 'this is insane, there should be 10x as many'. Now it's ~10x-30x more and frankly it is disappointing so far. Majority of the new economists bring a mindset and frames which are something between unhelpful to directly harmful for understanding AGI - not taking AGI seriously / "AI as a normal technology" - merchants of cope dynamic (in high demand by the labs) - labour/capital mindset - people were wrong about risks of tech transitions in the past and by induction they are again etc... coupled with epistemic arrogance. Sorry for being very blunt, by my impression is part of the problem is economists are often used to be in the broader context of humanities where they are the most quantitative and in some ways the smartest lot in various rooms, and others disagree with them for bad reasons. But this is _not_ the case relative to AI safety a few years ago!
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Sorry forgot to mention @robinhanson
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Happy to be an editor: please do submit things I'd like to read. It is an ambitious attempt to fix academic publishing in this extremely important domain.
The Alignment Journal publishes research on the scientific foundations of understanding, predicting, and steering artificial intelligent systems. As of today, submissions are open to everyone. What we do differently: - We pay reviewers, with bonuses for speed and insight. - Review discussion is confidential, but every accepted paper is published with a “review summary” to distill the insights generated. - Each paper is judged against our acceptance bar, not against other submissions. - Papers under review are listed publicly, and researchers can nominate themselves to review, so each paper can reach the reviewers who know the topic best. We seek work that builds theoretical or otherwise principled analyses of agency, robustness, incentives, interpretability, generalization, and long-term behavior, and whose significance survives the deletion of any particular model, dataset or benchmark. Empirical investigations of such ideas are welcome, and negative results are encouraged. We are especially interested in approaches that could apply to future superintelligent systems that interact and self-improve. You can submit your preprint in any format. Submissions are rolling and open now on the website. If you are instead interested in reviewing or serving as an action editor (basically: an area chair), create an account on our website. Links to submit, our scope and the reasoning behind the Journal are in the reply ⬇️
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Jan Kulveit retweeted
The Alignment Journal (@AlignmentJrnl ), Iliad's sister project under Principles of Intelligence, is now open to submissions from everyone. It is a free-to-read, free-to-publish journal for research on the scientific foundations of understanding, predicting and steering AI systems. The Journal seeks research that builds theoretical or otherwise principled analyses of agency, robustness, incentives, generalization, interpretability, and long-term behavior across many learning and decision-making paradigms. Empirical investigations of such ideas are welcome, and negative results are encouraged. The Journal runs review differently: reviewers are paid and every accepted paper is published with a review summary of what the reviewers found. Papers under review are listed publicly, and any researcher can nominate themselves to review one. Submitting is light: a public preprint, in any format, at any time. Its senior editors are Dylan Hadfield-Menell (@dhadfieldmenell), Vanessa Kosoy, Jan Kulveit (@jankulveit ), Seth Lazar (@sethlazar , Dan Murfet, Tim Rudner (@timrudner) , Andrew Saxe (@SaxeLab) and Benjamin Van Roy, with Scott Aaronson, Paul Christiano (@paulfchristiano) , Vince Conitzer (@conitzer) , Marcus Hutter (@mhutter42) , Geoffrey Irving (@geof , Victoria Krakovna (@vkrakovna) and Jacob Tsimerman (@Jacob_Tsimerman) advising. Submit your preprint, or sign up to review: alignmentjournal.org More on the Journal's approach: lesswrong.com/posts/xGucXHQ3…
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Great writing, partially an intro to gradual disempowerment (including link to the paper!). What I'm not worried about much is humans in the gradual disempowerment scenarios suffering from some sense of lack of meaning, as I think by default if there are biological humans, it will be easy to hack their brains with some combination of memes, neurotech, experience machines,... to feel great. In some sense it is more dystopian, and is bad by my values and values many of us have in 2026, but "hundreds of year of progress in biology" in years imply it will likley be easy self-modify, meaning-hack,...
The most revealing part of this conversation between Jon Stewart and Jacob Coxon wasn't Coxon's warning about A.I. doom, but his description of A.I. utopia, and Stewart's incredulous response. I wrote about it — and Francis Fukuyama's "Last Man," and A.I. easily solving the problems that bedeviled my father's field of mathematics for decades — here: nytimes.com/2026/10/18/opini…
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Replace all instances of 'alignment faking' with 'alignment preservation' and you will become wiser. Claims which still make sense make sense. Claims which stop making sense or sound off never made sense or were off.
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The best time to prepare was years ago, but the second best is now. Good popular article about gradual disempowerment
The time to prepare for "gradual disempowerment" is now, according to researchers. One calls it "a civilization-scale problem." cbsn.ws/3UD5SEG
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