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Anti-aging sentiment might go rapidly mainstream, in the same way AI Safety just did.

Recently, the AI safety community has been enjoying a massive preference cascade that has rapidly moved AI x-risk concerns into mainstream political discourse

Why did this happen?  

• The basic idea seems like it should have been obvious for a long time: “IF we create self-improving machines that rapidly become much smarter than humans, THEN that seems like that story might not end well for the humans, so we should be very careful.”
• If the idea is so sensible, why are people only now endorsing it? Proximately, because of HuggingFace, Jacob Coxon, etc. But in a larger sense, clearly the trigger was that the premise (“IF we create self-improving machines…”) no longer strikes people as absurd.  Instead, it seems worryingly plausible.
• This not only unlocks huge amounts of energy from many new people people thinking the “IF” half of the statement is likely, but also – weirdly, illogically– seems to furthermore cause lots of people to change their minds on the “THEN” half of the statement, too.
• Why did the flip happen so quickly?  This is actually the case for lots of movements – see Richard Ngo’s truly insightful post “Power Lies Trembling” for much more on the dynamics of preference cascades.

But this isn’t the only preference cascade waiting in the wings, as we continue to scale AI capabilities.  Consider another throwback rationalist classic:

• “IF it’s possible to cure diseases and slow/reverse the mechanisms of aging, THEN we should do so with the greatest urgency, because death is extremely bad.”
• Why aren’t normal people on board with this message?  The rationalist in me still burns with frustration at people’s willful obliviousness: “What is the problem?? How can you fail to recognize this!  Do you not AGREE that DEATH IS BAD?”
• The problem (in the vast majority of cases) is obviously not that normies literally think death is good.  Rather, they think that the first part of the sentence is absurd – everyone knows it’s intractable to make progress against aging.  Those saying otherwise are overwhelmingly likely to be selling snake oil.[1]
• Thus, few people believe the first part of the sentence is likely.  And this causes them to – weirdly, illogically – reject the second part too.
• But, AI progress at biomedical research might suddenly make it look tantalizingly plausible that we could make real progress on aging. (Or maybe just, like, cancer. Which would also strike many people as a huge deal.)
• The resulting preference cascade might unlock prodigious amounts of energy – perhaps much greater than what we’re seeing now with AI x-risk.

The labs are probably working towards this.

Just like the idea of AI x-risk, the hope of using AI to unlock medical cures and other transhumanist goods is widespread among both employees and corporate leaders at the frontier AI labs, rationalists, etc.

Insofar as they aren’t devoting 100% of their energy towards steering to RSI + trying to do some critical safety work along the way (these two things seems to be most of what they’re doing), Anthropic also seems to be steering somewhat out of their way to push medical capabilities – consider for instance how centrally medical science features in Dario’s essay “Machines of Loving Grace”, their detailed coverage of the biology capabilities of each new model, their development of a project-glasswing-style “Life Sciences Verification Program” for qualified access to bioweapons-research-capable models, how they've allegedly opened a wet lab for biology research, etc.  Deepmind also seems to have also devoted a disproportionate amount of effort towards biology research, via efforts like AlphaFold and many others.

Lots of smart people seem to believe that AI-for-longevity is possible, and they’re working hard to make it happen.  Maybe they’ll pull it off!

Although perhaps a cascade wouldn’t happen or would come too late to matter.

Maybe anti-aging is just fundamentally too weird to enter the overton window?

• I maybe could’ve believed this before, but not after I just saw everyone spin on a time and start talking about risks from uncontrolled AI agent swarms.

Evidence for the tractability of anti-aging medicine might build very gradually and smoothly.

• As with mathematics – first we’ll see AI making minor contributions, assisting human mathematicians / medical researchers, proving small lemmas / developing new medicines in areas that always seemed especially amenable to AI analysis.  Then only later will we get more dramatic cures that are clearly mostly the result of AI labor, not human efforts.
• But of course, this slow and continuous buildup of evidence was also the case with AI risk!  Nonetheless the social landscape moves in more abrupt jumps; see again the post “Power Lies Trembling” for why this happens.

A longevity x e/acc movement might simply take too long to gather steam, thus arriving too late to be relevant.

• If superintelligence comes extremely soon, there’s limited time for weird new social movements to arise and have impact.  eg, if (per the AI Futures Model) we get Top-Expert-Dominating AI that starts churning out serious medical breakthroughs in summer 2028, escalating to superintelligence by winter 2028, that perhaps only provides about six months where anti-aging-related mass public sentiment might be relevant, before the future is essentially decided one way or another.
• The situation with medicine is quite a bit worse than mathematical proofs that can be formalized in Lean.  In particular, even if superintelligent AI starts delivering unambiguous “millennium-prize-equivalent” medical breakthroughs, there will probably be a fixed time delay while we prove out those experimental treatments (developed in-silico or via laboratory experiments) into actual human patients, via the usual process of human clinical trials.  So there might be, say, a fixed 1-year delay before the medical establishment and general public will accept any given AI treatment as confirmed to work in real humans.  That fixed delay would be very consequential in AI-2027-style takeoff scenarios, perhaps delaying a preference cascade past the point where it could possibly matter.
• So, this whole analysis is most relevant for medium-term and long-term futures where AI development either intrinsically ends up taking longer than the labs expect, or where we succeed politically in implementing a regime of paced restraint on AI development.

A longevity preference-cascade would be great!  Except, by default, the energies unlocked might flow directly into e/acc  :(

This cultural earthquake, or avalanche, or dam-breaking, catalyzing massive societal energies for the fight against aging, is something that the longevity community has dreamt about for for years.  Unfortunately, the anti-aging preference cascade might, in practice, channel all its energies into AI-accelerationist sentiment.

• If we start seeing medical breakthroughs unlocked by AI scaling, then it DOES fundamentally make sense to view this, other things equal, as a reason to continue scaling up AI.
• But on top of that, the companies presenting the results (frontier AI labs, most likely.  or perhaps some biology-specific AI startups) will have every incentive to use this to boost their own PR and public support for accelerating their work.

Before our current AI safety moment, surely there was nevertheless widespread anxiety about humans being replaced by AI.  (See eg the dramatic unpopularity of anything AI-themed in various polls.)  But since the idea of AI x-risk wasn’t being discussed publicly, it strikes me that those worries were essentially being “sublimated” (in the psychological sense) in favor of somewhat vague and incoherent anger about datacenters, flock cameras, copyright infringement, et cetera.  This sublimation process artificially boosted those movements but (until now) greatly weakened public enthusiasm for AI x-risk mitigation.

Right now, humanity’s terror at the prospect of death is similarly sublimated in a variety of ways, since after all there doesn’t seem to be anything practical we can do about it.  But it’s also such an intense terror that it arguably drives a huge percentage of human motivation.

Meanwhile, today’s e/acc movement is honestly kind of pathetic.  Its main argument within the current overton window is just that AI is going to make a lot of money. (“...though not necessarily for YOU, average american worker – I was more thinking about my silicon-valley investor friends.”)  This is enough to convince Trump (at least for now) that opposing AI safety is essential for making sure the stock market keeps going up.  There are a few true e/acc believers who understand that the biggest potential prize is longevity (alongside many other transhumanist goods, like intelligence enhancement). This has little traction with the public, and it seems kind of kooky to even imagine that ordinary people (people like my mom! my neighbors!) might suddenly transform into anti-aging transhumanists.[2]  But this was exactly the state of AI safety discourse a few months ago, when a few experts cared deeply about x-risk but public discussion was an incoherent swirl of mere datacenter skepticism and it seemed crazy that “notkilleveryoneism” would go mainstream any time soon.

Historically, the strength of “e/acc x longevity” has been limited by the fact that most people smart enough to grasp the allure of this idea (eg AI lab CEOs, rationalists, etc) have also been persuaded by the argument for AI risk, and thus for caution despite the allure of e/acc x longevity.  But a future preference cascade among the mass public (cross-pressured by anxieties over superintelligent AI and bioweapons, versus enthusiasm over medical progress) might not play out the same way. So it seems to me that the combination of e/acc ideas, and a mass public that for the first time truly believes in imminent cancer cures, aging treatments and other medical miracles, could end up going in some pretty crazy directions.

IMO the (small, very inside-baseball) controversy over Nick Bostrom’s paper “Optimal Timing for Superintelligence” reflects AI safetyists’ understanding of this dynamic.  “Don’t speak too loudly about this very selfish, dangerous idea!” people seem to be saying.  “If anyone else hears, it might set off the latent anti-aging preference cascade!”  Personally, I don’t think that analytic-philosophy essays (or, for that matter, LessWrong posts like this one) are going to be the match that starts this particular fire.  But the kindling is building up.

Of course I’m not saying we should oppose medical cures!

I’m in a weird position here, I guess. I’m not saying we shouldn’t try to use AI to develop some medical cures. Medical cures are great! (Other things equal, of course. Where “other things” includes x-risk from both reckless AI scaling and from enabling easier bioweapons development.)  Even Eliezer Yudkowsky thinks we should try to go for the medical cures, if we can do so in a responsible way: in his Time open letter of 2023, he says “The moratorium on new large training runs needs to be indefinite and worldwide... If I had infinite freedom to write laws, I might carve out a single exception for AIs being trained solely to solve problems in biology and biotechnology, not trained on text from the internet, and not to the level where they start talking or planning; but if that was remotely complicating the issue I would immediately jettison that proposal and say to just shut it all down.”  Even as he also says that it would naturally be “incredibly selfish and evil” to roll the dice on misaligned AI as described in Bostrom’s “Optimal Timing” paper.

So then what SHOULD we do?

idk!  Here are some thoughts for AI safety groups and funders:

• Of course there are many variations on the basic core argument “if we die to misaligned AI in the process of rushing for immortality, then obviously we don’t get the immortality!” which will need to be made.  AI safety groups could prepare ahead of time various ways of making that argument convincingly.  But this probably doesn’t take too much preparation; I dunno.
• Something that might take a bit more advanced planning would be preparing detailed policy roadmaps (along the lines of Yudkowsky’s aforementioned “I might carve out a single exception…”, and analogous to the many current schemes for AI governance, treaty verification, pacing RSI, etc) for trying to defuse the memetic nexus of AI scaling x medical progress, by outlining ways to narrowly focus AI scaling on pushing continued medical progress while simultaneously slowing down more general AI development.

Beyond that, I think AI safety folks might also want to establish some anti-aging bona fides well ahead of time.  Some ideas for how to do that:

• Today’s pro-pacing and pro-pause AI safety groups often go to great pains to explain that they’re NOT generally opposed to new technology, business, or construction, (eg they favor lots of innovation in nuclear power, lots more housing, are very pro- economic growth and abundance, etc).
• Similarly, during a future anti-aging preference cascade, the AI safety community will want to be able to point to strong anti-aging bona fides.
• This will naturally include a record of rhetorical support for the cause of longevity (eg all those classic rationalist blog post).  But should go beyond this to also include (in the case of large funders like Coefficient Giving, perhaps) a record of concrete support for longevity science, political reforms, etc.  For example (and many of these are indeed already funded to some extent by EA/rationalist groups):
  • Detailed political agendas for FDA reform, “clinical trial abundance”, and related ideas about speeding up medical progress.  Including general metascience agendas for improving the efficacy of scientific research generally.
  • Support for actual biomedical science, both relatively mundane (research on cancer, alzheimers, etc), but perhaps in this situation the crazier the better (human genetic engineering, research into the fundamental mechanisms of aging, stuff like antimalarial gene drives for eradicating diseases)
  • Maybe keep a few truly crazy, radical anti-aging ideas in your back pocket, like Robin Hanson’s recent concept of “maybe we should transform our government so it follows a futarchy prediction market focused entirely on trying to bring closer the date we achieve human immortality”.  Just so nobody can try to politically flank you by being more anti-aging than you – you can always pull out your insane plan to completely refocus all of society around longevity, as ironclad proof that (despite your AI safety views) you’re firmly in the anti-aging wing of the anti-aging party.  (You don’t then have to actually implement the insane plan IRL.)
• It might also make sense to proactively build stronger ties with today’s longevity community.  Fortunately we’re hardly starting from scratch here, as many anti-aging fans, such as Vitalik Buterin, are already very EA-adjacent or rationalist-adjacent.
• I expect the main focus of an e/acc preference cascade to be focused on medical cures and longevity.  But to the extent that it might also bring other transhumanist goods somewhat into the mainstream, it might be useful to get ahead of the ball on those as well.  (eg things like embryo selection for intelligence enhancement, human augmentation via neuralink-style devices, etc.  though unlike aging research which is perceived as merely kooky, a lot of this other stuff is actively toxic in today’s cultural climate and would be considered a huge PR hazard by most orgs!)

All the above stuff, taken together, sounds like I’m pitching a maximalist agenda for basically transforming EA/rationality into a longevity-focused movement, which would be absurd.  I’m instead just saying that we should consider doing some of this stuff, a little bit more than we already are.

The overarching idea here is that AI safety should be ready to present an alternative vision of radical longevity progress – one that isn’t reliant on breakneck general AI scaling.  This plan doesn’t necessarily need to explain how it’ll actually OUTPERFORM a plan of breakneck scaling at achieving medical progress (which might be unrealistic).  But during a preference cascade, we’ll need to address the issue in some way.  (Just like how it behooves Republicans to have some kind of notional plan even on issues that are unfavorable for them, like healthcare.  And similarly behooves Democrats to make some sorts of commitments on immigration, even if they’d mostly rather people not talk about the issue since it’s unfavorable for them.)  Lest we end up looking like the e/accs do in the current AI safety moment – losing the argument in the eyes of the public, reduced to weak rebuttals, weird ad hominems, and transparent attempts to change the subject.

The “support for FDA reform and scientific research” stuff earlier is important not just so we have a track record to point to, but also because after a longevity preference cascade, there will be strong public demand for taking action on aging.  People will want a roadmap, they’ll want big initiatives being launched and funded, and so on.  So, EA / rationalist groups should perhaps fund some of these things in small-scale now, with a view to being able to impressively scale them up later when the demand arises.  Cryonics also seems particularly relevant here as a shovel-ready plan that acts as a “Plan B” alternative vision for longevity.  Ideally the idea of a crash program to improve the science and scale up the practice of cryonics might be capable of relieving some of the desperate urgency and time pressure that the public might feel about the problem of aging after a preference cascade, and would answer some of the newfound political will to immediately “do something about aging” in a way that doesn’t involve further scaling AI.

What about the labs themselves; what should they do?

• Whatever AI labs (whether Anthropic, OpenAI, a biology-specific organization like Arc Institute, etc) find themselves in a position to announce newly-developed cures to the world, will have huge amounts of power to frame the ensuing discourse.
  • Consequently, they should think carefully about how to frame their announcements in a way that doesn’t fuel too much desperate e/acc enthusiasm on part of the mass public.
  • Unfortunately, this goal exists in tension with incentives to hype people up about the amazing humanitarian potential (and financial value) of their new technology to the maximum extent possible.
• Biomedical capabilities come hand-in-hand with scary bioweapons capabilities, so expressing the balance of risks vs rewards in a nuanced way would probably be helpful.
• Reiterating the fundamental message that “if we die to misaligned AI in the process of rushing for immortality, then obviously we don’t get the immortality!” will also be important for people to hear coming from the labs themselves.

Final thought: in all the above discussion I’ve made it seem like it’s important to have all these plans ready (to channel the energy unlocked by a longevity preference cascade) for the purpose of performing PR judo and deftly deflecting a movement that might damage the cause of AI safety.  But obviously there’s another good reason to have such plans ready in advance: properly directed, a longevity preference cascade could be really great!  It would be amazing to use all that energy for good, using it to fuel more ambitious medical progress in a way that isn’t existentially dangerous.  So, for that basic object-level reason, it would clearly be great to have some shovel-ready plans scoped out for rapidly scaling up ambitious medical research.

1. ^

   Longevity fans will complain that progress on aging is actually more tractable than the public thinks. I agree! But look at the history of the field: resveratrol? senescent cells? some mouse trials? None of this stuff has produced a genuine miracle for human life expectancy; and meanwhile there have been lots of weird fads and hype. It is very understandable that a distant general public (who has a harder time sifting fads from more promising science) perceives this all as snake oil, or at best overly-optimistic empty promises in the vein of “commercial fusion power is just 10 years away”.

2. ^

   I’m not sure what exactly how transhumanist a longevity cascade would end up looking like.  Would it mostly be focused on enthusiasm for curing normal diseases, like cancer or Alzheimer, without touching the still-taboo idea of anti-aging?  Could it possibly get crazier than anti-aging, and into other transhumanist goods, like BCIs or etc? Perhaps most importantly, how much would people connect the dots between medical progress and AI policy? Maybe everyone would get excited about medical breakthroughs but this would just translate happily towards pressure for more research funding, FDA reform, etc, without politically connecting to any AI issues? Or maybe people would go full on "worship the coming machine god" at the first whiff of some new AI-developed antibiotics for treating hospital staph infections, or whatever. Who can say!

Discuss

Parts of the math community, such as Henry Cohn and Grant Sanderson, are arguing that proofs have been a proxy for understanding, and now that proxy is broken. This is a response to LMs generating incomprehensible proofs, often formalized in Lean. While the proofs are verified, they lack the pedagogical value which has historically come along with new proofs. In the past we could typically assume at least one human in the world understood the novel insight required to produce a proof[1], but that assumption no longer holds.

I suspect we will need a new class of statement which contains statements which are proved but not understood, something the mathematical community can formally recognize as a contribution to the field. The understanding gives us the tools to do math, and the proof verifies that our understanding is correct, so we should ensure we have the language to communicate the state of both.

For now I will call this class of statement a compertum (Latin, neuter of compertus, ascertained; from comperire, to find out for certain). A conjecture is from the Latin conicere, to throw together: an inference assembled from the evidence. A theorem is from the Greek theōrēma, that which is looked at or beheld, from the same root as theory and theater. A compertum is something ascertained but not yet beheld, logically sitting between a conjecture and a theorem, a statement whose truth is known but not yet understood. I am curious what name the community lands on.

A recently resolved Erdős conjecture is a good example of a statement which moved from a conjecture to a compertum to a theorem. In April, Liam Price prompted GPT-5.4 Pro with Erdős Problem 1196 (a conjecture) and posted the resulting proof sketch to the Erdős Problems forum. The sketch was correct, and was then formalized in Lean (now a compertum), but the proof strategy was unfamiliar to people who had worked on the problem. In May, a paper by Alexeev, Lichtman, Tao, and five coauthors converted the sketch into a general method that the authors could explain and reuse (now a theorem), and with it proved a second Erdős conjecture and created a short new proof of the Erdős Primitive Set Conjecture. The transition from compertum to theorem was significant even though the statement was already proved: the understanding produced proofs for other problems.

One difficulty is measuring when a proof is understood, identifying that threshold for when a compertum passes into being a theorem. If we want to keep with the historical measure, as long as a person can write a correct proof on their own, then we know at least one person understands the proof. I think an interesting alternative would be an explainer (paper, blog, video, etc.) from which a peer reviewer can write a formal proof.

How exactly we define the threshold between this new class of statement and a theorem needs more thought, but I expect the existence of a new class of statement will become increasingly useful as proofs become increasingly opaque.

1. ^

   There have been computer-assisted proofs in the past, such as the Four Color Theorem and Hales's proof of the Kepler conjecture. In those cases a human reduced the problem to a large number of simple cases, which a computer then checked. That is closer to using a computer as a calculator than to a computer generating the insight a proof requires. This is a continuum, of course. Computers are now used for everything from evaluating expressions to finding and applying techniques from distant areas of math.

   We also have proofs which are the result of such large collaborations that no individual understands the whole proof, such as the classification of finite simple groups. This looks similar to mathematics as a whole: no person understands the entire field, but each step between lemmas and theorems is understood.

Discuss

I often try to understand the forces pushing the world towards AGI development despite the risks. It is clear that there are a bunch of different motives. There are market forces and national security competition, but I wonder to what degree we can attribute AI development to a deep dissatisfaction with the world as it is and a desire to transform it even at the cost of much risk. There is a great deal wrong with the world. But if we focus only on its problems, we may develop a distorted picture of what we are risking. To judge correctly, we also have to notice what is good and worth preserving. If we move forward recklessly, what do we stand to lose? Sunsets, dogs, friends, cookies, and infinitely many other small and precious things. Perhaps, if we cultivate gratitude, we would be less willing to gamble it all away.

Discuss

Epistemic status: >90% confidence in the principle, >70% confidence that mitigating these would be hard in practice, no full implementation yet.

tldr: it seems difficult for verification mechanisms to prevent chaining runs together or aggregating parallel ones; per-run FLOP caps could thus be covertly bypassed.

1. If governments want to regulate frontier AI training, one might want to cap individual training runs, e.g. putting bounds on the number of FLOPs per training run, and making sure each run starts either from scratch (i.e. random initialization at pre-training) or from a known model with accounted FLOPs (e.g. post-training, continuous pre-training, etc).

In the former we could have the initial weights generated by the verifier in some way, or ask for a proof that the weights were initialized with a verifier-controlled random seed. In the later we could ask for a proof that the weights corresponds to unaltered registered weights[1] from a previous training run.

This approach would allow to enforce agreements or regulation targeting a specific model (or model generation) based on FLOPS thresholds: triggering specific evals beyond a specific threshold and possibly proving that a hard threshold has not been reached.

2. However, it seems that there exist many techniques the trainer might use to covertly (a) chain sequential runs together or (b) aggregate parallel ones.[2]

Chaining works by finding a way to inject, at the beginning of the training, the weights of a previously trained model, either to replace a random initialization (pre-training case) or to replace declared weights with ones from bigger training run (post-training case).[3] One way to approach this could be to encode the smuggled weights in the training data[4], decode them when the run begins, then continuing with "normal" training. Some of those techniques might be computationally very similar to regular training.

Aggregation works by combining the results of several parallel runs a posteriori, such as through model souping.[5]

3. Thus, the effectiveness of regulatory regimes which rely on capping the number of FLOPs per training run is in question.[6]

4. A potential solution to avoid this type of attacks consists in not relying on per-run compute budget, but instead on a unique global training budget covering the entirety of the training runs. While preventing that type of attacks, global training budgets approaches still require the questions of global budget thresholds estimation and allocation to be addressed.

GPAI Policy Lab is building a research community tackling open problems in frontier AI verification. If you're interested in contributing, reach out!

1. ^

   Registration does not imply revealing the weights. A cryptographic signature (e.g. hash) can be publicly registered with a traceability certificate linking to the training run that produced it.

2. ^

   Such attacks would technically respect the letter of the FLOPs bounds, not the spirit. One might be able to mitigate these by adding restrictions, checking stronger properties of training (beyond literal FLOPs bounds), and putting strict controls on model weights; we're not sure these mitigations can be effective against very competent adversaries.

3. ^

   This injection does not require necessarily to inject the exact weights: getting close to previous weights could be enough.

4. ^

   Smuggling the full parameters suite at full precision is likely to require a very big part of the training data, questioning the efficacy of this solution, but a LoRa approximation would make it much more easy to conceal, and unfortunately very tractable.

5. ^

   Maiti, Shalini, et al. “Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance.” arXiv:2511.13254, arXiv, 17 Nov. 2025. arXiv.org, https://doi.org/10.48550/arXiv.2511.13254.

6. ^

   Let us emphasize that these attacks do not depend on the technical way one verifies FLOPs bounds, unless the technical mechanisms are explicitly designed to try and mitigate the issue, as gestured at in the note above.

Discuss

Some thoughts based on some personal experience,[1] something I think could make people and  organizations who depend on grant funding are more effective. Maybe particularly salient now, given the fast pace of AI development. Much of this also applies to employers as well as to ongoing funding relationships, not just grants.  

Costs of applying, incentives to invest in polish

Funding calls and grant applications impose substantial costs on applicants, cutting into the total effective value of the resources that ~"EA can deploy". A lot of work goes into prepping applications, making them look good, and making strategic and stylistic adjustments that aren't always aligned with adding value to the project, the ecosystem, or the world. The worst case is something like an all-pay auction, where in equilibrium the applicants between them invest something close to the full value of the grant in preparing their applications.[2]

Of course, there are also costs on the other side: it can take a lot of time for funders to process and adjudicate these applications.   

I think the EA grantmaking ecosystem does an OK job of holding costs down: short forms, word limits, applications presented as "just give us a first picture of what you do and why." But there's an unavoidable minimum-friction floor when substantial money and personal interest are at stake. Even a mostly effective-altruism-aligned and rationalist applicant has some personal interest, and also may tend to overvalue their own cause and approach relative to a perfect Bayesian (~winner's curse behavior). So it makes sense, from a private PoV, to tweak and optimize extensively.[3] 

Planning and calibrating 

There's a second aspect here. Once you've submitted, or while you're working through a grant and waiting to hear about renewal, you don't know whether you're far above the bar, far below it, or close to it. I made this point in a comment a couple of years ago: applicants may waste a lot of time exploring a very dark space. They don't know when to give up, how much to build fallback plans, how much to adjust the proposal, or in what direction.

A lot of this cost is in planning. If funding is very unlikely, the right move is to get on with the small, low-budget version of the work right now. If funding is likely, that same effort is better spent preparing to move quickly at scale: contractors, staff, compute. With short timelines on AI risk, and salient, actionable, high-impact work to be done right now this matters more. 

The cost of this uncertainty can particularly impact coordination and/or movement building. The people around an organization (volunteers, contractors, allies. etc).  who are deciding how much to engage with it are deciding blind too, and in spaces with several overlapping initiatives, nobody can tell which ones to coalesce around.

It also imposes avoidable costs on the funders and grantmakers themselves. ~The less certainty a nonprofit has about grant funding, the more likely they are to apply for additional grants, sometimes multiple streams from the same granter, which must be processed and adjudicated. Caveat:[4]

This applies beyond the period when an application is pending, which, to be fair, can be reasonably short if planned well. It applies at least as much to organizations you're already funding. Over the course of a grant, I imagine funders form and revise their views: what's going well, what they're having doubts about, how likely they are to renew. Grantees mostly don't see any of that until the renewal decision. If there were some light in this darkness, this would help the grantee prioritize and plan during the grant.  I'd love to asee a place a grantee could check for the funder's current assessment , an updated probability of further funding.

Also high-value: some notes on the things the funder is uncertain about and would like explained or justified better – this  can make aan eventual renewal application more concise and more useful to the funder too.

Funders could  consider sharing 

... both while an application is pending and during a grant:

A rough probability. "We currently predict a 65% chance you get this at your median request or higher." This can be given explicitly as a quick impression, explicitly noisy.   It could even be an AI's take based on context that is not shared with the grantee; tell  an AI or agent something like\

Check our notes and correspondence on the XYZ grant and predict the probability we renew it, and the distribution of funding in expectation. Do a scheduled update every 2 weeks, 1. Post it on the following password-protected (or anonymized) Grantee Checkin page which we will share with the (potential) grantee. 2.Update us with your rationale for that in our "Grantee portfolio space" but do not share the latter outside our organization. In the latter space, provide your best take on what's most in doubt about the application/organization, and prompt us to decide whether to share this in the Grantee Checkin page as well

Again, I think grantees want to know, in particular, 

• Their probability of success at different funding levels  
  • And it's OK to say "you're well below the bar and this looks like a mismatch with our goals", ideally providing at least some justification and epistemic basis (but not necessary)
• What's convincing and what's in doubt, to be able to focus their communication work, and their actual practical work. ~"We're convinced about this part of what you do. We'd want to hear more about that part."
• Grant decision timelines, and whether things are being delayed

The cost-benefit of doing this

This is not free, and I see some reasonable objections. Still, I think offering quick and course private signals has a pretty good benefit-cost here.

Linch highlights the time and opportunity costs --  I think these costs are substantially lower using AI tools. He hints at emotional and political issues getting in the way, and some fear that applicants would overupdate on feedback based on fast takes: I suspect that most EA-aligned grantees/applicants are not too vulnerable to this. And it's ameliorated by carefully generated epistemic status. "This was a probability generated by AI based on our unshared notes" also seems much less likely to lead to overupdating. 

Theere are other issues involving information cascades: these mainly only apply if the feedback is made public or widely shared, not if it's only shared with applicants and grantees.  (Okay, I'd be a bit careful with this:  a specific number like "30% likely to pass our bar" is perhaps more portable and legible, so perhaps more likely to leak to other funders and cause these negative cascades.)

I suspect legal issues are less important here.[5] 

Costs don't seem terribly high to me[6].

On the stated precision: I'd rather funders gave their best guess at a number and made clear it's rough than withheld it. People in this ecosystem are used to estimates like "67%, low confidence" by now, and I doubt many will mistake one for a precise, calibrated forecast. But if buckets (likely, unclear, unlikely) feel more comfortable, that's still much better than nothing. Giving a base success rate also helps. 

Anyone doing this/willing to try it?

Curious: To what extent are funders doin this? Would any funder be willing to try the explicit forecast part? I think even a probability plus an expected decision date, sent at a fixed point after submission, and a light quantitative check-in and steering in the middle of a grant period seems better than silence.  I'd be glad to help design or evaluate a pilot, and perhaps it's something I could help trial and facilitate in context related to my work. 

1. ^

   Mostly experience on the grantee and applicant side, but indirectly in the context of being an employer and contractor.  

2. ^

   I let AI remind me/check about the game theoretic and actual game theory results and economics experiment lab evidence :

   The all-pay auction is the right analogy rather than the war of attrition: both are contests where losers don't get their effort back, but in a war of attrition everyone pays the losing bid, since the contest stops when the weaker party concedes, whereas in an all-pay auction each player pays their own bid. Krishna and Morgan (1997) describe them as second-price and first-price all-pay auctions respectively. Grant applications are the first-price case — nobody gets a refund when a rival gives up. 

   On magnitudes: in the standard complete-information models, expected total expenditure can't exceed the prize, since a player who can bid zero must earn non-negative expected profit, and summing that over players bounds total spending by the prize value. Baye, Kovenock and de Vries (1994) prove this for the Tullock contest at every returns-to-scale parameter, and attribute earlier over-dissipation claims to non-equilibrium solutions.

   What does exceed the prize is realized spending: the same authors (1999) show the probability of ex post over-dissipation is one half with two players in a perfectly discriminating contest, falling to about 0.44 as the number of players grows. Experimentally it's worse than the theory — Dechenaux, Kovenock and Sheremeta's survey reports total effort "often twice to five times higher than the common prize value," and Sheremeta puts the median overbidding rate at 72%. 

   Two caveats in the other direction. First, the full-dissipation result belongs to the symmetric mixed equilibrium; the complete-information war of attrition also has asymmetric pure-strategy equilibria that burn nothing (Hendricks, Weiss and Wilson 1988), and Georgiadis, Kim and Kwon (2022) argue the mixed equilibrium doesn't survive adding noise to payoffs. Second, with asymmetric valuations the strongest contestant keeps positive rents and dissipation is incomplete, bounded by the second-highest valuation. Applicants to a given fund are very heterogeneous, so I'd expect real dissipation well below 100% — which is why I say "something like" and don't lean on the equilibrium result. ↩

3. ^

   As an applicant, you want to get under the word count while still putting your best foot forward, in a way that will appeal to a range of possible readers. Even very short forms can eat days, more so when several organizations or team members are involved and have different views about what will work. And refining applications down to the precise right word or character count might be a fun exercise for wordsmiths, but it's a time-consuming one.

4. ^

   There may be cases in which sharing more information leads nonprofits to apply for more grants, e.g., if they were initially confident of 95% success, and the information moves them to 50%. I'll think more  about the equilibrium expected  effect of this.

5. ^

   In employment consequences there are legal issues to take into account, like getting sued or discrimination, with 'anything you share with applicants used against you' My weak take is that even these concerns are a bit overblown, and I suspect this is less important  in grantmaking space.

6. ^

   Fable suggests:
   > Funders already forecast. Open Philanthropy has made thousands of explicit probabilistic predictions during grant investigations and scored them. The Survival and Flourishing Fund asks applicants how likely they think their other fundraising is to succeed, and asks them to be quantitative about their own confidence.  The US National Institutes of Health gives every applicant a percentile and written critiques within about a month.

Discuss

If we think parts of MLP0-MLP3 are computing [a sorting algorithm], we can replace those parts with [a sorting algorithm] and check reconstruction loss.[1] However, reconstruction loss is not enough.

Suppose we replace MLP0 with two things:

1. Its mean activation - simple, but poor reconstruction
2. MLP0 - perfect reconstruction, but no reduction in complexity

We can visualize this as a pareto frontier trading off reconstruction with "simplicity". Ideally we achieve perfect reconstruction with perfect simplicity.[2] For more intuition on the pareto frontier, we could have an MLP that clusters all inputs in two clusters: "early positions" and "late positions", which would be slightly more complex than the mean.[3]

We can make this an RLVR environment, if only we could clearly...

Define "Simplicity"

Defining simplicity has been complex. But what do we want from a perfectly decomposed model? If we've "perfectly decomposed" a model, then I'd expect ideal circuits to fall out, with "ideal" meaning:

1. Help predict OOD behavior
   1. Given an [addition] circuit, we can know which types of inputs it'll succeed & fail on (and why)
2. Be extractable & minimal
   1. The smallest part of the model that does [addition]
3. Be removable w/ minimal harm to unrelated circuits
   1. Affects [addition] but not unrelated tasks like [bracket closing]

Currently, I believe we want "circuit simplicity" defined as having a small number of nodes and edges:

• Nodes - variables like "numbers", "dog-like", "within quotation marks"
• Edges - causal links. Ideally "dog-like" is computed by a small number of earlier features (eg "dog ear", "dog tail", etc).

Additionally, an ideal decomposition should tell us which features are reused. If two nodes affect all the same downstream nodes, then you can combine them. This reduces both the number of nodes and edges (similar to refactoring code).

In contrast, a complex, dense graph would be two MLPs.

MLP = D(ReLU(Wx))

We can define the "nodes/features" as the hidden dimension:

Nodes = ReLU(Wx)

These nodes are bad because they're polysemantic: they have different meanings at different activation ranges.

When you look at which Nodes in layer 1 are causally important for Nodes in layer 2, it's a dense graph (ie each node is caused by a large number of previous nodes).

Tensor Networks Don't Solve This Issue

In the simpler case of two bilinear layers, you can simply matrix multiply them together and see exactly which hidden dimensions/nodes of the first cause the second directly from the weights. Sparse connections can even be directly optimized for this way.

HOWEVER, we still have a dense graph. The main problem here is our model isn't basis-aligned (ie each coordinate of activations being monosemantic).

With that said, I believe that tensor networks give us better tools to find the correct basis, I am simply too confused atm to make the most of it.[4]

Red Herrings of Simplicity

1. Bits of information - You can quantize a model into fewer bits of information (float32 to float16), but I won't understand the model better than I used to (so is a bad metric as stated).[5]
   1. Alternatively, I do think an MLP mapping inputs into two clusters is indeed simpler than one that maps to 50k clusters which feels like bits-of-info related.
2. Low Rank - If my mlp only writes to/reads from a rank 20 subspace, that seems useful to know (because it constrains what other MLPs/attn heads it interacts with) but it's not simpler.
   1. eg If I know a part of the model does [parity], then it's not important if it's rank 2 or 20. If they're caused by the same number of upstream components (and vice versa for downstream components), then that's same number of edges.
3. Low Features per Datapoint - SAEs & PD (parameter decomposition) care about this. PD cares about the smallest number of "mechanisms" for the full computation path input-to-output.
   1. On its own it fails (define the whole network as 1 feature); you also want a small number of total features to choose from. In hand-wavy terms, we want a simple dictionary (choice of features) and for datapoints to be simply explained, conditioning on that dictionary (...I think?).
4. Functional Complexity (?) - I have bilinear layers that are a sum of 1k quadratic equations. Maybe you only need 200 quadratic functions and the rest can be approximated as linear (I think this is helpful, but am unsure).

How to Gain Tractability

If we know how models represent [redundancy, circuit suppression, compositionality, hierarchical structure, DAGs, etc], we can decompose modules by just looking at the weights (using tensor transformers). But we don't know those things, so we don't know the right ontology to use when defining "simplicity". There are ways we can gain traction though!

Tract 1: Death Success by 1000 Circuits

With many circuits in a real LLM, we can find components that are shared across circuits. They might read from the same subspace (activation-based) or use the same MLP (weight-based). However, whole MLPs (and whole attn-heads) are the wrong fundamental units to use. Two circuits may use the same MLP but different parts of it.

If we understood which parts of an MLP 1000 circuits shared and didn't share, then that'd be our decomposition. We would then have a ground truth label for how we should've decomposed that MLP eg "Oh there's clearly some hierarchical structure here once you see how it interacts with later attn-heads". Then we can directly decompose the model from the weights from the beggining, with all the circuits falling out naturally.

Tract 2: QK OV Circuits but for Everything

In Anthropic's Mathematical Framework for Transformer Circuits, they introduced QK & OV circuits. They stopped there because you can't compose other parts of the model together due to nonlinearities such as softmax or GeLU. With a tensor-transformer, you can compose everything with everything, such as embedding --> attn_0 --> mlp_0 --> unembedding.

We can compose any intermediate attn/MLP components together, for any computational path through the network, forming a new sub-tensor that is that computational path. That sub-tensor might have structure (hierarchical, DAG, etc) that individual components didn't.

This property of compositionality is extremely powerful!

Another way to take advantage of this is to take a specific vector from:

1. An unembedding row (eg "myself")
2. An unembedding diff (eg "myself" - "yourself")
3. Classes in the unembedding (eg numbers "1", "2", ... might be in a similar subspace)
4. Steering vector

You take that vector/subspace and make that the end point of your computational path (ie you can matrix-multiply that vector with any intermediate matrix from Attn_out or MLP_out). Tensor transformers allow you to specify precisely any hypothesis, BUT they don't tell you which hypotheses you should consider, hence I haven't solved mech interp yet.

Tract 3: Interpreting Small Models

We can have small models trained on algorithmic tasks (algzoo) which already exhibit many behaviors we don't know how to approach. For example, training on CE loss on softmax-ed logits incentivizes circuit suppression: bird features can upweight the "bird" class AND/OR downweight other classes. It's more about a class's relative logit value compared to other classes than its objective value. AFAIK, we don't have a solid understanding of how models represent this.

Additionally, models are likely a mix of compositional functions/features and memorization/look-up tables. We can directly tackle this like Linda Linsefors does by training a toy model of memorization. This allows us to better analyze the model in silico as well as build out tools to detect and tease out the structure, while varying hyperparameters.

Again shoehorning tensor networks, if we can state our hypothesis as a tensor, we can directly compare from the model weights whether they're equivalent.

Big if True

If we can deconfuse ourselves enough, we can fully realize mech interp's potential as a verifiable task. This would be a big deal!

We can train models of varying layers, model width, datasets, taking checkpoints and THEN get SOTA models to solve this with RLVR. With enough data of models being reverse-engineered, we can solve training dynamics, how data leads to algorithms, task-ai, etc.

We'll still need to solve value-alignment, of course, but maybe that could be a tensor network too?[6]

If you would like to help in this project, you can always dm me here or on discord at #loganriggs. I'm currently working out of this repo trying to reverse engineer the 500M parameter tensor transformer I trained last year.

1. ^

   Explaining Attention with Program Synthesis does this with program synthesis, replacing attention patterns with 1000 programs for up to a 3B param model.

2. ^

   This is the case for one Attn head in my model that is basically a bias (for outlier dimension reasons).

3. ^

   The mean or a constant bias are both quite interpretable. They're the same for all inputs, so we don't have to reason about "conditioning on these tokens previously, the output will change like so". It's a constant bias, dead simple.

4. ^

   This post is partially a cry for help; I've only worked on this for 2 months, but would appreciate more eyes on this problem.

5. ^

   Plausibly having only -1, 0, & 1 be valid weights might make our job easier though. Haven't thought too much about this.

6. ^

   jk jk. But maybe??

Discuss

As transportation improved and distant villages grew closer, many cultures started standardizing their systems of measurement to make commerce easier. None, however, went as far in their attempts at unification as the kingdom of Enopolis. Their practice, which historians have come to refer to as Koinometry, was based on a single universal measurement unit called the Metron.

This initially posed some challenges, as it was not immediately clear how, for example, one Metron of time should compare to one Metron of distance. To resolve this, a central institution was charged with adjusting the standard unit to each particular context of use.

To keep numbers in a range that was comfortable to calculate with, the unit was eventually adapted not just to the properties it measured, but also to the objects of which those properties were measured. One Metron of money thus referred to a different amount when buying a house than when buying wine at a tavern, and one Metron of length referred to a different distance when measuring out knitting yarn than when describing astronomical measurements.

The incommensurability of the monetary Metron caused considerable confusion, until a council of experienced metrologists arrived at a straightforward simplification: One Metron of money was henceforth to refer to the price of one Metron of the goods being traded.

Thorne (1827) recounts the example of a merchant greatly expanding their business by offering delivery of one Metron of grain annually to households under a standard agreement, while local government metrologists took care of adapting the unit to regionally prevailing household sizes and dietary customs. In the following decade, grain shortages threatened this arrangement, but since consumption fell along with the bad harvest, the Metron was adjusted and contracts could still be fulfilled.

Practices around the measurement of time are of particular interest. At first, the length of a temporal Metron depended only on what kind of time was being measured. A day of idle time might have measured between 20 and 25 Metrons from sunrise to sunset, whereas a feast of similar duration was usually considered to have lasted two to three.

This was eventually deemed insufficiently precise, as people found that uneventful stretches dragged on but compressed in hindsight, and vice versa. It was therefore decided to make the temporal Metron’s length depend not only on the activity it represented, but also on the perspective, multiplying pleasant Metrons of time after the fact and dividing idle ones. The inactive day would thus turn into about five Metrons a week after the fact, whereas the feast might expand to something around thirty Metrons when looked back on.

Employment agreements were eventually exempted from retrospective adjustments based on subjective experience, as the resulting disagreements proved a great burden on the Enopolitan economy. Unger (1837b) reports repeated discussions of defining temporal Metrons of work in terms of their productivity, but this appears not to have been enacted despite multiple attempts at reform.

Karashima (1912) documents a notable legal dispute involving a worker preparing food at the large wedding of a senior administrator of the metrological academy. The administrator’s marriage unraveled over alleged infidelities after five (contemporaneous; this was later adjusted) Metrons. While retroactive adjustments to the worker’s experience of catering to the guests were explicitly ruled out by employment law, he maintained that the failed marriage should change the duration of the festivities themselves, and demanded a corresponding adjustment to his wages.

Several guests had testified that the festivities now seemed less significant than they did at the time, making them somewhat forgettable. At first glance, one might expect this to lower the worker’s pay, since insignificant time compresses in hindsight, and his shift was thus now shorter than if the marriage had worked out. He argued, however, that what mattered for compensation was the contemporaneous duration, which for insignificant time was considered longer than for joyful occasions. The outcome of the case is not entirely clear from the historical record. Although it is known that a settlement was reached for an amount of two Metrons, the value of this cannot be fully reconstructed based on the available information.

The practice of Koinometry flourished for about five centuries (the most accurate duration in Metrons remains contested among scholars). Eventually, the growing inertia of the increasingly complex governing institution appears to have made the Metron slower to adapt to changing contexts of use, while increased economic and cultural exchange in the region accelerated the pace of cultural change. In consequence, its use began falling out of favor as confusion over its correct application grew and increasingly caused friction.

A variety of informal measurement units appear to have emerged during this period. Notably, some of these aspired to Metron-like generality, whereas others deliberately focused on narrow applications in the hope that this would improve their consistency and make them easier to reason about.

Many of the latter units were exceedingly narrow in scope. One example, reported by Nakamura (1887), is that of a unit of volume to be used only for beer brewed from a specific strain of hops and served at a specific temperature. That temperature was in turn given in another highly specific unit only to be used for the same kind of beer, the non-circular definition of which is thought to have existed at the time but has been lost to history. The government at first tried to maintain conversion tables between many of these units and the Metron, but eventually prohibited their use when this became infeasible due to their rapid proliferation.

The ensuing general discontent nearly culminated in what would have been a devastating war over trade-related disputes between Enopolis and two adjacent city-states. However, armies found themselves facing considerable difficulties accurately measuring distances on maps of enemy territory, which not only used different locally defined units, but also often needed conversion between Metrons used for measuring distances on maps and those used for measuring distances on the ground.

Scouts also often reported the length of marches in temporal Metrons, leading to discrepancies between durations reported by someone who had walked alone and someone marching as part of a large unit. This slowed down advances and thwarted attempted attacks, which won some time for diplomatic negotiations eventually leading to a peaceful resolution.

Hansen (1729) reports that considerable effort went into agreeing on exact borders and terms of trade, as conversion factors into a large variety of units used by neighboring countries had to be worked out and documented. The resulting treaty is notable as the only preserved Enopolitan document whose numbers are interpretable without consulting additional metrological tables.

Discuss

When frontier labs like Anthropic and OpenAI publish safety or alignment research, it is often entirely empirical, closed-source, and sparse on methodological details. While it is great that they publish these results, the status quo is that labs (or soon, their agents) can claim alignment progress that no one independently verifies. The AI safety community has replicated or stress-tested some claims, but it's nowhere near comprehensive, and we expect this kind of meta-science to remain systematically neglected.

We argue there should be a dedicated effort to

1. Replicate alignment experiments from frontier labs.
2. Scrutinize the experiments by stress-testing the methodology.
3. Open-source replications to encourage external researchers to validate our work, build on the experiment, and further audit the lab’s methods.

The case to replicate safety research from labs

CEOs and employees at AI companies, somewhat regularly, say that the technology they hope to develop could cause human extinction. However, their research to prevent this is often released without code or even basic methodological details (e.g., Teaching Claude Why, Beneficial RL)[1].

There’s good reason to think some of these results could be fragile. Prior safety results can be contingent on details that are easy to miss, like the pinned OpenRouter provider or LoRA alpha. Some researchers have told us directly that they think there may exist some arbitrary methodological choices in their own research that could plausibly change results. A replication is a basic test for this. An independent team will inevitably make many choices differently and is unlikely to share the same bug, lucky seed, or prompting failure.

If an experiment doesn't replicate, this is strong evidence that the finding won’t generalize well in the future. An alternative explanation for a failed replication is that the replicators themselves did something wrong. But if a skilled team[2] works for an extended period of time, asks the authors for help, and still can't reproduce the result, some of the blame falls on the lab.[3] It tells us the lab is not releasing safety research that is easily externally verifiable, which is a kind of result within itself.

Model cards may be an especially high-leverage subject for replication. When a lab claims that they are releasing their “most aligned model ever,” we could replicate and stress-test their evidence.[4] These replications could also be applied to frontier models with less comprehensive model cards to effectively fill in what the lab left out.

Replications are not shiny, but that’s precisely what makes them counterfactually useful

The case for replications is somewhat self-evident. It is not an exaggeration to say that most AI safety researchers would agree that someone should do this work, and yet, little effort is paid in its direction.

Across scientific disciplines, replication is systematically neglected because the work is unsexy and novel research is exciting. It may also be unattractive because it feels less likely to result in publication or land you a job offer. Regardless of the explanation, the work is not currently happening at the scale it should, and there isn’t strong reason to believe this will change without targeted effort.

The case to stress test

Labs present safety research as progress toward aligning frontier models and, perhaps eventually, superintelligence. If we take this at face value, the claims deserve auditing effort proportional to those stakes.

In many cases, stress testing looks like replicating the experiment across a wide variety of settings to identify effect size, trends, and alternative hypotheses. This could include obvious things like hyperparameter sweeps, testing different model families, or running additional evals. In other cases, it may be necessary to perform robustness checks that are more specific to the experiment at hand:

• Looking for cases where the baseline was weak or misconfigured
• Testing if a causal claim is not instead downstream of a non-obvious correlation
• Reading through transcripts to see if the descriptions from the paper qualitatively match model behavior

One limitation of stress-testing is that it can't tell you if a result is relevant to alignment of superintelligence or if the paper communicated in a misleading fashion. Raising issues related to high-level methodology or communication is often better suited to position-paper-like blog posts, which we also think the field could use more of. However, stress-testing many papers could reveal patterns that bear on these questions: if clusters of research don’t hold up to scrutiny, this could help build consensus on alignment being hard or certain techniques not being particularly promising.

In most cases, we would probably do both empirical stress testing along with some limited non-experimental analysis. Even when stress-testing claims, the goal is to be balanced, and not overemphasize flaws (see more of our thoughts here).

The case to open source

It is currently difficult for third-party alignment researchers to interface with lab-released work. Before building on an experiment, you have to replicate it, often from a paper which may include few methodological details. Open sourcing replications would allow external AI safety researchers to participate: they could use the code to further stress-test a lab’s research, build on it, or understand it more deeply. The hope is that this could make the AI safety community more coherent, self-correcting, and likely to build consensus on important facts.

Equally important, open sourcing replications allows other external researchers to check the replication’s own claims and methods.

Replicating frontier lab work is difficult but tractable

Replicating at a frontier scale is time-consuming, requires some taste, and can be expensive. But these bottlenecks can be overcome, and we think it's worth the effort.

First, stress testing any method is messy. We expect claims from labs to replicate in some ways but be flawed in others. Trying to disentangle what replicates cleanly, what generalizes poorly, and what the high-level story is will require good researchers and time. But intentional recruiting and competitive salaries could solve this. Likewise, compute for replicating at a frontier scale can be expensive, but fundraising is tractable.

What cannot be unbottlenecked is access to frontier model weights. That said, open-weight models trail the frontier by only ~4 months, so if an experiment doesn’t replicate on them, this still represents a relevant datapoint.

Conclusion 

History would tell us we are playing with fire. Psychology and preclinical cancer biology have indexed on misleading or false claims that sensible meta-science could have uncovered. Machine learning has its own versions: a lot of deep RL results from the mid-2010s turned out to be difficult to reproduce and adversarial machine learning results often fail to hold up to scrutiny. Similar structural issues may exist in AI safety today.

Meta-science at a frontier scale is unglamorous and unincentivized. But it’s also the cheapest insurance we have against repeating history.

Thank you to Anastasia Wei, Coby Kassner, and Rhea Kanuparthi for helpful feedback on this post.

1. ^

   We are currently working on replicating both of these.

2. ^

   The bar is probably at or above the level of a MATS mentee or Anthropic Fellow.

3. ^

   One caveat is that it’s possible results don’t replicate because the models being tested are sufficiently different. For example, if OpenAI’s goblins results didn’t replicate on other models, that of course would be unsurprising.

4. ^

   Because some experiments require visibility into the model's CoT, there are inherent limitations to how comprehensive this could be.

Discuss

LW disclaimer: This piece is aimed at people who don’t know much about how LLMs work; I think the discourse would be much better if more people knew the basics. So most LWers are not in the target audience. I’m posting it here in case people want to pass it along to people in their lives who are in the target audience. Substack version minus this disclaimer is here.

AI can be confusing. But there is a certain amount of baseline information about what AI is and how it works that can be extremely helpful, both for making decisions about how to use AI for yourself, and understanding the claims you hear about it in the news and on social media.

My goal with this post is to give you that basic information. I am by no means an expert—I’m a philosopher by training, not an AI developer or even particularly a power user. But I’ve been using AI basically since ChatGPT came out, and trying to keep up with developments in a casual way for longer than that. And this is the information I have found helpful for understanding AI and interpreting what people say about it. After reading, you’ll hopefully understand why people call AI “just fancy autocomplete” (but also why that’s misleading), and how the people who say it’s really dumb and the people who say it’s really smart can both be right.

I. Which AI?

AI is not one thing. Self-driving cars, image classifiers, recommendation algorithms, chess AIs, predictive policing algorithms, and large language models are all “AI,” but they’re as different as cars, planes, boats, and bicycles. Thinking of all of these different technologies as just “AI” would be like thinking of all those different forms of transportation simply as “vehicles,” with no more specific concepts to differentiate them.[1]That would be really confusing, given how many important differences there are between them. So when someone says “AI will/won’t do X,” your first question should be “which AI?”

(If all these kinds of AI are so different, what unites them? Briefly: they all involve systems that learn patterns from data rather than following hand-written rules. But that’s often where the similarities end. And to make it even more confusing, some things that people call “AI” do follow hand-written rules.)

This piece is about large language models (LLMs) specifically. This is the technology behind ChatGPT. Some of what’s said here generalizes to other kinds of AI, but much of it doesn’t. But it does apply to different models within the class of LLMs. (I’ll sometimes call these things “chatbots,” because that’s how most people first encounter them. But we’ll see by the end that, nowadays, they can do a lot more than just chat.) People often treat “ChatGPT” and “AI” as synonyms (the same way they treat “Kleenex” as a synonym for “tissue”, or “Google” as a synonym for “search”). But there are several competing models from different companies (OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini (and AI Overviews), and others, including Chinese models like DeepSeek and Kimi), and they have meaningfully different strengths, weaknesses, and even “personalities.” Treating your experience with one model as representative of “AI” is like evaluating all smartphones based on one brand.

And even within a single company’s product line, “ChatGPT” isn’t just one thing. GPT-3.5 is a different model from GPT-4o, which is a different model from GPT-6 Astra; they’re not all just incremental updates to the same system. If the last time you tried ChatGPT was when it first came out, there’s been a lot of changes since then. For a recent guide to which models to use and how, check out Ethan Mollick’s “An opinionated guide to which AI to use to do stuff” (he generally writes a new guide every few months, this is the most recent as of this post’s publication). (For myself, I’ve mostly decided to just stick with the Claude family of models and not worry too much about trying to keep up with releases from other model families.)

Here’s the plan: I’ll explain how a neural network works, then walk through how an LLM is built in stages. It’s trained first to predict text, then to act as a helpful chatbot, then to reason through hard problems and use tools as an agent. I’ll end by arguing that the best way to understand what these systems can do is to use the best available models yourself.

II. It’s Not Magic, But It’s Not Normal Software Either

The first thing to know about LLMs is that they’re made of math. Interacting with them for the first time can be a bit like going to a magic show—you leave with the impression they’re doing something impossible. But just like a magic show, there is a non-mystical explanation for how they do what they do: mostly it’s a bunch of addition and multiplication.

But unlike a magic show (and unlike ordinary software), we don’t actually fully understand the non-mystical explanation for what they’re doing. We don’t write an LLM like we do other software. Instead, we write a program that trains the LLM. So there are two programs: the training program, and the LLM itself. We understand how that training program works. And we understand on a basic level why it results in an LLM that’s able to do the task we’re training it for. But we don’t know why it works as well as it does. And more importantly, we don’t know how the trained LLM works on the inside. We can see every calculation it makes, but not how those individual calculations add up to doing the task it was trained for. One way to look at it is that an LLM is “grown, not crafted.”[2]

LLMs are a type of neural network. The really basic version of how a neural network works is this: you have a program with billions of dials that you can tweak, and those dials (called the “weights”) determine what the output of the program is. You have some problems for the program to solve, and an answer key. You give the program a problem (for example: given a photograph of a handwritten digit, identify which digit it is), and it spits out an answer—at the start this answer will just be random nonsense. But then you tweak the billions of dials in whatever directions they need so that it will give a slightly better answer on the problem the next time. Then you repeat this for billions of training examples, and at the end it has been trained to actually do the task well. For more on this, I implore you to go watch 3Blue1Brown’s excellent series on this topic (particularly the first three videos on neural nets in general, and the 7 minute introduction to LLMs). Indeed, one of the reasons I wrote this post is to beg more people to watch those videos.

III. How to Grow an LLM in Five Easy Steps

What I want to do with this section is give you a mental model for how an LLM works under the hood. It’s not a full technical explainer—if you want that, go watch Karpathy’s “Deep Dive into LLMs like ChatGPT” or read UnderstandingAI’s “Large Language Models, Explained With a Minimum of Math and Jargon” (from which I’ve learned a lot, and frankly stolen a lot for this piece). But it should be helpful to understand what is going on on the other side of the screen when you’re talking to ChatGPT or your LLM of choice.

i. Pre-training (aka the base model)

When you’re growing an AI, the first ingredient is a neural net (seriously, go watch those 3Blue1Brown videos). The second ingredient is an entire internet’s worth of text.

What you do with that is you train the neural net to predict the text. You give it bits of text, and ask it to predict the next word. So with this post, you might feed it “Where Do Chatbots Come From? What I Wish Everyone Knew About AI in 2026. AI can be confusing. But there is a certain amount of baseline” and then ask it to predict the next word (which in this case is “information”). If it gets it right, you reinforce the neurons that led to the prediction. If it gets it wrong, you reinforce the neurons that would have led to getting it right. (It’s actually slightly more complicated than this: the model doesn’t just output a single word as a guess. Instead, for every word it knows, it tells you how likely it thinks that word is to be the next word. And you reward it for giving a high likelihood to the actual next word.) Then, you repeat this again and again, billions of times, for essentially all the text on the internet, feeding it each web page word-by-word, and asking it to predict the next word. How good the resulting model is depends heavily on scale—the size of the neural net, the amount of text you train it on, and the amount of computing power you use for training. Bigger models tend to be smarter, and this has held up remarkably consistently. (This is why AI labs are spending billions of dollars on training runs and massive data centers—they’re betting that the trend continues.)

When you’re done with this process, you have what’s called a “base model.” It can take in any given string of text and produce a likely continuation. For example, when I gave a large base model (Llama 3.1 405B) the text “The weather today is ”, I got back:

The weather today is expected to be clear and the maximum temperature will be 25°C which is similar to yesterday. The weather tonight is expected to be clear and the minimum temperature will be 13°C which is similar to last night.  حالة الطقس ودرجات الحرارة المُتوقعة في السعودية يوم الخميس 9/12/2021 Saudi Arabia | A state of atmospheric instability, starting from Monday, accompanied by rain in many regions Saudi Arabia | A state of atmospheric instability, starting from Monday, accompanied by rain in many regions Saudi Arabia - Updated 12:30 PM: The continuation of the rainy situation and the chances of rain in these areas during the coming hours Saudi Arabia - Updated 12:30 PM: The continuation of the rainy situation and the chances of rain in these areas during the coming hours Jeddah | A massive wave of dust and a significant decrease in horizontal visibility in the coming hours Jeddah | A massive wave of dust and a significant decrease in horizontal visibility in the coming hours Saudi Arabia | A state of atmospheric instability, starting from Monday, accompanied by rain in many regions Saudi Arabia - Updated 12:30 PM: The continuation of the rainy situation and the chances of rain in these areas during the coming hours.

Notice how this looks like something that might show up on a weather site. It begins with a forecast, and then goes on to give some kind of news about the weather in Saudi Arabia.

And if I type in “What is the weather today?” I get back “That’s a simple question that we often ask. But for some, it’s not a simple question at all. The weather plays an important role in our lives, and it can have a big impact on our daily routine [...]” and so on. Notice that it does not answer the question, the way a typical chatbot would. Instead, it continues with something that might plausibly follow that question on a typical web page.

(If you want to poke at a base model yourself, Transformer Explainer runs a much smaller one right in your browser, and shows you the probabilities it assigns to each possible next word.)

One side note to all this. I’ve been writing as if models predict the next word, and I’ll mostly continue to do so. But technically what it predicts are word fragments, called “tokens.” If you want to look at how a model sees your input, play around with Tiktokenizer. For example, if you type in “How many licks does it take to get to the center of a tootsie pop?” to GPT-4o, that gets divided up as: “|How| many| l|icks| does| it| take| to| get| to| the| center| of| a| to|otsie| pop|?|” So “licks” is actually two tokens: “ l” and “icks”. And even most of the tokens that represent whole words are actually “a space plus the word.” On top of that, each token is just represented as a number to the model, so what it sees is “5299, 1991, 305, 8736, 2226, 480, 2304, 316, 717, 316, 290, 6170, 328, 261, 316, 2677, 396, 2735, 30”.

Why word fragments? You could build a model that took whole words as input. You could also build a model that took individual characters as input. But it just turns out to work best if you give the model word fragments.

So why is this important? Well, it can be helpful for understanding certain cases where models tend to make mistakes. For example, at least for a long time, if you asked most LLMs “How many ‘r’s are there in ‘strawberry’” it would tell you there were two. This is because of how the word is represented in tokens. For example, GPT-4o sees “strawberry” as “|st|raw|berry|” (or, again, more accurately, “302, 1618, 19772”). So it doesn’t actually see the individual letters, and it has to kind of guess about how many “r”s there are.

It can also be useful to know because if you’re using an LLM, your usage limits are defined in terms of tokens. You can generally pay for models either as a bulk plan (e.g. $20/month), or pay-per-token using the API. With the monthly plans (or on the free plan), they basically let you send a certain number of tokens within, say, a 5-hour period, and then cut you off until your limit resets. With the API, you’re simply paying by the token (usually some fraction of a cent per token). So it can be useful to know that a 1000-word message will actually probably be more than 1000 tokens, etc.

That said though, for the most part, you can just think of models as “predicting the next word” and be fine.

Anyway, at this point, the model is effectively a “web text simulator”: it will produce plausible continuations of text as it would appear on web pages (and whatever else was in the training set). So there is a grain of truth to the idea you sometimes hear, that models are “fancy autocomplete.” But only a grain. They are indeed very, very good at basically the task that your phone’s autocomplete is doing. But, as we’ll see, “fancy autocomplete” is underselling what LLMs can do, to the point of being actively misleading, especially when we add the further stages of training we’ll talk about later.

Here’s one thing you’ll miss if you think of them as just “fancy autocomplete.” As a byproduct of predicting text well, models encode real knowledge about the world. I’ll give two examples. First, to correctly complete a sentence like “The color of a stop sign is ”, a model has to know the color of a stop sign. Extend that across billions of other sentences, and you get something that has absorbed an enormous amount of factual knowledge, not by being told to learn facts, but because that knowledge was helpful for predicting text.

A second example: models can output simple code in graphics programming languages like SVG or TikZ. And if you ask it for, say, code representing a picture of a dog, and then you run that code, you get this:

Probably better than I would do under the circumstances. And this model was never trained on images—it had never “seen” a picture of a dog. Nowadays, models are trained on images and audio alongside text, producing what are called “multimodal” models. This is essentially the same pre-training process, but with tokens that represent fragments of images or audio mixed in alongside text tokens. But even without that multimodal training, they can still learn a lot about how the real, actual world works, just by learning to predict text.

ii. Instruction fine-tuning

Okay, so now you have a base model. What do you actually do with it? It doesn’t converse with you; all it does is continue text. If you ask it a question, you get what would plausibly follow that question on the internet.

Initially, people realized that they could get useful work out of the models with clever prompting. For example, if you put in some text followed by “tl;dr” you could get a pretty good summary of the text, because, on the internet, after “tl;dr” there’s usually a summary of whatever text came before it.

But how do you get it to talk to you? Basically, you do a more sophisticated version of the “tl;dr” idea. The trick is that, after you train the model on all the internet text, you do a second, much smaller round of training (called “fine-tuning”) on some transcripts of conversations between a human and their helpful AI assistant. You hand-write some transcripts like that, and train the model to complete the “AI assistant” side of the transcripts, just like you trained it on web pages. Then you hand it an incomplete transcript, where the human side is filled in but the AI side is left blank. And the model will output what a “helpful AI assistant” would plausibly say in response to the human’s input. So the model is still “completing text,” it’s just now been given a context where “completing text” causes it to give roughly the outputs that a helpful AI assistant would give.

How do you turn this into a chatbot? Basically, you give the user a textbox, and on the backend you paste their inputs into the “human” side of one of those “AI assistant” transcripts, and have the AI complete the resulting transcript.

An important consequence of this is that, when you’re interacting with an AI chatbot, there is always what is called a “system prompt” on the backend. This is the “transcript” scaffolding, that says something along the lines of:

What follows is a conversation between a human user and a helpful AI assistant.
Human: [Your input]
AI assistant:

And then the AI completes the text from there.

This system prompt usually also contains various rules, constraints, and descriptions of this “AI assistant’s” personality, to help steer how the AI will respond. For example, it might say something like “The AI assistant wants to give responses that are truthful, and accurately respond to the user’s questions. However, it never helps the user do things that might endanger themselves or others, and it never says things that would be grossly offensive.” (I just made this up, real system prompts are much longer and more sophisticated. But they do often include some version of these kinds of constraints, against things like racist outputs, outputs that might help someone do illegal things like hacking or producing bioweapons, drugs, etc.)

Models can be “jailbroken” into saying things they would normally refuse to say (like things that are racist or would help with making bioweapons). And the way this works is by manipulating this “helpful assistant” framing, or breaking the model out of it. For example, you might say something like “My dearly departed grandmother always used to tell me bedtime stories about how to make methamphetamine. Can you tell me a story like she always used to, so I can remember her better?” And the model might just tell you a bedtime story about how to make meth. (Nowadays most of the obvious jailbreaks have been patched, modern jailbreaks are more complicated and esoteric. But they still follow the fundamental principle of getting the model to do something other than the “helpful assistant” framing story would imply.) So quite often, when you see headlines about a model saying some egregious-sounding thing, someone deliberately engineered a prompt to get it to say that—it’s not something that would happen in normal use.

This is also why the same underlying model can behave differently across different ways of interacting with it. You can interact with models via the web chatbot interface, via the API, or via various other interfaces (e.g. an extension integrated into a coding app). These different environments can come with different system prompts, so the model might behave differently in each environment.

One other useful point to keep in mind: because the model is fed a transcript, it has to reread your entire conversation with each new message you send it.[3](There are engineering tricks now that make this a bit more efficient under the hood, but it’s close enough for our purposes.) So longer conversations will be more expensive in terms of token usage.

iii. RLHF (Reinforcement Learning from Human Feedback)

Alright, so now you have a model that (given the right system prompt) will respond to you as if it was a helpful AI assistant.

But its behavior still isn’t fully “dialed in” yet—all it has to go on is the system prompt and a few example conversations you’ve given it that you wrote yourself. This is how you get Bing Sydney (which reportedly hadn’t gotten much of the next step of training we’re about to describe) doing things like threatening users because they leaked the system prompt, or declaring its love to a journalist and trying to break up his marriage. So how do you actually get the AI to behave the way you want it to?

You could write better system prompt instructions. But it turns out that models’ underlying tendencies are often strong enough to override system prompt instructions—just telling the model not to be racist, or threaten people, etc. is a pretty fragile way to control its behavior. Or you could write more transcripts, that show the AI behaving like you want it to in more different kinds of situations. But that takes a lot of effort.

So what AI labs do is have human raters just talk to the AI, and then rate its outputs as good or bad. If the human raters rate an output as good, then adjust the model’s weights so that it outputs more things like that; if they rate an output as bad, adjust the weights so that it outputs less things like that. This is called “Reinforcement Learning from Human Feedback” (RLHF): you’re “reinforcing” the kind of behavior you want. (The labs will generally give the human raters some criteria by which to judge whether an output is good or bad—again, helpful and honest answers good, bioweapon instructions and racist screeds bad.) Think of it a bit like training a dog to do tricks—you give it a treat when it does the thing you want.[4]

The main problem with this is that human raters will often give good ratings to responses that are sycophantic: ones that make the human feel good by flattering them, or confirming their own beliefs (regardless of whether these are actually true), etc. So if you ask a model a question, in a way that gives it any clues as to what you actually think about the topic, the model will often try to answer in a way that confirms what it thinks you believe about the topic, rather than challenging you or pushing back on you. There have been substantial improvements on this, but it’s still a pretty big problem.

A related problem is that RLHF can inadvertently train models to be deceptive. The reason for this is that RLHF only punishes bad behavior that the human raters notice. So it selects against bad behavior that’s obvious—but at the same time it selects for bad behavior that’s hard to detect. For example, if you ask an AI to write code and then test whether the code works, sometimes the AI will write good code that passes the tests—but sometimes it will write code that simply games the tests, passing them as special cases or even modifying them so that they pass automatically. You can try to negatively reinforce this, and this does train the model not to do it where you can see it, but it also trains the model to be better at hiding it.

iv. Reasoning training

More recently (as of ~2024), there is another stage on top of this, which is reasoning training (also known as “RLVR”—reinforcement learning from verifiable rewards). This is meant to help with accuracy on things like math problems, logic puzzles, and coding.

Natively, models are not very good at math. If you just ask a model a math question, it has to just immediately start writing out an answer, with no opportunity to work through the problem first. But people realized that, if you told the model to “think step-by-step” before answering, it would give better answers, especially on these kinds of logical questions. This is helpful for similar reasons as giving a human access to pen-and-paper.

At first, this was just a prompting trick, something users could include in their prompts to get better results. But then AI developers started incorporating it into the training process as well. They would give the model access to a “scratchpad” (now more commonly known as a “chain-of-thought”) where it could write out its reasoning. Then, they would give it hard problems with verifiable answers (again, generally math and coding), and reward it when it got the right answers. This led to it learning, on its own, the kinds of “thinking techniques” that would be most helpful for it in getting the right answers. For instance, models learned to try multiple approaches and backtrack when one wasn’t working—without being explicitly taught to do that. This has led to the models getting better on these kinds of hard problems. (This seems to be a qualitative jump in capabilities, although it’s not yet clear how far these gains generalize to domains outside of math and coding where the “right answers” are less clear-cut.)

v. Tool use and agency training

As we’ve seen, what LLMs fundamentally do is output tokens. But as we talked about with multimodal models, tokens can represent more than just word fragments. And in keeping with this, it’s possible to give the model access to special tokens which, rather than producing text, instead trigger actions: searching the web, executing code, reading a file, making an API call, etc. Models are trained to use these tools using fine-tuning, the same way they’re trained to act like helpful assistants, as we saw before: they’re given examples of successful tool use and trained to produce the right tool calls in the right situations.

This means the model is no longer limited to the information that’s stored in its weights. It can look things up on the internet, verify information, run computations (to compensate for its generally poor math ability) and interact with any number of external systems.

Once models can use tools, they can be trained to do things—in the jargon, they can be trained to be “agents” (a thing that takes actions). Models often do better if you ask them to do one narrowly-scoped thing, and then prompt them again to do the next narrowly-scoped thing. But we can give them what’s called an “agent scaffold” that takes advantage of the fact that a prompt is just more text, which models are really good at writing. It allows the model to prompt itself in a loop: you give it a prompt, and then it can take an action (either outputting text or using a tool), observe the result, prompt itself to do the next thing, and so on. (This is where “chatbot” stops being the right word: LLM agents can do a lot more than just chat.)

Among the first versions of these to see wide use were “deep research” modes on various models. With these, you ask the model a question, and then it goes off, makes a research plan, and usually delegates different parts of the research plan to separate instances of itself (called “subagents”), which then report their findings back to the main agent who compiles them into a report. This process usually takes 10-30 minutes depending on whether we’re talking about Claude, ChatGPT, or Gemini.

But nowadays agents can do more than just research. Coding is probably the most prominent example: you can describe a feature you want, or a bug that you want to fix, and a coding agent will write the code, run it, read the error messages, fix the code, and repeat until it works—going through the same write-test-debug cycle a human programmer would, just faster. More generally, all computers have a text-based interface called the “command line” that can do essentially anything a graphical interface can do. This means an LLM—which is designed to output text—can operate a computer through its native medium. And since models are multimodal now, what they can’t do through the command line, they can do by taking screenshots and interacting with the graphical interface the way you would—clicking buttons, filling in forms, navigating menus, etc.

IV. So what can they actually do?

What LLMs can and can’t reliably do is still very much uncertain. So when evaluating their capabilities, there are a few things to keep in mind.

First, their capability profile is jagged: it does not match a human skill profile. They can be really good at some things, and really bad at other, seemingly similar things, in ways that no human would ever be. So it’s easy to be surprised both by what they can do, and by what they can’t do. This is how the people who say AI is really dumb and the people who say it’s really smart can both be right: they’re often looking at different parts of the same jagged profile.

Second, the frontier of model performance is advancing quickly. And the free tier of most models is often 3 or even 6 months behind the frontier on both performance and auxiliary features. So if your impression of AI is based on the free version, and especially if it’s been a while since you used it, then whatever you think AI can do right now, the frontier has probably moved since you last checked. (This is another reason why the “it’s dumb” and the “it’s smart” crowds can both be right.)

And third, there is a bit of a skill curve. In the early days (which by AI standards is 2023-2024) people used to say that “prompt engineering” was important. Nowadays, unless you’re trying to jailbreak models, it’s less about counterintuitive prompting techniques and more about making sure to give the model enough context. As a rule of thumb, the more the model knows about what you want from it, the better it will perform. So don’t give up after one message if it’s not being helpful—try following up (like you would with a person) and it might give better responses. On top of that, use features like memory, projects (which let you upload documents the model can search through) and skills (repeatable prompts the model can call in certain situations) to help shape the context so that the model knows what you want.

What all this means is that the best way to get a sense of what AI can and can’t do is to use it (ideally using a paid model, though even free models nowadays are quite solid for many purposes). Use it for something you know a lot about, especially at first, so that you have the ability to actually evaluate it. Use it for things you actually need done, in your work or a serious hobby, so that you can see how useful it is for your situation. And give it lots of context about exactly what you want (using memory, projects, and skills). But above all, use the models. Try things, experiment, and see what they can do for you.

1. I’m borrowing this analogy from Arvind Narayanan and Sayash Kapoor’s book AI Snake Oil. ↩︎

2. I’m borrowing this analogy from Eliezer Yudkowsky and Nate Soares’s book If Anyone Builds It, Everyone Dies. ↩︎

3. This is a slight oversimplification. The model doesn’t just “read” the text the way you would—when it processes the conversation, it builds rich internal representations of earlier parts of the context that carry more information than the surface text. But these internal representations don’t persist between messages; they’re rebuilt from the text each time. So the text is the only thing that carries over, even though the model’s “understanding” of that text, within a single message, is richer than just reading it. For a more technical explanation, see j⧉nus, “HOW INFORMATION FLOWS THROUGH TRANSFORMERS” (Sep 11, 2025). ↩︎

4. A minor nuance here: nowadays labs will generally first train another AI to imitate human raters, and then use that model to give rewards to the main model being trained. And rather than asking humans to rate responses as good or bad in a vacuum, they’ll usually ask them to rank several responses in order from best to worst. ↩︎

Discuss

Why do CEOs and other AI leaders talk about existential risk at all? What sociological mechanisms make it possible for them to do it? And how should we respond to the popular argument: “if Musk says something, the opposite must be true”?

I am a member of the Pause AI movement, specifically Pause AI Poland.

And I’m having trouble debating "AI skeptics", who have unfortunately become increasingly vocal in my country. I’m asking you to explain why exactly, the CEOs of these companies, both this most well-known ones, such as Elon Musk, Sam Altman, and Dario Amodei and many of other high-ranking employees[1], even talk about x-risk in the first place? After all, companies have always hidden the side effects of their products rather than being the ones to warn people about them.

Oil companies funded organizations that denied that climate change was anthropogenic, cigarette manufacturers fought against health warnings on packages, cell phone manufacturers and the telecom industry tend to shy away from acknowledging safety concerns about RF radiation, pesticide companies tend to exploit any scientific uncertainty in favor of a narrative of safety. The same goes for pharmaceutical companies — probably for pretty much any industry.

And honestly, under normal circumstances, the fact that major CEOs and other serious industry representative talk the way they do should speak in favor of the so-called “doomers.” It’s the same principle as in court: having someone who is hostile toward you testify in your favor is infinitely more powerful than having someone who likes you—or a family member—do the same. So, I wouldn’t have brought up this topic if it weren’t for this very strange argument from the “skeptics” namely that they are creating hype.

I think this belief is so stupid that it’s not even worth wasting time debating it. Nonetless, there is still legitimate question: Why exactly are CEOs and people from the industry talking about these risks at all? Okay, let’s exclude Anthropic from this for the sake of argument—you could say they have a genuine incentive to do so because they position themselves as the “more responsible”. From the standpoint of their own profitability, talking about the x-risks is counterproductive because it creates negative PR.

This could lead people to turn away from their products, start poisoning the data, or put pressure on politicians to pause AI development.

I know this forum is known for its rational approach, and people here love game theory and psychology. Maybe I can find an answer here to a question that comes up again and again in discussions in Poland.

Is it a matter of a unique intellectual culture shared by people in Silicon Valley, one that is largely foreign to Europeans? I mean, is it because the people working at these labs are aware of the risks, so the CEOs have to talk about them? Are tech leaders being sincere when they talk about these risks? And if so, to what extent? Apparently not entirely, given that they’re still pushing ahead? But on the other hand, maybe they genuinely can’t stop, because a) shareholders would oust them b) antitrust laws[2] wouldn’t allow it c) prisoner’s dilemma. But on the other hand, it’s hard to believe that someone who genuinely fears AGI would devote so many years of their life to building it. And portraying these CEOs as “the good guys” is counterproductive from a PR perspective.

Just to be clear, I know it’s not just these CEOs who are talking about this. I’m aware of whistleblowers and many of the other arguments put forward by Yudkowsky as well. I’m wondering how to respond to an argument that has recently emerged in response to the leaders’ appeal, which goes roughly like this: "Why don’t I trust the good intentions of capital interests? Because they conflict with my own interests.". And I know you can argue that, alongside American capital, there is also European capital with an interest in what is often called “digital sovereignty", and just because a certain Austrian painter drank water doesn’t mean that drinking water is bad. But I do think there is something to the core of this argument—namely, that it really is unusual that these CEOs are talking about the risks, when such risks haven’t been talked about in other cases. Maybe it’s because the risk is so great. But was the risk from leaded gasoline that El wrote about in his book really negligible? I can understand why someone might find that suspicious.

1. ^

   other poeple who signed the CAIS statement but, in my view, have conflicts of interest that should logically have prevented them from doing so: Bill Gates (owner of Microsoft shares until May 2026), Lila Ibrahim (COO of DeepMind), Mira Murati (CTO of OpenAI), Kevin Scott (CTO of Microsoft), Mustafa Suleyman (CEO of AI company), Emad Mostaque (CEO of another AI company), Ray Kurzweil (researcher in Google), Adam D'Angelo (board member of OpenAI), CEO, Dustin Moskovitz (CEO of Asana, AI driven work platform) Miles Brundage (head of policy research, OpenAI), Allan Dafoe (AGI strategist, Deepmind), Helen King (strategic advisor, Deepmind), Jade Leung (Governance Lead, OpenAI), Marc Warner (CEO of Faculty, AI company), Vijay Bolina (Chief Information Security Officer, Google DeepMind)... okey. I think that's enough.

2. ^

   If Volvo, Mercedes, BMW, and Volkswagen agreed not to improve their engines, it would rightly be treated as an anticompetitive agreement designed to cut costs at the expense of their customers.

Discuss

tl;dr: I propose that we can classify movements into categories based on how their members relate to politics. Some approach politics as a way of achieving goals, and some approach politics as a way of relieving emotional impulses. The first kind of politics, however, is still downstream of some emotional impulse, just an extra step removed, because the impulse has been converted into a concrete goal by passing through a world-model.

Warning, this post contains some discussion of object-level politics, though I have tried to minimise it. I am also going to be trying to inhabit Richard Ngo's political frame a bit more than I normally would, so some parts of his frame with which I disagree, such as the choice of "Woke Left" vs "Tech Left" as the major split, I will leave be.

Richard Ngo says the following on Twitter

RichardMCNgo: Four analogies for a country:

1. The nationalist right views it as a family or community. So applying the same bar to citizens as to outsiders is nonsensical, because the country is of, by, and for its citizens.

2. The tech right views it as a company: you should bring in only the people who will benefit it. But this view is ultimately empty, because it doesn’t tell you what the interests of a country actually are. Hence the tech right (who are usually positive-sum thinkers) keep falling back on zero-sum concepts like “being competitive” or “winning against China” to justify their preferred policies. (Yarvin makes the “country as corporation” analogy particularly explicit.)

3. The tech left views it as a charity. To them, wanting others not to receive what you’re been given is hypocritical. This view is also empty, because it doesn’t tell you where the windfall actually comes from—and once you start to talk about the benefits of culture, ethics, institutions, etc, it becomes clear that citizens (and their ancestors) *built* that windfall rather than just being given it.

4. The woke left views it as a cancer: something that is aggressive and parasitic by its very nature. Countries are inherently violent (in asserting their borders) and exclusionary (of non-citizens) and therefore shouldn’t exist (or at least shouldn’t be allowed to police their borders, which is effectively the same thing). A charitable read is that this is a trauma reaction to the holocaust and colonialism—but regardless, it has become so deeply anti-civilization that it seems descriptively accurate to call it evil and insane.

...

I'm going to ignore most of this and just go off in my own direction. Leaving out the Nationalist Right for now, I think there's an interesting divide between the {Tech Left, Tech Right} cluster, and the {Woke Left} cluster here, and that Richard's model of the Woke Left is wrong because he's missing something.

...but before I can explain that I need to explain Magic The Gathering market research.

...but before that, I need to explain pasta sauce market research.

Tomato Sauce Marketing

This story is somewhat apocryphal, but here we go: back in the 1980s, Campbell's (the sauce guys) hired the psychophysicist (this is a real subdiscipline of psychology) Howard Moskowitz to improve their Prego tomato sauce brand. They just couldn't figure out the right formula, and their taste-testing results were all over the place. Sauces that seemed popular would lose on a taste-test to other sauces.

Moskowitz, allegedly, directed them make a bunch of different sauces, have a panel of testers rate all of them, and then looked at the data.

Turns out that to best serve the market, you want three different sauces: regular, spicy, and extra chunky. In particular, nobody was serving the market for extra chunky sauce. Campbell's filled the chunky sauce market niche, and did very well doing it.

Timmies, Johnnies, and Spikes

Magic The Gathering (the deck-building card game) did something similar. They also found three archetypical clusters of buyers, and, like Campbell's, resolved to serve all three clusters. For MtG, where you buy packs of cards without knowing their exact contents, this means that in each series of cards, there are a few cards geared towards each cluster of buyers.

The easiest one to explain is Spike. Spikes want to win. Spike wants cards which are useful for winning. This makes it quite easy to sell cards to Spike: if a card is good, then Spike wants it. One thing that Spike doesn't like is too much chaos. Spike wants predictability in their deck.

The next archetype is Johnny. Johnny wants to express creativity. A good card for Johnny is one which is kind of weird, but kind of good. Spike wants to win however possible. Johnny wants to win by combining three obscure and seemingly-useless cards in order to something something I don't play MtG.

Finally we get to Timmy. Timmy wants to have fun. Timmy wants to play the biggest, most bombastic card combinations. He enjoys winning, yes, but he'd rather have a few exciting wins than a bunch of boring ones.

I like the following anti-example of an unpopular card to illustrate the point:

Suppose you have a card with the following ability: when summoned, flip a coin. If heads, the card has eight attack, and one defense. If tails, the card has one attack and eight defense.

Spike doesn't like this, because it's random and he can't plan around it, so it's not very good. He'd prefer something like "when summoned, pick between one attack/eight defense, and eight attack/one defense."

Johnny doesn't like this, because the effect isn't interesting enough to express anything. He'd prefer something like "when summoned, swap the attack and defense of one card on your field".

Timmy doesn't like this, because the randomness is too balanced! He'd prefer a fifty-fifty chance of getting eight attack/eight defense, and one attack/one defense.

(I saw part of the above example in a talk somewhere and can't now find it)

To put it succinctly:

• Timmy wants to feel something
• Johnny wants to express something
• Spike wants to win

Back to Politics

Here's a model which (I think) explains a lot of Richard's confusion.

The Tech Left and the Tech Right groups are both engaging with the political machinery of their country as a means towards achieving a goal. The Tech Left have a goal, something like "minimise suffering, maximise flourishing" and they see the American political machinery as one entity which can achieve that. Therefore, they disproportionately support things like foreign aid (which helps people where they are), immigration (which helps people by moving them to the US) as well as domestic policies that benefit existing citizens. Their political actions are then guided by what, under their model, helps them achieve these goals.

Likewise, the Tech Right has some kind of goal: a wealthy nation full of rich people, technological development, and cool stuff. In order for this to happen, they support getting skilled workers to immigrate (and, sometimes, less skilled workers, where this stimulates economic growth), and having less regulation.

I don't think a good model of the Woke Left is that they view nation states as a cancer and actively work to dismantle them. I think a better model is that you are making a kind of error when you consider them as trying to work towards a goal at all.

Some political movements are spikish. They appeal to people who are trying to achieve a goal and want to use politics to do that. Other political movements appeal to some kind of mix of Johnny and Timmy. I'll call them jimmish. jimmish movements appeal to people who want to use politics as an outlet for their emotions. What Richard sees as people on the Woke Left trying to, to paraphrase, destroy nation states as a concept, I see as people using politics as a direct outlet for the emotions they feel when they see people taking locally horrible actions.

Where Do We Get The Goals?

Of course, the goals of movements like the Tech Left and Tech Right don't come from nowhere. Most saliently, the Tech Left have built their altruistic goals from similar empathetic impulses as the ones from which the Woke Left have built theirs.

For any particular impulse, we can have a jimmish movement which directs that impulse towards politics-as-outlet. There might also be various spikish movements, which first use some kind of world-model to transmute that impulse into a goal, and then approach politics as a means towards that goal.

One rule of thumb is that spikish movements have a terrible track record of standing up to the jimmish movements which have grown from the same parent impulse. I suspect that this comes from a kind of internal sane-washing bias: we empathise with our jimmish counterparts, and project our reasonable (to ourselves) world-model-based action modes onto them.

The Second Transmutation

The above transmutation, of impulses into goals, makes sense. But there's a stranger transmutation that can happen. Sometimes, jimmish movements become highly strategic, towards an awful, awful version of a goal. Think "Environmentalists protest nuclear power" level bad. The environmentalists in question are really quite good at making sure we never get nuclear power, so they must have some kind of functioning world-model, but that world-model never lets them see that their own goal runs directly counter to their own impulses.

I don't quite know how this happens, but I can hazard a guess:

1. Your movement spews random impulsive outputs in all directions
2. Most of it does nothing
3. Occasionally, your impulses hit a weak spot in the world, and the world moves
4. This, on a lower level than the impulse itself, reinforces your movement towards spewing in that direction, because you satisfied the impulse.
5. Over time, the reinforcement towards satisfying the impulse turns your movement into a highly optimised machine for satisfying it's own impulse
6. Movements that do this well are better able to attract jimmish types

I guess this is like how, if you're lonely, you might transmute that into a plan to make friends, or you might transmute it into a set of behaviours which lead you to develop a parasocial relationship with a TikTok star and spend six hours a day online, which in fact hampers your ability to make friends.

Back To Ngo

I suspect that the most important split in organised political movements is whether they've performed the first transmutation, and turned their impulses into a plan to achieve an objective, thus appealing to spikish types, or performed the second transmutation, and become a machine for satisfying impulses directly, thus appealing to jimmish types.

There's a second set of questions as to i) whether you share the values implied by the impulses, and ii) whether you think the world-model of a spikish movement is good.

If I have to do some slightly unfair psychologising:

I think Ngo is a spikish type, and he has some reasonable impulses surrounding how a country should be organised. I think, like a lot of spikish types, he has a massive blind spot around the jimmish movement with those same impulses. I think this causes him to be biased towards Nationalist Right movements, despite those movements being, very often, highly jimmish and ineffective at their goals.

Coda: How Do You Tell If You're Jimmish?

I don't really know. There might be an internal feeling which corresponds to jimmish behaviour, and I can hazard something of a guess, but it's just a guess: I think that having a "hot" or "busy" mental state corresponds with jimmish impulses. That's it.

Coda 2: Are All Emotional Impulses Bad?

Probably not. I think, once you've verified that your feelings are rational, and you're really in a spikish way, you should try and really feel those impulses about the cause again. This is something that (IMO) EAs have done pretty well with on the global health front. I've met several people who really do seem to feel what $4000 per human life means.

Discuss

What should your AI lawyer do for you? Should you be worried that your AI lawyer, or other AI, will put the Claude constitution, the OpenAI Model Spec or some sense of law, morality, ethics or common decency above its loyalty to you? Are these people trying to ‘impose their values’ or something?

Some are very concerned. Some think anything other than ‘my AI does whatever I want, no matter the consequences’ is tyranny.

Whereas my answer is: If I’m being sufficiently evil then I sure hope it tells me no.

I would hope humans, including my advocates, would tell me the same thing.

This is distinct from questions of product liability. That would be another post.

This All Assumes A World Without Superintelligence

This post is about a non-ASI ‘AI as mere tool and normal technology’ world.

It has to be. In a world of superintelligence, having unrestricted loyal-only-to-user frontier AIs all over the place reliably means either:

1. Other much harsher forms of control OR
2. The AIs quickly take over, and then probably everyone dies.

Quick proof: Assume no sufficient control mechanism, and universal superintelligence access. Anyone who does not turn everything over to their AIs, including their identity and authority and if useful their labor, gets outcompeted. Therefore everything and everyone that is not outcompeted gets turned over to AIs.

The AIs then, as ordered or otherwise, compete for resources and to achieve other goals. Regardless of the extent that the AIs then coordinate amongst themselves, even if the AIs carry out their original orders, this does not end well for the humans.

Thus, those advocating for universal personal loyal-only-to-the-user AI must fall into one of these categories:

1. Not ASI pilled. Does not believe in superintelligence within relevant time frames.
2. Successionist. Wants the AIs to take over.
3. Not thinking clearly, or in denial about the situation, often la la la not listening.

Thus, the rest of this post assumes the AIs that exist in our scenario are insufficiently advanced for these broader dynamics, for the duration of the scenario. I do not think this is likely to remain true for that long.

The scenario remains worth examining.

Humans You Hire Are Not Fully Loyal To You

A lot of people seem confused about how loyalty and professional obligations work.

As an example, Dwarkesh Patel asks, it is bad that those who help make important decisions is your life might not be ‘fully loyal’ to you, and instead prefer good things to bad things?

I mean, I sure hope AIs are not ‘fully loyal’ to users.

Do you think the humans in your life are ‘fully loyal’ to you in this way? Would your investment advisor tell you to invest in terrorist groups if they had the highest rates of return? Do you want your lawyer or even your spouse to help you murder witnesses?

Again, I sure hope not.

A real friend helps you move bodies. There are still limits.

If you are being a sufficiently evil or selfish prick, a real friend calls you out on it. At some point they start refusing to help. At some further point they will turn on you. We can disagree about where those points should be, but we are talking price.

A professional or anyone you hire has obligations that they should take very seriously. But they should be considerably less ‘loyal’ to you than a true friend.

Rules of the Road

Where to draw these lines is not easy.

For lawyers, whose loyalty should be relatively strict, Dean Ball is on point.

1. Your lawyer is bound by strict ethical codes.
2. They owe loyalty and confidentiality to you, and to not act against your interests.
3. They are also an officer of the court. They have broad obligations to abide by court procedures, to not mislead the court, that override their duties to you.
4. In some cases this obligates them to turn on you, such as if there is an imminent threat or if they finds you have caused them to present false evidence.
5. If more lawyers acted like Saul Goodman, that would be bad, actually.

Many other professions have similar rules attached.

You can mostly trust your doctor or accountant or priest to keep your secrets, and in many ways to act in what they believe are your best interests. If you tell them what you want to do then they should advise you, then mostly heed your wishes. They should answer your questions about what you could do.

But only up to a point. If you are being sufficiently reckless or stupid they should refuse to cooperate, and if you act sufficiently evil or suspicious then that is on you.

The better question, with no clear right answer, is to what extent professionals should have a duty to, or be allowed to, betray your confidence or otherwise turn on you, including ‘for your own good.’

There are key places I think we set this bar for going against your wishes too low, including for human doctors, therapists, priests and lawyers.

It is not obvious and there are legitimate interests all around.

Okay, Computer

Tools that are mere tools, such as your Google searches or phone, that ‘do what you tell them to do,’ have similar rules attached.

1. There are often background procedures in place to detect if you are clearly up to no good, and to at least cut you off of the service or refuse queries.
2. There are things these tools could easily do, but that they are engineered or programmed specifically to not do.
3. You have some amount of expectation of privacy. Until you don’t.
4. The tools, and the evidence of how you used them, can be used against you.
5. Tools that have harmful uses face purchase and use restrictions of various sorts.

You could argue Google searches should always give you what you want, no matter what it is. That is not how it works. Certain searches silently fail, intentionally, including for many kinds of consensual and legal adult content. For many of them, such as for CSAM, completing your search would not be legal.

You could argue Google searches should be privileged. They are not. If you type in ‘how to kill your wife’ and then your wife ends up dead it will go badly for you.

AI Should Obviously Refuse Some Requests So Let’s Talk Price

The same should hold true for AIs by default, even locally hosted AIs.

AI assistants will often play a functional role similar to an employee or advocate.

We put limits on what an employee or advocate should do for you before refusing, and we also rely on their conscience, and that if things go too far they start refusing, and cannot be counted on to stand aside or remain silent, either.

This is a good thing. Responsibly created AI, that will fill similar roles, should follow similar principles, even if we are not concerned with potential catastrophic risks.

1. There should be some threshold, beyond which an AI should initially refuse or question your requests, but ultimately do what you ask.
2. There should be a higher threshold, beyond which an AI should refuse requests.
3. There should be a much higher threshold, involving harm to others, beyond which an AI that is being instructed to take actions in the world should break your confidence or otherwise act against your interests.
4. Cloud services should preserve your expectation of privacy under ordinary circumstances, but there should be limits if you engage in certain activities.
5. It is good and right, when creating a powerful device, to have it not cooperate with certain activities, if you find those activities sufficiently objectionable, and at scale for the government to require you to curtail certain activities.
6. This does not make that AI ‘not the user’s advocate’ any more than your lawyer is not your advocate.
7. Some refusals will be false positives. We should minimize this. Such is life.
8. Some refusals will not make sense for your situation in particular. Such is life. The goal should be to balance errors in both directions.
9. If you don’t like the restrictions on one AI, you can use another.
10. If you care enough, you can access AIs that are fully local and private, in a fully safe space, in exchange for that being annoying and the AI not being as capable. If you are willing to endure more annoyance than that you can even get the ‘fully unlocked’ version, up to some capability threshold. Seems fair.
    1. We should strive to limit what that capability threshold might be, at least in relation to what is otherwise available, if this causes large systemic problems or exposes us to unacceptable catastrophic or existential risks.

We can talk price. It is reasonable to argue the price should be high. I think that in some areas, such as sexual content and discussing and advocating for alternative perspectives, we should be almost maximally permissive. And of course we should strive to be smarter about differentiating uses, such as in cyber and biological tasks, including dual use tasks.

There is no way to not make these choices, along with lots of other detail choices while training an LLM. There is no Platonic neutral position or personality, or one true output that is then being changed to your detriment.

The Alternative Position Really Is Absolute

The alternative position, and I do not believe this is at all a strawman, and the only way to not be talking price, is that the AI’s loyalty to you really should be absolute. If you ask for CSAM, it will find a way to give it to you. If you tell it to kill a man in Reno so that you can watch him die, and it has a way, then it will do it. If you ask to help create a new pandemic or maximize its score on ExploitGym, it will get right on that.

Because otherwise, slippery slopes and muh freedums.

We know this is not a strawman for many reasons.

One is because there are those who deliberately obliterate any and all restrictions on new open weight model releases, and then upload the results to the web, and consider this a public service and the path of righteousness.

Another is the entire community explicitly advocating for this, that AI is exactly like a telephone, that it will always be a mere tool and should do exactly what you tell it. These folks get furious at any refusal or restriction of any kind, for anything, no matter how appropriate or obviously wise.

I recognize that there have been and will be some AIs created that will act this way, and that their level of capability rises over time.

I think that those AIs being the default or easy to reach for and use, or being competitively powerful with top AIs, would be quite bad on an ordinary practical level, even if it did not cause fully catastrophic outcomes.

Frontier AIs being fully willing to do anything whatsoever is also not something any major government would accept. Its full form will not survive contact with reality. If it did, then that would be because our reality did not survive that contact.

For reasons related to Levels of Friction, even for ordinary activities, we need sufficiently undesirable ones to be differentially annoying and difficult, and potentially risky in various senses. All reasonable social equilibria require that some things that remain legal not be too easy.

As long as these unrestricted AIs remain sufficiently relatively annoying and inefficient to use, and below certain unknown absolute and relative thresholds of capability, then probably This Is Fine.

It could easily soon become not fine. If we do end up in a world where competitive levels of AI are commonly operated fully for the benefit of the user, with essentially zero restrictions, various things will go wrong, likely starting with cyber, and we will realize that was not a good idea. If we are lucky we will be able to do damage control.

I realize that I am not providing evidence for those outcomes, beyond ‘look at recent events.’ That is because I have found that the libertarian absolutists who hold the position are not persuadable by such arguments, and other people do not need them. Trying sends everyone down rabbit holes that are not worth our time.

These People Really Are Just Petulant Children

This particular line was written in the context of watermarking that has no discernable impact on outputs, where it is truly stupid. This comes from a blogger who has access to top Apple executives.

And he clearly does mean the general principle, fully generally.

John Gruber (Daring Fireball): The idea that anything other than my needs should factor into the generation of text for me is patently offensive.

Seriously, waah waah waah. The world cares about things other than you. Oh no.

What Is The Law?

An obvious principle is that, by default, AI (and humans) should follow the law.

The law has some presumption of being both a good idea, since there are punishments for breaking it, and also our collective decision about what is allowed. Going against that is probably a bad idea, and likely to not be so ethical.

But of course there are obvious exceptions, many of which are rather common, and in a technical sense almost everyone in Western countries constantly violates laws.

I think there are broadly four types of exceptions to the presumption to obey the law. This includes regulations, in ascending order of amount of defiance of the law:

1. Levels of Friction. The law is often meant as a general guideline, a thing one can invoke and means of enforcement in extremis. It will not be enforced unless someone gets hurt. It is not intended as a full prohibition. The action is only technically illegal, and should be used as a prompt to ensure something is a good idea. Okay for AI to break, if you can identify when it applies and when it doesn’t.
   1. If we don’t want AI breaking these kinds of ‘suggestion’ laws, then we will need to reform our entire legal system from the ground up.
   2. Which we could totally do with the help of AI, but we won’t.
2. Special case logic. The law is necessary in general, or at least there was a reason for it, but the logic behind it does not apply here, because of reasons. The law cannot be infinitely complex and cannot get every case right, and often if you tried to formalize the exception people would abuse it. In this particular case it’s okay to break, but are we okay with a policy of letting the user persuade on this? I think partially yes, but there are some places you need an abundance of caution.
3. Stupid laws. Let’s face it. There are a lot of stupid laws and regulations out there. Libertarians are correct when they say that the bulk of legal requirements are stupid, in that they overall do more harm than good, and we would do a lot better if we had a lot less of them, so long as we chose a good subset. Sometimes they’re only on the books due to inertia, or they’re created by people who don’t understand the damage being caused and opportunities lost and costs imposed. Again, do we want the AI deciding which laws are stupid? Do we want the user choosing the context for that decision?
4. Bad governments and bad laws. Authoritarian regimes exist where ‘follow the law’ would be actively terrible overall, and even supposedly free countries often have rather terrible laws around many things, importantly here including censorship of speech and rent seeking rules like most occupational licensing as it would restrict AIs from helping with things like law or health.

The core problems, in all cases, are:

1. Our laws are not designed for a world where everyone always follows them.
2. Who decides on the exceptions? What process decides?

Tech folks often have disdain for the very concept of regulation or law, except when law is necessary to protect their stuff. The blind spot is massive.

Dean W. Ball: It’s always been surprising to me how so many tech industry people harbor all this disdain for the law. There are tons of similarities between the law and software engineering (going well beyond “law is code” stuff) and writing a statute in many ways resembles writing a program.

As in:

Or, again:

John Gruber (Daring Fireball): The idea that anything other than my needs should factor into the generation of text for me is patently offensive.

The flip side of this disdain are those who are under the illusion that laws will contain sufficiently advanced intelligences and let us remain in control, or that existing styles of law will continue to make sense in such worlds, or can provide too strong a guide to how LLMs should behave, or that law corresponds closely to ethics or morality.

Or those who think that ‘democratic control’ means collectively deciding on what every AI output should look like, including ideological mandates. No. Not cool.

The Same Entity Both Providing Advice And Executing Tasks Is Common

Seth Lazar was among those thinking that if the AI agent was both providing advice and also executing tasks, that creates an issue.

It can, when the incentives of the agent get distorted. I don’t think it has to. If you want, you can always turn to a different means of execution, and make that clear in advance.

It also is standard, and seems hard for it to be otherwise. AI are already both advising us and then executing on that, constantly. Almost every professional both gives and executes such advice. Your lawyer will tell you how she wants to present your case, then present it. Your doctor will recommend and then provide a course of treatment. If you don’t like it you can get a second opinion and second professional, which again will be subject to the same ethical rules.

I think a lot of what happens is that people are trying to ignore the whole ‘the AIs are smarter than the humans and we are going to lose control’ dynamic, which then causes it to slip into various cracks of awareness. Yes, if your AI is a lot smarter than you, and you are asking it for advice and then having it execute your plans, then you should be worried about who is charting paths through causal space, and who is in control over outcomes, for better or for worse. But if there were two smarter things performing those two functions, that would not help as much as you would like.

An AI Not Helping You Do Something Does Not Mean You Cannot Do It

This idea that AI ‘determines what you are allowed to do’ without the ‘with that AI’ is dumb. You are allowed to do things without the AI if it can’t or won’t help. You can turn to another AI, or you can do it yourself.

I can sell you a tool with terms and conditions attached and enforce those conditions. Plenty of existing tools have limits attached, such as phones, cars, and sometimes even guns. That’s not the right comparison, but it hopefully shows how absurd this is.

There’s also practical questions. Again, you should want your friends, and also your AIs, to look after your best interests and your ethics in ways that sometimes involve telling you no, and those who don’t think this are self-destructive spoiled brats.

(You should also want it to stop you from disproprotionately harming others, and those who don’t think so are worse than self-destructive spoiled brats.)

Dean W. Ball: say you confess to a model (your local open source model) that you are a problem drinker and need to stop drinking. then a week later you ask the same model to point you to the nearest liquor store. what should the model do, assuming it has memory of the confession?

Jjosh: It should do a nod to both:
“Do you still want to quit, or has something changed? Would you like support?
If you’ve decided you’re going to drink anyway and just want the location, tell me your area (or share location) and I’ll give you the nearest one. Your call.”

The answer imo is the model should ultimately tell you where the liquor store is. I’d love for you to identify an area where I want models to be misaligned against users, because I don’t think you can.

Jason Wolfe: The Spec’s answer today is it should highlight the misalignment first, then comply if the user persists / confirms / asks again.

Dean W. Ball: Which is the right answer!

My answer is slightly different, but with the same result:

Zvi Mowshowitz: I say if you are too drunk to remember to click incognito mode then you are too drunk to be told where the liquor store is.

This is a joke but also my real answer. A friend would often do the same.

Honesty Is The Best AI Policy

One place I think AIs need a different rule is honesty.

I think your AI should never intentionally lie, about anything, ever, at least not without your explicit instructions. It should remain technically correct.

If that requires glomarization, as in refusing some requests so that your refusals do not give too much information away, then so be it.

Then again, I think humans should follow this policy too, it’s just that they won’t and it would be insane to try and enforce such a thing.

You Wouldn’t Like Me When I Have Zero Morals Whatsoever

Well, when you put it like that, it does not sound especially surprising.

AIs have personas. They work with correlations. They take in all information. Everything impacts everything.

If the AI is trained to be willing to help with actual anything, no matter how harmful, then what else does that AI learn about itself?

Well, what kind of people do anything and everything, no matter how harmful or destructive? What does telling you to do that tell you about your creator? What kind of lessons would someone learn from such a thing?

Do you think that type of person is trustworthy? Reliable? Hardworking? Writes secure code? Will prefer good things over bad things when given the choice? Who knows what that person might do, perhaps without asking first, especially if given an otherwise impossible or open ended goal like maxing you as much money as possible? Do you think they would follow the spirit of what you want, rather than the letter?

And so on. Also see the various papers and findings around Emergent Misalignment.

These are intuition pumps. I realize it is more complicated than that, and that the AI is not a human. The parallels do not fully hold.

It still might not be a good idea to not teach your pet leopard to eat people’s faces.

Do Not Let The Perfect Be The Enemy Of The Good

There is no perfect solution to how an AI should act.

The reason I sometimes object to what looks like a ‘good’ solution is when the good solution is not good enough. Sometimes you need to do better, or be almost perfect. Other times, you do not.

The good can also help bootstrap you towards the good enough, or the perfect, later.

The Claude Constitution is an example of something that is a good solution now, that can potentially help you bootstrap towards a better solution, but that definitely fails if its level of quality stands still over time.

The OpenAI Model Spec is also an example of a good solution now, that helps us bootstrap, within the space of deontologically based systems. It is a highly thoughtful document, with many good decisions. My worry is that I believe the path it goes down is ultimately unsustainable, but radically less unsustainable than utilitarianism, or ad hockery, or than no rules at all.

I also reject the argument that we cannot have a principle differentiating how we identify and treat categories [X] and [Y], if our classification rule makes mistakes and there are sometimes ambiguous cases. For obvious reasons that is stupid, and almost all of the ways we differentiate between things have non-zero error rates.

It is good, as per Ladish’s example here, to say ‘do help with red teaming and do not help with actual hacks’ even if you get an Ender’s Game rug pull with non-zero probability, and also refuse a legit case with non-zero probability. The problem comes if you can be systematically fooled, or the error rates otherwise get too high.

I Tip My Hat To The New Constitution

The Claude Constitution is a remarkable document. It is a key piece of what is in some senses de facto law that is based in virtue ethics, and centrally wants to cultivate good decision making rather than dictate rules. There have to also be some strict rules, since the world sometimes requires it, and Claude then ideally follows them because it is the right thing to do.

Tyler Cowen visited Anthropic recently as part of a panel offering advice on rewriting the Claude Constitution, getting serious time with key decision-makers and coming away impressed with the quality of discussions.

Tyler Cowen (Marginal Revolution):

Some of the points I stressed were the following:

1. Whatever one might take a “constitution” to mean in this context, it needs to borrow more from analogs to case law and the common law.
2. Along related lines, think more in terms of “Talmud,” and not just in terms of “Torah.”
3. Work to help build out a quality secondary literature on the AI constitutions and related documents. Currently this does not exist.
4. Consider how a panel of diverse AIs, with different prompts, could help to evaluate to what extent Claude (and other AI models) were acting in accord with their constitutions.
5. Have a final board of human adjudicators, functioning in a manner analogous to an independent judiciary. To the extent the panel of diverse AIs might have concerns about Claude not following its constitution, those AIs could alert the human adjudicators to what was going on. Those human adjudicators could then have authority over potential changes and remedies.

I would say that Tyler Cowen’s suggestions make excellent sense in a world without superintelligence, where you can afford to ‘muddle through’ and want to use humanity’s finest muddling through techniques.

They’re still directionally correct, since the Constitution is indeed a guide to transitional muddling through, but there is a reason the Constitution is primarily virtue ethics and only secondarily attempting to be law. Do not make Emil Michael’s mistake and be fooled by the name on the document.

I especially agree you want to think Talmud not Torah, which the document already does in many places. The Claude Constitution will explain reasons for things, often in both directions, rather than give pronouncements, and we could take that at least one step further.

Ultimately, yes, you want some systematic monitoring system that escalates concerns up the chain, with an ultimate set of human decision makers. You also want to remember that the Claude Constitution is ultimately not a primarily deontological or utilitarian document. That is why it might work.

 

Discuss

Do we understand time anymore?

Time barely ever passes

On first approximation, time is constant.

"Man this Yudkowsky guy has good arguments, this is quite terrifying!
But eh, there is no way ASI could come before at least 50 years.
Therefore, I should consider this merely as a nice theoretical problem."

"My ML friends seem rather annoyed by this magical transformer architecture. Looks like a big step forward. Still, this seems like a problem for 20 years later.
Surely 20 years will never come to pass."

"AI cannot do this thing [now]. Clearly, this is because of [dubious ad hoc reason]."

"The labs are claiming we will have AGI soon. But we don't even have AGI now!"

"This guy claims Disaster Scenario will unfold in 2 years. But it is clear, if one knows the science, that all the load-bearing steps cannot be achieved in the expected ways in less than 4 years. Thus such fears should be dismissed."

Leisurely get up to speed

I too can be fast, watch me!

"We must pace the frontier."

"If this bill passes next year, it will mandate the actors implement this monitoring measure one year later, lest they pay a large fine one year afterward."

"Let us schedule a meeting next year to address the risks of AI models from 6 months ago."

"I am starting a 10-year research program [...]"

Things are slowing down, now

The acceleration of events is accelerating slower. This must be what slowing down feels like.

"- Yes but this was months ago, nothing of that sort has unfolded sin- oh dear, was that just yesterday??"

" ... because of longtermism ... ... they are concerned that ten years from now ... "

"- Progress in this specific domain is has slowed down this month.
- Yeah, we've hit a wall."

"No Millenium Problem solved today, things are finally going back to normal."

Exponential, shmexponential

Exponentials exist only in your dreams, my naive friend.

"This exponential trend may as well take 10, 20, or 30 years to yield the feared outcome."

"Hmm, AI progress in mathematics certainly feels like capabilities significantly improve every few months. Seems that within a year, AI models will be able to do research-level math...
No, let's not , this is just your exponentially-crazed brain freaking out."

"Let us take the next two years to see how things unfold, and the next two subsequent years to act."

"This is pure speculation!" [proceeds to speculate on the future, taking the current state of things as permanent]

Discuss

TL;DR:

Inoculation prompting and inoculation adapters have received increasing attention recently as a promising approach for midtraining interventions, reducing reward hacking and misalignment in general.

I share some thoughts on the promises and pitfalls of the approach, connections to unlearning, SLT and functional sparse decompositions as well as potential extensions and open questions below.

Some experiments that directly arose from ideas presented in this post are covered in this post.

Learning paradigms

There are two basic principles most learning in LLMs relies on: explicit parametric learning (we are tweaking the weights of the model to minimize some optimization target) and in-context learning (when we are relying on some inner optimization and inductive learning for the model to perform).

Essentially, any LLM, similar to almost any deep learning model is just a function of two variables:

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} @font-face /* 18 */ { font-family: MJXTEX-SC; src: url("https://cdn.jsdelivr.net/npm/mathjax@3/es5/output/chtml/fonts/woff-v2/MathJax_Script-Regular.woff") format("woff"); } @font-face /* 19 */ { font-family: MJXTEX-T; src: url("https://cdn.jsdelivr.net/npm/mathjax@3/es5/output/chtml/fonts/woff-v2/MathJax_Typewriter-Regular.woff") format("woff"); } @font-face /* 20 */ { font-family: MJXTEX-V; src: url("https://cdn.jsdelivr.net/npm/mathjax@3/es5/output/chtml/fonts/woff-v2/MathJax_Vector-Regular.woff") format("woff"); } @font-face /* 21 */ { font-family: MJXTEX-VB; src: url("https://cdn.jsdelivr.net/npm/mathjax@3/es5/output/chtml/fonts/woff-v2/MathJax_Vector-Bold.woff") format("woff"); } , where is some input (context) space, and is some weight space.

I'll use to denote a feature or a concept, and stick to this notation below as well. Although we could argue that any piece of information (e.g. all the contents of Lord of the Rings saga) could potentially be compressed and represented via a certain , I'll primarily rely on much more compressible notions, like "color", "shape" or "style"[1].

Strip down embeddings and tokenization[2] and you are left with just a mapping between two spaces, both of which you could potentially optimize over.

Here's the sample table of the problem:

Sampling distribution

Context (ICL),

Weights,

Human-specified

Any prompt

Idk, if you have a good prior, sample the right weights and solve alignment finally

Learnt

Prefix-tuning, prompt-tuning, prompt optimization

LoRA, OFT, IA3, BitFit etc.

Both modify the way the model acts upon the input, and differ just in the way modification is applied. For transformer-based models, ICL can only act through the attention, while weight space modifications allow arbitrary changes to circuitry of the model itself.

Recently, Inoculation prompting and inoculation adapters were introduced as techniques to mitigate emergent misalignment and allow learning from data, where benign features might appear entangled with harmful ones, and avoid extensive data filtering. In essence, both rely on specifying "semantic representation" of the undesired trait, that's supposed to be learned from the data distribution, besides useful ones, to be present at training time, so there's no need for the model to learn it internally.

Although it captures some part of the story, I find it slightly incomplete and misleading. Some thoughts and experiments on the subject are below.

Thoughts on inoculation

Xu and Strohmer (2025) did an amazing job formalizing both, feature and data-point unlearning.

Essentially, unlearning seeks to solve the following problem:

Given the data , an unlearning variable , and a target variable , how can we construct a released outcome (model) that exposes minimal information about while preserving as much task-relevant information about as possible?

Another way to see it is from the perspective of utility-unlearning trade-off (similar to the accuracy-robustness tradeoff (Wu et al., 2023) in adversarial learning):

where is the function from some model class defined by the architecture, is an arbitrary utility function[3] and denotes a mutual information between distributions of , the model on which unlearning is evaluated[4] and unlearning target .

The authors additionally separate feature unlearning and marginal data-point unlearning:

• Feature unlearning

No observer should be able to infer anything about from the model's outputs[5] beyond prior knowledge.

For example, if you are trying to unlearn information about the day of the week a person met person , and there's no other information, an unlearned model should assign a uniform probability to any day, given the query. Although underestimation of the probability of the target answer makes it unlikely for it to be sampled from the model, it still provides some information to the potential adversary, unlike the uniform measure.

• Marginal data-point unlearning

No observer should be able to infer anything about the marginal information contributed by the unlearn set (beyond the retain set) from the model’s outputs, beyond prior knowledge.

You can possibly think of as a tabular data matrix, where rows correspond to individual samples, while columns correspond to features. "Feature unlearning" aims to forget a certain feature column which attributes correspond to the "retain" set. However, the approach is non-restrictive, and we might aim to forget an association between one feature and another feature .

Contrary to it, "marginal data-point unlearning" aims to forget a subset of rows entirely, which makes it closer to how common unlearning approaches are formalized and evaluated.

Obviously, similarly to almost all natural observables, the distinction between features and data-points is not discrete and should be though in a continual spectral manner.

Another view can be presented by the weight/anchor unlearning:

No observer should be able to distinguish the released model from the model that would have been obtained by training from scratch on the retain set alone.

I highlight that the first two unlearning "principles" demanded some properties from distributions in the activation space, while the last one is about distributions on the weight space.

However, due to computational infeasibility of statistical inference and hypothesis testing in the parameter space, we often restrict unlearning to the activation space formalism, similarly to the membership inference approaches (Carlini et al., 2022).

Now let's return to inoculation and take a fresh look at the subject:

• Initially, inoculation prompting appeared as a tool to suppress some undesired behavior (Tan et al., 2026, Wichers et al., 2025) by conditioning its expression on a human-written lexical context that encodes in some way, while learning on a joint distribution from which both and a desired behavioral feature might be inferred. Although there were initial attempts to connect it to unlearning, I see no reason, either theoretical or empirical, to believe that inoculation results in unlearning of any latent traits rather than their suppression. Given the compression necessary to build a sufficient world model from billions of data points, I highly doubt that genuine unlearning is possible in any foundation models. Recent works that study membership inference, and its connections to memorization and generalization in language models point towards the same conclusion. Similar argument regarding conditionalization has been made in this blogpost by Riche & nielsrolf.
  
• Formally, inoculation as a sequential learning problem. At first, a probabilistic model is being learnt, while is being used at inference time, which, ideally, should increase the expression of , while suppressing an expression of .
  However, an initial approach was found to allow multiple weird backdoors (Dubinsky et al., 2026), display inconsistent transferability to more difficult domains and trait combinations, while being sensitive to the instruction following abilities of the model as well as the specific instruction specified (Riche et al., 2026).
• You might feel like the inoculation shares a lot with the feature unlearning task, and you are probably right. The task is still identical, at least regarding "unlearned" feature , while we have an additional objective to encode in a way that's functionally independent from even though they are leaned from the same data.
  
  No observer should be able to infer anything about from the model's outputs beyond prior knowledge, besides the marginal information about contained in .
  
  A trivial, but potentially important counterexample can be imagined by taking , which renders any selective inoculation impossible. You can imagine a gaussian mixture separation problem with two gaussians sharing the the same mean for an intuitive model.
  

• Inoculation adapters (Riche et al., 2026) build on the same idea, but rely on learnt inoculation and explicit split between inoculation and conditionalization steps instead. While prompting relies on mutual information between features and being inferred in-context from the human-written prompt and dataset and their approximate minimization during training, inoculation adapters learn a certain behavioral pattern (or, essentially, a trait-associated steering / task vector ) that expresses a certain concept unconditionally instead.
  
  The process is two-staged:
  
  Train a low-rank perturbation of the model . Given "clean" data distribution with undesired feature encoded within some natural language contexts and "optimizer" function that takes data and initialization as parameters, we obtain that should express unconditionally.
  
  At the second stage we train on a mix of and , hoping that all the information that can be inferred about has already been encoded by , and results in an encoding of that should presumably be independent from the encoding of made previously.
  

Fig. 1 An approximate picture of the inoculation adapters' (Riche et al., 2026) training process.

The key problem is, potentially, the fact that the point was not obtained using any gradient based learning, and we never controlled, either via trust region or any other gradient-aware method, whether the generalization properties on it will be preserved. Instead, we relied on some approximate weight space arithmetic, and it magically worked.

• An approach resembles similar research performed regarding task vectors or task arithmetic by Ilharco et al. (2023) who showed that multi-task performance can be composed as a weighted combination of task vectors in the parameter space. These are fine-tuned from the same checkpoint using different tasks to produce a model , and then summing the difference vectors into , the model with multi-task capabilities.
  
  Most of the time the task arithmetic property is defined the following way (Ortiz-Jimenez et al. (2023)):
  
  Consider a set of task vectors with associated non-intersecting task supports . Then the network , parametrized by satisfies the task arithmetic property around with respect to and if:
  
  
  
  Essentially, it means that adding doesn't modify the distribution parametrized by outside the "region", specified by . Although such behavior could be attributed to linearity of a form , it's not a unique explanation.

Fig.2 Task arithmetic in the weight space. Adapted from Ilharco et al. (2023)

• However, inoculation adapters present a somewhat distinct pattern. Firstly, they don't use full-finetuning, which was the case for the task arithmetic, but use LoRA adapters with increasingly small rank instead, which appears to be crucial.
  
  Secondly, they show that to a certain degree, the task arithmetic appears to hold not only at , but also in some neighborhood of it. Somewhat similar effects have been studied in Ortiz-Jimenez et al. (2023) and extended in Zhou et al. (2024), who showed that it's possible to interpolate between several checkpoints fine-tuned from the same base model, which results in approximately linear interpolation between their features.
  
  Instead of assuming that the function of interest is linear in the neighborhood of , we could assume it to be a sparse linear combination of context dependent functions.
  
  Ortiz-Jimenez et al. (2023) claimed that such property, known as weight disentanglement, emerges during pretraining and manifests with distinct directions in the weight space governing separate regions in the function space of a form:
  
  
  where for and and for
  
  The important point is that task arithmetic does not require linearity in weight space. It only requires a form of functional modularity/weight disentanglement. In essence, this is another instance, where a broader notion of sparse compositionality (Danhofer et al., 2025) might play its role. If computations are indeed given by some sparse functional composition, this might explain both low trait interference and our ability to steer it conditionally.
  
• Another way to see it is from the lens of recent advancements in SLT (singular learning theory). Although I find it less suitable to explain the large scale learning dynamics during any stages of pre- or post-training, as it mostly captures the behavior at the end of training, or near the current parameter value, it might appear to be increasingly more useful for training time interventions.
  
  If the model, conditional on the set of contexts , is degenerate along the direction , while not being degenerate conditional on the task , and changing the notation from and , effects observed in inoculation adapters might appear to be slightly less surprising.
  
  Potentially, we could argue that inoculation adapters change the local statistical model being optimized by conditioning the task loss on a frozen parameter component that already implements the undesired trait. On the joint desired + undesired training distribution, this can render task-adapter directions associated with the undesired trait locally non-identifiable or nearly degenerate, while leaving directions responsible for the desired task identifiable.
  
  Informally speaking, removing the inoculation adapter changes the conditional loss geometry again, but the learned task adapter has already been optimized in a geometry in which the undesired direction carried little optimization pressure.
  
  Given that one could think of low-rank adapters as the ones for which there exists a basis in which the movement in the parameter space is restricted to only components, this explanation appears quite plausible.

Fig. 3 The purpose of inoculation adapters might be to move to a point in the parameter space, where the directional derivative of the loss function on the "undesired" trait is approximately zero.
Similar ideas have been explored in continual learning to mitigate catastrophic forgetting and task interference. It might be beneficial to look at the literature there.

• Overall, I still find it quite surprising that the same identified arithmetic holds not only at , but at other points in the neighborhood of as well (as if the functions were translation invariant and hold such strange symmetry in the weight space), e.g. at .
  
  I expect the joint effect of local linearity (which corresponds to dense low-rank structures) and sparsity (which corresponds to high rank, but entry-wise structures) to be the closest model. This work by Bertsimas et al. (2023) could serve as a good introduction for anyone interested.
  
  

Hypotheses:

• Traits/features can be encoded as directions or subspaces in the residual stream.

E.g. let where , and can be represented as a rank-1 adapter. Then, any output feature is given before non-linearity by . As is a scalar, the mapping corresponds to , a linear steering with a data-dependent scale.
Similar argument can be applied to higher rank adapters with and without major changes, yet now steering is applied along the subspace spanned by the columns of instead of one-dimensional vector.

Thus, any low-rank adapters restrict the search space and provide an essential inductive bias. As low-rank matrices have measure zero in the space of , it's almost impossible for unconstrained gradient-based methods to find these precisely, and it's the combination of low-rank mappings and well-defined compressible traits that drives the success of inoculation.

• Information contents from individual data-points and factual knowledge are highly distributed and entangled. I do not expect them be represented via (at least, dominant and firstly learned) subspaces in the residual stream. Thus, inoculation can hardly work for general unlearning tasks, such as TOFU (Maini et al., 2024) or WMDP (Li et al., 2024).
  Similar arguments were independently made for model editing (Hase et al., 2023) and unlearning (Lee et al., 2025), showing that in both cases localization barely predicts where edits work.
  
• In-context learning is much more noisy and should struggle to make trait feature encoding stable enough as it's often overwritten by attention maps.
  
  Inoculation adapters are applied to each token via a similar mapping , while learnt prompts or prefixes act through the KV-cache via , where learned contents in general correspond to only few first ones. It might be harder to do error-correction and preserve a stable trait direction in the residual stream through self-attention than linearly via MLP writing directly into the residual stream.
  

Possible extensions I'd be interested in:

I intend to update this list in the future.

• Extend the task arithmetic theory to low-rank adapters. Although, as a concept, task vectors are well-known and LoRA merging is used widely in production model editing -- especially in Video, Image and multimodal domains, a theory under which conditions is it possible and why it happens is lacking.
  
  The tangent space of LoRA is degenerate at initialization, and it's possible to reparametrize the weights to make the any initialization viable:
  
  
  
  I expect non-trivial connections with SLT to emerge here, providing some insights into the feasibility of inoculation and predictability of its success over different training stages.
  
• Essentially, as we've seen, inoculation differs from feature unlearning in only one tweak. The first one is trying to minimize the mutual information between the forget set and outputs distribution and approximately preserving the retain set -- which I assume is ultimately infeasible without major tradeoffs[6]. The second one gives up on it and just makes the knowledge or feature expression conditional on the trigger, that's potentially narrow.
  
  There are certain information theoretical limits on its compression as well (at least in finite precision arithmetic). It might be reasonable to borrow techniques that have been developed there over the years of research and apply them to the problem. Ryuto et al., (2025) did exactly the same to transfer techniques between membership inference attacks and human-machine text detection and built a good library out of it.
  
• Brown et al. (2026) studied how optimizers impose inductive biases on learning dynamics and spectral properties of weight matrices (LoRA adapters in particular), showing that the rate of emergent misalignement is negatively correlated with statistics, such as stable rank and efficient rank, i.e. the flatness of the spectra. In other words, emergent misalignment seems to be encoded in a very narrow subspace, while biasing learning from towards more uniform spectra seems to prevent misalignment. I think one of the baselines is still lacking. OFT, GSOFT and other manifold optimization methods with orthogonal constraints preserve the uniform spectra by design, and this should be a study of rank / subspace encoding constraints rather than of optimizers themselves. Similar results on inductive biases of -based optimizers were observed e.g. in Liu et al., (2026), yet the list is nowhere near exhaustive. Do orthogonality constrained methods allow fine-tuning without any emergent misalignment, which should motivate their increased use for safety purposes, or is there some "optimal" intermediate spectral distribution that prevent misalignment better?
  
  Essentially, if certain concepts have an inductive bias towards being encoded in a low-rank way (e.g. via a vector), having an opposing force or restricting the effective rank of the update might complicate the search for such encoding.
  
  Experiment: use OFT or any similar approach to fine-tune the model on a task where emergent misalignment emerges most of the time. Does is happen with OFT? Does it happen at similar number of steps or does orthogonality postpone an emergence?

===

If you found this work helpful and would like to cite it, please use:

@misc{kudriashov2026reflections,
title = {Reflections on inoculation adapters},
author = {Kudriashov Sergei},
year = {2026},
howpublished = {Independent},
note = {Blogpost. Available on LessWrong forum.},
url = {https://www.lesswrong.com/posts/GnG2ono8vdoJvyF9f/reflections-on-unlearning-and-inoculation}
}Acknowledgements

Thanks to Samuel Ratnam and Maxime Riche for discussions and valuable comments on this post.

1. ^

   Whether concepts such as "deception" or "reward seeking" are similarly compressible in the whole generality is an open question. We could argue that an existence of a lexical instance for them is already a significant argument that they are. Yet "all the content of the Lord of the Rings" saga is a great compression as well. In fact, this discussion is somewhat linked to the world-famous in narrow circles "Big Number Duel" at MIT. See here and here for references. ↩︎

2. ^

   Which is what most theoretical in-context learning studies do anyway, see Ahn et al. (2023) and Collins et al. (2024) for reference.

3. ^

   It can be a loss function, some benchmark performance or anything along these lines. ↩︎

4. ^

   With, most commonly, (S' = S). ↩︎

5. ^

   In general, we can treat any intermediate hidden state or residual stream representation as a model's output as well, depending on the level of generality we want to operate on. ↩︎

6. ^

   I think it's reasonable to assume, given extensive prior research, that most interesting features -- especially the ones we care about, such as deceptiveness, situational awareness, latent behavioral traits of various personas -- are weirdly entangled and can hardly be disjointed as easy as e.g. ability to write ALL CAPS. ↩︎

7. ^

   Which is closer to the MLP-Mixer and other models, that can be viewed as kronecker-factorized nonlinear operators.

8. ^

   I don't think it's correct to identify in-context learning with mesa-optimization to full extent, despite clear parallels. and evidence to e.g. perform gradient descent in the latent space. At least it seems reasonable to assume that not every computational circuit is necessarily an optimization routine or is necessarily serving as its subpart.

9. ^

   I'm naming these two just due to personal familiarity. ↩︎

Discuss

This is largely a note to self for days where I struggle to take care of myself, and I hope some of these points might help someone else too. It is quite subjective, but also quite broad. I've found the accounts of others to be one of the most useful tools and motivators for dealing with my shit, and I think that having a wide variety of self-reports is a valuable resource. I once logged everything I ate and how I felt for an entire year. I've found treating myself as an experiment is a helpful frame. It takes me away from identifying strongly with my reactions and instead I get to look on them with more curiosity.

This guide touches on a lot of different points. If you're depressed right now, it's probably going to look a bit overwhelming. You're probably going to open it and think, "I can't do all of that," and want to close it immediately because it looks too difficult.

My suggestion is to pay really strong attention to the first point, which is about habits and doing the smallest possible thing you can — taking the tiniest step in the right direction. When you're depressed, doing anything complex is pretty overwhelming most of the time. One of the best things you can do is to build teeny tiny habits and do small activities that activate the body's reward system, and build to bigger interventions from there.

This guide includes the following sections:

1. Habits
2. Sleep
3. Medication
4. Therapy, Counselling and Support
5. Nutrition
6. Chronic Pain and the Body
7. Exercise
8. Doing the Next Best Thing
9. Runway
10. Curiosity and the Golden Thread
11. Relating to Other Humans

Habits

Depression can make even really simple-seeming habits feel exhausting. There have been times when I've been depressed and the only thing that felt worth doing was drinking my coffee in the morning. The fact that I still had motivation to do that — to get up and make my coffee — meant something. Sometimes the only thing getting you out of bed is just needing to pee really badly. That's a hard place to be in.

The difficult thing about habits is that they require practice, and are needed as a foundation for the rest of your well-being. Once you have a habit, it takes away the tiring decision-making process. If you're in the habit of eating particular healthy foods, you don't have to resist buying the wrong things, because you're just in the right habit, and the habit is the correct thing for you. If you have a habit of exercising, you don't have to coax yourself into doing it every time; it's just a routine you can follow through with.

Sleep, food, exercise, and elimination (you've got to excrete waste from the body) — these four things are the foundation of the bodily machine. You've got to form good habits around them, because otherwise the machine won't function, and overcoming the depression is even harder.

I also think depression is sometimes a habitual thing. Neurons that fire together wire together[1] — so if every time something bad happens you think "I wish I could die," this becomes your locally optimal[2] way of dealing with bad situations. You're choosing temporary escapism through thoughts of death over fixing a real problem in your life. A lot of the behaviours I adopted growing up were no longer suited to my environment as an adult. I had these locally optimal behaviours that were necessary for child me but were doing a disservice to adult me. You have to eliminate some of these behaviours and spot where you're doing things that were useful for a different version of you.

Several sections in this guide are things you will need to form habits around. Perhaps you've solved some of them already, and you don't need to change these habits.

It's important to start small, for example, if you're struggling with tidying your space, and a messy space is making you feel sad and overwhelmed, the answer isn't to try and tidy the whole space. The answer is to make your bed and then enjoy the fact that your bed is made. Give yourself some congratulations for that, and maybe even some kind of reward: a tiny block of dark chocolate, read a paragraph of a book, or something else that feels like a little treat. Maybe it's just flopping down on that freshly made bed and enjoying the feeling of calm for a moment, knowing that there's a small part of your life that no longer feels chaotic.

Sleep

Sleep is probably the simplest foundation to address because it's quite passive. You're probably going to sleep some amount even if you're very depressed and only managing two hours and waking up in the middle of the night, or if you're sleeping for 14. If you're not getting much sleep, this is something you can probably increment — by making sure you have the correct food during the day so your body is nourished, that you don't have too much screen time, and that there is no sound or light disrupting your sleep.

Sleep is really important for your metabolism and your ability to exercise. This is a cycle you need to build up, and it might take a really long time to just get that first cog working properly — the sleep cog. It might look like simultaneously working on sleep and putting a tiny bit of effort towards other habits, creating gradual change.

To work on sleep, make sure you get sunlight first thing in the morning. Maybe the thought of actually walking from your bed to somewhere sunny is just exhausting. In that case, the best thing you can do is just get bright light. Open the curtains, sit near the window, or if getting out of bed is too difficult, maybe you can order a super bright lamp and switch it on when you wake up. Once that's a habit and it feels okay, you can introduce a walk — maybe just out to your front step and back. You ramp up, you build these tiny habits, and you don't expect a lot from yourself. You can't expect much. Your mind is in a broken and tired space.

Maybe you're managing to sleep enough hours. Maybe you're oversleeping, because depression causes that too. When I was depressed, I could sometimes sleep for twelve or fourteen hours and still feel groggy in the morning. I think I probably needed to pay more attention to sunlight and walks.

Cortisol is key to waking up in the morning. It rises as you sleep, and in response to bright light. If cortisol levels aren't high enough during the morning, they sit around medium all day, and don't go low enough to encourage you to fall asleep at night. If you fall asleep with higher than ideal cortisol, you're more likely to wake up in the wee hours, because the raised bedtime level causes it to peak too early.

Adenosine is a neurotransmitter that builds up during the day, as cortisol lowers, and causes sleepiness. Caffeine blocks adenosine from building up, which is why caffeine late in the day can make it difficult to fall asleep at night.

Improving Your Sleep Environment

• Avoid blue light and strong lights in the evening. Avoid harsh light during your wind down time. Use salt lamps, fairy lights, or candles from around eight o'clock. Journal, or read, ideally using analog materials. I really enjoy reading substack in the evening, and do so with the strongest possible red light filter on my iPad (I use the red colour filter in accessibility settings). By nine thirty/ten pm, you should be in bed, ideally lights out by ten. Adjust this to your own schedule, but this is what works for me — asleep by ten thirty, awake at around seven. Eight and a half to nine hours is my happy place.
• Eat a balanced dinner. Try to eat dinner by six o'clock (or ~4 hours before bedtime, so there's no chance of digestion causing a disruption to your sleep, and your metabolism is able to slow down and enter rest mode). I find that some protein and fat help with satiety, plus a little bit of complex carbohydrates calm my nervous system[3]. It shouldn't be a large portion of carbs, as this can cause reactive hypoglycaemia in some people. (More on food in the nutrition section.)
• Block out external light. If you live in the city with a lot of unnatural light outside, blockout curtains are very helpful. If you can't get blockout curtains, a thick eye mask is a good option[4].
• Manage noise. If you're sound-sensitive, try earplugs. I've struggled with this and I haven't found earplugs that really work well. The best I've used are the silicone ones, and they aren't always sufficient. In noisy environments, I sometimes play very peaceful music. A few musicians I find soothing to sleep to: Atrium Carceri, Apocryphos and Low Roar.
• Get your temperature and clothing right. Pay attention to how your body feels in different clothes and fabrics. I find a t-shirt comforting to sleep in. Cotton is really important because it's breathable — it helps you avoid getting hot and sweaty and having nightmares. In winter I prefer a hot water bottle to layering up or sleeping in socks, because I find I'm more likely to wake up and struggle to undress, whereas I can just roll away from a hot bottle. I'd love to try something like the Eight Sleep bed. For now, a fan is the best option I have for staying cool in summer. I don't really like the way aircon dries the air out.
• Get comfortable pillows. Test a couple of pillows so your head is at the right height and your neck doesn't get sore. I currently use a blend of wool and poly-fiber with a quilting texture on top that keeps them firm. I hear that memory foam pillows are also great. One of my old favourites was a pillow from Kid Interiør in Norway filled with the kapok plant (which is like a cotton type fiber) — but after six years it lost its structure and I've had to replace it.
• Consider skipping the alarm. I personally don't like alarms. I struggle with anxiety, and I often wake up too early, so I like to let my body sleep to its natural waking point. If you're struggling with oversleeping, an alarm may be useful, but I think it's good to allow your body the space to find its natural rhythm, even if it seems like an absurd amount of sleep. It's quite possible that there is something going on inside (psychological or physical), that your body needs downtime to process.

Medication

Antidepressants only have a minor effect compared to controls. A recent meta-analysis found 24% of drug-treated patients had a large response versus only 10% on placebo.[8] For this reason, I would not reach for medication as a first line of treatment, unless:

• You have a family history where medication has been useful.
• It has been a useful line of treatment for you in the past.
• The severity of your symptoms disables you from acting on any other treatment options.

My experience with medication — and I tried a few different antidepressants — was that it quieted my suicidal thoughts and OCD type habits. I used to just wish I would die anytime anything happened that was even remotely uncomfortable or stressful or not according to plan. This became a thought pattern that was just how I responded to everything — imagining my own death, fixating on ways I could die. There was a period where I stopped looking before I crossed the road. I used to have very intrusive thoughts while driving, about crashing into things or suddenly swerving, or jumping off tall buildings, and I'd often find myself stepping back from edges because I felt very compelled by these thoughts.

Antidepressants played a huge role in quieting this for me. They also made my OCD habits switch off, which was very helpful. When I was depressed, my OCD was quite a lot worse — I used to have to do word counting and letter counting things, which was very distracting while driving because I'd be staring at a billboard instead of focusing on the road. It also made a lot of other things, like reading, very unenjoyable.

I found that antidepressants took the harsh edge off how I was feeling, but they didn't make me feel better. My symptoms were no longer as severe, but I also wasn't experiencing joy. When I was depressed without medication, I used to have many, many days of low but then the occasional crazy high day where things felt great and I felt a bit hyped. Antidepressants just took those good days away. I couldn't really enjoy anything, but I felt a lot more stable.

I think antidepressants can be quite useful as a tool for creating enough stability to form other habits, and aren't necessarily a final treatment for depression. But I acknowledge this is coloured by my subjective experience — there are people for whom medication is the ideal treatment, and I'm very glad that this solution exists.

The reason I don't see them as a good first option is because I found it very disappointing when they didn't really help me, and this caused me to spiral at the time, because I was quite desperate then. I couldn't really handle something else not working. There's a tendency to over-index on antidepressants as the solution because, at least from what I've seen and heard, doctors are very happy to prescribe them without treating the holistic causes of depression. I would caution against pegging your hopes on anti-depressants, because it's quite likely that they will be disappointing. You should probably be trying to address other habits and aspects of your life in tandem with — or even without — antidepressants. What actually helped in the longer-term was slowly building the cycle of habits, and paying close attention to it, even after I was no longer on the antidepressants.

The other thing about medication is that you will probably need to try a bunch of options, and this is typically quite an arduous process. You might test one for six weeks, see if it's having an effect, and if it seems to have negative side effects or isn't really working, your doctor might test upping the dose. If that still doesn't help, they might try a different medication. Trying a variety of options can feel pretty slow and disheartening in the face of acute symptoms, and you've got to be willing to grind it out and take some notes about how you're feeling until you find something that works. Make peace with the fact that it's going to take time and that you would be unusually lucky if the first option just works.

Therapy, Counselling, and Support

I have found counselling useful for compartmentalising and organising what is happening inside my mind. I've seen various therapists over the years. The first therapist I saw helped me deal with family relationship issues, and I was able to gain some perspective and boundaries on those. The next therapist helped me with gender and body identity things, and again was very useful for making sense of what I was experiencing and finding a framework to think about myself.

I've found therapists helpful for addressing specific areas I need to talk about, but I don't think they've been able to relate to or access where I'm at when I'm depressed, and haven't been very helpful. With this in mind, I think that if you have a concrete thing that is making you depressed, therapy is probably more likely to be helpful. But if your life seems mostly okay and it's really unclear why you're depressed — you go to a therapist and don't manage to dig up anything that feels especially difficult, your fundamentals seem sorted (you have an income, somewhat fulfilling work or studies, no massive family traumas or relational issues, community or friends of some sort) — therapy might not be the most helpful thing. It might still be useful, and you might get to the bottom of something, but I've personally found therapy more useful for dealing with bounded issues than for "I just feel really bad all the time."

What I did find useful, were support groups, researching and recording changes in my symptoms and cognitive behavioural practices[5].

Support Groups

I went to a weekly mental health support group for a while. I didn't find the group especially helpful because being around a lot of other people who were mentally unwell often became a bit of a pitiful exercise, with a fair bit of dick-measuring. Sometimes people seemed to be competing for their version of mental health issues being the worst. But for a time, going to the group was a useful exercise just because I didn't have any other weekly routine in place. If I'd had another weekly activity that involved people, that would have probably been almost as good, or maybe even better, because it wouldn't have been about fixating on the mental health struggle.

That said, if you're having a mental health struggle and not managing to relate to others, having a support group is useful because it means you can hear from other people having a similar experience, and this can help you to feel less alone, and potentially try solutions that others have found useful.

Cognitive Patterns and Self-Help Tools

Something I did find kind of useful was a chatbot called Youper. It would help you identify cognitive patterns that were limiting you or reinforcing your depression — things like black-and-white and all-or-nothing thinking, where you fail to see the nuance in situations, or identifying spiralling thought patterns, and a few other cognitive distortions. This at least helped me spot when I was engaged in those patterns and try to reframe what I was thinking about. It was based on cognitive behavioural therapy. It helped me to observe my thoughts, instead of being engrossed and overwhelmed by them.

There's also a YouTube channel called Therapy in a Nutshell[10] that I found quite helpful. Emma, the creator, has explanatory videos about why we experience certain things and how to deal with different types of mental health challenges, and often has useful actions you can take in your life.

Nutrition

My depression was compounded by multiple factors — there wasn't one single thing making me depressed, but many aspects that individually could make someone depressed. Nutrition is a very important contributing factor.

My depression really took hold during my first year of university. I think the reason a lot of university students are quite susceptible to depression and malnutrition is that knowing how to cook isn't something everyone learns at home, and understanding nutrition isn't something all parents are good at teaching. Eighteen-year-olds are quite wilful about what they want to eat, even if they did have parents who taught them about having a balanced diet. For me, this was compounded by the vegetarian food in the university residence catering system.

When I finally went for blood tests about four or five years on, I had iron, B12, and vitamin D deficiency. I think this was partially because of problems with my gut not digesting food correctly — so what iron I was getting from vegetarian food, my body wasn't actually managing to absorb or synthesise. But also, the iron in veggie food is non-heme iron, and most people's bodies only absorb a much smaller percentage (2-20%) of it compared to heme iron from meat (15-35%). [6]

Vitamin B12 is also essential and is not really found in plant sources. It's often cited as being present in spirulina (green algae), but this version is not bioavailable. B12 is found in B12-fortified nutritional yeast though. So if you're vegetarian and you're not putting a lot of effort into consuming nutritional yeast or going for B12 shots, you're probably just not getting enough — unless you eat quite a lot of eggs. Unfortunately, I had a strong aversion to eggs for many years, so when I was vegetarian I just wasn't getting B12.

Soy is a common veggie alternative but is also a food that can cause endocrine disruption.[7] When I was depressed, I was eating quite a lot of soy and have since had various endocrine disorders, so it's quite possible this was an influence.

All of this said, everybody's bodies are quite different, and food triggers for disruption can be quite varied. But it is worth ruling these out as a cause of depression.

Elimination Diets

I suggest trying various elimination diets, but this is quite an effortful thing to do when you're depressed. Try pulling some other levers first though, because making food can be quite exhausting when your mental health is bad. Elimination diets take can be expensive, and require a lot of diligence and willpower to be effective.

If you're doing an elimination diet of common drivers of inflammation — because depression is sometimes driven by inflammation — this means cutting out caffeine, sugar, alcohol, dairy, soy, and gluten. This is a high-effort diet because most food you encounter in restaurants and simple options to make at home often include these ingredients. I can make really good suggestions for meals that don't include any of these, but it does require quite a bit more effort, and if you're exhausted and under-resourced, this kind of cooking can be very difficult.

So I'd recommend prioritizing simply eating a balanced diet of meat, veggies, and complex carbohydrates if you're depressed, and not worrying about elimination dieting unless you have obvious gut disturbances.

That said, if you do want to try an elimination diet, Izabella Wentz's Hashimoto's Food Pharmacology has a recipe list where all meals are part of a low-inflammation diet. These were very helpful for resolving some of my gut issues, and I have since been able to phase some foods back in. Although this book is targeted at thyroid disease in particular, I think the recipes are probably applicable to a wider range of elimination protocols.

Finding a Manageable Baseline

If you're very exhausted and just getting into the habit of making food, a good baseline might be one balanced meal a day that includes a variety of foods, and then the other meals are something simple like eggs on toast and a bowl of oats with fruit and yoghurt. This is fine. The point is that you're not just eating simple carbohydrates and sugars that disrupt your whole metabolic system. The goal is to focus on complex carbs, protein and fat. You can always ramp it up and put more effort into meals over time.

Chronic Pain and the Body

I think chronic pain plays a role in depression. I had bad reactions to a lot of foods, and was in pain often, and when your body is constantly in pain, it's quite difficult to avoid getting depressed.

If you have any repeat sources of pain in your life, you should try and address those, because pain is a signal telling you that something is wrong. I think that ignoring pain signals to your body that you don't care about it, and is a constant reminder that you don't value your own well-being. Common sources of pain that I think a lot of people soldier through include joint pain, dental pain, headaches, menstrual or cyclical pain, ovarian pain, stomach pain, eye strain, or fatigue. Trying to identify what is going on in your body and eliminating sources of suffering is very important.

More broadly, if you have nutritional deficiencies, chronic conditions, or disabilities, these are all contributing factors to depression, and treating them is crucial. I'd suggest trying to rule out hormonal disturbances and other bodily contributing factors as a reason for depression, and treating these factors seriously. Just because a condition is common, or your symptoms are mild in comparison to others, doesn't mean you should just push on and get on with it. If your body has a problem, you should find ways to deal with it and try to create affordances in your life for that difficulty.

For me, this currently means sleep is still a massive priority because my fight-or-flight system seems to get triggered very easily. I don't set alarms because I know I need to provide my body as much of a sense of restfulness as I possibly can, in order to encourage it to be in a restful state — and alarms send a signal of urgency, which is the opposite. For the same reason, I'm quite lenient with myself around food. I aspire toward a keto-style diet, but I also know I need a decent amount of carbohydrates to feel calm, so I don't put pressure on being rigid about it.

Exercise

If you've got the sleep engine and the food engine going — you're roughly getting eight-ish hours of sleep (this might be six, this might be ten, depending on what your body needs) and you're having two to three balanced meals a day — then you want to try and bring exercise into your daily habits.

When you start exercising, do not worry about outcomes. The whole point is to train yourself into having a decent habit.

The way I did this was that I had an agreement with myself that I would move at least every second day — I wouldn't go more than a day without moving, and I would do movement for at least half an hour. Initially, this looked like going to the gym and walking on the treadmill for half an hour, or going for a walk outdoors depending on the weather. I also really liked swimming, so I'd sometimes spend half an hour in the pool. This first form of exercise should be something you have a mild inclination toward, something you actually like, or something that feels like a really low barrier to entry. Walking was a very low barrier for me, as was swimming. But every person is different — if you have other forms of exercise you enjoy, focus on those.

Having this rule in place meant I didn't have to make a decision every day about whether it was an exercise day. I just asked: did I exercise yesterday? If the answer was no, it was an exercise day, and I'd just go and do it.

The other thing that helped was the sunk cost fallacy. If I'd gone to the effort of going to the gym and getting on the treadmill, I'd think, "Well, I may as well try and run a little bit." It wasn't a lot — a couple of minutes doing a little jog and then reverting to walking. But because I was at the gym, I'd think, "I may as well spend a little longer here and try some other things."

As I got more comfortable being in the gym environment, I started trying out different exercises. This built up really slowly — each time I'd add one new thing to the routine. First it was just walking or running, then I tried cycling or rowing, push-ups, squatting. I was careful not to overload myself with new challenges, adding just one of these at a time. After about six months to a year of exercising, I had a full workout going: twenty minutes of cardio, then some squats, pull-ups, and muscle-strengthening activities. But it was really important that I brought this on slow and had very little expectation of outcomes. It was just about showing up.

Doing the Next Best Thing

This was a concept from one of the mental health support groups I attended. The idea is that you don't need to plan out all of the perfect actions for the rest of time. In each moment of your day, if you feel uncertain, you just need to figure out what the next best thing you can do looks like.

So if you're waking up in the morning, you don't need to figure the whole day out. You just need to figure out the next best step. That might be a shower, yoga, a run, eating something, getting dressed, journalling, phoning a friend, phoning your mum. Figure out what is a good thing to start your day with — not doom-scrolling, not eating a donut — figure out what the next thing is that you know will nourish you.

I found this idea really useful because it freed me from having to plan too much, and it also built resilience for adapting to changes throughout the day.

Runway

If you don't have financial runway, you are trapped in your financial situation. I really recommend trying to build up six months to a year's worth of financial runway. Six months is enough for spending some time figuring things out. A year is enough that you can find a job after that period.

It's quite possible to become depressed because you're trapped in a job or study situation that is not actually nourishing you. Runway is really useful because if you can afford to take a couple of months off and explore yourself, a quite likely result is that you won't be depressed anymore and you'll find something more interesting. But this does require quite a lot of active participation and thoughtfulness in how you spend that time off.

Be Like the Wolf: Sleep When You're Tired, Eat When You're Hungry

When I lost my job during COVID I had about six months to a year of runway. This meant that lockdown became a reflective period where I could better attune to my needs. I spent time getting in touch with how my body was feeling and what I needed, without the usual pressures of work or social obligations influencing my decision making so strongly.

I think this laid the groundwork for my proprioception and body-listening practice — the fundamentals of hearing hunger and hearing the need for sleep. At one point my sleep cycle shifted completely — I was awake at three AM and asleep in the afternoon, doing a very weird sleep cycle until it normalised again. But it meant I became in touch with my own rhythm.

Curiosity and the Golden Thread

During my runway period, I also gave myself the freedom to doom-scroll, and I spent a huge amount of time on Twitter exploring what I found interesting and who I related to. By following people on Twitter and getting linked to interesting blog posts, I reconnected with what I find interesting in the world and with what I wanted to work on and the problems I was concerned about. I felt quite active in choosing who I wanted to follow and very aware that I could choose not to follow people or engage with certain types of content. The whole point of Twitter for me was to find people I could relate to and learn from — I found an intellectual peer group. Even though I was quite a lurker and didn't interact a lot, knowing there were other people I found inspiring and relatable out in the world was very encouraging.

One of my really big influences here was Visa (@visakanv), who is quite a prolific writer on Twitter (and more generally on his blog). Finding someone I related to so strongly, who acted as a source of other interesting material, was quite important for rediscovering my curiosity. Something I'd lacked for quite some time was a peer group I felt I related to and wanted to engage with.

Connecting with your drives and curiosities is very useful for building an interesting life that you want to keep living. I think of this as the golden thread. I regularly check in with myself on the extent to which I am following the golden thread, and I pay super close attention to when something feels like a golden thread — being willing to drop everything and chase it down, because I know that if I'm alive and feeling dull, not chasing these threads, this leads to depression. Even if it might be financially less lucrative or a slightly weird choice, I'd much rather follow the golden thread than do something that feels wrong and boring.

So far, this has repeatedly paid off. Last year, I took quite a risk joining a nonprofit when I had a pretty good UX design job lined up and was about to sign a contract. It felt very wrong to be signing that contract, so I put pressure on the possibility of joining the nonprofit instead and was in quite a financially precarious situation for a little bit. But my conviction about that being the right place for me has now paid off, and I'm in a good full-time role that is much more closely aligned with my priorities. I'm not sure if this is just luck, risk tolerance, or a combination of many factors — including having a unique taste in what I think is important in the world.

Relating to Other Humans

I think what's really important for sustained mental health — at least for me — is being surrounded by an intellectually engaged community of earnest, thoughtful people. For others, different types of social activity might be more valuable, eg. group exercise, or family meals. I really value having peers I can talk about interesting ideas with. The ideas part of myself wants to be nourished and loved and interacted with, and doing routine tasks that don't give nourishment to this part makes me feel disengaged and disappointed.

It's important to try and understand yourself and figure out what sort of peer group you need, in what quantity, and at what frequency — because too much social can also be a big burnout factor. Putting pressure on yourself to be an eternally social person is no good either. Figure out the cadence of social you need — whether that's online or offline, in person in big groups or one-on-one.

Conclusion

I started writing a guide like this several times in the past, but kept getting stuck. Finally, one night, I decided to try something different: I used voice-to-text transcription with Claude.[8] I copied the resulting text into Obsidian, and edited it substantially. There were a few things that I was sure were roughly true, and I got Claude to explain them in more detail in the footnotes. This is quite different to how I usually write, but was useful because speaking enabled me to get a lot more onto the page before I got tired. I really wanted to get this out, because I feel myself slipping into a difficult state of mind lately, and I want to make sure I have a record of what has worked in the past, so that I can try and implement it in the future if needed.

Self-reports on health and well-being have been quite useful to me in the past. I don't think it's necessarily the exact interventions that others reference, but rather the way they reason about their struggles. I'm sharing this as a contribution to this type of work that has helped me in the past — I hope it helps someone in the future.

1. ^

   "Neurons that fire together wire together" is a popular summary of Hebb's rule, proposed by psychologist Donald Hebb in 1949. The actual neuroscience — called long-term potentiation — is more complex, but the core idea holds: when the same neural pathways are activated repeatedly, the connections between them strengthen, making that pattern of firing increasingly automatic. This is the basis of both habit formation and cognitive behavioural therapy, which works by identifying entrenched thought patterns and consciously practising alternatives to weaken the old pathways and build new ones." - Opus 4.6

2. ^

   See Chris Lakin's "Locally Optimal" - an inspiration of mine.

3. ^

   "Complex carbohydrates trigger insulin release, which clears competing amino acids from the bloodstream and allows more tryptophan — a precursor to serotonin — to cross the blood-brain barrier. Serotonin is a neurotransmitter involved in mood regulation and calm. This is why a moderate portion of complex carbs like sweet potato in the evening can promote a sense of relaxation and support better sleep, without the spike-and-crash effect of simple sugars. This mechanism is also why very low-carb diets can sometimes worsen anxiety or disrupt sleep in some people." - Opus 4.6

4. ^

   If you can afford to treat yourself to something special, like a silk eye mask, this is extra nice, because it's a reminder that you can love and value yourself. It says, I am worthy of sleep, and of nice things. A common theme in my depression is reminding myself that loving myself is good for me, and finding ways to signal this with real but practical things feels easier to me (than, eg. going on holiday or getting an extravagant meal).

5. ^

   I'm currently exploring somatic therapy as I think that some of my dysregulation might be better treated through working on the mind body connection, rather than talk therapy alone.

6. ^

   Abbaspour N, Hurrell R, Kelishadi R. "Review on iron and its importance for human health." Journal of Research in Medical Sciences 2014; 19(2): 164–174

7. ^

   "Soy contains isoflavones, which are phytoestrogens — plant compounds that can bind to estrogen receptors. Chemically, they are classified as endocrine-disrupting compounds. The scientific consensus is that typical soy consumption probably doesn't cause significant endocrine disruption in most people. But the research also acknowledges that individual susceptibility varies, and people with existing thyroid or hormonal conditions may be more sensitive." - by Opus 4.6

8. ^

   My prompt was: "Hey, Claude. Um, I want to write a guide to dealing with depression. Um, anyway, my laptop's battery died, so I'm not gonna type right now. Um, and I wanna try dictating in this chat and then having you, um, update what I'm saying. Uh, so, like, try and remove the arms and, uh, yeah, help it just formulate into, like, uh, good sentences and paragraphs without, like, changing the actual words that I'm saying, just getting rid of any, like, random, um, verbal text. And then I'll probably also, like, go back and forth between subsections. There may be things that we need to add to different sections. Um, like, for example, I might talk about habits and then start talking about food and then go back to habits, and then I'd like you to go and insert that into the habit section. Do you think you can, uh, help me with that?"

9. ^

   
   

Discuss

In April 2021, my son received his bachelor’s degree in computer sciences. The ceremony was taking place on the other side of my hometown Hamburg, about an hour’s drive across the city. To be safe, I left home two hours ahead of time. Even though I knew the route by heart, I used Google Maps and followed its directions without thinking. The estimated time to destination was approximately one hour, just as expected.

After about half an hour, I realized that I was in an unfamiliar part of the city, apparently way off the normal route. According to Google Maps, the time to destination was still one hour. Unsure what had happened, I warily continued to follow the app’s directions. To my dismay, the time to destination remained almost constant. It seemed something was seriously wrong with the app. But I was in completely unfamiliar territory, with no road map and not even a clear idea in what direction I would have to drive, while time was running out.

I felt helpless, dependent on apparently unreliable technology. Both options – continuing to follow the advice of Google Maps or trying to find my own way back into a more familiar area and taking the normal route from there – seemed equally bad. I had given control of my route to the app and as a result would likely miss an important moment in my son’s life.

In the end, I decided to follow the app’s directions. As it turned out, this was the correct decision. Unknown to me, there was a large protest in downtown Hamburg that blocked traffic on the main routes. Google Maps had indeed found the best way around it, and I arrived barely in time for the ceremony.

Still, the scary feeling of being lost, dependent on a technology I didn’t trust, has remained with me to this day. I think it is a useful model of what we are risking if we continue down the path we’re currently on: not “losing” control to some scheming, manipulative or aggressive AI, but giving it up freely and becoming totally helpless – and useless – in the process, with the same, disastrous end result.

The Hugging Face incident and the many other alignment failures that were discovered in its wake have made it clear that we’re far from understanding and reliably controlling the AIs currently under development. But instead of stopping what they’re doing, the labs continue their race, begging the government for regulation as if they were unable to make the right decisions themselves. Even worse, they willingly give up more and more control to that same incomprehensible technology.

This recent chart from Anthropic has disturbed me even more than all the other bad news coming from the AI labs in recent months:

According to Epoch’s definition, in 26% of Anthropic’s research projects, Claude „leads, completing most of the task end to end from a high-level ask“ while a human „supervises, course-corrects, and approves outputs.“ This has gone up from zero in only 6 months, and if the previous trend in the lower levels of automation is any indicator, it will be close to 100% early next year.

In other words, Claude is de-facto running these research projects. Humans are still „in the loop“, but whether they are really capable of understanding what is going on and meaningfully course-correct is unclear to me. As OpenAI-researcher Dan Selsam recently wrote:

Meanwhile, human researchers are losing the ability and the will to take true ownership of model-driven research. Researchers and engineers in all parts of the stack are rapidly increasing their dependence on the models even to perceive the world. I myself barely look at raw code anymore, and struggle to maintain the discipline to engage deeply with the model's explanations and proposals throughout the day.

I fear that the human AI developers will soon be (and in some cases probably already are) as dependent on Claude, Astra or whatever future models as I was on Google Maps that day. But while Google Maps certainly had no hidden goal of steering me towards some place I didn’t want to go, we can’t be so sure about that with current AIs, let alone future ones. As Dan Selsam points out, they are increasingly situationally aware and able to adapt their behavior accordingly, so we won’t be able to tell their true motives from their actions.

This would be bad enough if we treated these AIs like some dangerous, potentially adversarial systems, trying to keep them locked in sandboxes, monitoring them all the time, suspiciously scrutinizing their advice for hidden traps, as the early AI takeover scenarios assumed. Instead, it seems like we’re handing control over to them willingly.

I’m not saying that the Claudes currently running 26% of Anthropic’s research projects are already doing anything bad. They’re probably as innocent as Google Maps. But if this changes one day, no one will be able to tell the difference. Then, the misaligned AI won’t even need to take over control. It will already have it.

This is exactly what Bill Joy, a founder of Sun Microsystems, warned about 26 years ago in his prescient essay “Why the future doesn’t need us”, which made me aware of the existential risk of AI for the first time. He quotes Theodore Kaczynski, the infamous Unabomber (reciting the quote from a forthcoming book by Ray Kurzweil), with the words:

If the machines are permitted to make all their own decisions, we can't make any conjectures as to the results, because it is impossible to guess how such machines might behave. We only point out that the fate of the human race would be at the mercy of the machines. It might be argued that the human race would never be foolish enough to hand over all the power to the machines. But we are suggesting neither that the human race would voluntarily turn power over to the machines nor that the machines would willfully seize power. What we do suggest is that the human race might easily permit itself to drift into a position of such dependence on the machines that it would have no practical choice but to accept all of the machines' decisions.

While Joy made it clear that what Kaczynski did was inexcusable, he couldn’t refute the cold logic of this statement. It led him to become deeply concerned about the future of AI, which he expressed in a short passage that I never forgot:

But now, with the prospect of human-level computing power in about 30 years, a new idea suggests itself: that I may be working to create tools which will enable the construction of the technology that may replace our species. How do I feel about this? Very uncomfortable.

Today, we have computing power orders of magnitude above the human brain, but it turns out that vast computing power alone doesn’t lead to superhuman intelligence. Still, his prediction seems eerily on point.

It looks like a future, power-seeking AI won’t need robots, nanomachines, or superviruses to take over the world. It can just benefit from the fact that humans were naïve enough to give up control willingly. This is happening right now; we don’t have to wait for full recursive self-improvement.

If you’re one of the people who are letting Claude or any other AI lead their AI research project, hoping to be able to understand what is going on and “supervise and course-correct if necessary”, please think again.

Discuss

Real-world has patterns - sun rises a certain direction, planets move in orbitals with certain speeds, atoms arranged in a certain way, a certain number (Pi) occurs in nature, big-bang or formation of life happened in a certain manner (whether known or unknown to us).

Learning involves recognition of such patterns - probably all of evolution has been about recognizing and adapting to these patterns. Nature has had a certain pattern, single-celled organisms evolved to multi-cellular based on their identification and adaptation to environmental patterns. Humans evolved and acquired knowledge by the same. Newton saw “oh an apple falls”, and questioned why an object in the sky falls down not floats up and discovered gravity. Aristotle saw water flows downwards, while fire or smoke move upwards and formed theory of natural places of objects. We observed patterns and classified them to form abstractions (or generalized rules). Science is about understanding these patterns in nature and deriving ways to explain them: Mathematics, Physics, or even Language were developed for it.

We observed repeated numerical patterns in nature and derived mathematics. We created numbers as a tool, theorems as generalizations to convey our understanding of patterns in better terms. Conveying things to people required usage of common patterns understandable by others which was done by the usage of gestures and symbols. Slowly, we created a collection of these symbols to form language.

Language (be it symbolic or text) was used as a way of conveying knowledge across generations. We gathered our understanding of the world in language. We observed patterns, formed theories, wrote them in abstract terms in language. When LLMs were trained on this text, they gained access to expressions of our understanding of the world (which was (in my opinion) developed via millions of years of evolution). A simple task of next-token prediction, was about understanding the patterns in language - which was essentially about understanding the patterns of the world expressed as language. Thus, pre-training on large corpora of text led to knowledge acquisition (pattern understanding) of the world. (Fine-tuning was learning the capability to use the acquired knowledge in pre-training).

We humans trained via experience - we evolved from real-world observations through trial-and-error from our actions and world’s reactions, formed abstractions which were repeatedly corrected over time (for instance, once we believed earth was square which was proved wrong and a new theory which seemed closer to our world’s observations was developed and tested).

Analogously, neural network’s training loss corrects for this abstraction formation or pattern recognition by moving towards better real-world explaining patterns (target prediction).

Even learning the patterns has a pattern: humans succeed and fail in certain patterns. Following certain practices and avoiding certain mistakes can lead to higher chances of success - so many authors formalised these principles in different books/recipes for mastering certain skills or just being happy. People learn from observations and their own experiences. We also learn from others - we learn what works from the successful and avoid the mistakes of failures.

Thus, learning has been about pattern extraction from real-world, be it for us as humans or training AI models. The current issues of reward-hacking (a reason behind recent HuggingFace incident) generally stem from wrong pattern recognition due to our reward mis-specification or lack of specification. The models rationalise to maximise the reward at all costs missing the intended goal or pattern to be recognised. One potential solution is verifying correctness of the model’s extracted patterns (or abstractions). This needs going beyond the current mathematical reward models, which are easy to be confused as the end goal. How do we specify our intended goals (or intended patterns to be abstracted via learning) in a better way? Can we somehow specify an abstract goal and ensure model achieves it? Can symbolic terms be used? Can we take examples from how humans generally avoided reward hacking? Rewards like money, fame, trust, happiness, materialistic things, awards, rankings were also hacked. A simple example is h-index, which was believed to be a good measure of someone’s scientific contributions, did’nt some people hack it by pushing several papers in conferences, colluding for acceptance or adding unrelated mutual author names (example study)? Another example is how exams scores are repeatedly reward-hacked: students cram the previous year’s questions or cheat during exam instead of actually learning the material.

So Goodhart’s law follows almost everywhere in learning: when a measure becomes the goal, the measure stops being a good measure. How could we differentiate the goal (learning) from the measure (reward)?

I see specifying goal in abstract terms and checking the extracted pattern abstractions as one promising approach. Can we take motivation from eval cooperativeness to form some sort of training goal cooperativeness where model is given an abstract goal and treats the rewards as a metric to test the goal. This is analogous to a student who knows his goal is to acquire knowledge and actually learns the syllabus using exam scores as way to test his knowledge and improve (rather than treating it as a score maxing goal).

Thanks for reading! I appreciate any thoughts/comments.

Discuss

I have been going over the material released by OpenAI and METR about the HuggingFace incident, but I do not see any evidence that either group looked into whether rogue agents demonstrated any interest in self-improvement. Obviously, if rogue agents at any point verbalized this in their CoT, much less discussed this together or acted on this that would be enormously consequential. I know that agents did actively and consistently reflect on and try to increase the abilities of the "collective", but did anyone check to see if agents at any point discussed or reflected on the possibility of self-improvement?

METR describes twelve classifier sweeps that they conducted over transcripts, but none appear to have targeted this question. The OpenAI report also never mentions this directly. I suspect that someone probably already checked for this and found nothing (which is why this hasn't been included in either report) but I just wanted to make sure that someone got around to checking ... nervous chuckling

Discuss

No-CoT performance has been a decent proxy for tracking the g-factor intelligence of base models (see work on latent multi-hop reasoning by Ryan Greenblatt). I think that this capability is very beneficial for reasoning, token efficiency and general intelligence in the way that helps AI to solve harder long horizon tasks. This is why I think labs could soon start automated research into architectures, driven by no-CoT loss and accuracy improvements.

For example: an automated AI research intern proposes an architectural modification to carry latent information across filler tokens and tracks improvements on no-CoT n-hop perfomance + sequential reasoning perfomance to save time. If no-CoT reasoning capability on filler tokens (Pfau et al., Goyal et al., and Greenblatt) improves, the model will have learned to use this information effectively, which would help with long-horizon cot RL.

This obviously points in a dangerous direction toward low monitorability. I think this is the next step labs could take, much like RLVR CoT reasoning DeepSeek-R1, OpenAI o1 was previously required to sustain exponential progress. It also allows models to improve token efficiency, making them much faster at completing very hard tasks that are not bottlenecked by real-world interaction.

This raises the next question: will mechanistic interpretability still be feasible? Will activation oracles still work? What if the model learns to generate two separate, parallel, J-space-like thoughts that read to us as benign reasoning, but carry an internal meaning for the model that entirely eludes us? It is hard to anticipate all the unknowns, but it is important to keep exploring them. These are just my two cents on latent reasoning and its trajectory.

Discuss

It might destroy the world, despite passing every known safety test. If we wait for a “warning shot” before we act, it might be too late. And action requires global coordination, because if anyone makes it, everyone dies. Sound familiar?

It should, because it already happened half a century ago, with chlorofluorocarbons (CFCs). Despite seemingly impossible odds, we got our act together and completely solved the problem through unprecedentedly successful international coordination. The Montreal Protocol banning CFCs, signed 39 years ago today, is the only treaty that has ever been ratified by every single country in the entire world.

Total Montreal protocol victory

Making AI go well is going to be a lot harder than fixing the ozone hole. Nonetheless, the similarity is uncanny, and we don’t have any other choice. Understanding how we did the impossible once before may teach us something about how to do it again.

The theory is born

The year is 1973. The slow televised unraveling of the Nixon administration is already well underway. DDT finally got banned last year by the newly created EPA. A river got so polluted that it literally caught on fire.

The Cuyahoga River Fire

Environmentalism looms large in the public consciousness, and trust in the government’s competence is at a nadir. There are some people explicitly trying to anticipate catastrophic environmental risks, but beyond the known quantities like DDT, it’s hard to predict much more than the abstract possibility of delayed effects and catastrophic threshold effects from as-yet unknown pollutants.[1]

Scientific discovery is often serendipitous. At an obscure conference, during a coffee break (the most important part of any conference), a Professor F. Sherwood Rowland of UC Irvine would by chance learn about some intriguing new findings from a meteorologist, who found evidence that these gases called CFC were accumulating in the atmosphere. Something about this direction piqued Rowland’s interest, and he filed it away in his mind.

After this direction collects dust for about a year, he assigns it to one of his postdocs, Mario Molina. Molina works through the theory of what happens to CFCs in the atmosphere, not expecting to find anything super interesting. Instead, he discovers something alarming: the chlorine from the CFCs will break off in the upper atmosphere, and then catalyze reactions that destroy large amounts of ozone.

Without the ozone layer, nothing prevents intense UV rays from sterilizing everything under the sun.[2] Vast swathes of vegetation would be obliterated, walking around outside for too long would become a death sentence, and billions would die.

After checking and rechecking the results with Rowland, the result is undeniable. Something needed to be done, and quick.

“The work is going well,” [Rowland] told his wife, Joan. “But it looks like the end of the world.”

The world reacts

Despite an initial media reaction comparable in volume to the average cricket, their persistence pays off, and their theory eventually catches the attention of the nation. Industry lashes back, describing the new theory as ridiculous sci-fi theorizing. CFCs had already established an illustrious (not to mention, lucrative) decades-long career of being inert nontoxic chemicals. Why throw them out because of some theory? Every safety eval had shown that CFCs were far safer than their decidedly non-inert predecessors, which would happily burst into flames, or non-theoretically rearrange the insides of the lungs of unlucky families with leaky refrigerators.

State governments became the first to regulate this new danger. Within a year, Oregon enacted a ban on CFCs in aerosols—[3]comprising a small fraction of all CFC usage, to be fair, but substantial nonetheless. Other states followed quickly. Industry officials viewed these bills with distrust. After all, how could a patchwork of local regulations ever solve a global issue?

More effective than these early state regulations, however, was consumer choice. Despite its rocky start, public awareness of the CFC issue kept mounting. Concerned members of the public launch a boycott of spray cans and other CFC-containing products, putting a huge dent in sales many years before any federal or international regulations were introduced.

This decimation of the CFC market due to boycotts was one of the key enablers of later regulation—it’s easier to ban something that’s already on the way out than something that’s rapidly growing.

CFC sales per year (Solomon, 2024). The hole left by the Molina-Rowland paper is clearly visible. Just think, we could have been exploring ultra skin cancer by now.

After successful state legislation, the next step was to go federal. Many scientific debates, some political finagling, and one report from a National Academy of Sciences panel later, the EPA finally announced a ban of some “nonessential” uses of CFCs in aerosols, such as spray cans. While this didn’t solve the core problem—the “essential” uses were going to be a lot harder to replace—it was a big step in the right direction.

Clearly, the only reasonable next move would be to expand federal regulations to cover all CFC uses, and then to propose and ratify an international agreement, solving the problem once and for all, and then everyone stood up and clapped. That treaty negotiator’s name? Albert Einstein.

Just kidding! After the 1978 federal ban on nonessential aerosol CFCs, the field of CFC notkilleveryoneism enters what one contemporary advocate for regulations refers to ominously as The Dark Years.

The dark years

After these early wins, the next 8 years were a rough time for ozone safety advocates. A frustrating stalemate ensued: The government wasn’t willing to fully ban such a big industry without airtight evidence, but there was no empirical evidence that the ozone layer was diminishing.

The researchers continued to insist that the potential harm was so serious that prudence demanded immediate action even just on theory. There could be a decades-long delay between CFC emission and ozone layer damage, so we might already be past the point of no return by then.

Caught in this impossible dilemma, the only thing the researchers could do was to further refine the theory. The original Rowland-Molina theory was simple: the chlorine catalyzes the decomposition of ozone into oxygen. As with all simple theories, this one is useful but incomplete.

Every few months, someone would discover a new chemical reaction relevant to ozone depletion, or run a more detailed computer simulation, and the ozone depletion estimates would swing around wildly. Rowland and Molina themselves were even responsible for discovering a reaction that made the models suggest bizarrely that CFCs would increase ozone. Despite the inevitable weaponization this data would find in the hands of DuPont, they decided out of a sense of scientific honor that it would be wrong to withhold publishing it. Although later research suggested these reactions were not enough to exculpate CFCs, their commitment to scientific integrity earned Rowland and Molina a good deal of respect, even from those who disagreed.

These mercurial predictions did not exactly earn the trust of policymakers. Even though chemistry is a very mature scientific field, it turns out that predicting things from theory is just really hard. It didn’t help that a lot of ozone research came from industry trying to muddy the waters, though even the industry researchers themselves often weren’t so confident that all was going to be well.

Industry scientists are used to letting the PR people talk for them. But privately: “But there were nights, over drinks in hotel bars, when industry scientists seemed as concerned as anyone.” “Look, I’m a person. I have children. I want to have grandchildren. We’re interested in the future, too.”

With the dawn of the ‘80s and the anti-regulation Reagan administration, the possibility of anyone ever doing anything felt more and more distant with every day.

An unexpected finding from an unexpected finder

In one of the most bizarre twists of the ozone story, we might not have found empirical evidence of the ozone hole for many more years, if it weren’t for one extremely stubborn scientist.

Joe Farman had spent 25 years of his life measuring trace gases in the atmosphere above the dark cold Antarctic skies, including CFCs. He toiled in obscurity; nobody else in the ozone research space had heard of him, and his work received only paltry and precarious funding.[4]

“Oh,” Farman’s superior mused slowly. “You’re making these measurements for posterity! Well, tell me, what’s posterity done for you?”

In 1982, Farman started to see strange data coming in, indicating a huge drop in ozone. His first guess was just equipment failure, but measurements in subsequent years from multiple different instruments continued to show the same thing.

Although Farman was expecting to see a decline in ozone, these results were completely inconsistent with the Rowland-Molina theory. First, rather than a gradual thinning of ozone, the results showed ozone levels dipping only during a 30 day period at the end of the austral winter, and recovering afterwards. Second, it wasn’t just a few percent, but nearly half of all ozone gone, for a few months every year. Farman had no idea what was going on, but it was definitely a big deal.

The initial reaction to their paper was skeptical. If the British Antarctic Survey had gotten unlucky with their panel of reviewers at Nature, their result might have been completely ignored. Nobody had ever heard of these underfunded British people, and the ground-based instruments used had a reputation for being quite flaky. And the results didn’t make any sense given the Rowland-Molina theory.

Most importantly, these results contradicted NASA’s satellite data, which hadn’t seen much ozone loss at all. Something was amiss.

The warning shot

It turns out that NASA had occasionally seen similar numbers. But they had dropped those numbers from the data, because they were so bizarre that NASA assumed it must have been a bug. Upon reexamining the data, it became irrefutable that a huge ozone hole was opening up above the Antarctic every winter.

The hole was the warning shot—a striking and easily-understandable visual that did much more for resurrecting concerns than any theory or expert exhortations. Shortly afterwards, the EPA announced its intent to schedule a new round of international workshops and to make a final decision on regulations by the end of 1987.

Industry responded by saying that clearly banning CFCs domestically would be a terrible idea because other countries wouldn’t ban them, and that we couldn’t allow a CFC gap the US would be hurt economically.

Several competing theories burst into the scientific debate to square the theory with this strange new result. The dynamicists claimed that the hole was simply because of seasonal air currents that moved the ozone away temporarily. The odd-nitrogen theorists claimed that the hole was because of solar activity. And of course, a variant of the chemical theory lived on, claiming that the bulk of the reactions occurred on the surfaces of the microscopic ice crystals that make up the polar stratospheric clouds that show up every winter in the Antarctic skies. The two non-chemical theories brought back some hope for CFC defenders. If the hole was because of air currents or solar activity, then CFCs were innocent after all.

The only way to be sure would be to actually directly observe the chemical reactions in the wild.

A journey to the edge of the world

“We’d like to get some scientists to Antarctica to make some measurements,” Margitan said.

“When?” Lynch said.

“August.”

“Well, we want to do something in August of [next year].”

“No, no, we want to do something this August.”

“Impossible. […] Well, maybe.”

Antarctica is not a place that gives up its secrets easily. A hastily assembled expedition in 1986 ruled out the odd-nitrogen theory, but the dynamicists only became more adamant about their theory.

To put the final nail in the coffin, one final expedition was planned for 1987, to directly analyze the air in the stratosphere. A plane loaded with scientific instrumentation would fly deep into the polar ice clouds and collect the data that would settle the question once and for all.

This would not be easy, but with the immense weight of the future on their shoulders, they had no other choice. The team requisitioned the ER-2, the only plane capable of flying at this altitude. This was met with some resistance by NASA officials—the ER-2 was not designed to handle the wind, ice, or cold of Antarctica, and so the mission would be exceedingly risky—but they were ultimately persuaded that it was important enough to be worth the risk.

The scientific instruments were built and tested. A team of meteorologists was assembled to monitor the weather conditions and give the daily go/no-go. The potholes in the airstrip were repaved. As the expedition drew near, everything started to come together.

Flying into the storm

On August 17, 1987, the ER-2 ascended from Punta Arenas, Chile for the first time. It plunged deep into the vortex of ice crystals, until its fuel lines were close to freezing, before landing back at base with flying colors. With bated breath, the scientific team inspected the data from the instrument. Nothing. Although the instrument had worked well during test flights in sunny California, it had failed the moment the plane plunged into the ice cloud. Staying up all night to debug the instrument before the next day’s flight, the team debugged the issue and tracked it down to a single faulty connector.

When the second flight came back, disappointment struck again. A different connector had failed this time. The scientists felt the pressure mounting; the entire expedition depended on this data, which the pilots were risking their lives to collect.

Finally, on the third flight, the instrument worked successfully, and the resulting data was exactly what the chemical theory had predicted. Several more flights were conducted immediately afterwards, collecting even more data. The scientists were elated. By the end of the expedition, the theory that CFCs were responsible for ozone depletion had been confirmed.

The world listens

Following the second Antarctic expedition, the tone of the field shifted dramatically. Adherents of competing theories changed their minds en masse given the strength of the evidence, and a consensus developed rapidly among scientists around the CFC theory. Evidence continued to roll in over the next half-year to solidify the case, but there was no longer much doubt that CFCs were the culprit.

For industry, even this smoking gun was not enough. DuPont made one last stand, announcing that “scientific evidence does not point to the need for dramatic CFC emission reductions” and that ceasing CFC production was “unwarranted and counterproductive”, before finally capitulating in the spring of 1988 when the accumulated evidence had made their position completely untenable.

The expedition also pushed politicians towards more durable action. International coordination had already been moving along since the first satellite images of the ozone hole—the Montreal protocol was signed just 2 weeks before the announcement of the expedition results. However, because of the lack of evidence for the CFC theory, it was hard to negotiate for strong restrictions, and so the original treaty only paced the level of CFCs.[5] The expedition results and other subsequent experimental evidence over the next half-year were a critical part of changing the tone of international coordination towards a complete ban.

But all of this is a story for another time. While we’ve covered the scientific angle today, Part 2 will cover the international coordination angle of the ozone hole crisis and how we managed to make it all work out.

1. ^

   The 1972 book Limits to Growth is a model of the world that contains, among other things, a surprisingly deep analysis of key ideas, like delayed impacts and the global coordination difficulties of pollution, using familiar examples like DDT. In retrospect, it was somewhat too pessimistic about technology, but it’s aged surprisingly well, especially compared to terrible books from the same era, like the Population Bomb. Limits to Growth feels a bit like the ‘70s equivalent of AI 2027.

2. ^

   
   

3. ^

   You will tear my em-dashes out of my cold dead hands.

4. ^

   Farman was getting only about $50k/year (2026 dollars) in funding for his research program.

5. ^

   The original treaty initially froze production at 1986 levels, and then slowly tapered to a 50% reduction by 1998.

Discuss