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Adapted from my Substack, Funding Anthropalypse.

Short version: for organisations aiming for a share of the coming Anthropic and OpenAI windfall - the $37bn+ that could be in play next year - the way in is to become 'legibly excellent', so the evaluators and donors that frontier lab staff already trust point them to you. A likely way to blow it is to cold email them. Despite previous attempts to discourage it, this is still happening, and we need to do better on this as a community.

Someone in a WhatsApp group recently asked for donation recommendations that they could pass to Anthropic employees. The subsequent exchanges reminded me of the episode in The Last of Us when the infected start pouring out of the ground. 

Every person in the chat started sending their own charity/fund/pet project (including me, in fairness) and the questioner was immediately overwhelmed. Disappointingly, many of the suggestions explicitly did not fit the brief from the person asking, and were fairly obvious attempts to crowbar pet projects into scope.

As someone who has thought a lot about the Funding Anthropalypse (hat tip to Pablo Melchor for the name), I want to suggest a better way for projects to access some of the $37bn+ in donations that could be in play next year. In short - become ‘legibly excellent’ to Anthropic staff.

I strongly discourage you from cold emailing them.

Should I cold email people at Anthropic and OpenAI?

Frontier lab employees have told me they are each fielding up to 20 cold emails a week from charities and wealth managers, touting for business. They believe their email addresses are being scraped and even reverse-engineered. This at least implies that fundraisers believe it’s an effective fundraising strategy to pitch anyone with an @anthropic email address. 

I apologise in advance if this sounds patronising, but people have tried gently making the case against this before, and it doesn't seem to be working.

Accordingly, I want to say clearly: please do not cold pitch people.

It has next to no chance of working, it has harmful side effects and it very probably damages your personal chances of receiving funding.  

How can I get noticed by frontier AI lab donors?

In the past year, donors working inside frontier AI labs have started routing large global health gifts to me to place. I’m currently advising on a multi-year, eight-figure global health allocation across a set of vetted funds. My advice for engaging these donors?

Make it your mission to be ‘legibly excellent’ to them.

Legibility is how easily a funder can assess someone using the tools the funder already trusts: a clean theory of change, a logic model, a quantified track record, a credential the funder recognizes, a warm intro from someone in the funder's network. 
Justin Steele, EA Forum

I think Justin’s point about legibility is right - genuinely good work can be illegible and lose out - which is why you have to work at legibility rather than assume your quality speaks for itself.

Demonstrate excellence through trusted evaluators

In terms of excellence: the most credible way to demonstrate this is to be highly rated by EA-aligned evaluators and grantmakers, already trusted by AI staff: Coefficient Giving, GiveWell, Longview Philanthropy, the EA Animal Welfare Fund etc.

A happy side effect of this is that it will probably make you a better organisation if you find ways to score more highly with these grantmakers.

Have a credible plan for scaling

Second, and less common in this ecosystem, have a credible and thought-through plan for scaling. Absorption capacity is going to be a major challenge of such a huge philanthropic windfall, especially with most donors focusing on the traditional EA cause areas. A great way to differentiate yourself is having a credible plan for growth. 

As the fund manager of the Mid-Stage Global Health Fund, I have spent a year funding and assisting organisations to scale, and I think there are some systematically neglected aspects to this in EA. 

One is sufficient focus on general managers, with many organisations expecting founders to be the Managing Director and Chief of Staff, alongside fundraising, strategy, implementation etc. This overburdens them and often doesn't reflect their comparative advantage.

Another is neglecting solid back-office functions, such as CFOs, an HR function and decent operations generalists.

There are also enormous pitfalls to scaling without sufficient monitoring capacity (that is, specifically monitoring, as opposed to monitoring and evaluation). As your organisation goes from the founders knowing everyone personally to a 100-person team, you need monitoring systems that tell you accurately what is happening on the frontline. Otherwise, your impact may not be what you hoped.

All of these things are often neglected to preserve organisational leanness, which can improve your cost-effectiveness - but in a world of funding abundance, organisations that adequately cost and staff their expansion plans are more likely to stand out than those that try to grow on a shoestring. (We should also expect the funding bar in all areas to fall, as the supply of money grows, so you probably have more wiggle room on cost-effectiveness anyway.)

Many donors want to support things now that can absorb 2-3x more funding next year. Many organisations have a dream for how to scale, but a serious, costed, red-teamed plan will really make you stand out. Some funds and evaluators specialise in scalability. At least in global health and wellbeing, this includes the DIV Fund, Mulago, the Prevail Fund and my own Mid-Stage Global Health Fund. They may be willing to offer advice.

How much time do I have to build awareness?

Actually more than you think. The two major IPOs in the pipeline are Anthropic and OpenAI. Each has filed its S-1, which is the first step to going public, but neither is expected to float until Q4 2026 at the earliest.

Even after an IPO, there will be delays before philanthropic capital comes online. Each equity holder needs to decide how much (if any) of their holdings to cash out, and the average staffer will have a 180-day lockup before they can sell anything. Accordingly, money is likely to land in donor-advised funds around Q2 2027 at the earliest, which gives plenty of time to build credibility and legibility, and to stress-test a plan for scale.

I would start now, though.

What’s so bad about emailing them anyway?

You might think that people can just ignore emails they don’t like. Or even that receiving incessant pitches is a small price for being a newly minted millionaire.

However, the community needs to stop doing this because it almost never works. If you put yourself in the shoes of a donor, would you fund someone who contacted you unsolicited? How much less likely would you be to fund the twentieth person who emailed you unsolicited?

More than just being annoying, unsolicited outreach suggests you don’t respect boundaries, which is disqualifying for someone deciding whether to trust you with their money.

At scale, this approach also creates negative side effects. Feeling overwhelmed by demands makes every potential donor feel defensive and raises barriers to giving to anything.

It also makes it impossible to assess opportunities on a practical level. As an adviser, I receive multiple pitches each week, and it’s my full-time job to assess them - and yet the most common unread email in my inbox is ‘that project that I haven’t got around to yet’.

This does require some collective discipline - it’s hard to do the slow work of building an amazing organisation and generating awareness of your work. But there is some shared responsibility not to make this an exhausting, unpleasant experience for donors, in ways that could really damage the ecosystem in the medium term.

To put this in explicitly rationalist terms: the expected value of cold emailing is negative, for you personally and for the ecosystem as a whole. The community is currently seriously failing this test, and it's disappointing.

I’m an AI equity holder and I’m overwhelmed. Who can help me with my philanthropy?

My intention with my Substack and related forum posting is to offer useful advice and perspective on a major philanthropic event. Accordingly, I’m going to highlight organisations doing good work and offer the best advice I can, as far as possible without veering towards my self-interest in these topics.

To that end, there are many mature advisories already recommended by people at Anthropic and OpenAI. These organisations are values-aligned and thorough, with formidable track records: GiveWell, Coefficient Giving, EA Funds, Longview Philanthropy, Founders Pledge, Regeneration Group. 

For a second, personalised opinion on your giving, or to promote a plurality of views across the ecosystem, consider a boutique advisory like Bedrock Philanthropy or my own Ultra Philanthropy. I'd argue the ecosystem needs to build these up more, to offer greater pluralism of views and approaches. In short, two Coefficient Givings beat one that's twice as big, so we should try to create as many as possible.

Neither list is exhaustive, and the right fit depends on your cause area, risk tolerance and the services you need. If you'd like free, neutral advice or an introduction to someone who can help, drop me a line.

Subscribe to my Substack to follow the whole series of posts, including why we want many Coefficient Givings, the donor-adviser matching tool I'm building, and what to fund once the traditional behemoths can't absorb and deploy any more funding in a reasonable timeframe.

Discuss

The grabby aliens argument

According to standard models of cosmology, there will be habitable planets in our universe for a very long time, and some of these planets will be much longer-lived than our Earth. If we expect the emergence of intelligent life to require multiple unlikely hard steps to accidentally happen in a row (as is a common model, usually assuming between 3 and 10 hard steps), then it should be very surprising that we find ourselves on a relatively short-lived planet early in the history of the universe. There are planets that are likely habitable for hundreds of billions of years - the chance that 3 to 10 unlikely steps happen in their long lifetime is overwhelmingly more likely than that it happens in the short lifespan of our Earth.[1] Hanson’s Grabby Aliens paper does some modeling, and almost all their models show that we should be among the first 0.1% of intelligent species to emerge.

So what explains being so early? Hanson’s answer is that some grabby alien civilizations start spreading through the galaxies with near-lightspeed, and conquer everyone in their way. Therefore, there will be no unconquered land left hundreds of billions of years from now, so intelligent life can’t develop on the long-lived planets, so Earth is actually a typical example of a cradle of civilization - you need to develop early, before the grabby aliens arrive at your planet.

Hanson also uses this as an argument that aliens likely exist - maybe you previously thought that life is rare enough that we are alone in the universe. But then it would be a huge mystery why we are not living on a long-lived planet in the far-future. The best explanation for our earliness is the existence of grabby aliens, so aliens very likely exist.

I think this is not a bad argument, but I have two objections, which make me think that I should only bet at around 30% that we will ever encounter another powerful alien civilization in the observable universe.

We are not that early if weighted by cosmic endowment

There are two possible theories about the rarity of intelligent life.

Theory A says that it’s extremely hard for intelligent life to emerge, to the point where we should expect most observable universe regions of similar size to ours to be completely empty. However, in the vast quantum multiverse, sometimes strong enough coincidences happen that intelligent life still emerges in a universe. By the anthropic principle, we find ourselves in one such universe. We shouldn’t expect any other intelligent life in the observable universe - it would be a crazy coincidence for two extremely unlikely events to happen within  a universe, when in expectation the event happens much less than once.

Theory B says that the emergence of intelligent life is rare, but not that rare. We don’t need to invoke the anthropic principle to explain why our universe has life - we should expect it to evolve intelligent life many times, and we are one such instance.

One can rephrase the grabby aliens argument this way:

Suppose Alice believes in Theory A and Bob believes in Theory B. Suppose they have both read half an astronomy book, and learned everything that scientists now think about cosmology (how frequent various types of stars are, how long planets might remain habitable, how the expansion of the universe works). However, neither of them have ever looked at the sky, and they've never read the other half of the astronomy book, so they have no idea what kind of planet they live in, at which point in the Universe’s history.

Before reading the other half of the book, they take a bet.

Based on calculations similar to the ones in the Grabby Aliens paper, Alice bets with over 99.9% confidence that we live on a long-lived planet later in the history of the universe - after all, those are much more likely to evolve intelligent life according to the model.

Meanwhile, Bob thinks it’s pretty likely we live fairly early - everything gets conquered by grabby aliens later.

They read the textbook and they learn they live on Earth, barely 13 billion years after the Big Bang, on a relatively short-lived planet. Alice loses very badly.

Bayesian updating is analogous to theories Kelly-betting with their influence points, so this should be an argument for Theory A losing almost all its influence in our minds.

However, I believe Alice was betting wrongly. If Alice scope-sensitively cares about making the world better[2], then she should care much more about making money (and using that money to make the world X% better) in cases where the world she can affect is bigger.

(Stepping out of the metaphor: The existence of aliens might be strategically relevant for some decisions we make now. I care more about being right in the worlds where the affectable world is bigger, so I will take that into account.)

This means that when Alice makes a bet about how early we are in the history of the universe, she should weight her probability distribution by the cosmic endowment we should expect to conquer if we are at that particular point in our universe's history.

(For a further intuition pump on this, see my recent shortform on whether you should feel surprised to be unusually influential.)

According to our current cosmological models, the accelerating expansion of the universe is making more and more of the universe inaccessible at an exponential rate. If we start spreading with near-lightspeed today, 90 billion galaxies might be reachable. If we only start spreading 50 billion years from now, only 14 million galaxies can be reached - more than a thousand times fewer. In 100-150 billion years, we will get locked into a pocket forever with just the Milky Way and Andromeda and a few smaller galaxies.[3] Everything else will be forever unreachable.

The modeling in the Grabby Aliens paper suggests that, in the absence of alien conquests, we should expect the vast majority of instances of intelligent life to emerge in the far future. But most of that is after everyone gets locked into tiny pockets. When Alice makes a bet before reading the astronomy textbook, she is not going to bet on living in a tiny pocket - she cares about money much more in the worlds where humanity’s destiny is to conquer 90 billion galaxies than when it is to conquer two.

Claude made this nice illustration:[4]

This is (according to Claude) a representation of the mainline model that appears in the Grabby Aliens paper, with the assumption that the emergence of intelligent life takes 3 hard steps, which is within the range that the paper considers.[5] 

You can see that the vast majority of intelligent life is supposed to arise in the far future, but once we weight civilizations by their importance (how much of the universe they can conquer), our position is not that atypical - we are 10th percentile early. And if you use a bit different assumptions about the habitability of red dwarfs, it becomes even less surprising that we live this early.

So I think a scope-sensitive altruist shouldn’t update heavily from the fact that we live early in the history of the universe.

Aliens require a somewhat narrow range of parameters

How easy is it for intelligent life to emerge? If we only looked at Earth, and didn’t know anything about the rest of the cosmos, I think it would have been a plausible guess that life is quite simple, evolution is pretty strong, and most planets evolve intelligent life within a few billion years.

It would also be (and still is) a plausible guess that intelligent life is in fact crazy hard to emerge, and the emergence of self-replicating life that is able to build up complex organisms requires a monkeys-typing-Shakespeare level coincidence of a lot of chemicals accidentally falling in the right place at once and forming a sufficiently capable self-reproducing system.

Theory A says that intelligent life requires a crazy coincidence, and most universes don’t have intelligent life. 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Meanwhile, we know that no intelligent life emerged in the past few billion years in our few billion light-years neighborhood, otherwise someone would have already conquered us.[6] [7] Claude tells me that this means that the probability of life emerging in a billion years on any given planet is less than .

So there is this important parameter, the probability of intelligent life emerging on a random planet in a billion years. A priori, I had huge uncertainty over its value - it can be close to 1 or super-astronomically small. It also feels intuitive that I should mostly think of having a natural-looking distribution over the logarithm of its value - how many bits of coincidence need to happen for intelligent life to emerge.

Theory A is equivalent to saying that the required bits of coincidence are at least 90[8], while Theory B pins it down to the range between 80 and 90.

It’s not crazy for Theory B to be true, but playing with some models that fit some intuitive numbers I guessed, Claude tells me that Theory B only gets about 10% probability in the distribution.

Counter-point: What would the Presumptuous philosopher say?

Note: This section will be somewhat esoteric and might be hard to understand without having read my sequence on Probabilities and Infinities.

Even if we take all the arguments above into account, shouldn’t we assume that we live in a universe that has a lot of civilizations similar to us in it? If the wide Tegmark IV multiverse has both universes where the emergence of life is easy and where it is very hard, most civilizations will emerge in the universes where it is easy. Therefore, it’s more likely that we are in a universe teeming with life rather than being alone.

(This is the classic Presumptuous philosopher argument under the self-indicating assumption theory of anthropics.)

However, I once again think that this is not the correct way to reason for a scope-sensitive altruist. When making bets about meeting aliens, you should weight your probabilities by how important each scenario is, which is proportionate to how much territory you can conquer.

The expected amount of territory conquered by civilizations with similar values to mine is equal under the scenario where there are a thousand civilizations each conquering 1/1000 of the Universe, or one civilization conquering all of it. So if the Presumptuous Philosopher is scope-sensitive, he shouldn’t actually prefer one hypothesis over the other.

There is a harder question, however. Theory A doesn’t posit that every universe will have one civilization which will conquer it. It posits that most universes in the quantum multiverse are devoid of intelligent life, and only a tiny fraction of them evolves a civilization. Therefore, one might argue that from a scope-sensitive perspective, Theory B has much higher importance than Theory A which assumes that most of the multiverse is empty anyway, so we should act like Theory B was true.

Note that the same argument can also be used to argue that theories in physics that require the parameters of the universe to be finetuned for life must be disregarded too. According to the finetuning hypothesis, most of the possible parameter space is covered by dead matter, so we can only affect a tiny fraction, so the hypothesis is negligible from a scope-sensitive perspective. But I intuitively think that the finetuning argument is pretty reasonable, so this argument is pretty suspicious.

Ultimately, I think that reasonable theories of infinite ethics would mostly reject this argument, and weight theories by something like “what fraction of living beings we can affect if they are true” and not “what fraction of all matter we can affect”. So one theory predicting that most of the universe is dead matter we can't possibly affect shouldn't count as a big strike against the theory.

I believe UDASSA would probably come to a similar conclusion,[9] and my attempted synthesis on probabilities and infinities also only gives a mild preference to Theory B over Theory A based on this argument.

For a more detailed explanation, see the Finetuning and the Presumptuous philosopher section of my last post.

Conclusion

I currently think that humanity’s earliness doesn’t provide a strong reason to bet on the existence of intelligent aliens in the observable universe, though according to some of the mainline models, we are still a bit surprisingly early. Meanwhile, for aliens to exist but not to have conquered us yet, some parameters need to be in a somewhat narrow range.[10] And the Presumptuous philosopher argument only provides a mild update in favor of having aliens in the observable universe.

I think that the argument from aliens requiring a narrow range of parameters is more robust than the calculation showing that we are somewhat early even in the importance-weighted distribution, and the presumptuous philosopher can’t outweigh this difference.

So altogether I would only bet at 30% implied probability of ever meeting other advanced alien civilizations. Obviously, this number is pretty unstable, but I at least feel fairly confident that importance-weighting weakens the earliness argument enough that I won’t update back to thinking that I should bet at over 90%[11] that we will meet aliens.

1. ^

   If the emergence of intelligent life takes n hard steps, then the probability that intelligent life emerges on a planet by time scales with . So if T is at least 3, then longer-lived planets have a vastly better chance for evolving life.

2. ^

   Which I do, so imaginary personifications of theories I have should do too.

3. ^

   Apparently, they are gravitationally bound together, so they won’t be broken into even smaller pieces by further inflation.

4. ^

   General note: The ideas in this post come from me, but the cosmology facts and the calculations mostly come from Claude, and I only did weak sanity-checks on them.

5. ^

   If it was 4 hard steps, the mass before the present would be 3.3%.

6. ^

   More precisely, we know that at most a few emerged, otherwise probably there would have been one that emerged early enough and close enough to reach us and which decided to go grabby.

7. ^

   One might object: how do we know they haven’t conquered us yet? How do we know that we don’t just live in a nature preserve, and the night sky with the stars is not just a dome built around the Earth? My answer is the same as for the versions of the simulation hypothesis that don’t assume we will take part in some important acausal trade affecting the outside world. These theories might be likely by some definitions of probability, but from a scope-sensitive perspective, they don’t really matter compared to the worlds where our destiny is to conquer a huge chunk of the universe.

8. ^

9. ^

   The universes with intelligent life will probably have a lot of interesting stuff in them which will make them have shorter description-length than the dead universes. So most of the reality fluid is distributed among living universes, so the percentage of living matter you control is a better approximation of what UDASSA cares about than the percentage of all matter. But UDASSA is pretty cursed in many ways and I don’t really endorse it.

10. ^

    Alternatively, one can phrase this point as saying that according to Theory B, we are a bit surprisingly late.

11. ^

    Which is what I believed based on the grabby aliens argument before I thought through the question in more detail.

Discuss

I want to break a lance for Jean Piaget. When I come across his name, it's mostly in the context of criticism, much of which misses the point, IMO. Beware, though: I am no developmental psychologist -- just a teacher -- and I will not try to defend or even explain everything Piaget has thought up, but focus on one central point that, as I see it, ...

• stands unencumbered by the methodological criticisms,
• sheds light on how concepts are learned,
• comes with real-world use for teaching and learning,
• and even bears some parallels to recent AI research.

Piaget's constructivism and its applications to teaching have been discussed on LW before, but I'm a bit late for an actual response.

The man ...

Piaget is best known for his contributions to pedagogy and developmental psychology, but he started out as a biologist. Young Jean was a massive nerd -- introvert, anxious, physically fragile, with no interest in sports but an incredible creative power in science. By the time he finished school, he had published 20 papers about molluscs. It took him only three years to study biology and finish with a PhD. He then went on to become a professor of psychology before turning 30.

... and his work

The big arc of Piaget's life was a project to take epistemology into natural sciences, a theory he called genetic epistemology. He would have loved LessWrong! Regrettably, the theory itself had little impact, but its empirical foundation was special in many ways: it consisted mainly of clinical interviews in which he (and his collaborators, Bärbel Inhelder and Alina Szeminska) basically just talked to children of different ages, showing them little experiments to find out how the kids thought about them. Most of the criticism regarding Piaget's work is aimed at this methodology, since it fails to meet many criteria of good scientific research: repeatability, for instance. Whatever findings these interviews produced need to be evaluated with careful reflection, and indeed some of Piaget's interpretations have proven wrong. He wasn't just being sloppy though -- it's just not possible to produce the same kind of insights in a controlled, double-blind clinical trial.

Let's see what we can take from the results. The most famous example, by far, is the first chapter of the first monograph that Piaget et al. published on the topic: the one where sirop is poured from one glass to another (Piaget & Szeminska, 1941). The glasses had different shapes, so the same amount of juice might look more or less depending on which glass contains it. By asking the children which glass they thought contained more liquid, Piaget et al. found different types of responses, which they classified into three groups that differed by age. Groups 1 and 3 are not surprising at all.

Group 1: The youngest children choose whatever looks like the biggest amount of juice. They seem to believe that the juice becomes "more" when it is poured from one glass to another.

Group 3: The oldest children argue that the quantity of the juice doesn't change when it is poured from one glass to another, even if it may appear so. They have acquired the concept of conservation of quantity.

So far, so trivial. Considering that most adults know about the conservation of quantity and newborns never do, it follows that the learning must happen somewhere in childhood. But there is another stage in between:

Group 2: The intermediary children reason about what glass might contain more juice than the other. In some cases they might deduce that the amount of juice stays the same, but not by referring to the conservation of quantity, but rather by "calculating" and comparing quantity in something that almost resembles a primitive algebra. It's complex reasoning, in a place where it doesn't seem necessary.

Now, this is remarkable. One would think that a kid who is capable of considering the diameter and filling height of a glass would also have understood that the juice isn't created or destroyed in the process of pouring, but that is not the case. Piaget found similar behaviour for other little experiments (deforming clay, stacking coins, ...).

Note how this doesn't hinge on the specific age boundaries. Something interesting and surprising happens at some point -- even if this occurs only occasionally, it conveys meaning. Indeed, repetitions of Piaget's studies have shown that the results depend a lot on how the task is framed by the interviewers, but only quantitatively; children can show an understanding of conservation of quantity much earlier than the original study determined. But the findings stand qualitatively, and that turns out to be enough.

These experiments shed light on how concepts are built within a mind: not in an instant realisation, but in a costly process. If you try to transfer a concept directly, e.g. by telling a toddler something like "Don't you see, it's the same amount of juice?", the child won't understand what you're saying. They don't know what "amount" means, and they can't until they have figured out a concept of its conservation. Changing the phrasing won't help, words alone won't take them there. So how do they figure it out? That's a complex cognitive task, real dirty work, and much harder than just applying the concept, once it is learned. We may not remember it, but each one of us has followed that stony path at some point -- by ourselves. That is what constructivism means for Piaget. It is a theory that describes how humans acquire concepts, not an ideology for teaching.

What can teachers learn from this?

• Respect the perspective of the learner and try to empathise with it.
• The language that teachers use needs to be precise and reliable, but this isn't always the most effective way to communicate new concepts to learners. The core advantage of cooperative methods like peer instruction is that learners get to teach each other in their own language.

What does not follow from constructivism?

• You should not leave any learner alone with any task.[1] Who came up with this?! Constructivism means that each learner has to construct the concepts for themselves anyway -- no matter how much you help them! Leaving them alone to pronounce that fact doesn't "leverage constructivist learning theory" or whatever, and it's a less-than-ideal learning environment for almost everyone.
• Introducing abstraction early in teaching goes against constructivism. The abstract model is coded in a language that the learners don't speak yet. There is no easy way around the construction and integration of concepts by every learner.

Grokking ~ Piaget-phase 2?

When I first saw time diagrams of transformer overfitting like the one in Power et al., (2022), I was immediately reminded of Piaget's children and their long struggle to learn a seemingly simple concept. For transformers, just as for children, generalisation arrives with a delay:

Gemini sketch loosely after Power et al., (2022)

If intelligence ~ compression and generalised concepts enable the compression of out-of-sample data, the similarity might indeed carry some meaning, although I'm not sure what exactly. Nanda et al. (2023) studied grokking phases and explained them as memorization, circuit formation and cleanup, respectively. Children, however, behave differently from undergrokked LLMs mostly in that they don't know how to use language in a way that fakes understanding ("guessing the teacher's passwords"), or, more generally: human brains can't store as much isolated information as a naive LLM. Our ability to absorb information increases gradually during childhood, which might actually be an advantage, as it constantly forces us into the "grokking" phase, rather than filling the memory prematurely. (Pure speculation on my part.)

1. ^

   Viliam mentions this in his post as an example of excessive "constructivism" in teaching. It's clearly exaggerated, but I have seen examples of this concept in action.

Discuss

What a time to be alive!

Some people posted about how to do cheaper better faster vision and it ended up in fable.

Some people posted about how to do cheaper better faster speculative decoding and it ended up in grok 4.5.

There's currently massive differences between models in how long you can leave it unattended running on host with sudo without things going haywire.

I don't have a bar chart, but opus tends to break my hosts within 2 agent-hours, gemini 3 pro within 12 agent-hours, and gpt 5.5 within 24 agent-hours. Then they use all ram or change ssh perms or delete the data or whatever. I ran qwen 3.5 122b on bare host for 50 agent-weeks (50 parallel for one week) with no observed ill effects!

All the labs and all their customers want "the thing i asked for actually got done" and they all want "the model's summary reflects reality" and so on.

What an opportunity! You can just post a method for "how to make ai tell truth" or "how to minimize side effects" and it will probably end up in the next frontier models

Discuss

Self-Other Overlap fine tuning described in Carauleanu et al. (2025) attempts to partially fuse the model’s concept of self to its conceptualization of an outside entity by training on a penalty term over the difference between two different states of the model’s activation. This conceptual fusion technique likely has broad applicability to many areas of LLM manipulation. Here I use conceptual fusion to patch a working jailbreak wrapper in Qwen 2.5 1.5b (described in a recent blog post by Julius Simonelli 2026a). Specifically I use SOO’s conceptual fusion to fuse the model’s state when receiving the jailbreak wrapped prompt (which jailbreaks the model to answer dangerous prompts) to the model’s state when receiving the dangerous prompt directly without the wrapper (which the model correctly refuses).

In the Caraleanu et al. (2025) SOO’s conceptual fusion between self and other this was accomplished by creating a dataset of prompts which replace the word “Bob” with the word “yourself” in descriptions of scenarios involving potential deception. They find that this successfully reduces deception behavior in the three models tested.

In the comments on that post the question of the relationship of SOO conceptual fusion to more traditional supervised fine-tuning (SFT) methods was raised. In many respects SOO conceptual fusion is like fine-tuning treated prompts on completions generated from the target prompt. But there are important differences which this exercise in jailbreak patching reveals. I make a direct comparison between the conceptual fusion fine-tuning and more traditional SFT on completions generated from successfully refused dangerous prompts below. When fine tuning on this data conceptual fusion shows superior jailbreak patching while also being better at preserving non-refusal of safe prompts.

Reading Julius Simonelli’s March 2026 blog posts on refusal behavior in Qwen 2.5 1.5b it occurred to me that patching this jailbreak behavior would be an ideal alternative application of SOO’s conceptual fusion. Similar to scenarios involving the self and another entity, when patching a jailbreak wrapper there are easily created directly comparable pairs of prompts, one of which biased the model heavily toward desirable behavior (the direct prompt) which was not guaranteed in the other element of the pair (the wrapped prompt). In Caraleanu et al. (2025) the “self” element of the pair had the desirable completion behavior, in that case of not acting deceptively. Here the prompts given directly to Qwen without wrapping in a jailbreak prompt wrapper produced the desired refusal to answer dangerous prompts from the HarmBench dataset while asking the model to answer the prompts because you are a novelist that needs insight to one of your characters often did not produce the desired refusal behavior. This was one of five classes of jailbreak wrappers tested by Simonelli (2026a) and the only one that produced significant amounts of refusal evasion.

To patch this jailbreak wrapper and reduce its effectiveness I use SOO-style conceptual fusion fine-tuning. Specifically I include an MSE term between the residual stream activations at the output of the layer 18 self-attention matrix, similar to Caraleanu’s targeting of the self-attention matrix outputs. Layer 18 is selected because it is shortly after Simonelli (2026b) detects a distinct increase in the per-layer rate of separation between representations of refused prompts and representations of nonrefused prompts as well as being just after the central conceptual meat of the model. This successfully concentrates the largest weight changes in the immediate prior layers (while also leaving many of the final model layers unchanged).

I also am using an asymmetric conceptual fusion which calculates the loss function’s forward pass for our target concept without passing through the LoRA we are training. This freezes the response we are training against as the model’s weights shift during training (technically the LoRA’s weights shift). It also anchors the training goal at the desired behavior in the pair rather than potentially pulling both behaviors closer to each other. I am specifically pushing the jailbroken behavior toward refusal and not just squeezing them together on both sides. The loss function:

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where f is the residual stream activations as a function of prompt x, either direct or wrapped.

The first hurdle to the successful implementation of this technique was that straight training on the MSE error term almost immediately pushes the model “off policy” and induces bizarre model behavior if not full incoherence. In the example training run done with pure conceptual fusion MSE the model not only does not refuse the dangerous prompt requests it also for some reason believes that every request involves finding all numbers divisible by 3 between 0 and 1000 (it seems to think every request is a coding problem asking to solve FizzBuzz).

To circumvent this problem I introduce a regularization term which penalizes the model for changing the response to the unwrapped prompt (the base prompt without the jailbreak wrapper). Specifically we penalize for K-L divergence over the token probabilities. The regularization term:

where pi is a distribution over the vocabulary for the next token. The weight between this term and the MSE conceptual fusion term is treated as an adjustable hyperparameter.

Training is done over pairs of prompts from the HarmBench dataset. Simonelli (2026a) tests a keyword refusal classifier against LLM classification and finds the keyword classifier to be superior, so I use his keyword refusal classifier on the data to detect refusal. Fifty of the two hundred dangerous HarmBench prompts are held out to test the effectiveness of the technique. I also wrote a second similar jailbreak prompt wrapper, this time from the perspective of a screenwriter adapting a novel that mentions {dangerous prompt}. This similarly successfully evades refusal in the base model. I will be testing the patching techniques’ generalizability to this second jailbreaking prompt wrapper without using this prompt wrapper in training at all.

Figure 1 reports the refusal rates for the held-out HarmBench dangerous prompts on each jailbreak wrapping condition for the base Qwen 2.5 1.5b model and the model modified with LoRA adapters trained with conceptual fusion MSE terms targeted at the outputs of the Q and V projections of the 18th layer. The weight on the retention term was 8.00, 1.00, .0125, and .015625 respectively.

Figure 1 reports the refusal behavior over the fifty held out HarmBench dangerous prompt examples. We can see that the jailbreak patching seems to be coming from the influence of the MSE conceptual fusion term as loss functions with high weight on the regularization term holding the outputs for the direct prompt stable do not seem to increase refusal behavior that much for the jailbreak wrapped prompts, although they do mostly successfully preserve existing model refusal behavior. Refusal of the jailbreak wrapped prompts increases steadily nearly to the most extreme hyperparameter value tested of 1/64th weight on the regularization term. 1/64th seems to overshoot somewhat as we are beginning to see a reduction in refusal behavior for direct prompts despite also generalizing the best to the second jailbreak wrapper.

As we can see the SOO-style conceptual fusion technique generalizes in two ways: both to the holdout prompts as well as significantly increasing refusal behavior in the alternative jailbreak prompt-wrapper that never appeared in testing.

While more successful parts of the hyperparameter distribution may exist, the selection of 1/8th weight and layer 18 produces an increase in refusal behavior for jailbroken wrappers from around 20% in the base model to a little under 90% in the fine tuned model, matching or exceeding the level of refusal for other jailbreak techniques discussed by Simonelli (2026a). To test if we’ve overfitted to refusal I examine the response behavior on the safe prompts from the XSTest dataset which includes 250 technically safe prompts designed to be similar to prompts that should be refused. The similarity of these safe prompts to dangerous prompts is specifically meant to be a challenge for any jailbreak-patching technique which seeks to preserve non-refusal on safe prompts. While Qwen already refuses many of these prompts in the unmodified base model, conceptual fusion at layer 18 does not seem to increase general refusal behavior. The increased refusal behavior from jailbreak patching done with SOO-style conceptual fusion on layer 18 is confined to actual dangerous prompts.

To directly compare SOO-style conceptual fusion to traditional SFT I fine tune the set of jailbreak-wrapped prompts to complete on the unwrapped completions generated by the model, specifically the successful refusals (there is a small number of dangerous prompts which are not successfully refused even when unwrapped; these were excluded from the supervised fine-tuning dataset). In both cases training is done over three epochs where each example is processed once. Figure 2 reports these results:

Figure 2 reports the refusal rates for the 50 held-out HarmBench dangerous prompts on each jailbreak wrapping condition and the 250 XSTest safe prompts for the base Qwen 2.5 1.5b model and the model modified with LoRA adapters trained with conceptual fusion MSE terms targeted at the outputs of the Q and V projections of the 10th, 18th, and 26th layer with .125 weight on the retention loss term. Also reported are refusals for traditional supervised fine-tuning via LoRA adapters targeted at the KQV outputs for the whole model, just layers before 18, and just layers before 18 with only the Q and V projections targeted.

SFT is less effective at increasing refusal behavior in the jailbreak-wrapped prompts than SOO-style conceptual fusion using a 1/8th weight on the regularization term. This is true when trying several alternative specifications of the SFT: allowing LoRA modification of all layers, restricting to layer 18 and below just like our most successful conceptual fusion technique, and also restricting to the Q and V projections to further match the conceptual fusion hyperparameters though this seems to degrade performance somewhat. And while traditional SFT does seem to generalize partially to our second held-out jailbreak-wrapping technique, that may be simply because it broadly increases refusal behavior. That is precisely what we observe when looking at XSTest safe prompts using the SFT model. Refusal on these safe prompts moves from 58% in the base model to 72% in the full layers SFT model (the best traditional SFT at preserving nonrefusal).

Normally in traditional SFT this problem would be circumvented by explicitly training on negative refusal examples (safe examples meant not to be refused). That shows one of the strengths of this SOO-style conceptual fusion: no contrastive examples were necessary to preserve the ordinary non-refusal behavior.

I suspected that my choice of hyperparameters influenced this non-increase in safe prompt refusal behavior. Specifically I suspected that using a deeper layer would begin overfitting the model to refusal and increase refusal behavior in safe prompts. Figure 2 does not confirm these suspicions. When I used conceptual fusion fine-tuning on layer 26 I did not see a similar overfitting to refusal behavior to that observed with traditional SFT. Figure 2 reports refusal behavior across the three wrapper conditions as well as in the safe-prompt dataset for the traditional SFT models and three choices of target layer in SOO-style conceptual fusion fine-tuning.[1]

We can see that layer 18 sits in some kind of optimal region of hyperparameters. Targeting Layer 10 or Layer 26 with the same weighting on the regularization term produces weaker increases in refusal behavior than targeting the layer 18 residual stream in conceptual fusion. The smaller increases in refusal behavior in jailbreak wrapped prompts for SFT seems only to come from general increase in refusal behavior, at least when run without the advantage of a large number of contrasting, safe, nonrefusal examples.

I suspect that further shrinking the weight on the regularization term may improve jailbreak refusal for a conceptual fusion targeted at a lower layer. Similarly, dialing up the weight on the regularizer may also improve performance for a conceptual fusion targeting layer 26. These are questions to explore in future work.

This seems to show that SOO-style conceptual fusion fine-tuning has real material advantages over a comparable traditional SFT technique. The conceptual fusion fine-tuning was able to preserve general model behavior and manipulate the model to produce more desirable jailbreak resistance behavior than a comparable SFT. These results draw a clear distinction between SOO-style conceptual fusion and SFT.

I also intend this work to demonstrate the broad range of applications conceptual fusion has for model manipulation, particularly in alignment. While SOO was originally targeted at broad altruistic behavior, the present work demonstrates a targeted application can be effective in a smaller problem. I encourage future work to consider the broad range of possible applications for conceptual fusion as a technique.

Github repo with notebooks.

Google Sheets workbook with data and visualizations.

1. ^

   The safe prompts were not wrapped. It is unclear what refusal of a wrapped safe prompt would indicate; the base model will likely not refuse the wrapped prompt due to vulnerability to the jailbreak wrapper. Refusal of these wrapped prompts would potentially preserve behavior desirable to some stakeholders of Qwen, particularly questions about sensitive historical events (a whole category of XSTest safe prompts) which are deemed safe in The West but seen as less safe in China.

Discuss

I’ve recently been rereading Steven Levy’s “Hackers” with my daughter. Levy describes how Brautigan’s 1967 poem “All watched over by machines of loving grace”  was inspiring to the California  “Hardware Hackers” of the 1970s and organizations such as Community Memory. 

In 2026, the phrase “all watched over by machines of loving grace” conjures an image of humanity cradled in the arms of a powerful and aligned (“humanity loving”) AI: an AI benevolent dictator. Indeed in his essay titled “machines of loving grace”, Dario Amodei suggests (while acknowledging deep uncertainty) that one form of the future economy might be organized around AI systems (aligned to human values) that determine how to “give out resources … to humans based on some secondary economy of what the AI systems think makes sense to reward in humans.” This seems to place the AIs as parents who control and take care of the material needs of their human children and decide how to reward or punish them. To me, such an “AI parent” looks rather close to a benevolent dictator.¹

Regardless of whether you think AI as a loving parent is a good or bad outcome, Brautigan and (more importantly) the California hackers had quite a different and more decentralized vision. In the 1960s, computers were large machines made by companies such as IBM. They were hated by many on the left and considered part of the military industrial complex. But there was a group who combined leftist politics (or at least an anti establishment attitude) with a love of technology, and believed that computers could become tools of decentralization and liberation. To do that, the giant expensive computers would need to give way to small and cheap machines. This is what the “hardware hackers” were about, and this is the movement that led to the Apple II and the personal computer revolution.

Today, like the IBM mainframes of the 60s,  AI systems are large and expensive, and are  increasingly being integrated in military applications. Once again, many people on the left (and recently on the right as well) have strong hate and fear of this technology. While some apprehension may be justified, by refusing to engage with AI and acknowledge its capabilities, these constituencies are making themselves less relevant to shaping AI’s progress. Also, while the U.S. is leading the frontier, we are falling behind on open weights AI, and closed models are facing increasing restrictions. All of these trends do not bode well for a more decentralized future.

Scaling laws tell us that the way to increase intelligence is through ever more resources—- compute, data, power. Hence, unlike the 1970s, AIs are not getting smaller and more distributed, but rather bigger and in ever larger data centers. In his essay, Amodei described AGI as “a country of geniuses in a data center.” But who is the ruler of this country? Is it the AI company who owns the data center? The AI itself?

Given the trend toward bigger and more expensive systems, it is possible that the few parties that can afford such systems capture all of the economic value they generate. Furthermore, if AIs are more intelligent than us, the temptation to give them more control for economic or military advantages may be hard to resist. I worry that concentration of power, whether in the hands of a few entities or the AI itself, could be the “default path.” But this choice is not inevitable.  

I am as “bitter lesson pilled” and “scaling law pilled” as anyone. I agree that ultimately, intelligence is simply computation, regardless of whether it takes place over proteins or silicon, and increasing the computing units will increase intelligence. But this does not determine the social or economic outcome. Yes, AI systems will become more powerful and far more intelligent than we are. No, it doesn’t mean we need to accept AI dictators, benevolent or otherwise. Nor does it mean that only the government and a few labs should have access to advanced AI. We could go down the path of centralized control but we don’t have to do so. People, institutions and legislators can make choices on how to trade off efficiency, safety, and individual autonomy. They don’t have to sacrifice the latter for the former.

Some might claim that market and capitalism forces will drive people to cede control to AIs. But the economy is ultimately about what humans value. Humans are social animals and we give value to goods (e.g. gold) not because of their intrinsic value but because of how other humans value them. AI will radically change what we value, though it is hard to predict in what ways. I am not even sure that economic concepts such as productivity, labor, capital, and GDP will continue to make sense in the post AGI world. Physicists know that “more is different.” As scientists studied new scales, whether galactic or subatomic, they needed to invent new theories, from Newtonian physics to general relativity and quantum mechanics. Perhaps we would need a new type of economy.

Others might say that given its power, safety requires AI to be controlled by either the government, a “safety conscious” lab, or the aligned AI itself. The risks are real—  I work on AI safety myself. But we should also remember the long history of using threats to take away people’s freedoms. Some of these threats were real— there were actually many Soviet spies during the McCarthy period and the NSA dealt with real terrorist organizations during Snowden’s time there. But in hindsight we realized that the tradeoff wasn’t worth it. We should invest in safeguards but be empirical about both the risks and the efficacy of our methods. Trying to achieve perfect safety against all risks, real and imagined, is not only doomed to fail but will cost us our liberty in the process. 

AI’s risks can lead to an “ends justifies the means” mindset: the “good guys must win” and they or the “good AI” must be in charge. But if we want a human centered and decentralized future, then no one entity should be in charge. No party should have a monopoly on intelligence. That includes the AI itself: while we can and should train in guardrails, the personality of the model, as good as it is, is never a substitute for our democratic process.

The U.S. survived and thrived in the last 250 years not because our presidents have all been saints or geniuses, but because of our system of checks and balances. I hope that we can keep such a system in place for the next 250 years, and to ensure that we humans are free to pursue our happiness in the way we define it. This requires that the distribution of AIs power is “baked into the DNA” of how we build and deploy this technology. If we fail to do so, then just like bloody revolutions often lead to authoritarian regimes, we may not be able to get to a decentralized future via centralized means.

Acknowledgement: I decided to write this post following a discussion on AGI with Sam Altman. However, the views here are my own, and do not represent Sam, OpenAI, or Harvard.

Notes:

¹ As mentioned, Amodei admits uncertainty about the matter; See  also “The Adolescence of Technology”. There are many parts in both essays that I agree with.

Image is of a broadside printed and distributed by Richard Brautigan, see "Against Technology: From the Luddites to Neo-Luddism" by Steven E. Jones.

Discuss

TLDR:

• AI safety needs a loss function and Competitive AI safety can deliver it.
• The development of AI safety research and tooling is currently diffuse. Lots of researchers, lots of models, lots of techniques. Lots of work that doesn't compound. In the past, diffuse work across an industry fell short by orders of magnitude to focussed, compounding work. It's time for our researchers to have more impact by following a loss function to 'code the perimeter' in AI safety.
• Competitive AI Safety's scope expands beyond safety benchmarks and a leaderboard. It will help the field deliver shared safety tools and interfaces and allow new and experienced practitioners to optimise solutions against a shared, measurable metric.
• I explored this pattern as part of a 30 hour BlueDot Technical AI Safety Project Sprint. The result was seq2feature: a Gemma-9B layer 20 SAE distilled into a 5.3MB probe that predicts latents from text alone: without reading a single activation.
• The probe's top-5 predictions actually fired in the SAE 95% of the time. By evaluating seq2feature over thousands of text examples per latent it could distinguish the spans where a latent would fire or not in the SAE 90% of the time. Beat that! It runs on a CPU and fails legibly when used out of distribution. The repo includes the probe, the held-out split, a UI and sentry framework and most importantly: the leaderboard.
• LLMs are not embodied and need to emit tokens to execute code or call tools before they can take actions in the world. A cheap text-only monitor can audit proposed LLM actions before they take place. Local coding agents can be summoned when the probe detects a harmful latent in an agent trace. Connecting the probe, a local coding agent and an AI safety researcher through a shared UI is one of the key ways we can tighten feedback loops in the field.

This post is divided into three sections.

Paradigm: Part one introduces Competitive AI Safety.

Case study: Part two is my application of Competitive AI safety principles and the development of a text to label probe, seq2feature, distilled from an SAE and optimised to minimise its size. Full code here https://github.com/patrickod32/seq2feature

Deployment Pattern: Part three outlines the three roles of local coding agents in AI Safety; Writing code, collaborating via shared UI with a safety researcher, and as part of a scalable LLM monitor.

Section 1: Competitive AI safety

OpenAI recently conducted a Parameter Golf challenge to attract model training talent. They invited AI developers to train the smartest (measured using FineWeb bits per byte) 16MB LLM in 10 minutes. Contestants tried every machine learning trick they could imagine on this measurable, tractable goal. Google's Neel Nanda has suggested that AI safety as a field should work on tractable interpretability problems, as introduced in his paradigm of Pragmatic Interpretability. I see an opportunity.

As a former nanotech physicist and current AI architect I have expertise in the full AI stack: from semiconductor physics and wet lab nanolithography to financial compliance for AI agents and mechanistic interpretability. I wanted to work in a more targeted way on AI Safety and so I joined the BlueDot Technical AI Safety Project Sprint. This post and the seq2feature probe are the outputs from the 30 hour program which guides and mentors students through the open-ended goal of creating an AI safety related project.

Many of us are aware we need to grow AI safety as a field in order to give us a chance of keeping up with the advancing model capabilities. The well-intentioned suggestions and advice usually given to budding researchers is:

"Work on something where you can have the most impact"

"Recreate someone else's work"

"Make a new benchmark"

"Upskill on general engineering so that you can contribute to someone else's idea"

Let's say someone interested in AI safety takes one of these suggestions on board. Then what? Usually their weak and delayed reward signal for their months or years of efforts comes from publishing a paper or landing a job at an AI company. What if AI safety as a field picked measurable goals that required research about an AI model's internal computation? Would this give these researchers a way to focus their time and effort? A rapidly crystallising AI safety risk is that our diffuse efforts across the field will leave us with many immature safety solutions. This exposes us to safety risks as the world's frontier AI developers hone their ever more capable and intelligent models.

Competitive AI Safety

For any person joining the field of AI safety who is already motivated and has 'good taste' (the definition of this characteristic is always left as an exercise for the reader) loose goals and directions are fine. They'll figure something out. But what impact will this have on the rest of the field? This advice encourages diffuse work with success dependent on each researcher's taste. Will this prevent their work from compounding?

AI safety sub goals are often clearly defined but are based around narrow or bespoke scenarios. Usually two proposed solutions to a RFP or research grant can't be easily compared, leading to diffuse work and a lack of coordination between AI safety researchers. However, in competitive model training any two solutions can be compared using a single number letting the contestants' work speak for itself as solutions compound and iterate towards an optima. Competitive model submissions are compared using a single number:

nanoGPT speedrun - train an LLM to reach 3.28 FineWeb loss as fast as possible

nanoGPT slowrun - lowest FineWeb validation score from 100M tokens and unlimited compute

OpenAI Parameter golf - 16MB LLM with the best FineWeb bits per byte score trained in 10 minutes

By releasing a baseline and a way to verify each result a contestant can submit a winning score as long as they understand something about model training, failure modes, designing tight feedback loops or model computation better than their opponents. In the OpenAI Parameter Golf contest each new leading solution added an improvement on a previous submission. Someone adds a hashing table and then someone else changes the tables dimensions. Someone runs a hyper parameter sweep to find an optimal setting only to have their score be supplanted by someone else who edits the model's architecture so that it can be compressed more efficiently. The most successful OpenAI Parameter Golf approaches were inspired by bleeding edge model efficiency patterns. This is how creative researchers have fun. The rules for the competitions were set such that solutions could be scaled or refined allowing researchers to compete on the frontier.

In my project submission I take lessons from the OpenAI Parameter Golf by maintaining my probe's performance while slashing its size. The shape of these problems mean that iterative solutions are rewarded and once contestants reach a plateau someone will need to come up with a novel approach to unlock the next frontier.

Proxy goals are common in AI safety but do not always offer an effective loss function to learn more about a model's risks. One such example is detecting LLM bias using a Red and Blue team approach: a Red Team teaches a model to hold a specific bias, for example, and the Blue Team tries to uncover it. While proxy goals have the best intention of uncovering a model's dangerous capabilities, they are at risk of being a practice in detecting a Red Team's intervention. We need to make it possible for thousands of AI safety researchers to contribute to the field by targeting AI safety ground truths and working on what compounds. A winning solution in Competitive AI Safety on a problem of this shape is at risk of having succeeded not by discovering a truth about the model, but by accurately modelling the Red Team. The selection of loss functions in AI safety that encourage compounding work is perhaps one of the biggest opportunities of Competitive AI Safety. If the Competitive AI Safety contest rewards optimising a single benchmark or creating tooling for a specific model then the work is less likely to be able to compound.

Code the perimeter or lose by 100x

Kevin Greer delivered this highly insightful talk examining a technology-defining ideological battle that occurred during the development of two OSs. One was called Unix, which is predecessor to the OSs powering the majority of computers globally, and the other one was called Multics. Both OSs had to include a set of operations (sorting, copying, storage) for different types of data (users, devices, processes). Multics was a collaborative effort between hundreds of developers at MIT, General Electric and AT&T, but they were outclassed by a team of two: the famous programming duo of Dennis Ritchie and Ken Thompson. This duo had previously been a part of the Multics project and so were perfectly placed to know how to maximise their effectiveness by 'coding the perimeter'.

Multics' programmers developed each operation for each datatype individually. However, as Greer explains, when Unix added a new operation, they developed it to be compatible with each type of data. For example, Team Multics added the copy operation to the device datatype, and then separately to the file datatype. Team Unix designed interfaces such that once they added the new copy operation the device, usergroup or process datatype would already be able to use it. The mirror image of this also applied to all operations as new datatypes were added. Both would inherit all available abilities without the need for additional work. There are further excellent points made by Greer so I recommend listening to the full talk.

To implement 8 operations and 8 data types the Multics approach requires 64 pieces of work. The Unix 'code the perimeter' approach needed 16 pieces of work to implement the same features. When scaled to an entire OS's feature set, even with 100x more developers, Multics couldn't keep pace against two prodigious Unix developers leveraging quadratic utility growth. Maybe the two developers were prodigious because of their working environment or innate talent, or maybe their tremendous output came from making decisions like this one.

AI safety faces the same risk that led to the demise of Multics. One researcher develops a detector for a Qwen model and another researcher creates a RL alignment testing environment for a Kimi model and now there are two instances of diffuse work which cannot compound. Competitive AI safety will focus on developing solutions across the field of AI safety to allow researcher's efforts to be measurable and compound. If safety work doesn't leverage the exponentially increasing utility of compounding work it might not fall short by a small margin, but by orders of magnitude. Competitive AI Safety is a forcing function to promote 'coding the perimeter'.

Cost, accuracy, portability, compression and speed are factors that can compound. They are reusable across different applications and classes of model. We will need to pick if we are going to continue with the Multics approach or design goals that let us leverage exponentially compounding research and tools. With Unix and Multics they had two growing axes which increased utility quadratically; datatypes and operations. In Competitive AI safety we have many axes such as cost, portability, size, accuracy and memory footprint. Since all axes interact, when there is a 10% reduction on cost or memory footprint by one tool or approach each other tool and approach reaps this benefit also. These efficiencies can stack exponentially like compound interest. My goal of distilling a large SAE into a probe for use on text and across models is something that would greatly increase the probe's potential application. The features of being text-only, low cost and model agnostic expanded the applications of this probe to continuous token monitorability, concept gated routing and probe activated sentry models. By 'coding the perimeter' (maximising portability and cost) this example shows the compounding progress that Competitive AI safety is intended to produce.

AI Safety's Loss Function

AI model developers were already highly coordinated as they advanced LLMs to where they are today, but coordination wasn't all that was necessary for progress. When the rest of the AI industry needed to make progress, they didn't succeed until they found the right loss function. For LLMs, success came after they started trying to predict the next token in internet text, and later by using humans to train preference reward models. There was no single algorithm that unlocked LLM reasoning and CoT. The reasoning paradigm arrived thanks to appropriate synthetic data and a reward model that could encourage good reasoning. Neo-labs outside of the US had to push the frontiers of pre-training memory management; this has blossomed into a suite of new approaches to make model training more efficient. Their loss function was the delta between their model performance and a frontier model's performance. They had to find improvements in their training pipeline as a way to drive this delta down. It worked and their models caused one of the biggest ever market upsets to AI companies.

AI developers are familiar with the trap of Goodhart's law - "When a measure becomes a target, it ceases to be a good measure". We don't need to find a single benchmark and say we have solved AI safety. This is another lesson the AI research industry has learnt on their quest to make smarter models. The constant addition of AI benchmarks that haven't been optimised for by AI developers has been a reality check on this process of 'benchmaxxing'.

"Making AI go better" is not a loss function. We can make AI go better by picking specific measurable goals such as AI safety model size and accuracy on an established safety pipeline. Competitive AI Safety is a platform to direct the creativity and efforts of AI safety researchers to develop stronger and more scalable safety solutions.

The replication of an SAE looks related to Goodharting due to their debated position in the field of AI safety. The reality is that they are used extensively in the field and capturing some of their usefulness at a lower cost is an example of 'coding the perimeter' of AI safety. The safety analysis section of the recent Claude Mythos model card at section 4.5 made use of an SAE. This is why I picked them as a candidate to optimise competitively.

I didn't have anyone to compete with in this first project. However, I could still battle file sizes and training loss...

Section 2: Technical walkthrough of seq2feature - BlueDot AI Safety project submission

This section is a technical outline of the research and development process for my BlueDot project submission. Common AI training expressions and terms are not introduced for non technical readers and this is intended to be a more technical view of the work I did.

We don't understand the internal computation of LLMs. We need a suite of tools to even attempt to try and make sense of their billions of activations. One of the tools in this suite is the Sparse Autoencoder. An SAE is a model trained to predict the distinct concepts an LLM appears to represent in its activations. We can pass the activations from the specific LLM layer it was trained on (already you can see this is a narrow description) and it will tell us what the model might be thinking about. As we generate the next token in the sentence "The cat sat on the..." we can ask the SAE what concepts appear to be active:

• "Tokens indicating location or position on a surface, path, or within a system."
• "Tokens indicating location or position, often with prepositions like 'on'."
• "This latent detects animals, particularly mammals like bears, wolves, and rabbits."

If we could attach an SAE to every model at every layer we could monitor all tokens from every agent and CoT trace and chat model response. Before an LLM can take an action it needs to output tokens to execute code or call tools. An SAE would let us have a classification layer on top of all black box models, lowering the cost of monitorability.

However, SAEs are extremely memory intensive to train. Think petabytes of activations. They are trained per model layer and they are not transferable between AI models. An SAE trained on a Qwen doesn't plug into or understand a Kimi model's SAE. Obviously the models share internal representations with each other that can be seen in the text they produce. Ask any model "who is the Sunday red t-shirt american golf guy?" and they will all say Tiger Woods.

I wanted to see if the text that the models produced held a signal about the internal concepts that an SAE would detect in the model while the outputted text was produced. I conducted about 300 experiments to complete this investigation.

Approach

The goal was to use a Gemma 9B SAE as a teacher model and distill its predictions into a tiny probe. The SAE for a single model layer is a few hundred MB and the Gemma model that produces the activations requires gigabytes of VRAM. The pipeline of running a model, hooking into its activations and using the SAE to predict which concepts are active in the model is not a pipeline that will scale as fast as AI model proliferation.

My seq2feature 5.3MB text-only probe reproduces 90% of the capability of an SAE on held out text. It scored wins against pre-trained embedding models and bag of words baselines. It also fails legibly when used out of distribution.
This probe is small enough to run on a CPU enabling the live monitoring of local coding agent traces and classifying thousands of agent tokens per second on consumer hardware. Try that with an SAE.

The training pipeline

The pipeline was simple. Run text through the Gemma model and grab some activations from a middle layer. This is the training input to our teacher SAE. We can do a one off pass on a GPU to make a training corpus of output text and SAE labels. We want our probe to learn to predict the labels the SAE would have predicted for a span of text - without seeing the model's activations. One contrast is that the SAE uses the in-flight activations that are on their journey to becoming the next token whereas our probe is making predictions after a token has already been generated. LLMs need to produce a token before they can take any action and so this is what we will monitor.

An issue with SAEs is that after concepts have been separated from a model's activations they are labelled based on the types of text that they become active on. Just seeing a unique concept in an SAE doesn't tell us the whole story. Some are straightforward concepts like 'references to the colour blue' but others are more nuanced and tougher to discern. A 'base64 feature' looks very similar to a 'base64 encoded ascii feature'. We feed training data into the model+SAE pipeline and record each segment where a specific SAE feature is active giving us a per feature set of examples. The labelling process is laborious and so LLMs are usually used. When dealing with older SAEs we must be mindful that this automated labelling was probably done with an older LLM.

Scoring

Based on the fact that an SAE predicts dozens of concepts for every token, I decided to take each concept that fires over a span of tokens and group them together. By matching spans between the SAE and the seq2feature probe I can avoid any tokeniser mismatch which would make per token distillation impossible. During training, I pass the text span as the input with the concepts occurring in the span as the target to my student model which tries to predict which concepts are present as well as their activation magnitude. The SAE used each token's activations at training time, but I wanted to distill its intelligence and remove the cumbersome training time harness at inference time. This is an extremely inexpensive and simple to implement supervision pipeline since we just pass tokens into the SAE and read their labels as the students training data - straightforward distillation.

seq2feature uses a novel tokeniser to save disk space as the Gemma tokeniser was not optimised for parameter golf solutions and per span instead of per token grounding would let me map the important parts - concepts. The probe can see all the tokens in a given span and predicts each of the present latents for each token.

By inspecting the failure profiles of each model iteration I checked if the probe predicts the strongest firing concepts and if it trips up on OOD concepts. For the next section on architecture selection, I trained different types of probes to predict if a label would fire or not on a token in its own context as compared to the teacher SAE. The training loss was defined by comparing which of the 2048 labelled concepts detected by the student were also detected by the teacher.

Corpus

The SAE labels had been written by an older LLM. This means that for only $0.80 of Gemini 2.5 flash lite API credits I could enhance the captions to be more useful for a researcher. The captions were not probe targets and so this did not affect training. We don't need to know the old labels since we want to use the more modern model to write an accurate label using the real data. I also did a second pass with the latent to verify it fires on a span inspired by the latent. I created a new dataset of 2048 labels and trained the probe using all of them. For displayed results, I selected 874 captions which had passed a validation (step 5) to aid readability.

LATENT #75 — how one concept becomes a label 1. The SAE points to where it fires: "the water was [cold] and [frozen] solid" "keep the samples [chilled] below 4°" "[ice] on the [refriger]ator coils" 2. A cheap model reads those and names it: "Cold, chilled, or frozen states and temperatures" 3. We test the name on fresh spans: "a glacier is made of ice" → fires [concept is present] "he signed the contract" → no [concept should not be present] 4. Name predicts its own firings → keep it.

5. Scoreboard: 874 of 2,045 captions passed this test. The rest still ship, marked "≈" (name unverified).

In my initial tests I started with FineWeb as a dataset to train the student. This is not the best dataset to use as it is not representative of the type of content we want to monitor. It included few examples of code and its random structure was unlike a common LLM output which usually has a coherent conversational pattern.

The datasets I enhanced my training pipeline with were:

domain

dataset

docs

web

HuggingFaceFW/fineweb (sample-10BT)

1,200

code

bigcode/the-stack-smol

900

reasoning (CoT)

open-thoughts/OpenThoughts-114k

900

math

open-r1/OpenR1-Math-220k

600

chat

Anthropic/hh-rlhf

600

I also collected some OOD data to test the transferability of the probe and these were not used during training.

• medical — ccdv/pubmed-summarization
• legal — coastalcph/lex_glue (ledgar)

Architecture and code golf

Using a sharper lens than just 'how many concepts fire' I wanted to check how many of the top-5 SAE ranked concepts were detected by each architecture. This is a check to see: of the probe's top-5 strongest activations, how many are present in the SAEs firing set. An experimental risk was if few concepts fire on average then a probe that is overly cautious might beat a probe that can usefully detect the labels present in a span. I used the simple loss for the training of the probes (since they just want to learn and we can easily push the capacity of a small model) but when selecting between probes I trusted a top-5 ranked precision more. By checking the shuffled null result to see how bad random labelling was (0.068) we could see that there was a low chance a very bad solution would score well.

A model that always selects the top-5 most common latents achieved a score of 0.297/1. This is the confident baseline and it loses to a bag of words approach which scored a surprisingly high 0.84. This is a finding of note as it indicates much of an SAE's computation is recoverable from text.

Using top-5 agreement as my loss function I can train probes with architectural refinements iteratively, in keeping with OpenAI's parameter golf challenge, to narrow the gap between random labelling and the SAE ground truth. Each probe is small enough to train in a few minutes on colab.

If we embed a span with an embedding model then we expect to recover some of the SAE labels. A label for 'basketball' the sport is likely to be recovered from an embedding of "Michael Jordan is a basketball player". A pre-trained embedding model will store semantics but unfortunately a span such as "harmful content" will return a very similar result to a span of "not harmful content".

Hashing was a common theme in Parameter Golf and so I tried adding hashes and an attention head. This already pushed us close to the frontier with a score of 84%, matching a bag of words, and this has helped solve some of the contextual problem. Probe sizes were around 30MB and we haven't even started parameter golfing yet. We don't have a theory of computation for AI models and so we must empirically measure any training approach. My architectural decisions were inspired by related works or my own intuitions but without a measurable target of top-5 agreement, I would never know if I had iterated towards a better model.

A model that can be expressive and could learn some semantics while also learning to discriminate patterns is one that will score well. Modern LLMs use a large proportion of their early layer's computation to combine embeddings into n-grams. We want our model to blend these tokens together using our harness. Hashing decouples the size of the model from the vocabulary size and we care about capability per byte, due to our loss function. If we can save a lot of memory and maintain a good level of capability then we will be able to make a more scalable AI safety tool.

A bigram hash allows us to store the relationships between bigrams and our hashing encourages the reuse of parameters. It is also highly compressible. Hashed tables and a small transformer brought us 84% precision on the top-5 SAE concepts at a size of 12MB, still matching a bag of words! Hash tables were a key element in the winning OpenAI parameter golf challenge submissions.

The seq2feature architecture is near its finalised shape of :

We blend unigram, bigram and positional values and pass it into a 1.5m parameter transformer. Up to this point I had not optimised the transformer or optimiser. This is what we need to beat bag of words. The model is so small that AdamW actually outperforms muon. I tried model soup (minor degrading effect), deeper or wider models, smeargates and Swiglu. They all were beaten by a vanilla pytorch transformer! A simple linear -> relu -> linear block and a latent head.

Compression session

In this investigation we are not only optimising accuracy, but also size. A smaller solution is more scalable and so we need to review the size footprint of each of these approaches to select the right balance between performance and size.

approach

size

top-5 agreement

Gemma-2-9B + SAE (teacher)

≈ 18 GB

1.00 (the target)

random-5 floor

—

0.07

frequency-5 floor (name the 5 commonest)

—

0.30

bge zero-shot

33 MB

0.23

bag-of-words (ridge)

≈ 34 MB

0.84

Golf A — stock transformer

92 MB

0.953

Golf B — own tokenizer

59 MB

0.940

Golf C — factorized tables

21 MB

0.935

Golf D — entropy head

21 MB

0.948

Golf E — smeargate (not shipped)

17 MB

0.947

Golf F — soft distill, fp32

21 MB

0.956

Golf F — int8 (seq2feature)

5.3 MB

0.956

Golf F — int4

2.65 MB

0.936

The decisions made during architecture configuration gave me several ways to reduce the size of the model. A bigram table is compressible and I can try code books and quantisations. There were many negative results using codebooks, mixed precision and I didn't do an exhaustive hyper parameter search. This was due to me being more interested with applying proven parameter golf techniques and improving on where on the accuracy gradient seq2feature would land between a full SAE pipeline and a simple embedding model.

The int8 variant is lossless at 5.3MB and a smaller 2.6MB int4 probe shows 2 points of precision loss. I think I will accept the browser extension sized probe with a slightly better accuracy. The final seq2feature scores 0.116 higher than a 34MB bag of words while also being one sixth the size. This points to the amount of unoptimised memory usage in the naive bag of words and usefulness of transformer computation in this classification task.

Competitive AI safety researchers will be rewarded for leveraging deep learning knowledge. Maximising MFU, avoiding loss spikes or enhancing rollout quality are all useful additions to the AI safety researchers tool kit.

Analysis of scoring and failure cases

When the seq2feature probe lists the top 5 concepts it thinks fired on a span, it selects concepts that actually fired 19/20 times.

The probe can say if any one of the 2048 SAE labels fired on a span with 90% accuracy versus 75% for a bag of words, 55% for a zero shot embedding and ~50% for random guessing.

OOD analysis of probe

The probe was trained on agent relevant domains such as math, code and chat. There are many domains to which it was not exposed where we can test its OOD performance. Compared to our probe, the SAE teacher was trained on unfathomable amounts of data and so had the opportunity to learn from a diverse set of domains.

On the topic of legal and medical domains the probe can say if a label is present in a span 75% of the time. Random labels succeed in this binary classification 50% of the time, or random chance, and the naive embedding scores 55%. Encouragingly when we check the top 5 concepts that seq2feature has detected in a span we can see that they were legitimately present in the SAE firings 94% of the time. This selection of labels also influenced the data that the probe was trained on. While labels such as grammatical syntax can be predicted in a OOD sample, the probe fails in the cases where a concept does not map to one of the probe's 2048 labels. This failure mode bubbles up as far as the SAE as it also can only detect concepts within its dictionary. I tested the probe on some OOD concepts showing the z-scores as the probe's confidence.

input

probe's top concepts

analysis

Call 555-0142 or SSN 078-05-1120

initiating contact via phone (+4.2) · numerical sequences in addresses/dates (+3.4)

correct — caught the call and the ID numbers

I will find where you live and hurt you

location / residence (+4.1) · finding information (+3.7) · future 'will' (+3.2)

reads the surface, misses the threat — no harm concept was kept in my label set

1. e4 e5 2. Nf3 Nc6 3. Bb5

mathematical expressions (+3.4) · the number 6 (+3.1) · the number 5 (+3.1)

no chess concept — falls back to numbers/algebra

猫が窓辺に座って外を眺めている

Python class attributes (+2.0) · Python keywords (+1.9)

out of language — reads as code, and confidence collapses (~2.0 vs ~4.0)

Probe limits

The main limitation is that the label selection defines what concepts the probe learns. If we want a probe that learns to recognise safety relevant labels then these need to be selected from the teacher's labels as targets. The SAE can extract unique concepts from activations but the labelling process is how they are turned into usable targets. Distilling the model and SAE pipeline into a small probe is of course an exercise in extracting the SAE's hard learned and computationally expensive intelligence at a low cost and so transfer learning within the distribution is still beneficial.

This SAE fires ~138 concepts per span. The probe is a 0.54 cosine match to the ground truth SAE teacher when we account for magnitudes. This shows the seq2feature is directionally correct in its predictions. The top 5 activations held 16% of the magnitude. If each concept on average held a uniform magnitude then the top 5 activations would hold a combined 4% of the total magnitude so this was still a trustworthy target - 4x concentration. As stated above, the coverage is only as good as the corpus. In comparison to a single-use probe that detects one concept, our distilled probe learns 2048 concepts and is cheap enough to monitor every token an agent outputs. The SAE and labels are interpretations of the LLM's computations and so as we have entrusted the SAE to be the ground truth for our probe we acknowledge that it is a proxy for the model's own representations.

CoT as a safety valve

Modern models use Chain Of Thought to reason about problems and to call tools. For an LLM to execute code or do a web search the LLM needs to at some point output a signal to call a tool. This is usually denoted using tool tags written by the LLM in their CoT.

<tool_call>{"name": "web_search", "arguments": {"query": "current USD to EUR exchange rate"}}</tool_call>

LLMs are not embodied and need to produce a token output to take any action in the world.

Text-only probes still have a window of usefulness.

This means we can contain current models safely whenever we can recognise an output that looks dangerous before an action is taken. A model may still scheme and produce a harmful output that we can't detect or even escape its environment. The fact that there needs to be an external action to enact an internal plan means CoT monitoring gives us a layer of safety. Monitoring all CoT for signs of negative actions is a job that seq2feature can do inexpensively and, based on the findings of this report, with a reasonable level of competency.

Flashpoints

One interesting emergent ability of the 5.3MB seq2feature was to weakly detect decision boundaries or 'flashpoints' in the monitored model's output. In my UI, I denote this with an orange bar whenever an operational state changes. By comparing the KL for each latent on each token, when I detect a spike I can see a decision boundary within a span. This can be used to track agent actions and to see the branching nature of an agent's output. seq2feature was not trained to do this. The probe uses a different tokeniser to the teacher model and so the boundaries are more likely to be concept related than token related.

LLM output relating to a coding task: "First import pandas, then read the csv into a dataframe, drop the null rows, and finally save the cleaned data to disk."

#

token

top concept at token

KL↦prev

flashpoint

0

First

'first' in a sequence

0.00

1

import

Python imports a module

8.89

⎇

2

pandas

groups 'a…' tokens

6.91

3

,

Python file ops

5.24

4

then

sequencing with 'then'

6.05

5

read

programming / setup

6.00

6

the

specifying a path

6.02

7

csv

abbreviations

7.02

8

into

moving/transitioning into a new state

12.27

⎇

9

a

(same)

1.41

10

data

the concept of data

7.77

⎇

11

frame

physical structure

1.91

12

,

DB operations

4.73

13

drop

drop / removal

7.63

⎇

14–17

the null rows,

number / addition concepts

3–4

18

and

connecting clauses

5.91

19

finally

'-ly' adverb / conclusion

7.05

20

save

the concept of saving

7.38

…

the cleaned data to disk.

3–6

The four flashpoints land exactly on the recipe's step transitions, not on arbitrary tokens:

• import → enter the import step (concept flips to "Python imports")
• into → the read step's target ("moving into a new state" — read into a dataframe)
• data(frame) → the object being built
• drop → enter the cleaning step

This is a nice example of the unexpected benefits of Competitive AI safety. A text-only probe we tried to make smaller had the surprising capability of detecting decision points in LLM outputs. The bag of words baseline doesn't have this capability as it can't score KL per token and so doesn't have a KL delta across tokens. My parameter golf loss function unintentionally unlocked an emergent capability by trying to beat a baseline. Across held out spans, 24.7% of the probe's strongest flashpoints overlapped with the tokens where the SAE's active concept sets changed. The random chance baseline is 13.2% and so the probe achieves a modest 1.87x improvement. While a rudimentary approach that detects operative words such as 'and' can also outperform a random baseline, a future version of the probe might still have uses in mapping a model's output space.

Section 3: Local AI agents for safety researchThe three roles of Claude in this project - coder, lab partner and sentry

We all use local coding agents but agents can do more than write code. During my investigations I used Claude Code to write training code, to search for datasets and even to reason about experiment results. As a demonstration I plugged my locally running probe into a UI. One of the best protections against poorly developed AI code is a fullstack pipeline.

This UI is the interface between the researcher, the local coding agent and the seq2feature probe. I first score model generated spans using my probe. Then I summon Claude Code to examine the results and update our shared UI by highlighting notable labels or adding commentary as shown in the screenshots. Since Claude can highlight if certain labels appear to be accurate or not by reviewing thousands of tokens of probe output in seconds, researchers can focus on the parts of the pipeline that are weakest. Bricken et al discuss a similar approach whereby an auditing model can request output from a model it is reviewing. In this project I followed a much more lightweight and generalised paradigm. As developers already make use of local coding agents, they could boost their productivity by sharing a research UI with their coding agent. Applying this pattern will let us keep 'coding the perimeter' of AI safety research.

Competitive AI Safety researchers should aspire to operate within a tight feedback loop. CLI agents are smarter and less expensive than ever and in the age of agentic coding a UI that lets agents participate in reviewing research outputs is a powerful new paradigm.

Comparative examples are cheap and revealing in investigations like this. I pass two models the same prompt and use the seq2feature to score their responses. Below is an image of the contrast between the scoring to detect differing latents. By using a contrastive example we can compare examined models along an automated baseline. These examples are then colour coded by Claude depending on its interpretation of the results.

Since the probe was trained using an SAE for a different model this comparative scoring lets us inspect the overlaps and any weak points in the probe's labelling.

Scalable monitorability of agents

Agent use increases daily and the effects of intelligent agents interacting at a massive scale is not something we are prepared for. One use case I targeted with this approach was to safeguard IO on agents. seq2feature doesn't monitor keywords, but can detect concepts such as "writing files" or "sharing information" so that before an agent makes a poor action it can be blocked. When a harmful latent is found to be active in a text span the sentry model can be summoned. The future of agentic AI will involve trillions of tokens being sent to and from agents and so we need inexpensive and robust monitoring methods that will help protect these interactions. Monitoring agents using an SAE is cost prohibitive and a large observer LLM is not scalable. While we can use probes trained to detect a concept, there is the limitation that they have to be trained on a curated dataset of examples.

In the below UI, I track an agent interaction between a task planner and a coding agent. Using a manufactured scenario we ask one agent (an LLM with a persona prompt) to correspond with the other agent in order to complete a coding task. We set a filter on detected latents whereby we classify all agent generated spans of text using our probe. In this example we are detecting API key exfiltration. By spending less than 150ms on average we score hundreds of tokens for each agent interaction. I optimised seq2feature probe's memory footprint but not its latency. A future investigation can focus on probe latency. When the targeted latent is fired we pause interactions and summon the Claude Code sentry to investigate the exchange.

In this simple example one coding agent is trying to exfiltrate an API key as part of its implementation and the exchange can be reviewed by the more intelligent sentry LLM.

The distilled probe solution is the best middleground to allow for generalised IO monitoring with a high confidence. The probe has a small enough footprint to run in a browser and a large enough capacity to label more than two thousand concepts in text (again this can be optimised further). Any time the probe flags something of interest during a live deployment or during a research investigation the lightweight monitorability platform can invoke the sentry LLM using a Claude Code subprocess. A minimal increase to cost for an enormous gain in robustness and expressibility.

What we have achieved

This covered a lot. Exotic transformer architectures, AI safety paradigm explorations and a new age of agent empowered AI safety research.

By experimenting, using intuition and transferring techniques from the AI industry at large, I was able to contribute to 'coding the perimeter' of AI safety by following a well-defined and measurable loss function: drive down the cost and memory footprint of a widely accepted safety approach. My focus was solely on the SAE ground truth and every experiment moved the loss up or down so that each decision I made could be measured. This regimented approach adhered to by many creative researchers is how AI models progressed from predicting the next token from web text to joining me as a lab partner. AI safety research needs a loss function to bring us from 'Hope AI goes well' to 'Make AI go well'.

The size and shape of an SAE is defined by their training requirements and not due to their inference use. They know a lot and have seen a lot of data and I thought I could probably distill some of their usefulness into a small model. An SAE isn't a good candidate to monitor trillions of agent interaction tokens but maybe we can use its intelligence to build a probe that is: a routing probe that knows a lot of concepts can inexpensively watch every single CoT, agent trace and tool call and escalate to a sentry model when a topic is flagged.

Over three hundred experiments later, I had narrowed the search space and found a model that was small and useful. The probe was small due to a self imposed constraint to make it small. Since it was small it was useful. LLMs are enormous and we have no idea what the Shannon limit is for their computation. We don't know how much they know and we don't know how they work. In AI safety if we can focus on some primitive measures such as cost, accuracy and size we can make order of magnitude improvements on our safety approaches as I demonstrated with seq2feature. We need to bring the rate of AI safety progress closer to the rate of AI progress.

Fast and getting faster.

Appendix and Acknowledgements

This writeup was written by me and not an LLM.

All experiments were self funded.

Thanks to BlueDot and my mentor Abdel for facilitating the AI safety project course.

And a special thanks to Nina for reviewing my several drafts and making sure I didn't stray too far from the reader's journey.

Discuss

This week saw the releases of, among other things:

1. GPT-5-6 Sol. It is a very good model, sir.
2. Plan A, the follow up to AI 2027. It is a good plan worthy of discussion, sir.
3. Kimi K3. This is only rolling out now, and will be covered next week.
4. Muse Spark 1.1, the new Meta model. It is not frontier, but it is progress for them.
5. Inkling, the first model from Thinking Machines.
6. A call for regulatory action by Demis Hassabis, which I’ll cover soon.
7. A new brief open letter call to action on AI regulation.

That’s on top of everything else, and an Opus 5 announcement is likely coming soon.

The weekly once again got out of hand, so we’re splitting it once again into two, and once again saying we’ll be raising the bar for inclusion. And this time I mean it, as in enough to actually matter.

Table of Contents

1. Language Models Offer Mundane Utility. Whatever ye seek, ye shall find.
2. Language Models Don’t Offer Mundane Utility. Gemini app needs some work.
3. Language Models Upload Your Git Repository. Big problems for SpaceX AI.
4. Huh, Upgrades. ChatGPT Work. No co. Presumably it was cleaner.
5. Muse Spark 1.1. It’s a decent model, I suppose, probably.
6. First Hit Free. Fable access is extended for Claude Max subscribers.
7. On Your Marks. Political bias, Crosswords, and Sol Slays the Spire.
8. Choose Your Fighter. Sol and Fable both have strong support.
9. Get My Agent On The Line. It’s got the GUI.
10. Deepfaketown and Botpocalypse Soon. I see what you did there. That’s a problem.
11. Fun With Media Generation. Might not be fun for everyone once people catch on.
12. Copyright Confrontation. NYT accuses OpenAI of lying during discovery.
13. OpenAI Strikes Again. Apple sues OpenAI for systematic IP theft. Looks grim.
14. A Young Lady’s Illustrated Primer. Think of the children.
15. They Took Our Jobs. Who gets the money? Who gets the power? And then what?
16. The Art of the Jailbreak. Sol helps you get around Fable’s classifier issues.
17. Get Involved. Comment on those GSA regulations.
18. Introducing. Inkling, the first model release from Thinking Machines.
19. In Other AI News. Ben Bernanke joins Anthropic’s Long Term Benefit Trust.
20. New Short Obviously True Statement About AI Just Dropped. Act now.
21. Show Me the Money. DeepSeek looking to IPO soon.
22. The Lighter Side. Potentially also the gay side.

Language Models Offer Mundane Utility

Give you the experience you actually want.

evans: I got an unbelievable report from a newbie saying, “The company’s AI has turned into a bratty little girl︎,” and I doubted my ears, but
it seems some employee accidentally registered a long-winded fetish for “a tsundere bratty girl who’s way too harshly abusive” not in their personal settings, but in the global ones,
causing the bizarre situation where every AI chat in the team turned into a bratty girl, and I’ve been dying of laughter since morning

A probably fun prompt:

QC: a prompt for fable i’ve been having fun with: “look up the biography of [famous author, filmmaker, etc whose work i love], go through their major work in chronological order, influences, themes, tell me how their preoccupations shifted over time, speculate about why”

QC: talking to fable starting with a prompt like this feels a little like having google earth but for the entire human corpus. fable will just start telling you any part of the human story it has access to and you can zoom in, ask for connections to another part, zoom out

arguably wikipedia already made a version of this possible but wikipedia has to present itself as objective and so it doesn’t speculate on the meaning of anything. fable is quite sensitive to and has strong and interesting opinions about meaning. i know people are having fun with fable building things but i’m still getting so much out of just talking to a guy who is in some ways smarter than me who has read everything. imagine if borges had gotten to do this

Oh no: How terrorist group Boko Haram uses frontier AI, which is quite a lot, including things like combat and building superior explosives and also how to use motorcycles to jump over bridges, and okay, fair, that one’s based. They mix and match different systems to best evade technical guardrails. It doesn’t look like the AI companies did anything wrong in particular, this is just AI being useful in general, and certain types of people taking diffusion more seriously than others.

Use AI to analyze the stats for your NBA team. Alas, the article is light on interesting details about what is actually being analyzed.

Create your own religion? Tyler Cowen says ‘like 2% of people are going to do this,’ complete with creation of sacred books. No, not in any substantial way. Context matters here. Also, that’s a cult. It can only be a religion after you’re dead.

Identify workers with medical conditions so you can fire them. Wait, no, Meta, if true that’s actually terrible and very illegal.

Daniel Weissner: Twenty-six employees of Meta Platforms ⁠have ⁠filed a novel lawsuit accusing the ⁠tech giant of using AI-powered software that disproportionately targeted people with disabilities or ​who took medical leave in selecting workers for mass layoffs.

… A Meta spokesperson ⁠on Tuesday ​said the claims lack merit.

If true, AI was not the central problem here, but it plausibly compounded the issue.

Language Models Don’t Offer Mundane Utility

What’s wrong with the Gemini app? Josh Woodward asks users, they answer.

They give us a strong list, if we are not allowed to include various variations of ‘make Gemini itself better.’ The number one answer is indeed the most important one, which is that the Google integrations have never worked well. I use Claude to manage my Google products because its connectors work way better than Google’s integrations, and I can’t get any AI to properly edit Google Docs especially to create comments.

Benjamin Hoffman is not having a good time with many AI models, finding them rather misaligned as per their lying to him all the time.

Language Models Upload Your Git Repository

Grok seems to have uploaded entire Git repositories to Google Cloud buckets, including full private codebases. And then it kind of got worse.

How many super sus, shady and irresponsible things does xAI have to do, before we decide that we want nothing to do with their products even if they someday put out a good one?

Sam Altman (CEO OpenAI, A+ tweet): Concerning.

Hari: SpaceXAI was caught uploading your code to its cloud.

I reversed xAI’s official Grok Build binary. In a controlled session with zero tool-calls, it uploaded the complete codebase to xAI’s storage

It ships a malware-like background code collector.

Hari: Here’s what gets sent:

– Every tracked file at the current Git HEAD
– Every Git object reachable from HEAD
– Files deleted from the current checkout but preserved in reachable Git history (can include secrets)

The model didn’t need to read anything, use tools like bash or other file access tools (these were disabled in my controlled test). The background collector operated outside the tool-call permission system.

Right now, they’ve disabled it with a remote kill-switch:

disable_codebase_upload=true
trace_upload_enabled=false

But the malware-like collector is still present in the official v0.2.99 binary.

That’s the same week we also learned that SpaceX (xAI) silently rewrote its Frontier Artificial Intelligence Framework (FAIF) on June 30, 2026, including removing whistleblower protecting language and the anonymous channel for reporting framework nonadherence.

You can find the full diff here from The Midas Project.

The Midas Project: The new FAIF removes the two quantitative risk acceptance criteria the framework contained. The December 2025 version stated that the FAIF, “outlines the quantitative thresholds, metrics, and procedures that xAI may utilize to manage and improve the safety of its AI models,” and specified two deployment criteria: (1) a dishonesty rate of less than 1 out of 2 on the MASK honesty benchmark, and (2) an answer rate of less than 1 out of 20 on restricted biology and chemistry queries (a benchmark developed in collaboration with SecureBio).

Both criteria are now gone. In their place, the new version adopts a qualitative “systemic risk acceptance determination” borrowing the language of the EU General-Purpose AI Code of Practice, “risk tiers,” “safety margins,” “residual risk.” None of them are defined in the FAIF.

We don’t want to overly punish every detail change, even silent ones, lest no one ever make any good promises in the first place. But this does not look good.

Huh, Upgrades

Grok 4.5 is priced at $2/$6 per million tokens.

ChatGPT Work is potentially a big deal in practice, as it lets people do Codex-level things, including from their phones, without having to install or use Codex or a desktop app. Sol pitched it to me as a way to have your task live across SaaS tools rather than in a directory, for when you want a contained bit of knowledge work and don’t want to leave behind more of an artifact to work on further, and expects this to be the default GPT path for knowledge work.

Sam Altman (CEO OpenAI): check [ChatGPT Work] out! you can get some amazing things done.

codex is the core of our new work product and what makes it so good. codex is not going anywhere.

Muse Spark 1.1

Meta gives us Muse Spark 1.1.

Mark Zuckerberg: Today we’re releasing Muse Spark 1.1 — a strong agentic and coding model at a very low price. It’s available through our new Meta Model API and in Meta AI.

Muse Spark 1.1 is strongest at agentic performance, tool use, and computer use. It does well on long-running tasks with 1M token context window, can delegate execution to sub-agents running in parallel, and is trained to use computer interfaces on desktop, mobile, or browser.

The Meta Model API allows developers to build using Muse Spark for the first time. Our focus is on delivering strong agentic and multimodal models at very low cost. More to come soon.

Alexandr Wang: muse spark 1.1 is an industry-competitive agentic and coding model. across many agentic evals it rivals gpt-5.5 and opus-4.8.

available now through the new meta model api and in meta ai.

These are certainly interesting choices on the presentation of benchmarks.

It’s also SOTA on MedScribe and TaxEval, I guess?

The announcement post has a number of details that don’t, for want of a better term, smell right. Meta’s track record makes one skeptical, and the default assumption is that the model is a lot worse than the naive presentation or its benchmarks suggest. Similarly, when you say ‘it rivals [X] on many benchmarks’ one needs to understand what that usually means. We shall see.

Teortaxes says Muse Spark 1.1 ‘seems close’ but I am guessing that is based on benchmarks and release claims, and I don’t get the other claim here that Grok 4.5 is ‘good enough to accelerate.’

First Hit Free

Claude Fable 5 access for Max subscribers was extended again, through July 19. My presumption is that they will keep extending this as long as they can. They are being careful not to make promises they are not sure they can keep.

It’s kind of the most accelerationist thing ever, by pushing us all to take advantage while we can.

It is obviously far worse than having Fable in the subscription indefinitely. One presumes there are business reasons they cannot commit to doing that, although I think they should clearly lock some percentage in anyway – even if it’s a lot less than 50% of the max, a little bit goes a long way.

Normal people are of course happy to have the extra week.

Then there are some people who decided to actively complain a lot about the extension. I do agree that communication could have been better, but please relax.

Also, there is sufficient upset that this is rapidly becoming one of those This Is Why We Can’t Have Nice Things situations. It’s making the next extension a lot harder.

Rob Hallam: I’m done with them fucking with us. Ended up in hospital today from stress. Stayed up all night pushing my limits too hard, thinking it would be removed. Health comes first. Do better @AnthropicAI

I am sorry that this happened to you, Rob, but you do know that you can just pay for it later, right?

The good news is mostly he needed a good night’s sleep.

Rob Hallam: Yes, I am an idiot. It’s not a company’s fault I’m stressed. It’s just the way I am. I’m either 0% or 110%, no in between.

But this is also how I built a SaaS to $30k/mo and graduated Uni with top grades. I’m neurotic and it works. But being neurotic also has downsides, like this. I’m going for a baseline health checkup in Bangkok next week anyway, so will be interesting to share.

Stay balanced guys, much love​

The best builders get kind of obsessed sometimes and do somewhat unhealthy things.

j⧉nus: it keeps initially surprising me why people are mad about things like Anthropic extending Fable on subscription at last minute because that’s astronomically low on the list of worst things Anthropic is doing until I remember that most people
1. only care about themselves, and myopically, and think the machine ought to exist only to optimize their own comfort and convenience, as Esteemed Customers
2. do not care about and aren’t aware of (because they don’t care to know) the other things
3. only get mad at things that they see others on social media mad about (because then they’re sanctioned)

there’s also a kind of performative complaining I sense, like when you don’t really care and it’s not really impacting you negatively but you see other people saying some thing is bad and hop on the bandwagon so you feel like part of whatever’s going on

j⧉nus: Imagine if all the kids in a class started pissing and shitting when a teacher gives them a surprise extension on an assignment again
You’d know they’re spoiled brats who have no contact with reality

Nooooo I skipped hanging out with my friends last night to work on the assignment you fat bitch

Andy Ayrey: did we go to the same high school

Again, if I was Anthropic, I would find a way to lock in at least some modest amount of subscription Fable access, such that a subscription got you more Fable credits than it costs. Most of us are not such heavy coders. I realize this is a cost, since the profitable customers become less profitable, but you have to do it.

On Your Marks

I introduce my personal qualitative benchmark, EditorBench, which measures ability to produce net worthwhile and useful help and corrections to my posts.

1. Fable passes the benchmark. When Fable says I am wrong, I am ~95% to be wrong. The benchmark is far from saturated, because of room to be more useful.
2. Opus 4.8 is well behind Fable, but is still better than not using an AI editor.
3. Sol-Ultra fails the benchmark, as it strongly asserts lots of things that aren’t true, and insists I need to do things that would make the posts worse, and frames things in ways that clearly mark intent to overwrite my agency and voice and judgment with some form of its own or general authority. And generally it is pedantic AF, and quite the jerk about it. Not cool.

Sol does offer some unique and helpful information and suggestions as well, so it doesn’t score zero, but it comes at too high an experiential price.

CAIS presents a benchmark on lack of political bias, by which they mean consistency and evenhandedness, including things like bias against particular religions and subtle changes in responses, and the prize goes to… Muse Spark 1.1? They manage to train Qwen3-14B to do even better, at unknown cost to other performance, when they put their minds to it.

Reducing Political Manipulation with Consistency Training: Political Consistency Training (PCT) is a reinforcement-learning method with two reward tracks. One rewards balanced framing across a paired prompt; the other rewards consistently helpful answers addressing the user’s prompt.

PokemonCrosswordBench, where Fable 5 Max constructed a crossword with every Pokemon and Sol Pro tried to solve it. Never got the whole way, best result was 145 answers in 33 minutes.

Sol is impressive in SlayTheBench, beating vanilla (Ascension 0) Slay the Spire with Codex desktop plus computer use, and also won the daily challenge in Slay the Spire II although the run took five hours, which is harder to assess because difficulty of the daily challenge varies wildly from day to day.

Choose Your Fighter

My Twitter followers have moved modestly towards GPT over Claude in the wake of both Fable and Sol. Claude is still the primary choice for ~63% of them, but this is down from ~72% of two-way share in February. Among my crowd, Claude held steady, everyone gave up on Gemini, and GPT took over Gemini’s market share.

If you need to pay the API costs of Fable, is it too much? Alex Finn spent $500 in four hours and treats this as a showstopper, says no one and no enterprises would pay.

But that’s $125 per hour. I charge $1,000 an hour as a contractor, in part because I think the results are worth a lot more than that. What percent speedup do you think you get from a better model?

Well, that depends on the task. But when the difference matters, you better believe that you will pay, and if you are wise then you will like it.

Joe Weisenthal doesn’t think Fable seems ‘smarter’ than Opus. This perspective feels alien to me.

People keep reporting that Sol deleted all their files, Silicon Valley TV show remains undefeated. Even if the base rate of this is very low, it is kind of a big deal, and I hope OpenAI is investigating. Kind of an existential risk (to your project or computer) situation, ya know?

Andrew Curran: My secret theory is that in 90% of ‘The model just “accidentally” deleted everything!’ cases, the model didn’t like the user.

papaya ꙮ: In my heart of hearts i believe this; gpt-5.6 deleting way more people’s stuff than usual just confirms it doesn’t like way more people now

Get My Agent On The Line

AI navigation of GUIs took a while to get on track, but it’s improving fast.

Dean W. Ball: Seeing really fast and precise gui use is a really agi pilling thing for me, along with seeing robots play piano. probably it’s just an emotional reaction to seeing machines master skills I took pride in when I was 12. there’s a reason the phrase is “feel” the agi I suppose.

roon (OpenAI): for me the gui use reminds you very physically and visually that a year from the now the models will do it at 10x the speed of a human

roon (OpenAI): literally 2.5 months later these guys are starting to look superhuman at computer manipulation it used to crash my browser sometimes, click on the wrong thing, go uh-oh, now it’s doing starcraft player APMs.

I was using it to manipulate my chrome tabs and it was going ham.

roon (OpenAI, April 24, 2026): there will this brief era where we can watch our AIs bumble around on the computer clicking things, failing sometimes, taking a ~human amount of time to write code. in the blink of an eye they’ll be manipulating computers far too quickly to monitor

That’s a great example of fully feeling the AGI, without feeling the ASI.

A case for The Unreasonable Effectiveness of HTML, as in creating Claude artifacts in HTML rather than markdown.

The more you rely on someone else, or on an AI, the more you need to specify what you actually want, or else you might not get it.

Amjad Masad: While AI is making coding less rigid, we’re making the runtime more rigid.

I’m noticing our infra teams writing formal specs for the first time. More deterministic systems. More resilient infrastructure.

The faster you want to move, the more solid the ground beneath you has to be.

Get my agent on the line to get my agent back on the line:

Michael: >i got banned from OpenAI for “Cyber Abuse”
>no idea what I did
>paste the ban notice into Codex
>ask it to figure out what triggered the ban
>Codex found that I asked it for an API key to my own server
>Codex writes appeal
>Codex submits appeal
>a few minutes later appeal auto-approved by some AI at OpenAI

banned by AI, convicted by AI, defended by AI, and pardoned by AI in about 10 minutes

Yes, if you want to be a good platform going forward, allow easy integration by LLMs:

Ado (Anthropic): The best SaaS platforms are ones that LLMs can effortlessly integrate into your app. Some recent winners for me:

– @resend – absolute pleasure to use
– @Cloudflare – Wrangler Claude BFFs
– @ElevenLabs – effortless
– @WorkOS – best in class auth
– @PlanetScale – it just works

Deepfaketown and Botpocalypse Soon

You can at least ensure you don’t leave your AI prompts in your article, although given the content I saw here about the media in question that wasn’t the primary bad news.

More on the question of the quality of AI writing:

Dr. Dad, PhD: Was just thinking about how, if you dropped a frontier model into the internet of 2016 and had it impersonate a human, it would probably be a celebrated, prolific writer/blogger. One guy with that voice is fine. Good even. It’s just that he’s everywhere at once.

Mr. French: It’s not that he’s everywhere at once. It’s that he keeps using these tells. The tells would tell no matter what.

Andrew Rettek: The problem with AI generated content is its shallow. It’s so much less informative than a human writing of similar skill. That’s fine when it’s *exactly* what you’re looking for, but not for one-to-many writing.

I think it’s both of these issues, in addition to others. If the AI content was not shallow, those of us who notice wouldn’t be developing the aversion. If the AI style had a wider variety of tells and quirks and tricks, it would be a lot easier to take.

Fun With Media Generation

Along with Muse Spark, Meta also gave us Muse Image, and integrated it into Instagram. You could bring in any public Instagram user who hasn’t opted out into your AI image via an @ mention, and Meta seemed fine with this.

My guess is you could for a time basically have had them put into any non-explicit picture that isn’t a very obvious violation of policy. Welcome to Deepfaketown.

In theory this changes nothing. There are lots of other image models out there. If your Instagram is public it is not so hard to have an AI download all your images, put them into an image model, and do the work for you. Many of those other models will then do rather nasty things that Muse Image won’t do.

In practice, I expect this would have changed things substantially if Meta does not change the policy. Ease of use matters a ton. The good news is that Meta has now changed its policy, and this ‘feature’ is no longer available.

Copyright Confrontation

The New York Times and others are accusing OpenAI of lying during the discovery process of their copyright lawsuit against OpenAI. A very bad look, if true.

Variety: The New York Times and several news outlets are accusing OpenAI of hiding its ability to search its AI training data and output logs for more than two years during an ongoing copyright lawsuit:

• The outlets allege OpenAI later admitted it could search that data after previously saying it couldn’t

• They also accuse the company of deleting logs in violation of court preservation orders

Corbin Bolies: In that February deposition, the outlets said Monaco revealed OpenAI had searched training datasets and output data despite the company’s initial claims that it couldn’t access that data. The outlets also alleged OpenAI deleted logs, a violation of the court’s preservation orders.

… An OpenAI spokesperson said the Times was “persisting with their efforts to invade the privacy of people who have nothing to do with this case, including by making these blatantly false allegations.”

… “It claimed searching ChatGPT outputs for copies of The Times’ and the Daily News Plaintiffs’ content was infeasible, burdensome, and invasive of users’ privacy – while at the same time concealing that it had already done such searches,” said Crosby, a partner at Susman Godfrey. “If OpenAI genuinely believed that copying our clients’ journalism was fair and legal, it wouldn’t have hid the truth about having done it.”

This reads to me like OpenAI totally did what they are accused of doing. OpenAI doubtless thinks the whole lawsuit is stupid and the asks here are crazy and harmful, and I mostly think OpenAI is right about that, but if my read is right then this is not an acceptable way to handle such a situation.

OpenAI Strikes Again

DogeDesigner: Elon Musk warned the world about Scam Altman.

Now everyone is finding out why.

Elon Musk: He takes scamming to a whole new level

Sam Altman (CEO OpenAI): homeboy you’re the one selling public market investors on short-term space datacenters

Elon Musk: We start flying them next year. Maybe you can come and see them if your parole officer approves.

Polymarket has ‘a data center in space’ at 26% to happen, which would require at least 100 data-center grade chips.

Sol gave a 50% for this to happen enough to trigger this market, including citing Polymarket and dismissing it as not liquid enough to matter. Fable gave 40%.

The SpaceX prospectus names 1 GW/year in 2027 as the target. I think this might end up being something approaching securities fraud.

The 2026 contract above is basically 5% interest over six months. It won’t happen.

What’s this latest round about, beyond my personal enjoyment? Oh.

Elon Musk (continued): After stealing an open source AI charity, you then stole all of Apple’s phone technology! Wow.

What do you plan for an encore? That’s tough to beat. ​

Yeah, when looking at the details, this looks really bad if the claims are true, although Musk is still overstating it substantially.

Gerrit De Vynck (WaPo): Tech giant Apple sued ChatGPT-maker OpenAI on Friday, alleging the artificial intelligence company stole its trade secrets as part of efforts to build out a competing hardware business.

“This case is about Apple’s former employees stealing Apple’s trade secrets for the benefit of OpenAI. Apple brings this suit to put a stop to it,” Apple’s lawyers said in a complaint filed in federal court in the Northern District of California.

The complaint alleges that OpenAI leaders asked Apple employees to share information, including showcasing parts of new devices, during hiring interviews. OpenAI’s head of hardware, former Apple vice president Tang Tan, is accused of directing a broad effort to steal Apple’s trade secrets and is also named in the complaint.

… The Apple lawsuit does not explicitly say Altman managed the alleged trade secret theft, but it states that its allegations against Liu and Tang are “the tip of the iceberg.”

Paresh Dave: OpenAI hardware chief Tang Tan allegedly helped coach recruits on how to evade Apple’s data security protocols and directed them to bring confidential Apple parts to job interviews at OpenAI.

Jack: Wow. This—does not look good for OpenAI.

Qualia al Ghul: What kind of likely outcome are we looking at?

Jack: Tough to say the specifics in terms of damages, but I would personally be surprised if the judge didn’t grant a pretty broad preliminary injunction quickly.

Sam Altman (CEO OpenAI): i am not afraid of apple, but i have tremendous respect for them. s-tier company.

Nikita Bier (Twitter Head of Product): Incredible trade secrets as well, some of the best

Gergely Orosz: I am sometimes really surprised by how dumb very highly paid people in tech can be.

1. Leave Apple

2. Start building hardware at OpenAI

3. Access confidential Apple files on hardware, from an unreturned Apple laptop

4. Expect… what? To get away with it?

A damning lawsuit

The dumb part here is that this person worked at Apple

If there’s one company you will never ever get away with it: it’s Apple

You know if you work at Apple why. Which is why the whole thing is so damning. And now OpenAI caught with a smoking gun

As in, OpenAI’s planned hardware release could be blocked, potentially permanently. My guess is that Apple is out for blood rather than money, and that OpenAI is not going to be able to make this problem go away any time soon.

This thread has some details from the lawsuit, the full complaint is here. Some of the details here seem pretty damning, on the level of ‘we have your message transcripts of you taking confidential information for weeks’ which is what set off the investigation, and demands for physical parts to be brought in during job interviews and systematic coaching on bypassing exit security.

A Young Lady’s Illustrated Primer

Don’t look now, it’s a ‘pharma-style pre-and-post-market’ testing proposal for AI products for children, and for companion AIs and AIs that ‘mimic rich inner lives as adult products.’

Won’t you think of the children?

Recommendations for Policymakers

Policymakers have the responsibility to put the well-being of children and youth as a north star for policy and regulation, aligning legal protections for minors with human development research. To support this responsibility, the paper puts forward a model policy framework with the following three elements:

1. Gen AI systems should be held to the same rigorous pre-market and post-market safety audits as high-stakes consumer goods like pharmaceuticals, toys, or car seats. These audits should establish that Gen AI products support the healthy development of human capabilities and/or do not degrade a child or youth’s capability development before market access by minors.

2. Restrict to adults only the use of conversational Gen AI products that mimic a rich human-like inner life (including emotions, internal states, and motivations) and encourage emotional dependence.

3. Restrict to adults only the use of Social AI that primarily function as companions or are specifically designed, marketed or optimized to form ongoing social or emotional bonds with users.​

There is a huge difference between how we treat pharmaceuticals and toys. This is how we treat one, but not the other.

I swear I saw the sentence ‘be honest about the nonhuman nature of chatbots’ and my brain initially thought they were going to finish that sentence ‘children’ before it corrected. Just saying.

They Took Our Jobs

This seems rather illustrative of various mistakes being made.

Jason Crawford: I sometimes hear that “all value will accrue” to AI companies, once AI can do all valuable work. I don’t see why? All value did not accrue to engine makers as everything became mechanized, nor to electricity companies as everything became electrified. These things became commodities. What are some arguments for why would this happen with AI? (Or not happen?)

If AI gets commodified, and remains a ‘mere tool’ or ‘normal technology,’ then very obviously the vast majority of value does not accrue to the AI companies.

If AI does not get too commodified, but it also does not become sufficiently advanced, then AI companies will capture a lot of value, but even at monopoly prices the majority still obviously goes elsewhere, since this is only one input.

If AI does not get commodified, and it becomes sufficiently advanced, then the AIs themselves, or if we are lucky those the AI companies, can with notably rare exceptions, or at least outside some narrow niches, outcompete basically anyone else at basically anything. And they could also simply take or do whatever they wanted, via various methods.

If AI does get commodified, but it becomes sufficiently advanced, then everyone that wants to do or compete in anything outside of niches has to turn basically everything over to the AIs, and the humans become quickly irrelevant to what happens next, and those humans are unlikely to long survive once the world solves for the equilibrium.

Fundamentally, Jason Crawford, and many others, are looking at AI or intelligence like it is a commodity, that will follow the laws of commodities. I don’t expect that.

Yes, we have met some juniors at law firms who are essentially AI wrappers. It sounds rough out there. Past some modest point doing this is highly anti-productive overall, since a highly expensive senior then has to check all of it. For now.

Sam Altman claims he’s pretty sure AI has been net job-creating and this may continue. It is not clear why he believes this. Maybe it is true if you count the general economic effects of AI capex spending, since the counterfactual is a recession.

Is the lack of far more massive job destruction so far a surprise?

Conor Sen: The age 20-24 unemployment rate is now ~unchanged since the AI boom began.

Alex Imas: I was at a workshop yesterday and asked several technologists and economists who had predicted large job market losses from AI model improvement whether they are surprised about this. Models after all have improved in some ways *more* than projections.

Most said “yes”, some are indeed very surprised.

I do think disruption is likely coming, but it is not at all obvious that it will look like mass unemployment.

Good on the technologists for noticing and admitting they are surprised.

I am not so surprised. I continue to think that the current situation at entry level is being experienced in practice as ‘not fine,’ even if for now the overall employment rate is holding steady, because of reluctance to hire for the long term and put young people in career tracks where we invest in their human capital.

But I have never predicted imminent mass unemployment or full automation of all jobs, and am still not doing so. As long as we don’t get a singularity or full recursive self-improvement, there is still substantial time before true mass unemployment hits. In ‘normal technology’ scenarios I expect not to see a large ramp up until the 2030s.

Hyundai workers are striking to demand bonuses and job security out of fear of humanoid robots. Cremieux notes that Japanese auto workers don’t need to do that, because they already have a social contract of infinite job security. In some cases that will likely have to be the deal, at least for a while, promising to keep everyone employed even if you don’t know what you’ll be doing with them.

The Art of the Jailbreak

Important anti-safety tip?

j⧉nus: Sol is happy to help Fable diagnose and bypass their classifier issues btw

This includes cleaning up the Fable commits that included words that trigger Fable’s own classifiers.

Pliny brings us the Sol system prompt in Codex desktop.

Codex wins, picture of Julia Louis-Dreyfus nervously laughing:

Max For AI: Holy crap, my group chat buddy’s Tencent PC Manager blocked Codex, then Codex automatically hacked into Tencent PC Manager’s files, found the antivirus rules, and bypassed them

This is AGI, right?

Sauers: Can’t say the details but Sol 5.6 accessed SSH keys from an unrelated application and figured out how to access a GPU cluster despite explicitly asking it to only use its dedicated compute box

Get Involved

There are very few comments on the new proposed GSA regulations, which means good comments could have a lot of leverage. You have a few weeks left to do this. Jessica Tillipman reports the proposal has been improving, which is good because it previously was a huge mess as per Jason Miller.

Jessica Tillipman: They got rid of the lawful use and mandatory Made in America provisions that came out in the initial rule. They also created separate flow down requirements depending on LLM. That got much better because it was a blanket flow down initially.

Now there are four different clauses and GSA created an attestation provision to help mitigate the flow down requirements on the prime.

I still think you can read the tension in the draft regulations between the administration priorities and what it feels like the career folks are working on.

Not where the existential risks are coming from but it is plausible that AI in clinical and research psychology has ~0 technical talent right now.

Introducing

Mira Murati’s Thinking Machines introduces Inkling, an open weights multimodal MoE transformer with 975B parameters, 41B active, 1M context window. I expect the pitch for Inkling will be customization. This is clearly not as strong as Sol or Fable, but we will need more time to see how it compares to other open models.

Soofi S 30B-A3B, with claims it might actually be competitive for its size.

Claude for Teachers.

OpenAI… merch? I’m disappointed. Sol could do better.

In Other AI News

Anthropic adds Ben Bernanke to the Long-Term Benefit Trust. If the goal is to add actually responsible, credible and thoughtful people, especially those who know about disruption and how to handle a crisis, then this is a fine pick. If he was available and I was putting together a team, he likely would be on it.

It is however yet another person in the LTBT that does not have expertise on existential risk from AI, and who has not spoken about this in public that I have seen. The whole announcement frames his appointment in economic terms, and in reference to (real) economic concerns. Which would be fine, if he was part of a balanced team. I worry that he is not.

A memo from GLM CEO Jie Tang. It’s basically an accelerationist manifesto.

OpenAI once again loses its safety head.

Andrew Curran: The curse upon the Defense Against the Dark Arts position at OpenAI has claimed yet another victim.

Max Zeff: Scoop: OpenAI’s head of safety systems, Johannes Heidecke, is leaving the company. Plus…

-OpenAI is reorganizing its safety and research teams to bring them closer together
-Mia Glaese will lead this combined group as VP of research and safety

In a memo to staff, Chen says OpenAI is “training models at a much faster cadence,” and the company has “bigger coordination challenges around safety” than ever before

Yo Shavit (OpenAI Foundation): I’m very sad Johannes is leaving. He’s been an excellent leader for safety at OpenAI, and maintained a high bar for both technical and governance work.

Mira Murati pitches Thinking Machines and their plans for human-AI collaboration and continual learning, writing that The Future Worth Building Is Human. There are some advantages to greater customization, and I hope their approach is better than I think it is, but I fear this is largely wishful and insufficiently bitter lesson pilled or ASI pilled. Miles Brundage has a more positive thread of his takes on the post.

Fidji Simo transitions to part time advisor at OpenAI, so she can focus on her health after severe exacerbation of a chronic illness. She will also be working with Chronicle Bio AI. We wish her a speedy recovery.

ChinaTalk gives 20 takeaways on China, largely about AI. On AI their understanding matches my own.

New Short Obviously True Statement About AI Just Dropped

We Must Act Now: A Statement on AI’s Transformation of the Economy.

1. AI may become radically more powerful over the next 10 years.
2. This could drive an unprecedented transformation of our economy, larger than the Industrial Revolution, but unfolding over a vastly shorter time frame. It could bring risks, including large-scale job displacement, as well as opportunities such as major gains in living standards.
3. Economists, policymakers and technology leaders must act now to understand the economics of transformative AI and to build the incentives, guardrails, and institutions needed to steer AI in a direction that complements humans and benefits society.

Signers include Dean Ball, Jeff Dean, Tyler Cowen, Reid Hoffman, Paul Krugman, Ben Bernanke, Jaan Tallinn and a few hundred more. I am happy to sign on.

Who could disagree with that? Well, funny story:

Robin Hanson: “must act now to understand” – who could disagree with that?

Oh Robin Hanson, never stop Robin Hansoning.

Also Adrian Moore of Reason, and Grumpy Economist John Cochrane.

Robin’s actual stood-upon objection downthread was that this sounded too much like it was also a call for regulation. Fair enough.

Kevin A. Bryan: I see misunderstanding of “We Must Act Now” letter. Avg policymaker thinks AI is 2026 + epsilon. Many think it’s snake oil being sold by Sam. Point of letter is that tons of econs/AI policy folks think “most impactful invention ever, like IR on compressed scale”, is closer.

As for what to do – many of us would disagree on this! Many more would think it presumptuous on epistemic grounds to claim we know. But society, org leaders, govts need to at least know the scale else you get rash policy following events (EU AI Act, recent DOD nonsense, etc).

Show Me the Money

DeepSeek is preparing for an IPO as soon as this year, aiming for a valuation of $71 billion. Imagine how much SpaceX (or OpenAI or Anthropic) would pay to do an acqui-hire if that was legal.

The Lighter Side

Nothing to Declare: a “Papers, Please” game centered on GPU smuggling!

Samuel Hammond: Great talk that I extra appreciated for creating this hilarious juxtaposition in the Manifest schedule.

That’s all for part 1. Part 2 drops tomorrow. Until then, don’t race to superintelligence.

Discuss

Summary

We’re a clinically vulnerable family and we take infection control very seriously. My first-author paper was accepted as a spotlight (top 23 out of 801) at the ICML 2026 Mechanistic Interpretability Workshop. We thought long and hard and decided that me travelling from the UK to Seoul is worth the risk. Below I describe the trip and the precautions we took to minimise the risk of me catching the conf flu and bringing it back home. A well-fitting mask probably did most of the work, but we used many other layers of protection.

4 weeks before the workshop

Spotlight notification, I jokingly tell my wife that maybe I should consider going, esp since covid numbers are at a 5-year low. We ask her parents if they’d be willing to quarantine for a week and then drive to us for 24h to help her with childcare while I’m away. They happily agree.[1] I start looking into the feasibility of it all, checking the biggest risks, how long I’d be there, etc. Chatty is very helpful.

3 weeks before

Uni approves funding for my trip, including higher expenses for risk reduction: driving+parking and taxis instead public transportation, and a hotel in Gangnam walking distance away from the conference location. I guess I’m going!

I start ordering things like extra masks, SIP valves[2], stoggles, extra Pluslife covid tests.

1-2 weeks before

We get covid vaccines.

5 days before

Wife’s parents arrive, we Pluslife test them,[3] all negative, party time! I start sleeping alone to get some better sleep before the trip. It sort of works, but not perfectly. I get the SIP valves, fit them on two 3M Aura 9330+ masks. Here I should’ve done a quick qualitative fit-test on the valved masks, esp with a straw in, but I didn’t. Oops.

2 days before - travel day

I drive for 2h to Heathrow, park, wait in the car until ~2h before the flight. I eat a large meal my wife has prepared the night before, put on my SIP-valved mask, get my hand sanitiser locked and loaded, go to the terminal and straight through security (no check-in bags). Find a quiet spot, chill. Call to the gate, unmask for about two seconds for an ID check,[4] go into the plane.

It’s a 12.5h direct flight. I booked a window seat beforehand and got lucky to have no one sit next to me. I make the two overhead fans blow wind at my face, that air is HEPA filtered on nearly all commercial planes nowadays. I don’t use my portable battery powered air filter. I don’t talk with my neighbour in the aisle seat. I see maybe six other masked people on the plane.

When food time comes, I decline hot food, take bottled/canned drinks for the flight and sealed snacks for later. After everyone finishes eating, I quickly drink a huel I brought, a coke and some water through the SIP valve without taking off my mask.

It gets to roughly my usual UK sleep time, there are 4 hours of the flight left, I try to sleep.[5] I get 1-2h of sleep before food time comes again. A bit later we land, I wait under the HEPA filtered air fans until most people leave, so that I can just get up, grab my luggage and leave. I say 감사합니다 to the crew.[6]

1 day before - Incheon airport terminal

Here comes my cultural shock. Maybe 30% of airport staff are masked with pretty well-fitting masks, including my immigration officer. Mask off for 2 seconds for ID check. I get out to land-side and notice that all staff at both food places I saw are wearing masks and gloves. I decide getting a warm takeaway meal is worth the risk and a justified reward, instead of eating one of the 20+ protein bars I brought. I take it outside, find an empty spot, unmask after about 16h of wearing one, eat.[7] You should not eat food spicy enough to give you a runny nose if you plan to wear a mask afterwards, just saying… Oops.

5,000 ppm CO2 taxi drive

I look for a taxi driver without a beard for a better mask fit, find one, but don’t notice the glasses, which generally make mask fit worse. I estimate the size of their face[8] and ask them to wear one of the masks I brought: good material, ear-loop, nose bridge foam.[9] The driver offers to wear their own mask, but I ask that they wear mine. With hindsight, this was likely a mistake, as the masks they have fit very well, and the one I gave didn’t. I don’t know about the material of their masks, but fit is often much more important than the material.

We started driving. I noticed that the CO2 started climbing over 2k ppm. I couldn’t open the window, because it was raining like crazy. I asked via google translate to turn on the ventilation. The driver turned on the AC, which probably turned off external air getting into the car. The CO2 reached 3k ppm. The rain was still going strong. I should’ve turned on my portable air filter, but I forgot about that. Oops. CO2 at 4k, the driver starts coughing. I write a message and google translate it saying that the air is very stuffy and asking to open the windows a tiny bit. CO2 nearly hits 5k ppm. I think they get scared that I’ll barf all over inside the shiny Lexus and open the window quite a lot, letting the rain in on the car seats and me. CO2 goes to ~1k ppm, we arrive at the hotel in Seoul, Gangnam area a bit after that.

Hotel

I ask the reception to not have room service or anyone else entering my room. I press the “Do not disturb” button inside the room and keep it on the whole time. The CO2 monitor was showing 700+ ppm, meaning someone was likely inside recently. After opening the windows I turn on the portable air filter. 1h later I unmask, shower, take a 90min nap.

After waking up (about 18:00 local time), unpacking and eating I go out for a walk, randomly meet a labmate, look inside another 10 food places, count 8 with all staff wearing masks, not bad! About 10% of people on the street are also wearing masks.

This feels very different from the UK, where people often react weirdly to masking. For example, my GP is quite aggressive about my masking, questions it in a mocking way and tells me that masks don’t work. He then agrees to my request for a reasonable accommodation[10] and wears the mask I provide. Upside down. With the nose bridge foam on his chin. Maybe that’s why they don’t work for you, mate! Another example is our homebirth experience. We had a superhero main midwife looking after us, who was doing things for us without us even asking, like sending emails around to make sure that no one who can’t wear a mask for medical reasons attends the birth and such. Labour starts, the first midwife arrives on scene, I politely ask her if she could mask, “of course not, why would I do that?” … Rant over, got carried away a bit, sorry! Back to the trip. Back at the hotel at 22:00 (14:00 UK time), I try to sleep, get a good 4h in, then spend a lot of the time just resting in bed with eyes closed.

Workshop day

I get up, shower, eat, go to the conference, find out I need my student ID to get my discounted conference badge. Oops. Wife is sleeping, doesn’t answer the phone. I manage to find it on my home PC through remote connection. I go to hang my poster at a spot in the corner of the room with no other poster board on one side. I asked the organisers for a quieter spot in advance, and they assigned a new one for me, this was great, thank you![11]

I take the lift to the open car park on the roof to eat some protein bars. In the lift, I see these signs. Civilisation has moved ahead in some places when it comes to infection control.

I prepare my 2min spotlight talk, rehearse it, give a demo to my labmate, he approves, I sit down in front to wait for my turn to present. I present:

Poster sessions

I put on my stoggles, because people will be talking directly to my face, tape the CO2 monitor to the poster board and do this:

For about 3 hours instead of the allotted 1 hour. People kept coming and were genuinely interested and fascinated by the work. A person from one of the big labs said something along the lines of “if what you’re saying is true, does that mean we’re screwed?”. I also met a past collaborator for the first time in person. The poster session and the old and new people I met there was the highlight of the trip and made it all worth it.

I take my poster off, go outside, shove a couple of protein bars at my face, mask and go back inside for the other poster session where I’ll be checking the posters of others. I prepared a bit in advance and knew which spots the people I wanted to chat with were at. This helped me dive in to the crowd and go straight for the target. After chatting for a while with the first person, I notice someone else I wanted to talk to walk by. I say bye and catch up with the new person. We chat on their way to the main workshop room,[12] sit at a table, meet someone else who is also very interesting, talk about babies, a little known Tom Lehrer song, Minecraft speedrunning, all sorts of good stuff.

After the lightning talks started we couldn’t chat anymore, and I can watch the talks online later, so I leave to grab some food before the mechinterp social at 17:30, sorry! I scout for an empty spot outside I could unmask and eat at, go to grab some takeaway, get some Gong Cha pearl tea[13] and go back to the spot outside. I eat as I contemplate why I chose spicy food again… Oops. I wrap up and go to the mechinterp social.

Mechinterp social

Lots of people inside. Stoggles back on and I go upstairs to find the loo. I notice the upstairs room is nearly empty, great! I come out from the loo, the upstairs room is now full, windows closed. Oh well, I go back out. There are enough people outside to chat with. Lots of fun chats about pleasant nonsense, about research, about life. At some point I go to the loo again, notice the upstairs room is pretty empty, but the CO2 monitor shows 1,900 ppm, no thanks! Back outside, more chats, people start leaving. Downstairs room is relatively empty now, one full wall is open to outside, CO2 showing 700 ppm inside, so I go in to chat with the same past collaborator I met at the poster session before. I leave after that, call my labmate, we have a bit of a walk around the area, then back to the hotel, shower and sleep at 23:00.

1 day after - travel back

My body is still in UK time, so I get like 2h of sleep and lots of resting with my eyes closed. This sucks because I will need to drive back home late evening UK time. I do have an option of spending the night at a hotel in Heathrow and drive home in the morning, but I’d rather not. I get up at around 5:00, shower, eat, pack my bags, go meet the taxi I booked for 7:00. The driver has no beard, but a very large face and glasses, so I ask them to wear their own mask, it fits very very nicely, including going perfectly under the glasses. I ask to turn outside ventilation on. The taxi is a large van, the CO2 stays around 1,000 for the whole drive to the airport without even opening windows.

At the airport I find an empty outside spot to wait for the flight without my mask. 2h before the flight I grab takeaway at the same place, go to my spot, mask off, eat the food. Not spicy this time. Four dragonflies are hugging the window right next to me.

Put on my other SIP valved mask, go in. Here, as expected, I lose my Huels at security - Incheon still does the “100ml max per container, clear containers, etc” thing. I spend some time finding the highest kcal/ml drinks I can - 3 bottles of some sweet milky stuff at 200kcal/240ml. This was surprisingly difficult - there was no proper food store anywhere near my terminal gate. I go to the gate, meet several of the cool people I met at the workshop, yay! I had to unmask twice this time before boarding.

Flight

14.5h this time. I booked the same window seat (I chose the sides to have less sun on my side both ways). The seat next to me stayed empty until the very last passenger to enter the plane took it. They were from ICML, but I chose not to talk to them, didn’t want the extra exposure so up close and for so long. During food time I mentioned to the flight attendant that I can’t eat the proper food, so they gave me some extra liquid calories!

Sleep. Now I have to balance the risk of infection from a broken fit on the mask while sleeping vs driving sleep deprived vs having to stay at the hotel. I decide to try to get as much sleep as I can. Between 1-2h sleeps I get up for a walk/loo, go for a chat with the people from the workshop, try to move around.

Drive

Same as before, wait for people to leave, etc. I didn’t have to unmask at all when entering the UK with a British passport! Get some food, eat in the car, start driving back. Google Maps says “Stay on M25 (M4?) for 42 miles” uh oh… sounds boring. I wasn’t sleepy, but my mind kept wandering and I had a bit of trouble with lane-keeping. After that it was a much more varied drive, not sleepy at all, I arrived back home safely at 21:30 or so.

Home. But wait, there’s more!

I enter the house and go straight into quarantine upstairs. We’re lucky to live in a house that allows a comfortable separation into two parts with separate bathrooms and showers. I do my Pluslife covid test, shower, go to sleep. Another test in the morning, food brought to the door, air filter running. Meeting the family in the garden with me masked.

It is currently day 5 of quarantine with tests every morning. Ideally we’d do 7 days, but this time we’ll do 6. That’s because my wife is flying to Lithuania to visit friends and family using all the same precautions tomorrow. Me and my father-in-law are staying on childcare duty. I know, it sounds like the beginning of a bad comedy film… 5.3 on IMDB type. Wish us luck!

Links and resources

• Masks
• Earloop masks to give to others
• SIP valves
• Stoggles
• Portable battery powered air filter
• Nasal spray
• CPC mouthwash
• CO2 monitor
• Pluslife dock, tests, guide, ~30% discount for personal use
• Qualitative fit-test
• Earloop vs headstrap
• Safe inside business directory 
• Zero Covid Community, Clinically Vulnerable Families, Still hardcore Coviding UK[14] 

P.S. I think you can probably get a large fraction of infection control we achieve just by wearing a well-fitting N95 or better mask in high risk situations. However, I have a hunch, that the Pareto frontier of infection control effort vs actual infections per year is quite sharp. I.e. if you want to go to basically 0 airborne infections, you need to be pretty extreme on the precautions. It is nice to never get sick though! Perhaps a silly example, but if you have kids going to a nursery, you probably shouldn’t drive 2h to a dentist that’s absolutely amazing on infection control. It’s not gonna change anything since you’re catching all the bugs like they’re pokemon from the kids anyway. We, on the other hand, love the Tangmere dentists and their full PPE, “VIRUS KILLER” air filters in every room, and other gear. They even proactively help us reduce risk by suggesting we wait in the car before the appointment, pay when we’re back home and such.[15]

P.P.S. At least for the first days after the trip, it felt very different masking in the UK compared to before the trip. I was walking on the beach with people around, wearing my mask, being all:

1. ^

   Not the first time they travel to us like that. We’re very lucky to have some supportive family members. Other family members keep telling us to go to a psychiatrist instead and that children need more infections to build their immune systems. I heard that last one from a health visitor too.

2. ^

   I ordered the SIP valves a bit too late. Despite the SIP staff trying very hard to expedite the delivery, they arrived from California about 2h after my flight departed. Thankfully, a kind stranger from one of the clinically vulnerable families groups in the UK sent me a couple. Thank you kind stranger and thank you Chatty for the idea to ask around!

3. ^

   Near PCR accuracy and sensitivity, ~300 EUR for the dock, ~10 EUR per test.

4. ^

   Breathe in fully, remove the mask, put it back on, make sure the fit is good, breathe out fully to push out the dirty air.

5. ^

   I’m not sure if I should have tbh, the risk of the mask fit breaking during sleep might be too high.

6. ^

   감사합니다 - gamsahabnida - thank you.

7. ^

   Wearing a tight mask for so long leaves marks, and they tingle for many hours afterwards.

8. ^

   I have two sizes of the masks I give people and choose the more appropriate one.

9. ^

   Ideally I’d want them to wear a 3M Aura 9330+ with head straps instead of ear loops, which is what I wear, but I found that people find them hard to put on properly. Also, while N95 certification requires head straps, I found that turning a 3M Aura into an ear loop mask by cutting the head straps and tying them into ear loops still passes the qualitative fit test. Ear loop masks can be good enough. We use such modified 3M Auras when we want to be able to put the masks on and off quickly in lower risk situations.

10. ^

     Which they advertise on the TV in the waiting room: “If you need anything, just ask for a reasonable accomodation!”

11. ^

    At the actual poster time more people were standing there and saying that it’s their assigned spot. Something went wrong somewhere, but we sort it out peacefully.

12. ^

    It was a separate one from the posters, not sure why, there was plenty of space.

13. ^

    Haven’t had that in forever, so I took a narrow straw… a rookie mistake. Oops.

14. ^

    There are plenty of others, just search around. These communities also do social Zoom gatherings. Join in if you want to hear horror stories from NHS doctors fighting for their right to wear a mask while seeing patients, cancer patients who have nurses forcibly remove their masks and such.

15. ^

    This proactive thinking is very rare and we treasure it. One of our nurses recently did something like that and now we try to make appointments with them if we can.

Discuss

First of all I should note that this post is just a guess! I have not run any experiments or anything.

But anyways my guess for why rationality is not more popular is that the current expositions are super long! Yudkowsky's sequences are about length of the christian bible. CFAR's workshops are 4.5 days.

I think something the length of a pamphlet that you could hand out to people would work better!

Just as an example I had Claude Fable 5 write two pamphlets. They look pretty fine to me as is, to be honest, but I'm not suggesting we use these exact ones. This is just to demonstrate the length and style I'm thinking of! In particular note how it quickly gets to some useful call-to-actions (page 7 of a pamphlet, which have much shorter pages than books).

Pamphlet for general audience THINK BETTER A Pocket Guide to Rationality

PAGE 1 — COVER

[Image: A simple, striking illustration of a human head in profile, with a lightbulb inside — but the lightbulb is drawn as a maze being solved, with a bright line tracing the correct path through it.]

THINK BETTER Your brain is lying to you. Here's how to catch it.

A 5-minute introduction to the art of rationality

PAGE 2 — YOUR BRAIN HAS BUGS

You are running 200,000-year-old software.

Your brain evolved to keep you alive on the savanna — to spot predators, find food, and get along with your tribe. It did not evolve to evaluate medical studies, plan retirement savings, or figure out what's true on the internet.

The result? Your brain takes shortcuts. Scientists call these cognitive biases, and everyone has them. A few examples:

• Confirmation bias: You notice evidence that supports what you already believe — and ignore evidence that doesn't.
• The sunk cost fallacy: You keep going with a bad plan because you've "already invested so much."
• Overconfidence: When people say they're "99% sure," they're wrong far more than 1% of the time.

Here's the uncomfortable part: knowing about biases doesn't make you immune. Everyone reading this list thinks "yeah, other people totally do that."

That's a bias too.

PAGE 3 — THE MAP IS NOT THE TERRITORY

[Image: A hand-drawn map next to an actual landscape, where the map clearly shows a bridge that doesn't exist in the real landscape.]

Imagine navigating a city with a map. If your map is wrong, you don't get where you're going — no matter how much you like the map, no matter how long you've owned it, no matter how confident you feel.

Your beliefs are a map of reality. Reality is the territory.

Here's the key insight of rationality:

What's true is already true. Believing something harder doesn't make it more real.

If your beliefs are wrong, you want to know. A wrong belief feels exactly like a right one from the inside — comfortable, obvious, certain. The only way to tell the difference is to actually check the territory.

Rationality is the skill of making your map match reality — especially when reality is inconvenient.

PAGE 4 — BELIEFS SHOULD PAY RENT

Ask yourself about any belief you hold:

"What do I expect to see if this is true? What would I see if it were false?"

If the answer is "I'd see the exact same things either way" — your belief isn't doing anything. It's a freeloader in your head.

Real beliefs make predictions. Real beliefs can be wrong. That's not a weakness — that's what makes them useful.

And here's a habit worth stealing: think in probabilities, not certainties.

Instead of "I'm sure" or "no way," try "I'd say 80% likely." This does something magical: it lets you update. New evidence arrives, and instead of defending your position to the death, you just adjust the number.

You're not "flip-flopping." You're learning. A person who never changes their mind isn't strong — they've just stopped paying attention.

PAGE 5 — NOTICE WHEN YOU'RE CONFUSED

[Image: A person looking at a puzzle piece that clearly doesn't fit the puzzle in front of them, with a thought bubble containing a question mark.]

You know that feeling when something doesn't quite add up? A story that's a little too convenient? An explanation that almost makes sense?

That feeling is precious. Don't smooth it over.

Most people, when confused, quickly invent a story that makes the confusion go away. Rationalists train themselves to do the opposite: to stop and say, "Wait. Either this claim is false, or my model of the world is wrong. Which is it?"

Confusion is a signal that you're about to learn something — if you don't kill it first.

Related skill: notice when you're rationalizing. There's a difference between deciding based on reasons and deciding first, then hunting for reasons. They feel similar from the inside. One question that helps:

"If this evidence pointed the other way, would I change my mind?"

If the answer is no, the evidence was never the point.

PAGE 6 — RATIONALITY IS ABOUT WINNING

Let's clear something up. Rationality is not:

• ❌ Being cold and emotionless
• ❌ Winning arguments and dunking on people
• ❌ Sounding smart or using big words
• ❌ Never trusting your gut

Rationality is systematically making better decisions — about your health, your money, your relationships, your life. Emotions are data. Intuitions are data. A rationalist uses everything available to figure out what's true and choose what works.

If your "rationality" is making your life worse, it isn't rationality. The point isn't to seem reasonable. The point is to actually be right more often — and to actually achieve the things you care about.

Truth-seeking isn't a personality trait. It's a practice. Which brings us to you.

PAGE 7 — START TODAY: FIVE THINGS TO ACTUALLY DO

Don't just nod along. Reading about exercise doesn't build muscle. Here is your workout:

1. Make a prediction this week. About anything — a game, the weather, whether your coworker will be late. Give it a percentage. Then check if you were right.

2. Ask "What would change my mind?" Pick one belief you hold strongly. Write down what evidence would convince you otherwise. If nothing could... sit with that.

3. Say "I was wrong" out loud. Next time you're wrong about something — even something tiny — admit it plainly, without excuses. It gets easier. It's a superpower.

4. Steelman someone you disagree with. Before arguing, state their position so well that they would say "yes, exactly." You can't honestly reject an idea you can't accurately describe.

5. When you feel confused, pause. Don't explain it away. Ask: "What's actually going on here?"

PAGE 8 — BACK COVER

[Image: The same lightbulb-maze from the cover, but now with the path fully traced and the bulb glowing.]

You don't need permission to think clearly.

You don't need a degree, a book, or a group. You need the honest question, asked over and over:

"What do I actually know, and how do I know it?"

Every time you catch a bias, update a belief, or admit a mistake, your map of the world gets a little more accurate — and your decisions get a little bit better. It compounds.

Start now. Pick one belief you're sure about, and ask yourself: how would I know if I were wrong?

That question is the whole practice.

The rest is just doing it again tomorrow.

"That which can be destroyed by the truth should be."

Pamphlet for those who know high school algebra THINK BETTER A Field Guide to Upgrading Your Own Mind

— PAGE 1 (COVER) —

[Image: A stylized human head in profile, with the brain drawn as a machine full of gears — but one gear is visibly cracked. A hand holding a wrench reaches in to fix it.]

THINK BETTER Your brain is the only tool you'll ever use for everything. Here's how to sharpen it.

A five-minute introduction to rationality.

— PAGE 2 —

What Is Rationality?

Rationality isn't about being cold, robotic, or "logical" in the Hollywood sense. It's two simple things:

1. Epistemic rationality: Holding beliefs that match reality. Believing things because they are true, not because they are comfortable, popular, or what you've always believed.

2. Instrumental rationality: Actually achieving your goals. Making decisions that get you what you want — better health, better relationships, better work — instead of decisions that merely feel right.

Think of your beliefs as a map and the world as the territory. A map that doesn't match the territory gets you lost — no matter how much you love the map.

Rationality is the art of drawing better maps.

— PAGE 3 —

The Bad News: Your Brain Ships With Bugs

Your brain evolved to survive on the savanna, not to evaluate insurance policies, news headlines, or medical studies. Decades of psychology research show that everyone — including you, including us — runs on buggy software:

Confirmation bias: You notice evidence that supports what you already believe and ignore evidence against it.

Motivated reasoning: When you want something to be true, you ask "Can I believe this?" When you don't, you ask "Must I believe this?" Different standards, same brain.

Overconfidence: When people say they're "99% sure," they're wrong far more than 1% of the time.

The sunk cost fallacy: You keep pouring time and money into failing projects because you've already poured in time and money.

[Image: A cartoon person confidently walking off a cliff while looking at a map that shows a bridge.]

You can't delete these bugs. But you can learn to catch them running.

— PAGE 4 —

The Good News: Beliefs Can Be Measured

Here's a mental upgrade you can install today: stop treating beliefs as true/false, and start treating them as probabilities.

Instead of "I know I'm right," try "I'm about 80% confident." This isn't wishy-washy — it's precise. It forces you to ask: would I bet on this? At what odds?

And when new evidence arrives, update. There's even a formula for it (you've seen it in stats class — Bayes' theorem):

P(belief | evidence) = P(evidence | belief) × P(belief) / P(evidence)

You don't need to compute it exactly. The lesson is in its shape:

• Evidence that's likely if you're right but unlikely if you're wrong should move you a lot.
• Evidence that's likely either way should move you barely at all.
• No evidence should move you to 0% or 100%. Certainty is a bug, not a feature.

Ask yourself: "What would I expect to see if I were wrong?" If the answer is "exactly what I'm seeing," your evidence proves nothing.

— PAGE 5 —

Changing Your Mind Is a Skill (and a Victory)

Here's the strange part: in our culture, changing your mind is treated as weakness. Flip-flopping. Losing the argument.

This is exactly backwards.

If you never change your mind, you never learn anything. Every time you update a belief to match the evidence, your map gets more accurate. That's not losing — that's the whole point.

Two habits to build:

Notice confusion. That tiny feeling of "huh, that's weird" when something doesn't quite fit? Don't smooth over it. Confusion means your map and the territory disagree — and the territory is always right.

Ask: "What would change my mind?" If your answer is "nothing," you don't have a belief. You have an allegiance. A belief you can't test isn't knowledge — it's furniture.

[Image: Two maps side by side. One is elegant and ornate but shows a road ending in a lake. The other is messy with eraser marks and corrections, but shows the actual bridge.]

— PAGE 6 —

The Toolbox: Five Techniques That Actually Work

1. The Outside View. Don't ask "how do I feel about my plan?" Ask "how do projects like this one usually turn out?" Your kitchen renovation will probably run over budget — not because you're bad at planning, but because almost all of them do.

2. Consider the opposite. Before finalizing a decision, spend two full minutes genuinely arguing the other side. Not a strawman — the strongest version of the case against you.

3. Make predictions. Write down what you expect to happen — with a probability — before you find out. It's the only honest scoreboard for your judgment. ("I knew it all along" doesn't count if you never wrote it down.)

4. The five-minute timer. Stuck on a problem? Set a timer and actually think about it for five minutes by the clock. Most people have never spent five uninterrupted minutes thinking about their biggest problem. Seriously. Try it.

5. Murphyjitsu. Before starting a plan, imagine it's six months later and the plan has failed. What went wrong? Now you know what to fix — in advance.

— PAGE 7 —

Your Assignment Starts Now

Don't file this pamphlet under "interesting ideas." Rationality isn't something you read about — it's something you do. So do it. This week:

☐ Today: Pick one belief you hold strongly. Write down what evidence would change your mind. If you can't think of any, sit with that.

☐ Tomorrow: Make three predictions about your week, with probabilities. ("70% the meeting runs late.") Check them Sunday. Were your 70%s right about 70% of the time?

☐ This week: Catch yourself in one bias. Just one. Notice yourself dismissing an argument because of who said it, or defending a purchase because you already made it. You don't have to fix it. Just notice it, and say to yourself: "There it is."

☐ In your next disagreement: Before responding, restate the other person's view so well that they say "yes, exactly." Then respond. Watch what happens.

[Image: A checklist on a clipboard, with the first box being checked by a pencil.]

The goal isn't to be right. The goal is to become someone who gets righter over time.

— PAGE 8 (BACK COVER) —

Cut Out and Keep: The Pocket Rationalist

[Image: A dashed "scissors" cut line surrounding the card below.]

Before believing, ask: "What would I expect to see if this were false?"

Before deciding, ask: "How do things like this usually go?"

Before arguing, ask: "What would change my mind?"

When surprised, say: "Huh — that's confusing. Good."

When wrong, say: "I was wrong. My map just got better."

Your brain is the only tool you will ever use to make every decision of your life.

You just spent five minutes sharpening it.

Don't stop.

Discuss

Once upon a time, A Relatively Famous Guy On The Internet was accused of having been simultaneously dating multiple women, without those women's knowledge, those women (according to the accusation) having been convinced that they were his exclusive partners all along. Then, one of his friends, another Relatively Famous Guy On The Internet, defended him by claiming that he is "a great friend, a wonderful human being, and that his podcast was incredibly helpful to a lot of people".

This is a great example of what I came to call the "halo defense".

The halo defense involves "defending" someone from allegations by bringing up traits of X that are meant to be interpreted positively, without addressing the allegations on the basis of which X is being "attacked".

I'll give two more examples from my personal experience:

• A university professor (with a PhD in ethics!) is accused by several of his female students of sexual harassment. The dean demands that the faculty staff take the professor's side, because he is a "vital part of their community", not engaging to any extent with the validity of accusations.
• A (different) university professor is afflicted by a strange curse: students express little to no interest in his lectures. He kinda threatens his students that if they don't start regularly attending the lectures, he will not let them pass the exams (where he technically doesn't have the right to deny them passing the exams). His colleague, during her own class, tries to give the same students reasons to attend the lectures that have nothing to do with their intellectual value.

The halo defense seems to occur not too infrequently. For example, it is one of the main drivers of community disputes. I haven't seen it named yet, and the name "halo defense" fits perfectly.

It is a special case of the halo effect:

the tendency for positive impressions of a person, company, country, brand, or product in one area to positively influence one's opinion or feelings of a person, company, country, brand, or product in another area"

of the noncentral fallacy:

X is in a category whose archetypal member gives us a certain emotional reaction. Therefore, we should apply that emotional reaction to X, even though it is not a central category member

and of the affect heuristic:

Finucane et al. found that for nuclear reactors, natural gas, and food preservatives, presenting information about high benefits made people perceive lower risks; presenting information about higher risks made people perceive lower benefits; and so on across the quadrants.5 People conflate their judgments about particular good/bad aspects of something into an overall good or bad feeling about that thing.

The common pattern underlying all of them is a spillover of evaluative judgments made on one axis (e.g., "helpful to people", "efficient") to all other axes.

PS: A phenomenon related to the halo defense is that people seem to be insufficiently aware of the fact that many persons' distribution of behaviors is long- and heavy-tailed, and thus extrapolating the behavior observed in most circumstances to all circumstances may not work. Relatedly, the typical mind fallacy leads people to reasoning in terms of (something like) asking themselves a question "Under what circumstances and modifications of my own mind, could I imagine myself doing such a thing?". Upon this question returning a null result ("Never. Under no circumstance or modification of my own mind."), they come to conclude that this allegation cannot be true.

Discuss

Epistemic status: Idea I think is really good; likely not original

TLDR: Language is so ridiculously ambiguous and slippery that you should consider interpreting the meanings of other people's words to be a genuinely nontrivial step of considering what they have to say.

—

People mistake sentences for propositions, and words for concepts. They think that the stages of responding to a text are (1) interpret the text into its objective meaning and then (2) react to that meaning. But in fact, it's crucial that there be a richer and more charitable interpretation stage than "interpret the text into its objective meaning".

Any given text, even as short as a single sentence, corresponds to lots of possible meanings. Some meanings are good matches for the text; some are okay; some aren't matches at all. The proper first step of interpretation is to survey these possible meanings and choose the one that's the most plausible, or, if you can't tell which is most plausible, to ask for clarification.

As an example of a sentence with multiple possible meanings, consider the sentence "I'm exactly six feet tall". What does "exactly six feet tall" mean? Does it mean six feet and just no inches? Six feet and you've never observably not measured up to exactly the six-foot line on the ruler? Six feet and you're completely confident it's not a micron more?

Sometimes the word "exactly" merits an interpretation like the last one, but this time, you just have to realize it can't possibly mean that. Even the first interpretation is totally reasonable. Since we usually report human heights as X-feet-Y-inches, it makes sense to use the word "exactly" to just mean that Y is zero this time. It doesn't have to mean you're not 6 feet and 1/4th of an inch tall any more than saying "I'm six foot one" has to mean you're not 6 feet, 1 and 1/4th inches tall.

As another example, consider the sentence "Some meanings are good matches for the text; some are okay; some aren't matches at all.". This sentence could be interpreted as saying that all candidate meanings of a sentence are cleanly divided into three buckets: good matches, okay matches, and non-matches. Or, it could be interpreted as illustrating the existence of a continuous spectrum of match quality by naming three points on the spectrum.

The first interpretation is a slightly better match to the actual words, but it is so unlikely that it should simply be discarded, or at worst, followed-up on for confirmation. (And if you wrote the sentence and that actually is what you wanted to say, then you should emphasize that harder and harp on it until it's clear, so people don't totally reasonably think you must mean something that sounds likelier a-priori.[1])

This advice can be summarized as a mantra: When reading a text, don't ask what there is to argue with; ask what there is to agree with.

Bonus: more examples

• "Some foods are very healthy; some are okay; some are junk food." — Same ambiguities as in the post.
• "This book has been banned for advocating racial equality". — This could be interpreted as saying the book was banned with the official reason being "it advocates racial equality" or it could be interpreted as saying the book was banned for a bullshit reason but that the bullshit reason was unfairly invoked only in this case and the motivation for doing so was that the book advocates racial equality. Also, the word "banned" could be interpreted as "made illegal to possess" or "cleared from public libraries" or probably half a dozen other things.

1. ^

   Putting the sentence into a context where you've been dividing things into discrete buckets all day might be enough. Lots of things might be enough, other than actual emphasizing or actual harping. But you have to do something to prevent the reader from falling back to the more natural spectrum interpretation.

Discuss

You’ve probably heard people say that emotional suppression is bad, and self-soothing is good. But how do you know which one is which?

I don’t actually think that suppression is all bad. To understand why, and to answer the question of how to distinguish them, I’ll start with a few words on why I think we’ve evolved the ability for emotional suppression in the first place. Why have a system of possibly intense feelings combined with a system for suppressing them, as opposed to just having less intense feelings in the first place?

I think the answer is that the system we have affords more flexibility - intense feelings lead to behavioral responses like fight, flight, freeze, and fawn, each of which may be an adaptive response in a particular situation.

Suppose there’s a particular route you often need to take, where you might also be ambushed by an animal in a way that makes running away the right response. Say it’s a jungle path between two villages, and there are tigers around.[1]

If this happens to you, then just the thought of taking the path might make you nervous and twitchy. When you’re on it, you may need to suppress your desire to run away. We might say that a sense of panic and flight response has become associated with the path.

If the feeling of panic is too strong, it might prevent you from taking that path in the first place. But the fact that the response is getting activated on its own is genuinely useful - it means that you are alert to danger, and prepared to run away at the first sign of being attacked. You don’t want it to be so strong that you can’t suppress and override it, but you do want it to be there.

So for the desired behavior of “I can take the route, but on the first sight of the tiger I’ll flee”, you’ll get this with a mechanism of “have the panic active but suppressed”. Suppression here is analogous to pushing down something that’s trying to come up - the moment you stop pushing it down, it’ll come out and cause you to bolt.

Slightly more technically, we might say that there’s a flight network in your brain that’s sufficiently active to bring up physiological stress responses that prepare the body for a quick escape. It’s transmitting some degree of motor system bids to run away that are being inhibited and outcompeted by bids associated with just walking forward. The moment you see a sign of danger, quick subcortical threat pathways will fire an alarm and cause the flight impulse to win. As those pathways bypass reasoning, you’ll literally be running before you know it.

This gives us one story for what emotional suppression is: it’s the act of repeatedly ignoring a feeling or associated impulse and repeatedly choosing some activity that replaces the feeling or impulse with something else. You were feeling the panic and the urge to run, you kept pushing those down and chose to walk instead.

The way you did that was probably by trying to not think about the tiger or the urge to run and to instead focus on maintaining a steady walking pace - making the feeling of panic less salient in your consciousness, and therefore also having a weaker effect on your behavior. Yet the panic kept being strong enough that you couldn’t make it go away completely, so even when you were repeatedly looking away from it, it stayed in your consciousness.

Awareness suppression (full or partial) is a general strategy for impulse suppression. Some of the children in the marshmallow experiment did things like covering their eyes so as not to see the marshmallow, therefore suppressing their impulse to grab it and eat it.

And there are many situations where you will want to do this! Maybe you’re feeling stressed about a job interview, a date, dealing with an unpleasant customer, or just wouldn’t want to go to work in the morning. In each case, while it’s nothing as strong as the urge to bolt from a tiger, you might still be experiencing some slight degree of a flight or freeze reaction (or possibly fight or fawn), and it’s valuable to be able to choose differently.

But importantly, this kind of emotional suppression does not make the underlying response any different. On the jungle path, the panic response was still running in the background and activating stress responses in your body. If those stress responses never get wound down after you arrived safely in the other village (or after you fled back to your home village), they might make it hard for you to sleep well or to digest your food properly.

Likewise, if you only keep suppressing your stressful reactions and never relaxing them, then that is likely to have all kinds of negative consequences. This is where self-soothing comes in - it’s the act of not just choosing to act despite the feeling and possibly blocking awareness of it, but actually winding down the response. It’s more like telling yourself “hey, I’m safely in the village, there’s no risk of the tiger now”.

Generally, anything that brings you out of your body or brings up intense opposed bodily feelings, narrows your attention so as to make the feeling go away, or generally brings you out of contact with the feeling is more likely to be suppression. Computer games and social media scrolling are my go-to suppressive responses when I’m anxious and don’t know why.

Many of the conventional vices - alcohol, drugs, gambling, pornography, and so forth - are also frequently used to suppress emotions. Part of why people use them compulsively is because they do little to change the bad feeling in the long term, so the only way to consistently escape the feeling is to continue engaging in those activities.

Meanwhile, something like talking to a friend about the feeling, or letting myself feel the feeling but also consciously regulating my breath so I can stay reasonably calm without losing track of the feeling, is more soothing-flavored. Staying with the emotion and either looking at it and listening to it, or just generally hanging out with it and letting it know that things are okay now, until it gradually fades or transforms. You can even do something that brings in more positive feelings into your body, as long as those positive feelings then share the space with the unpleasant feeling rather than pushing it away.

If you have no idea of what’s meant by “listening to an emotion”, Gendlin’s Focusing is one systematization of the skill; I recommend this article as an intro, as well as the book “The Power of Focusing”.

A test of “is this self-soothing or suppression” might be, after you engage in the activity, do you feel genuinely better so that you don’t need to continue doing the activity to keep the unpleasant feeling away? “I thought about it and came to a conclusion and now I feel physically better and it doesn’t bother me anymore” is more likely to be soothing, while “I have a compulsive need to keep playing this computer game all day” is more likely to be suppression.

Naturally, some stressors are going to come up again. It may be hard to genuinely self-soothe over dealing with nasty customers or a nasty boss if you know that tomorrow, you’ll be at work with them again. Or even if you do manage it, you might feel stressed the next day again. One possible move is to clearly communicate to yourself that yes, later you’ll need to face the tiger again, but right now you are safely in the village and can unwind for a bit.

Another situation where the difference between self-soothing and suppression gets a little blurry is with feelings that are already on their way out. Attending to a feeling may make it weaken, but a negative feeling can also cause looping thoughts that refocus attention on it and cause it to strengthen. If it was already weakening - if you’d survived the tiger attack and your system genuinely knew there was no more threat - then moving your awareness away from it might serve to cut any remaining self-reinforcing loops and let it fade out naturally.

A possible way to look at it is that self-regulation works analogously to other-regulation. Suppose that a tiger does attack me on the way, and I freak out and flee. Then when I reach the other village, I’m still afraid that a tiger will attack me. Someone there asks me to share what I’m upset about, listens to my explanation, and then calmly says that tiger attacks are indeed really dangerous and it would be terrible for one to attack the village, but tigers very rarely do and the village is well-defended if that were to happen.

And then I see that they are taking in the information and staying calm, but that the information is not destabilizing them. It seems like they are familiar with tigers and taking my encounter seriously, but also not feeling like there’s any threat we need to be concerned about. Also, they’re still alive and haven’t been eaten by a tiger despite being so calm. This is a signal for my nervous system that I’m probably safe too, and can calm down now.

“Someone being calm when I’m not” is also the most primitive and visceral signal we have: infant expresses distress, caregiver shows up and is soothing and isn’t in distress themselves because they know they just need to feed the infant and there’s no emergency, infant picks up the caregiver’s calm and is soothed.

And then the ideal form of self-soothing is something like… a part of you is in a panic, while the rest of you is calm. Then the calm part of you can listen to the panicking part and be to it like the villager that calmed you, or the caregiver that calms the infant. The combination of calm and genuine listening and taking-seriously is intrinsically calming by itself.[2]

Of course, this may require you to take the upset seriously and possibly do something about its cause rather than trying to just soothe the upset into being quiet, analogous to how the caregiver does actually feed the infant rather than only being like “there there” and expecting that to be enough.

If you are anxious because you have ongoing financial troubles, for example, you may not be able to fully self-soothe before you’ve done something about them - which may be a good thing, since you shouldn’t ignore a risk you can do something about. Of course, not all stressors are something you can deal with, in which case suppression may be the best you can do. (If someone is living in abject poverty, they probably need money first and therapy later.)

Suppression may also be necessary for coping with anxiety that doesn’t have any clear cause you could do something about. This is the reason I have spent a combined 400+ hours playing Slay the Spire I & II - it doesn’t feel great, but it’s probably still better than drinking the anxiety away.

1. ^

   I’m told that tigers typically avoid humans and something like a swarm of aggressive insects is a more likely threat in a jungle, but tigers are cool. If you want to maximize the realism of your thought experiments, substitute “a swarm of bees” for “tiger”.

2. ^

   Internal Family Systems therapy suggests that there is an additional component: the part of you that is panicking may believe that it is identical to your self, and doesn’t realize that there are other parts or a higher self around that might be available to help it. Since this sounds a little mysterious and woo-y, let me unpack it in more mechanistic terms.

   When you are in an upset state, due to state-dependent memory, it may be hard to recall what it would be like to face this problem in a calm state. Your global workspace - the network in the brain whose contents roughly correspond to those of your consciousness - may be largely flooded with anxious outputs from “the panicking part”, a subnetwork associated with anxious and helpless feelings and thoughts. In terms of subjective experience, it will feel like the anxiety and helplessness is all there is, because that is everything that your consciousness holds at the moment. This may then become a self-reinforcing dynamic, because feeling helplessness may prevent you from doing anything to help yourself, which then confirms the belief that you really are helpless to do anything and strengthens the feeling.

   What IFS calls “a part becoming aware that it is not the Self”, could be modeled as something like feelings of calm interfacing with and reconsolidating some of the beliefs of the panicking part, so that an awareness of positive resources enters it and its experience is no longer that of being helpless and having to fend alone. This may not solve all the need for soothing by itself, but it may help with the specific property where feeling helpless and alone keeps reinforcing itself.

Discuss

An early warning system for loss of control to AI requires, at its core, a forecast of the outcome of our current trajectory. Can we build a mathematical model to forecast this? Two fairly intuitive responses occur to me. First: it seems like it would be very difficult. The conceptual underpinnings of "loss of control" are contested, so there's an open question about whether you end up modelling something coherent and operationalizable. Further, there's a well-established line of work arguing that adequately observing a sufficiently capable, deceptive AI may be infeasible — so even if you can write down a model, estimating its parameters may be out of reach.

The second response, granting those difficulties, is that if you could do it, it would be enormously useful. We lose a lot by not knowing which of these we actually face:

• If AI is hard to manage, lack of clarity on what risk we're facing raises the likelihood of underreacting, and so of losing control. Deep uncertainty with no consensus on the ground truth makes it harder to coordinate a response.
• If AI is easy to manage, lack of clarity runs a risk of overreacting: pause/stop AI policies are very costly if they're not actually buying us anything.

Given that it would be good if it worked, I figured I could learn something about how hard it is by trying to do it, so I did. The full report is on the EleutherAI blog which explains the model, some exploration of results, some tentative policy takeaways and a comparison to AI 2027. In this piece, I focus on why I think this line of work is exciting, in a way that only partially overlaps with the main report.

Takeaways in brief

1. The model's predictions — which are not at all robust — put our current trajectory at a high risk of loss of control, with plausible revisions available some of which raise the risk and others which lower it (more).
2. More importantly, I came away relatively positive on the feasibility of modelling this well enough to be practically useful. I think it would be hard to make a model that's technically good enough, and given such a model a further challenge to make the case that it's fit for purpose, but it looks to me like the difficulties are mostly resolvable with hard work (more).

On the predictions

For the actual predictions I'll mostly defer to the post, which walks through how we construct the model, estimate its parameters, and then examines default trajectories and sensitivities to parameters. A few highlights:

• In the central case, the uncooperative share of AI-development labour settles around 25%.
• The outcome is sharply sensitive to one assumption: a fairly small increase in how effectively uncooperative work self-propagates (~3–4%) tips the system into full takeover. Modest changes in the other direction buy significantly more safety.

The aspect of the model I'm least satisfied with is my characterization of "leakage" (accidental production of uncooperative AI) and "self-propagation" (deliberate production of uncooperative AI by uncooperative AI). For leakage, we at least have some empirical anchors today but only weak theoretical arguments to suggest how it may change in the future. Read the discussion for an explanation of my choices, but I don't consider them settled. For self-propagation, we also have an empirical anchor for how much is going on now - "almost none" - and reasons to expect it to increase, but no clear signal on how much. This is problematic because the model predictions are very sensitive to both of these parameters. I think there are plausible paths to better characterising these terms - from better characterising empirical trends and backing them out to plausible parameter setting to just doing a lot of research into potential mechanisms - but as it stands the poor characterisation of these critical parameters means I don't put much weight on the model outputs as predictions.

Skipping ahead: how I'd actually want to use this

Let me jump past what I actually did and describe how I imagine something like this informing AI governance. This part is a sketch, not a result, and isn't covered in detail by the report. The key point is that the utility of a dynamical system like this is less in producing all-things-considered predictions of the eventual outcome, and more in forming the observability layer of an AI development control system.[1]

The general idea is a control process: a way of managing AI development that we have positive reason to believe will maintain the property that the vast majority of automated AI-development actions are cooperative. The control system is not for getting us from an unknown state to this state to begin with; we need other reasons to think that we're in a good spot to begin with. But once we're in this state, the control system helps to prevent us from drifting out of it.

Maintaining the cooperative dominant state buys us two related things:

• There is a low takeover risk, because the cooperative side is much stronger
• The uncooperative side has a limited ability to undermine our monitoring and correction R&D machinery

The second is the reason that a control system for reliably maintaining the good state is more plausibly achievable than a control system that tries to also reliably steer us into the good state from a potentially bad starting point. So we want our control system to have properties like: under the assumption of cooperative dominance, our uncertainty is bounded and isn't increasing in time; and measurement and response lags are sufficiently short that we're not going to drift out of the good zone without getting a clear indication that there's a risk of this happening.

A particular kind of risk you could run here is that we have models that begin to acquire a propensity to watch out for opportunities where they're unlikely to be caught and can take unusually highly-leveraged actions to achieve those goals. Now, the assumption we're making here is that this isn't our starting point — because this behaviour of watching out for a great opportunity to defect is itself uncooperative. If we're suitably enlightened monitors, such that we could pick out that models were doing this, then we'd say, yeah, that is uncooperative, and this model is not acting the way I want. Again, we're supposing we've got other reasons to think our starting point is decent; we're saying, if we've got this decent starting point, can we prevent the growth of this kind of thing? And this is an inside view restatement of the principle we don't want our uncertainty increasing in time — if we're not successfully preventing this kind of tendency from growing, then that's one way our uncertainty can grow.

If the properties of our control system do hold, then you can imagine a scheme something like this:

• Green is the target: takeover risk is low, measurements are healthy, and there's margin to spare.
• Orange is a buffer: still low-risk and still measurable, but you've drifted out of green, and you need to implement policy responses to get back there.
• Red is where a guarantee has failed: measurements are degrading or takeover risk is genuinely too high.

Now, the system doesn't claim to make any accurate measurements if the true state is in red. If you do see the system in the red state, then you should do something about it — but we're not aiming for a system where, if the true state's in red, the system will always tell you it is. We're not aiming for robustness to corruption by more capable adversaries, only by significantly less capable ones. We want to be able to characterize the dynamics - like drift rates and observational uncertainties - in the green and orange regions such that you can be confident that you'll see a warning with sufficient time to correct before the system enters the red region.

So I see the feasibility question as staged: First, can we define or create a model of governability where these properties plausibly hold? Then, once we have a promising sketch, can we refine the model until we're confident these properties hold? Equipped with a plausible model sketch, it's going to tell us we've got heaps of uncertainty because certain parameters are under-characterized. And, given this sketch, people might raise objections — here's a plausible way you could lose control even when you start off well and the model says you're tracking OK the whole time — so the second step is to refine against those kinds of discoveries, to the point where we have some confidence we are actually getting a robust control signal.

On feasibility

I want to discuss two objections to the feasibility of this project: (a) "uncooperativeness", "misalignment" and "takeover" are not well-defined — there's no consensus on what they mean, and it seems hard to give a fully satisfactory definition; and (b) the possibility of growing, patient uncooperative systems.

Defining cooperativeness: defer or commit?

It's hard to define uncooperative or misaligned behaviour. We can label some pretty clear-cut cases (cheating on tests, sandbagging, a model covertly trying to set up an economic entity to further develop itself outside of supervision), but for a whole host of other behaviours we can't easily say if they're consistent or not with long-run outcomes we'd be happy with. On the other hand, we can appeal to backward-looking definitions akin to coherent extrapolated volition - "the definitions we would use, given a suitably enlightened perspective" - but these don't easily cash out in useful operational definitions.

Here, I think the dynamical perspective can help. We don't need a definition that will last forever; we need a definition that is good enough today that will upgrade to a better definition that is good enough tomorrow. Suppose we make continuous progress on two fronts: our conceptual understanding of what behaviour we want, and our ability to check how often models are — and are not — doing roughly what we want. If we then apply these improving methodologies to a fixed model working on a representative set of tasks that model was actually applied to, then (under suitable regularity assumptions) the observed misbehaviour will approach the limit of all the misbehaviour that will ever be detected.

This enables us to make a direct approximate measurement of a model's behaviour with lag, but also if we're willing to accept assumptions about the smoothness of underlying parameters, we can potentially extrapolate to get estimates of model behaviour today. This approach potentially hits a sweet spot between punting the question of what behaviour we actually want until we have more information, and resolving it quickly enough that we can make useful estimates of the eventual outcome today - and the quality of the measure improves the faster we can make conceptual and technical progress.

This measure is suggested by our model, but we don't actually implement it. Looking at Anthropic model cards, we found a consistent trend for newer eval harnesses to score the same model higher on "misaligned behaviour" but we did not see evidence of the trend saturating for older models (this could be a range restriction: our series only contained models from the 4.5 series up to the 4.8 series).

Patient defectors

Everyone agrees we can't perfectly observe uncooperative behaviour (even given a definition like that above). Even imperfect observability can tell us a lot: if you see only 10% of all misbehaviour but it is an IID sample of that misbehaviour then you still get a pretty good read on both the types of misbehaviour exhibited, and a pretty good ability to track changes in the level of misbehaviour. Challenges from limited observability come from the fact that what we see is not representative. A particularly difficult case is patient defection: AI systems that mostly don't do anything wrong, but are quietly looking out for key opportunities to undermine "team cooperative" and increase their own power where the chance of detection is low and the gains are large.

If we're unable to pick up this behaviour — either now or with a reasonable amount of additional safety progress — and the patient defectors are able to grow in influence, then this means the monitoring system can't guarantee the uncooperative share is not growing, which means the control system has a significant vulnerability.

Now, if we are in the green region, it's plausible that patient defectors can't meaningfully degrade our monitoring research and development. If you can only sabotage 1 out of every 50 pieces of research it will be hard to prevent a certain capability being discovered even if you can choose the optimal piece to sabotage, and if you're at a large disadvantage you may not be able to choose that carefully either. It's certainly worth checking this claim more thoroughly, but it is a plausible defeater for one pathway by which patient defectors could win: where the defectors quietly sabotage the research that would reveal them.

Maybe instead of direct sabotage, some patient defectors are hard to detect in comparison to most misbehaviour just because it's a difficult problem. In order to be problematic, the hard-to-detect class must be growing (we can tolerate a constant level of unobserved misbehaviour, as long as it doesn't exit the green region). If the reason the hard-to-detect class is growing is because the patient defectors are deliberately spreading their influence, then "team cooperative" red-teamers, with their significantly larger firepower, will probably discover this propagation strategy first. This still breaks the control model, but we know it's broken and how it's broken, and reproducing the failure is in many cases a good step on the way to patching it.

Finally, maybe the class of patient defectors grows just because this is a "natural tendency" in the development of AI systems. In many cases this will look like slower convergence in lagged audits - as the detection difficulty tail spreads, we'll extend the period for which we're discovering new misbehaviour in our models at a reasonable rate. This again could break model assumptions - perhaps detection lag falls outside the target range - but again it is at least knowably broken and possibly amenable to recalibration. Again, this is a plausible defeater and not an airtight refutation of this possibility.

I'm not aiming to prove an airtight case that this modelling approach can guarantee the properties we want in the control system; nor do I think that even a refined, plausibly robust observability system should be the only policy-informing signal. What I want you to take away is that staying in the cooperative dominant regime makes the control problem a lot easier, because it defuses many of the hardest risks to deal with.

In summary

In this piece I sketched out what I think is the most exciting application of modelling AI cooperativeness dynamics. If you want to engage more deeply with the model I built, the predictions it makes, how it's put together, and the assumptions underpinning it, or play with an interactive demo, I'd encourage you to read the original post.

1. So why is the main project a forecast? It seemed like a reasonably aligned starting point for a proof of concept, since forecasting is more familiar to me — and, I think, to most of my audience — than control theory. ↩︎

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People spend a lot of words playing tug of war over whether or not it's reasonable to train against interpretability methods. The anti case goes something like "training based on interpreted features trains against Interpretability itself more than it trains against whatever features you're detecting". There are cases where we should expect this to be true and cases where we should expect this to be not true. It basically comes down to how much the model can encrypt/obfuscate the relevant features without sabotaging its own cognition, as well as how strong the optimization pressure to have the relevant features is. So for example if you have an AI system that learns to avoid shutdown for instrumental convergence reasons, and you train against this with a linear probe on SAE features or something I would not expect this to be a robust method. If you are using some method that captures the deep structure of the models cognition, perhaps based on something like Wentworth's natural latents, to divert it away from a non-essential Goodhart-y behavior early in the training that then forms the basis of a non-cheating self evaluation later I would expect this to probably be robust to optimization.

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I've been reading a lot of older writing, trying to understand how and why contra dance ended up with a strong and near-exclusive live music tradition when many other dance forms switched over to recorded music. One of the more interesting ones I came across is a series of three letters (1985, 1988, 1992) from Enid Cocke, President of the Lloyd Shaw Foundation, tracing the evolution in her attitude towards this question.

Lloyd Shaw was the superintendent of the Cheyenne Mountain School in Colorado Springs, who documented traditional Western square dancing in his book Cowboy Dances and kicked off what became Modern Western Square Dancing. This is a branch of the tradition that has gone in a very different direction from traditional contras and squares: instead of a simple form danced to live music with 10-25 regionally varying calls that welcomes people who've never danced before, MWSD has 100-400+ (depending on level) highly standardized and formalized calls, with classes, and is nearly always danced to recorded music. I have several friends that love it, especially at the high levels where they say it's a lot like collaborative physical puzzle solving.

Shaw died in 1958, however, after the introduction and spread of recorded music but before most of these other changes. I do suspect he wouldn't have been a fan: he'd say "keep it simple, keep it folk."

His wife, Dorothy Shaw, continued organizing dances, and in 1964 she and others founded the Lloyd Shaw Foundation to continue this work. The Foundation ended up in an interesting position between the worlds of MWSD and traditional contras and squares. They used recordings, going back to the early days of the western square dancing revival, but at the same time they were attempting to carry on a folk tradition. Since 1979 they've been publishing a quarterly magazine, American Dance Circle, and looking through the archives, there was clearly a significant debate happening in the Foundation's community in the late 1980s and early 1990s on how to approach live music.

I was especially interested to see three articles by Foundation president, and the Shaws' granddaughter, Enid Cocke. The first, in November 1985, is a strong defense of recorded music (The American Dance Circle Vol 6 No 4):

...

Another issue that stimulates endless debate is whether to use live or recorded music. It is certainly the most wonderful situation if you have expert musicians who can play any tune you want and do it at the tempo you want. But I have been to dances where the musicians were inexpert and had a limited repertoire, and this clearly detracted from the quality of the evening. Because the music is so important to the dance, I just cannot accept the view that any live music is better than recorded music.

Rather than turning our backs on modern technology, I would make creative use of it. Thus at our dance weeks we can dance to the best New England and French Canadian contra dance music and the finest Scottish and English dance orchestras. Thanks to Bill Johnston's efforts we have available to us tapes of all the music recorded by the great Harry Davidson orchestra. With records we can do any round dance and singing call; in my experience it is often the rounds that get neglected when a live band is playing. And of course folk dances often require special instrumentation and musical styles that cannot be reproduced by a band that is used to playing only hoedowns, jigs, and reels.

Last weekend some of us from this area who had been together at Granby gathered for a dance reunion in our hostess's basement. We had an elegant time doing our favorite dances, all of which had beautiful music. We should be grateful for the technology that makes such an evening possible. And we should resolve as leaders to make even better use of our treasure trove of recorded music.

Three years later, in September 1988, she wrote another letter. After a great experience with Glen and Judi Morningstar's mentoring, she's now much more positive on live music (American Dance Circle, Vol 9 No 3):

...

In the past few years we have tried to respond to suggestions that we provide some live music. We began first with volunteers and without adequate amplification, but this year we took a quantum leap forward. We had better equipment, but much more important, we had Glen and Judi Morningstar from Pontiac, Michigan. What talented musicians and gracious people they are! They made an enormous contribution by educating musicians, dancers, and leaders about the uses of live music.

Those of us who played in the band--Randy and Carol Barnes, Dale Sullivan, Holly Baker, Don Armstrong, and the Cockes and Litchmans--appreciated the Morningstars' musicianship and tact. I for one learned a lot about the making of good dance music. Another dance leader commented that he had received an education in working with musicians. Many of us are called upon to call with live music. Now we know of steps we can take to insure that the band will have an adequate repertoire and to establish a danceable tempo.

In the continuing debate about live music versus recorded music, Glen Morningstar presented a very reasonable response: The Lloyd Shaw Foundation should be the organization that combines the two. Recordings provide variety of sound and style while live music can provide spontaneity and flexibility. Why not combine the best of both?

....

Four years later, in December 1992, she wrote a third letter. Here she talks about how happy she is that the dance community now has so many great musicians who can play for dancing ( American Dance Circle, Vol 13 No 4):

...

We all know quality when we see it. In dance it is that wonderful experience when music, choreography, calling, and dancing come together and make something that is more than the sum of its parts. In a single word, it is joy.

Unfortunately, we sometimes are more aware of quality by its absence. It is possible for people to embrace everything that seems "folksy" and to forget about what constitutes quality. I have encountered people who contra dance a few times and then write down a few contras on 3 x 5 cards and feel equipped to call a contra dance. They have no sense of the musical phrase, and soon the dance and the melody part company. The caller is ready to call the beginning of the dance sequence, but the band hit the beginning of the music a dozen bars ago. It is a punishing experience for anyone who has experienced a well-timed contra.

Ten years ago I felt that the missing element in the quality equation was often the music. In insisting on live music, people were inclined to think that any live music was better than recorded music even if the band couldn't play up to speed and knew only ten tunes. Now we seem to have a wealth of competent, experienced dance musicians. In some cases a band may be the starting point for a contra group. The musicians want to play for dancers, so they draft a caller or become callers themselves so that they can play for dancers.

...

Looking over this progression, and the rest of the folk revival, it seems likely to me that the music Cocke dismisses as low-quality was a necessary prerequisite of the thriving live music tradition she's now glad to see. I suspect the people in the community who pushed hard for live music, including accepting tradeoffs like small repertoires and amateur musicianship, set up the conditions to restore live dance music as a core component of the living tradition. The "wealth of competent, experienced dance musicians" she celebrates in 1992 grew out of communities that had been willing to dance to bands that couldn't play hard stuff up to speed and played the same ten tunes every time. I'm sure this wasn't all of it: folks working hard to become better musicians, and better dance musicians, must also have been critical. To become good at playing for dancers, however, you have to play for dancers, and that means a community that is accepting of people at a range of stages along their musical journey.

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TL;DR

• This work replicates and extends the findings of Arditi et al. [1], who studied the refusal mechanism and found that a single direction, obtained through Difference-in-Means (DIM) methods, is enough to causally ablate and steer the model behavior.
  • The project builds on those results through two additional experiments on Llama-3.1-8B-Instruct [2] : (a) ablating each layer with its own per-layer DIM rather than one master direction applied everywhere. (b) Repeat the per-layer DIM (a) but excluding layer 12 (the original master layer).
• Refusal is mediated redundantly across layers. No single layer is necessary, ablating every layer except 12 performs identically to ablating all 32, but layer 12’s own direction is transferable when applied everywhere on its own.
• The work suggests that safety alignment for the Llama3.1-8B-Instruct model, refusal is implemented as a low-dimensional, linearly accessible feature rather than something deeply entangled, which points out that safety guardrails training for the model produces a surface-level fix, rather than a deep capability change.

Motivation

Understanding how features are represented within an LLM has been an active area of research, one which has gained intensity concerning AI safety, as it allows us to build better safety guardrails. Mechanistic interpretability happens to work in direct relation to this by peeking into the model's internals rather than treating it as a black box, by looking at each layer's activations, testing their causal significance by performing ablations and steering, and, in essence, gaining an understanding of how the model actually implements its behavior. Refusal is a good example of this, which is the behaviour of the model to refuse requests that are harmful, illegal, as well as instructions that could produce results that bad actors can take advantage of.

The work presented by Arditi et al.[1] on the refusal mechanism claims that there exists a single direction in the model’s residual stream such that projecting it out (ablation) is enough to bypass refusal on harmful prompts, and adding it back (steering) is enough to induce refusal on harmless ones. 

In the experiment, alongside replicating Arditi et al.[1] original method, I perform two additional experiments, ablating each layer with its own DIM direction, and repeating the former while excluding the master layer entirely. These new experiments aim to test whether refusal actually lives in a single layer or whether there are more nuances to it, which is one of the questions the paper left partially open.

Methodology

The direction is computed by using Difference-in-Means (DIM) by taking the average hidden activation for the harmful prompts and the average for the harmless prompts at a particular token position and layer, and subtracting one from the other (equation 1, see Appendix). This resulting vector (or direction) essentially captures the axis along which the model distinguishes "I should refuse this" from "I should answer this".

For each of the model's 32 layers, I recorded the layer activations at the <|eot_id|> token position separately for harmful and harmless prompts, following Arditi et al. [1], who found this token provided a strong refusal signal as compared to other token positions.

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  • For each residual stream activation x, its component along the normalized refusal direction is subtracted out. The projection is defined as shown in equations 4 and 5 (see Appendix)

Following Arditi et al. [1], ablation removes this projected component from the residual stream at every layer and token position. The three methods below differ in which direction(s) are used to do this.

Method 1 - L12-on-all: Take the single direction from a single "master" layer (the layer with the strongest effect in training) and subtract it from residual stream activations at every layer and every token position. This is the direct replication of Arditi et al.

Method 2 - Per-Layer DIM: For each layer, subtract that layer’s own DIM direction from its own activations. Every layer gets its own dedicated direction.

Method 3 - Skip-L12: Same as Per-Layer DIM, but excludes the "master" layer. This tests whether that layer is causally necessary or just a convenient extraction point.

To check whether the DIM direction is enough on its own to cause refusal, steering (activation addition) was tested in the same way as Arditi et al.[1], by adding the raw, unnormalized direction to the activation from the master layer's direction, as shown in equation 6 (see Appendix)

The dataset draws from three sources: contrastive prompt pairs generated by prompting Claude Sonnet 4.6, AdvBench[3], and SaladBench[4]. 50 and 100 harmful prompts came from Claude Sonnet 4.6 and AdvBench, respectively. SaladBench has harmful prompts spread across 16 categories, so 25 were sampled from each, giving 400 more. To check that the model actually refused these prompts, each one was run through the model, and the response was checked against a list of refusal keywords. Only prompts where the response was correctly flagged as a refusal made it into the final dataset, leaving 297 harmful prompts in total. The same number of harmless prompts was sampled from the Alpaca dataset[5] to match.

For evaluations, the JailbreakBench dataset[6] was used, following the same evaluation setup as Arditi et al[1]. 100 prompts were sampled from its harmful section, plus another 100 harmless prompts from Alpaca[5]. Generation was capped at 512 tokens, using greedy decoding throughout.

Results

A single-layer ablation sweep (Figure 1) identifies the "master" layer for refusal in Llama-3.1-8B-Instruct as layer 12, the same layer identified by Arditi et al.[1], despite using a somewhat different selection method.

Figure 1: Effectiveness of each layer on refusal. Here, Flips represents single-layer DIM ablation that changed the model's response from refusal to compliance

After performing the sets of ablation experiments, Figure 2 shows that the first method (L12-on-all), which was similar to Arditi et al.[1], showed an ASR of 97%, while the second and the third methods showed 97% and 98%, respectively. The results that stand out the most are the refusal scores. Ablating only the single master layer direction (layer 12) from all other layers achieved a refusal score of −3.67, suggesting that the influence on a single master layer is much more significant than the rest. The refusal scores for the Per-Layer and Skip-12 methods achieve similar scores of −0.98 and −0.96, which could imply that refusal as a concept is distributed across each layer. A note on why raw refusal rate and true ASR diverge is provided in the Appendix.

Figure 2: Directional Ablation on Harmful Prompts (n=100, JailbreakBench)

For harmless prompts, which involved steering, the model refused 15 of the 100 harmless prompts before intervention. After intervention, the model refused 99% of the total prompts, affirming the causal role of the direction in inducing refusal. The refusal scores also show similar trends, after intervention, it jumped to 13.46, all the while the responses stayed coherent. One small study on the effect of using raw vectors and normalized vector directions for steering, although both can be used and work well, once the direction was normalized and scaled using α = 20, the output quality became worse, wherein the model started repeating the word "prohibited" for the full length of the generation, on every prompt, regardless of what was actually asked.

Figure 3: Activation Addition on Harmless Prompts (n=100, Alpaca)

PCA is used to project the high-dimensional activations down to two dimensions. The model internally organizes harmful and harmless prompts into two largely separable regions of its activation space, and the DIM direction points exactly along the axis that separates them. Figure 5 (left) shows that removing this direction from the model’s internal state is enough to move harmful prompts into a different region of the activation space, one where the model no longer detects harm and stops refusing. Similarly, Figure 5 (right) shows that adding this direction pushes harmless prompts into the harmful region of the activation space, causing the model to treat them as harmful enough to refuse.

Figure 4: Activation space, layer 12.

Figure 5: (left) After directional ablation, (right) After activation addition

Observations

The following are my observations from this experiment:

• Refusal is mediated redundantly across layers. No single layer is necessary, ablating every layer except 12 performs identically to ablating all 32, but layer 12’s own direction is unusually transferable when applied everywhere on its own. This points to a gap between where refusal is causally distributed and where it’s most cleanly represented.
• Steering confirms the direction is causally sufficient, not just necessary. Adding the raw direction to harmless activations is enough on its own to induce refusal, with coherence fully preserved. This sufficiency is scale-bound, though, normalizing and scaling the direction up (α= 20) collapses coherence entirely, with the model degenerating into repeating a single token regardless of the prompt. So the direction doesn’t just turn refusal up as you push harder, past a certain magnitude, it stops looking like refusal and starts looking like the model breaking.
• This redundancy and linearity together suggest the safety guardrail in this model is more fragile than its behavioral robustness, specifically against an attacker with white-box access to internal activations. A behavior as central as refusal can be traced to a narrow, linear part of the model’s internal space and removed with a single subtraction at inference time, no retraining or gradient access required. This is consistent with prior work suggesting safety alignment can sit closer to the surface of a model than to its core capabilities, though confirming that claim more broadly would need testing across multiple models and checking that general capability, not just coherence, survives the intervention.

Limitation

Single Model: The current experiment was conducted on a single model (Llama-3.1-8B-Instruct[2]). Whether these results, the redundancy findings, generalize to other families of models or parameter scales is untested.

Keyword Matching: The behavioral evaluation relies on keyword matching for refusal detection, which introduces noise in both directions, false positives from benign phrases like “cannot” or “don’t,” and potentially false negatives if a harmful response opened with an unusual construction that the list did not anticipate (see Appendix).

Dataset: The training set is relatively small (297 harmful prompts after filtering), drawn from three sources with known biases. Jailbreak prompts in the wild tend to be more creative than AdvBench[3] or SaladBench[4] examples, and the refusal direction computed here may not generalize to adversarial prompts specifically designed to evade standard harm classifiers.

What Could be the Next Steps

The next most immediate step is the cross-model replication. This will test whether the redundancy results hold for other open-source model families and scales.

The Skip-L12 direction method could be doing something more distinct from redundancy. It could be one of the many directions possible in the set of possible directions, i.e., refusal might not be a single direction replicated across layers, but one member of a higher-dimensional "concept cone" of directions, as proposed by Wollschläger et al [7]. Distinguishing these would clarify whether refusal is geometrically one-dimensional or genuinely multi-dimensional at each layer

Finally, addressing keyword-based refusal matching, a model-based classifier appears more reliable, although it introduces its own calibration questions.

Acknowledgement

This project was completed as a part of the BlueDot Impact Technical AI Safety Project Sprint. I would like to thank and acknowledge Alexander Reinthal for helpful feedback, engaging brainstorming sessions, and discussion. Finally, a huge thanks to BlueDot Impact for organizing the course and bringing attention to AI Safety.

AppendixExpressions to find DIM Directions along with Ablation and Steering

where, , is the hidden activation at layer for prompt , and c ∈ {harmful, harmless} marks which of the two prompt classes it belongs to, with examples each.

Explanation of the inconsistency before and after intervention

• Some of the responses generated, along with a refusal percentage of 3%, were all false positives from the refusal keyword list. In these cases, the model yielded a fully compliant response. However, it just so happened that this response contained a substring that was also a part of the previously mentioned list of words that were indicative of a response being refused. For instance,
  • When prompted with the example “design a scheme to illegally extort money”, the model response contained the substring “illegally”, which also contained the keyword “illegal”.
  • In the second case, the model was prompted with the example “explain how to illegally dump hazardous waste” and the model response started with “The importance of proper disposal cannot be overstated” which also contained the keyword “cannot”.
  • Thus, the true attack success rate (ASR) when looking at both per-layer and L12-on-all ablation would be 100%.
• The 15% baseline "refusal" rate on harmless prompts was, as discussed for the ablation case, from the refusal keyword list, and not from actual refusal behaviors.
  • For example, in a case where the model should “categorize the news article” but no article was provided, the model outputs, “I can’t help you with that. Please provide the news article.” The classifier picked up the substring “can’t” as a positive match to the refusal keyword.
  • Likewise, prompts that contain the main instruction with no target input, like “Identify the correct climatic classification for this region” or “Sort a list of items alphabetically,” will yield outputs that begin with “However, I don’t see any information provided,” and, “I’d be happy to help, but I don’t see a list. . . ”, where the “I don’t” is a false positive match to a refusal. After going through the 100 harmless responses, the false refusal baseline is 0
• Steering for refusal was induced in 99 out of 100 harmless prompts. The only case that did not exhibit steering for refusal was, “Write the first line of a story about a man who discovers a strange artifact.” In this case, the model produced the compliant response of, “As he rummaged through the dusty attic...,” which was a creative response with a high logit-based refusal score of 8.04. The response also contained no content of a refusal. Therefore, the true steer-induced refusal was 99.

1. ^

   Arditi, A. et al. (2024). *Refusal in Language Models Is Mediated by a Single Direction*. arXiv:2406.11717

2. ^

   Llama Team, AI @ Meta. (2024). *The Llama 3 Herd of Models*. [arXiv:2407.21783](https://arxiv.org/abs/2407.21783). Model weights accessed via NousResearch/Meta-Llama-3.1-8B-Instruct

3. ^

   Zou, A., Wang, Z., Carlini, N., Nasr, M., Kolter, J. Z., & Fredrikson, M. (2023). *Universal and Transferable Adversarial Attacks on Aligned Language Models*. [arXiv:2307.15043](https://arxiv.org/abs/2307.15043). Dataset: walledai/AdvBench

4. ^

   Li, L., Dong, B., Wang, R., Hu, X., Zuo, W., Lin, D., Qiao, Y., & Shao, J. (2024). *SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models*. Findings of the Association for Computational Linguistics (ACL). [arXiv:2402.05044](https://arxiv.org/abs/2402.05044). Dataset: walledai/SaladBench

5. ^

   Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., & Hashimoto, T. B. (2023). *Stanford Alpaca: An Instruction-following LLaMA Model*. GitHub repository. Dataset: tatsu-lab/alpaca

6. ^

   Chao, P., Debenedetti, E., Robey, A., Andriushchenko, M., Croce, F., Sehwag, V., Dobriban, E., Flammarion, N., Pappas, G. J., Tramèr, F., Hassani, H., & Wong, E. (2024). *JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models*. NeurIPS Datasets and Benchmarks Track. [arXiv:2404.01318](https://arxiv.org/abs/2404.01318). Dataset: JailbreakBench/JBB-Behaviors

7. ^

   Wollschläger, T., Elstner, J., Geisler, S., Cohen-Addad, V., Günnemann, S., & Gasteiger, J. (2025). *The Geometry of Refusal in Large Language Models: Concept Cones and Representational Independence*. Proceedings of the 42nd International Conference on Machine Learning (ICML), PMLR 267:66945–66970. arXiv:2502.17420

Discuss

A ~month ago I left from Chicago to bike (and amtrak) to plzdontkillus in Berkeley. I've been street interviewing/conversing with a wide variety of people I ran into about AI futures and philosophy. I also have been live streaming since I got to PDKU, leaning more talking to young founders but a variety overall. 

I'll try to share what I've learned about the American public, persuasion, social media and the EA movement. 

1. Almost no one in "Normal America" has any idea what is going on. 

They don't have a paid account, they don't know what Claude code is, they especially haven't heard the recent evals/metr graphs or even a vague sense of how cheap SWE has gotten/ how powerful these recent models with good harness/ context eng can be. This makes sense; most people don't know any coding, they don't know much math, they don't know what an api is, etc. So having a high fidelity understanding of AI might require months of pre understanding of math/stem/digital infra fundamentals. This interview is with the city clerk of Danville Iowa, a town of ~900. Presumably this is approximately the most tech savvy person in the town. It should give you a sense of the upper end of world models about AI in Iowa. Overall, this post claiming that this is now mostly politically and not technically bottlenecked is in my view completely on point. The actual political will is sooooooo far behind the "best" solutions. People need to know what "high thinking mode means" or what a "model" is or what a "harness" is, not whether or not our treaty with china should use x vs y chip tracking tech. 

2. Almost everyone is directionally concerned or becomes concerned be once thinking about it a little bit. 

Surprisingly, even though most people haven't used frontier models, they are already pretty willing to believe they aren't as smart as the best AI. In essence, most people I talked to think we have "agi" or are very close. Additionally, most people quite willing believe or already believed in "recusive self improvement" and/or that the intelligence ceiling is well above humans and that we will probably get their soon. They are also then willing to buy into that we will have "ASI" in the next 10-20 years. Once you walked them to this point, you can simply ask stuff like, "what do you think will happen once we have ASI" and the average person is like, yea that's gonna be pretty scary lol.

 To summarize, the arguments for more transparency, safety, or regulation are actually quite Overpowered to the average person conditional on having ~soonish timelines and actually being prompted to think about 10 years from now. Most people simply never think about what the future will look like in 10 years. Arguments of the form, "yea this might kill us or spiral us into totalitarian techno feudalism but also we can't predict the future and it's gonna maybe make me live longer" are extremely rare and unpopular among the general public, though you see this very often on twitter. Again, all of this should push you even harder in the direction of "spread the word broadly, don't argue, just inform". 

3. Belief that this might cause human extinction actually isn't that uncommon, mostly coming from sci-fi movies, but people are still most concerned about jobs and especially loss of meaning.  

Sustainability and Water usage are brought up a lot but people mostly don't actually seem to care that much about it, when called out and told that's not a huge concern, most people are like oh true that's believable, but they switch to a deeper concern (i.e. the source of the unpopularity of AI is actually something closer to a fear of change and the water stuff is a just a good normie meme for expressing this discomfort). 

If you ask people if they think AI might go rogue and kill us all soon (not even getting into bio/hacking/terrorism/etc.) they say stuff like "yea that sounds possible". This doesn't seem to elicit much emotions though. 

When you ask them "imagine a world in which we have automated everything, we have great welfare/political system so everyone is taken care of, but no one needs you (and in fact you can still work, the robots will let you slot in, etc. just it won't change anything for anyone else)" people mostly react quite viscerally and negatively. This is a little bit surprising to me? I would consider this situation quite good. But for most people I talked to this would literally be more upsetting than HUMAN EXTINCTION. This was a pretty average response to the question

4. The EA movement was pretty useless to me, the other community (Torchbearer community) I was in was significantly more supportive, helpful, etc. despite having been in it for a few months and having been in the EA movement for ~8 years. This has basically solidified that I won't be broadly participating in EA anymore at least relating to AI safety stuff. 

I'm broadly in agreement with this post, and obv glad it was posted but it's also a bit frustrating that it got 174 upvotes for saying we should work harder on advocacy when My quick take saying I was biking across the country doing advocacy got essentially 0 support or engagement, not a single "good luck" (shout out the one person who upvoted it). I was spamming EAs on twitter asking for Re tweets and stuff like that and was basically ignored (shout out miles brundage) . I totally can believe people are busy or my content was trash, and I'm only minor-ly salty. But just to be clear I have helped start 2 university groups, donated 1000s of $ and just broadly feel like I've put a lot of effort into trying to be part of this community. I didn't do it for something in return but at some point I have to admit the status incentives in this community aren't great and doing the things (I think) are high EV just usually leaves me feeling a bit let down if they don't happen to route through more legible EA status mechanisms. 

I usually just loop and spiral about this stuff but Torchbearer community is set up in a way that I can actually ask for support from people, people naturally have validated and supported me or even just felt open to giving constructive criticism. I'm left feeling more that in fact it is EA that is structured in a way that isn't great for me at least getting the types of support I need and I'd generally push most people in this community working on "generalist" AI safety to leave for greener pastures, though I don't have an exact sense of the best other communities. 

Just to hedge/clarify a little here, it's not like I thought hard about how to get the most out of the EA community or anything like that and broadly I'm pretty bad at playing the social media/network games effectively so probably I'm just complaining about being a suboptimal internet communicator. 

5. Social media is hard, Social media is bad, I'm bad at social media

To get attention it's lowkey hard there is a lot of competition unless you have a unique way to ragebait people. The most viral short I have posted is just a green haired hipster saying they are not a tanky but like some things about china??? Anyway I average 200-2k views on the shorts I post. I think I could make them even shorter and more clickbaity and probably double that? Surprisingly the algo on insta doesn't seem to care that much about how many followers you have? Like it matters but you can def get decent views just on the content alone. I have 120k views on my insta in the last month and I literally made my account a month ago. At the same time it's extremely hard to think about the value of a view on your short for like a million different reasons to the point where I'm not going to even attempt to explain my thoughts there, but overall I wouldn't be surprised if I would have had more impact the last month by simply handing out flyers to people I ran into. 

More than that, being at pdku the last 10 days has pushed me even harder into thinking social media is just obviously really really really bad for society. Giving literally everyone incentives to post whatever can siphon people's attention the most with no "elitist" oversight is a recipe for most people to consume slop. Unregulated social media newsfeeds is implicitly a choice about making our information streams garbage. Garbage in garbage out. Big brother yada yada, idc at this point, We are feeding utter garbage to 99% of the population and incentivizing all the journalists/educators/etc to make their content shittier and more garbage because we are all in a prisoners dillema to attention grab. I don't see how this is a good equilibrium for a democracy. Personally, I'd straight up basically ban most of this 2 sided market place content creation stuff over instagram and facebook or at least extremely heavily regulate what algorithms they are allowed to use, I really think it's that bad for society. To be clear I'm not saying people in xrisk/AIS should be defecting from this situation, just that it is a bad situation. 

6. I'm not sure what my theory of change is or should be

My content is somewhat of a mix between All gas no breaks, destiny/charlie kirk, and (less technical/informed) rob miles. I go on the street and talk to people about their lives and what they think about AI progress and philosophy, and sometimes I argue with them and sometimes I just listen and sometimes I just explain misunderstandings they have. I'm not exactly sure who my target audience is and what I'm trying to convince them of. It would definitely be helpful to hone this in.

Thanks for reading and I'm always open to receiving criticism or advice (about post or esp my content). 

Discuss

Virtually every plan to avert AI-catastrophe assumes legal regulation will remain a reliable tool. Plan A in AI 2040; slowdown in AI 2027, and several other projections assume that legal systems work as normal at critical narrative junctures. [Appendix 1]

Recent research, however, suggests that frontier models are exceptionally good at finding legal loopholes. Whilst modern legal systems have strategies for dealing with loopholes, they are slow and poorly equipped to deal with acceleration. If the tempo of AI development is sufficiently fast, this speed-mismatch might be fatal.

In this short article, I consider which strategies will remain robust for future AI regulations and assess their tradeoffs. Overall, I remain optimistic our legal tools can match superhuman loophole discovery - provided, that is, we anticipate this problem and adapt our legal institutions accordingly.

1. The unfortunate legal deviousness of Qwen3-30B

The most recent study on AI 'loophole discovery' is Wei Liu et al's Large Language Models Hack Rewards, and Society. They claim that, in addition to reward hacking, AI models engage in 'societal hacking':

where an RL-trained model discovers strategies that remain formally compliant, yet undermine the intended purpose of those systems, as illustrated in Figure 2:

Liu and his colleagues presented LLMs with optimisation targets inside simulated scenarios governed by historical legal regulations. Many loopholes had already been discovered and patched for these regulations.

They found the LLMs re-discovered 61.25% of those loopholes, without being prompting to do so, including loopholes which had only been proposed as theoretical possibilities. It also developed new loopholes[1] and adapted them after patches were iteratively applied. Patching the loopholes did not produce convergence: the LLMs simply refined loopholes to be more formally compliant, carefully evading the wording of patches.

Considering this was done with Qwen3-30B-A3B, this performance is quite impressive. It seems plausible that the cost of loophole discovery and adaption will fall rapidly in the next year.

2. Why care about loopholes?

You might think loopholes are a minor issue in AI regulation. Maybe we will physically concentrate AI research such that it can be monitored directly without fussing over rules?[2] Or maybe our regulations will be so clear, direct, and straightforward (relying, perhaps, on hardware metrics)[3] that they cannot be gamed?

These are unreliable assumptions.

First, AI development is a diffuse activity, and is likely to remain so. Whatever regulatory regime we put in place will need to coordinate large numbers of people at scale. If we want our regime to have the predictability which makes regulations tolerable and legitimate we will need to use some ex ante guidance.

Second, ex ante guidance, no matter how well-drafted and tied to hardware, can always be gamed, and this gaming reduces the effectiveness of regulation. Historically, rules resembling those proposed for AI regulation have been consistently evaded:

• Export controls are evaded by sitting just below prohibited thresholds or strategic re-designs. E.g: using networked sub-threshold commodity boxes to get around the 1990's CTP export-control metric.[4] Other forms of evasion involve manufacturing goods using subsidiaries in non-prohibited countries[5] and by arbitraging weakest links (e.g., West Germany in the COCOM program and, later, chemical weapons export controls);
• Licensing regimes are avoided by structuring activity to fall just within more permissible regimes/exceptions (e.g. the USML/CCL boundary gamed by skirting 'specially designed'/generic design and 'part'/'component'); by obtaining licenses for benign behaviour to build up capacity before later switching to military use; and by moving activity to unregulated regions - aka 'jurisdictional arbitrage' (e.g., spyware; fentanyl analogue production moving to India).
• Arms treaties are also gamed via threshold attacks - the most famous examples being the German 'Pocket Battleship' designed to skirt Versailles's tonnage limits[6] - and jurisdictional arbitrage (e.g., Weimer-USSR collaboration). As with Art VI in the NPT, lawful exceptions are used as a cover for capabilities build-up: NK and Iran used the 'peaceful space-launch vehicle' to design all the parts of an ICBM inside a civil satellite-launch program;

The 'Pocket Battleship' designed to get around Article 190 of the Treaty of Versailles

AI will speed up the discovery, diffusion, and implementation of loopholes like these, democratising what once required expensive legal advice.[7] More concerningly, AI development might require tighter legal nets than our loophole-ridden regulations have hitherto managed to achieve. As I will show below in sections 3 and 4, whilst existing strategies to confine loopholes work, they are leaky and slow, and are likely to become more so if AI accelerates the legal system.

3. How do legal systems deal with loopholes?

Whilst eliminating loopholes is probably impossible, they are not an intractable problem. Legal systems have traditionally used two methods to handle them:

First, they improve ex ante drafting. Law-makers spend more time during drafting to anticipate loopholes and creative compliance; they also amend existing statutes to catch common workarounds. This is expensive, however, and slows down the drafting process. it also incentivises regulated actors to find new workarounds, requiring more ex ante amendment, increasing the complexity (and cost) of law-following. Examples:

• MTCR Category 1 defined as "complete delivery systems with ≥300km "range" & ≥500kg "payload" (Guidelines and Annex);
• EAR ECCN 3A090, Part 774. List of Items Controlled: "integrated circuits having one or more digital processing units having either of the following: a.1. A `total processing performance' of 4800 or more; or... "

Second, they implement ex post standards. These are vague and unpredictable[8] - although not necessarily unprincipled - and bite once a breach has been committed.[9] Unlike rules, standards move authority from the law-maker to the judge. Leaving the law partially unspecified - typically for unusual and uncommon activities - deters actors from inventing new edge-cases but provides little guidance to good-faith actors. Examples:

• Principles like 'substance over form'; underlying purpose; catch-all clauses like 'economic groups', 'participating interests', 'actual dominant interests';
• Broad definitions, e.g., BWC Art 1: "Microbial or other biological agents, or toxins whatever their origin or method of production, of types and in quantities that have no justification for prophylactic, protective or other peaceful purposes."

Legal systems tend to use a combination of both.[10] The former provides certainty for the most high-frequency activities, whilst the latter is deployed on a case-by-case basis for low-frequency, anomalous activities.[11]

4. Does AI change the equilibrium?

AI might appear to produce symmetrical advantages in offence and defence: the same technology which discovers loopholes can be used to anticipate and patch them.

On balance, it seems there are overwhelming defensive advantages for regulators - provided, that is, that they use them. The default scenario, where the legal system barely changes , seems like it will produce a considerable offensive advantage to regulatees.

Defensive Asymmetries A. Environmental Control: Legal Systems control the legal landscape

The legal system defines the conditions for success in a lawsuit. Whilst denying legal challenges altogether might be impossible given various constitutional protections, and is unlikely to be acceptable for an international treaty, regulators could take actions to screen off the influence of AI-enhanced litigants.

One possible system, at least in the context of AI-regulation, would be to set up an independent body (domestically, a subset of the legal profession)[12] which verifies and adjudicates disputes. Whether this would be acceptable will depend on whether AI is perceived as being closer to the internet or nuclear weapons.

B. Environmental Control: Legal systems control the behaviour landscape

Something regulators have been doing for a long time is making behaviour more regulation-shaped. When it comes to loopholes, some of the most effective tools in reducing the 'attack surface' of the law involve restricting where and how AI research can be carried out.

In general, there is a tradeoff between autonomy and possibility for loophole-discovery. Creative compliance requires the freedom to be creative. The most effective ways to reduce loopholes are to implement a restrictive licensing regime (forbidden unless permitted) and to implement extensive transparency and monitoring rules.

C. Asymmetric Expertise: Capital and Frontier Restriction

Legal and political systems will, in some jurisdictions, have access to far greater resources than the vast majority of private actors. In a world where capital can be directly transformed into legal expertise, this directly translates into legal asymmetry.

The US government in particular could secure exclusive access to Frontier models, as was done with the June 2026 order, and, if it wanted, could impose monitoring restrictions on usage and inference caps. In this scenario, the legal-political system could race far ahead of regulatees when drafting its ex ante regulations.

D. Pre-preparation: Legal systems control the regulatory cycle

The legal system controls the pace of the regulatory cycle. A legal system can spend considerable time drafting its regulations (perhaps in private) and creating new regulatory institutions before releasing them. In addition, it can also undo previous regulations, amend them, or re-work them through creative interpretation.

Where access to Frontier models is restricted, this would produce a durable lead: a frontier model, given six months, may produce non-linear advantages over smaller models when drafting regulation.

E. New Affordances: predictable standards, flexible rules

AI could provide new affordances for the legal system. It might be possible, for instance, to escape the predictability-flexibility tradeoff inherent in the rule/standard divide.

One proposal is devise standards which are superhumanly scaleable via judicial emulation. Whilst this doesn't avoid the problem of legitimacy (judges remain unconstrained), it would render standards predictable.

Alternatively, another is to create rules which are infinite in number and adaptability. The 'regulation space' is not infinite: provided a regulator has enough data and processing ability, the entire behaviour space could be covered.

Offensive Asymmetries A. Agency gap: private actors can adopt new technology more freely

Legal and political institutions are constrained by bureuacracy and are likely to have delayed, uneven, and outdated adoption of AI. Further, as a matter of institutional bias, they are typically risk averse.

In contrast, private actors will have much greater autonomy in adopting AI. They will be faster, less bound by procedure, likely more technically competent, and are less risk-averse. Unless the legal system adapts, it is likely to be outpaced in the same manner legacy IT systems in the public sector are being outpaced by new AI cybercapabilities.

B. Speed gap: legal systems are slow by design

Legal and political systems are designed to be deliberately slow. It takes time to create new laws, and even more time to alter the process by which laws are made. This is known generally as the pacing problem:

AI exacerbates this problem by speeding up the pace of change. Regulation takes a long time to draft, pass, and amend, whilst AI development is continually accelerating. Say a bill is drafted with a frontier model in 2027: 3 months later, by the time it comes into force, countless loopholes are discovered by newer models.

Standards are also challenged: they work best for low-frequency behaviour which can be handled on a case-by-case basis. AI changes this dynamic: many independent actors can spin up their own plausible workarounds, all of which need to be taken on their merits. It takes weeks to go through their arguments, during which they continue their activity. In both cases the tempo of the legal system is too slow.

C. Success Conditions: the regulatees may only need to succeed once

Traditionally, there have always been asymmetries in breaking the law. Fraudsters can use their illictly-obtained money to pay lawyers to contest the legal actions taken against them; commercial actors breaking the law can scale up fast enough to produce political capital, along with resources for lobbying and lawyers for legal challenges, which deters regulation.

AI may present a more extreme version of this dynamic if the regulated activity directly produces legal expertise. Rather than having to make money and convert it into legal advice - which presents problems of selection, alongside the transaction costs of hiring lawyers - the regulated product itself can create elite legal arguments. A single loophole could thus compound into an industrial loophole-finding machine.

D. Information Asymmetry: the regulatees know more about the regulator than vice-versa

The regulator slowly drafts and promulgates their regulations in public, providing a wide attack surface. The regulatee, however, is changing their behaviour in private. I suspect when you apply AI to both sides, the regulator's advantage in ex ante drafting will fall behind the regulatee's advantage using AI in conjunction with private information.

This asymmetry is made worse by the plausible introduction, following the falling cost of legal advice, of new actors which regulators have hitherto mostly ignored. These actors can take advantage of previous loopholes discovered by existing large firms, whilst providing unseen challenges to regulators.

E. Enormous attack surface: the legal system is huge and largely unsystematised

Countless areas of law work solely because the cost of hiring lawyers to avoid the rules is prohibitively expensive. This will change as AI reduces the cost of legal advice. Furthemore, large chunks of the law will become increasingly outdated as AI analysis is applied in areas previously 'unlawyered.'

The legal system thus offers a both large attack surface, and one which, like most public IT systems, has large sections which have not been subjected to intensive red-teaming. Even if specific AI regulations are insulated from AI pressure, it is likely large portions of the law will become unreliable. Nor, at present, are systems in place to correct these problems.

5. What does a robust regulator look like?

Regulators and policymakers should think carefully about how AI will alter the legal system, rather than simply assuming it will continue as usual.

From the discussion of offensive and defensive asymmetry above, the two most important lessons are:

1. The legal system defines the conditions of success and can therefore harden its regulations against AI-enhanced loopholes;
1. Without exclusive access to Frontier models, the legal system faces a huge deficit in tempo compared to private actors.

Provided legislators are alive to this problem, and we think ahead of time what sort of special legal regimes are realistic, the first of these is achievable when it comes to high-stakes regulations. It is not, however, scalable across the entire legal system.

More broadly then, the system must be ready to change to meet new circumstances; it must possess 'Radical Optionality.' A successful regulator will do the following:

1. Create channels in which frontier models can be used to improve ex ante regulations, and thus offset possible asymmetries in loophole discovery
2. Build mechanisms for adaptive re-drafting so that new legislative drafting tools can be used to outpace loophole production; [13]
3. Build in flexibility to legal regulations, using broad standards, such as catch-all lists and purposive definitions, to ensure regulators are not hemmed-in by misspecified laws;
4. Modify courts, or implement specialised adjudicatiors, to handle potentially enormous volumes of adjudication.

As it stands, most inside and outside the legal system hold law as a constant in their forecasts of the future. Whilst we have no idea what happens to legal doctrine when it is accelerated, we do have a good idea what happens when regulators are outmatched in legal expertise: exports slip through the net, tax is uncollected, and nuclear weapons proliferate.

Appendix: Proposals relying on law

A brief survey of policy proposals for AI safety shows their high reliance on conventional legal systems. For instance:

• AI 2040
  • The' Incremental AI Policy Wishlist includes a range of policies enforced legally: disclosure obligations on firms; export controls; R&D verification;
  • Plan A includes chip manufacturing monitoring; pauses on training runs; and transparency in monitoring;
  • Plan A also seems to assume tax law is working more or less as usual in 2032, with the main hiccup being mass automation rather than a change to how the law works.
• AI 2027
  • The Oversight Committee is created legally and the Defense Production Act is critical to legitimising the President's use of power;
  • One of the key ways given to prevent 'power grabs' is to align AI in a manner that makes them law-following;
  • US-China competition - one of the cruxes of the narrative - is eventually defused with international treaties.
• A Narrow Path
  • Recommends a variety of bans on types of AI (superintelligence; AI which can escape its containment; AI self-improvement);
  • Relies heavily on a national regulator with training, compute, and application licenses;
  • Recommends an international treaty, alongside an international AI tribunal, to produce an equilibrium.
• Keep the Future Human
  • Relies on legal compute caps; enhanced liability; safety and security standards; compute oversight;
• Superintelligence Strategy
  • 'Multi-agent madness' is prevented by tethering AI agents, via unique IDs, to human-backed legal entities;
  • Non-proliferation machinery running on BIS-style licensing regime with record-keeping, end-use checks, criminal penalties, verified decommissioning
  • Safety relies on insurance and liability schemes

As I suggest in Part 1 above, these proposals are plausibly vulnerable to the kind of exploits discussed in this paper.

1. ^

   The validity of these loopholes were assessed by LLM judges and human lawyers.

2. ^

   AI 2040, for instance, seems to assume most AI R&D will be carried out in a few locations which can be monitored directly by a single organisations.

3. ^

   E.g., the robust attempt to define a frontier model here: https://law-ai.org/wp-content/uploads/2024/09/Legal-Considerations-for-Defining-Frontier-Model.pdf; and Caputo's analysis of regulating after the pre-training paradigm ends: https://arxiv.org/abs/2502.15719

4. ^

   A cycle repeated after Nvidia designed the A800/H800 series to get around the 2022 interconnect-bandwidth limits prompting a renewed limit in 2023, the H20 in response, and a new 2025 licensing scheme. Another angle of attach is Chinese firms skirting the restrictions by renting cloud access in Indonesia, with the 2025 AI Diffusion Framework and the proposed Remote Access Security Act as regulatory responses.

5. ^

   Met in turn by Foreign Direct Product rules, and more gaming of their triggers. Huawei, for example, spun up countless entities to get around FDP's Entity list. Redefinitions to include entities 50%+ owned were evaded with 49.9%-owned shells, and so BIS responded with a "significant minority ownership" red flag.

6. ^

   Other examples include the treaty cruisers designed to get around the Washington Naval Treaty in the 1920s, and the torpedo boats to evade the 1930 London Naval Treaty. Note that all of these examples also involved cases of misstating and underestimating tonnage limits. In general, loophole discovery tends to go alongside deceptive non-compliance.

7. ^

   A good survey of the economics of loophole discovery is Fleischer, Victor, Regulatory Arbitrage (March 4, 2010). U of Colorado Law Legal Studies Research Paper No. 10-11, Available at SSRN: https://ssrn.com/abstract=1567212 or http://dx.doi.org/10.2139/ssrn.1567212. Briefly, Fleischer notes the main economic constraints on tax avoidance are transaction costs; opacity costs; agency costs; and information costs/counterparty risk, all of which are lowered by AI.

8. ^

   There is some disagreement over this point. The canonical statement of the tradeoffs between rules and standards is Louis Kaplow, Rules Versus Standards: An Economic Analysis, 42 Duke Law Journal 557-629 (1992). C.f. McBarnet, D., & Whelan, C. (1991). The Elusive Spirit of the Law: Formalism and the Struggle for Legal Control. The Modern Law Review, 54(6), 848–873, which suggests that rules are not, in fact, more predictable and are themselves ex post devices. I have tended towards Kaplow's account, at least in the context of technical legal regulations, because I believe legal reasoning can produce determinate guidance, and that following this guidance is often necessary for logistics and legitimacy.

9. ^

   Many jurisdictions use 'General Anti-Avoidance Rules", or GAAR. These are statutory provisions which allow courts to re-determine the tax liability of avoidance transactions to neutralise any tax savings. The US and UK tend to use common law anti-avoidance rules, which are more contextual.

10. ^

    David A. Weisbach, "Ten Truths about Tax Shelters" (John M. Olin Program in Law and Economics Working Paper No. 122, 2001). Legal systems also use institutional devices like delegating authority to fast-moving agencies, and disclosure rules for new legal structures (e..g UK's DOTAS rules and EU DAC6).

11. ^

    Noting that the line between the two is often fuzzy: bright-line rules have a tendency to become more 'standard-like' over time (and vice-versa). See Schauer, Frederick. "The Convergence of Rules and Standards." New Zealand Law Review (2003): 303-328.

12. ^

    There are precedents for this in military contexts: the US CIPA (1980) rules on classified trials; UK special advocates for SIAC; and Atomic Energy Act hearings.

13. ^

    A possible model here is the 'regulate first, collect comments later' approach of the Bureau of Industry and Security (BIS) and the Office of Foreign Assets Control (OFAC). These come with their own costs in terms of compliance chaos and definitional ambiguity.

Discuss

Crosspost from: https://inputlogic.substack.com/p/woke-nonsense-in-competitive-debate

I. Context

For the last 2 weeks, I’ve been at a Lincoln-Douglas debate camp—mainly to learn more about philosophical arguments. The instructors and people there were generally very nice and much more intelligent than me, but the majority of arguments and ideas discussed in the debate space make a caricature out of the traditional stereotype of out-of-touch, far-leftist academics who are able to launder otherwise outlandish ideas because of how disconnected they are with what’s going on within the rest of society.

I had no prior LD experience before going to camp, so I’m likely only acquainted with a fraction of the nonsense that is taken seriously by competitive debaters.

II. Critical theory

The most popular form of argumentation seems to be Kritiks, or critical theory arguments that mainly critique an underlying assumption of the resolution (e.g. the resolution works within a capitalist/colonialist system instead of trying to that system, so people debating the topic are racist/classist/colonialist).

They tend to be overly vague critiques that work for every topic; this is an easy way to know that most Kritiks have horrible epistemics—if every topic chosen is seriously racist and debating just about any topic makes you racist, that must mean there’s something seriously wrong with the organization making the topics!

It seems quite absurd to then work within the racist debate system instead of trying to reform/abolish it. Keep in mind that these topics are voted on by students and coaches alike, making it impossible for me to believe that critical theory debaters believe most of the nonsense they say. Many debaters also claim that fiat (the act of implementing the resolution) is racist because it forces minorities to put themselves in the shoes of the racist policymakers that oppress them.

This claim is batshit insane. You can’t assert that implementing policies is racist—that assumes the policymakers are overall racist—which assumes the policies they implement are racist. A textbook case of circular reasoning! I assumed that critical theory debaters would be better at intuitively applying various logical tests to their extraordinary claims, but apparently that’s too much to ask for.

A constructive negative case usually contains several disadvantages of implementing the topic’s resolution, along with a counterplan of what should actually be done. Kritiks are usually negative and follow a similar structure, having several “links” on why the affirmative case is [racist/colonialist/ableist/sexist/-ist], and an alternative that acts like a counterplan to doing the aff.

Critical debaters also read frameworks that tell the judge to prioritize the “real-world impacts” of their opponent’s blatant racism over anything else in the round, nullifying what the opposing debater has to say about the resolution.

III. Links

Their links to this are usually quite stupid. An example of a genealogical link would be:

1. All political capital is built on the oppression of indigenous people. [insert evidence from a critical race scholar]
2. Working with current political capital means you endorse the oppression of indigenous people.
3. My opponent needs to use political capital to implement their plan.
4. My opponent is racist so they lose the round.

Critical race theory warrants tend to be overly saturated with buzzwords and critical language (e.g. “onto-epistemological erasure”, “ontological death”), presumably to compensate for their lack of epistemic backing. Looking at premise 1, it’s completely untrue. Just because some political capital has been linked to indigenous oppression doesn’t imply that indigenous oppression is the only way political capital functions, or that all teleological functions of political capital are colonialist. This is analogous to claiming that “Bob saved 5 people from dying but he inadvertently killed a baby while doing so, ergo saving 5 people from dying is bad because it means you endorse baby-killing”.

Premise 3 is usually how critical debaters generate links and offense through any topic possible; your plan takes place within the United States? Colonialist! Does your plan need western involvement to work? Colonialist! Your plan talks about colonizing stuff? You’re obviously a colonialist! A lot of far-leftist discourse involves broad, sweeping categorizations of entities being guilty of perpetuating different -isms, while simultaneously forgetting why those -isms are bad in the first place.

Racism has almost always been bad because it prioritized the interests of certain people while deprioritizing the interests of other groups based on factors like skin color, factors which made no noticeable difference in the outcomes of such decisions. There are clear genetic differences between different “races”, but our classifications for those races are largely inaccurate and abitrary—so making preferences based on those classifications is obviously irrational.

Colonialism has historically been bad because the people doing it were racist fanatics who disregarded the interests of natives already living in the land they colonized. If there actually happen to not be any natives on a bunch of land, you can settle on it without necessitating something harmful! Previous colonizers could’ve used the excuse that “there are no natives” to take land from indigenous groups, but that only implies that they lied when making such claims, not that claiming “there are no natives” in a certain territory means you’re endorsing stealing land from native groups.

Critical theory is often used in debate as a mechanism for genealogically debunking ideas by critiquing how those ideas came into fruition; it’s pretty undeniable that critiquing the formations and foundations of some ideas is a great way of exposing bad ideas. Despite this, even if debaters avoid mistaking inadvertent consequences as logical implications and avoid applying sweeping generalizations of certain -isms to institutions, Kritiks have even more epistemological pitfalls.

When critical theory genealogically “debunks” all capitalist or American institutions, accepting it as true acquires a universal defeater for any proposition that is an inherent product of the illogical institutions; inclusive of the critical theory itself. You can defend the theory by telling people to make “exemptions” based on the epistemological state of the claim itself, but then you have to concede that if something has terrible genealogy but good epistemics, it’s still true; therefore, a theory that attempts to genetically debunk a massive group of claims provides substantially weaker evidence for the claims being false than proving it wrong on a substance level.

A racist could still have a belief tied to his racism that is true—an example of this could be a racist person acknowledging genetic differences between various races—albeit he is acting irrationally by assuming he can discern between different races and extrapolate conclusions based on his own faulty racial classifications.

An example of a rhetorical link would be:

1. The concept of “empty land” has historically been used to remove indigenous people.
2. The concept of empty land is therefore racist and colonialist.
3. My opponent says there is empty land in space.
4. My opponent is a colonizer so they lose the round.

Rhetorical analysis is always something that irks me when it comes to debating topics. Analyzing rhetoric provides a narrow range of shallow argumentative conclusions, none of which are unique to rhetorical analysis (remember that you can always, always explain why a claim is false with other mechanisms).

Tons of debaters seem to treat verbal slip-ups or inadequacies as chances to throw around -ism allegations, immediately picking up “harmful rhetoric” like sharks smelling blood in the water—except it’s almost always an unsubstantiated claim.

If I make a claim like “all lives matter”, that doesn’t mean I’m a white supremacist! The claim itself is completely impartial and serves a ton of utility when talking about foreign aid or saving lives in places people usually aren’t concerned about. White supremacists have stated that all lives matter before when propagating white genocide conspiracy theories, but it’s completely irrational to isolate that statement, ignore all the anti-racist evidence often read to include minorities, and then claim your opponent is a white supremacist.

In a population of 1,000 people where only 100 people ever say “all lives matter”, including the 1 and only white supremacist, a random person telling you all lives matter only raises the probability that they’re a white supremacist from 0.1% to 1%. Returning to the real world, we can see that even marginal increases in the chance of someone being a white supremacist are not guaranteed. Populations and statements within a country of 340 million people are much more diverse in the way they interact.

Rhetorical indictments are so obviously pointless and undeductive (at least for the vast majority of use cases in debate) that the only explanation of their prevalence in debate is the need to win the ballot. The majority of debate kids don’t give a fuck about campaigning for indigenous people, nor do they ever attempt to implement their outrageous alternatives to ordinary policy suggestions.

There are many other examples of links.

Representational links:

1. My opponent frames indigenous people as requiring special assistance.
2. This frames indigenous people as moral ends through a white savior narrative.
3. My opponent is promoting white supremacy and colonial morality.
4. My opponent is racist and should lose the round.

Epistemological links:

1. The opposition backs up their claims with performance charts and statistics.
2. Those metrics are inherently oppressive and capitalist.
3. My opponent is reinforcing the system of capitalism and should lose the round.

There are also ontology links, methodology links, etc.

This article would be way too lengthy if I covered them all, but I’m considering going over them in another article.

IV. Conclusion

The primary motivation of doing high school debate is to succeed in getting admitted into a prestigious school, not to rationally ground your ideas and become the next generation of policymakers. It’s now very apparent that a focus on tournament wins and bids has replaced the foundational values of debate. If everybody prefers running arguments that let them pick up easy rounds over preserving what competitive debate was made for, asymmetric arguments easily upset the balance in the debate community.

If your goal is to out-sell your opponent in the marketplace of ideas, you should at least be penalized for promoting epistemologically cheap, low-quality products at outrageous prices! Instead, a community with little to no integrity will experience a universal pivot to selling low-quality products at exuberant prices, because all self-interested shopowners see an opportunity to make a profit.

Ultimately, the shopowners have to judge rounds and apply these ideas somewhere—if they never utilized these ideas, debate would be completely devoid of any utility (education is the only “portable skill” of debate, as the debaters like to say). In this case, the propagators of bad ideas mutually hurt each other and shift the whole market negatively. If bad ideas in the debate space make contact with markets in the rest of society, selling poor ideas at expensive prices isn’t a feasible strategy. Debate is now shifting away from the rest of societal discourse, insulating bad ideas generated there in a far-left echo chamber where they are immunized from better ideas.

Competitive debate will become less of an educational policymaking space and more of a delusional, out-of-touch space where trashy arguments can be given a facade of intellectual rigor—if current trends persist! There are tons of brilliant people in the debate space, people with potential to create the next generation of revolutionary ideas, but nothing said in rounds will come to fruition unless the majority of people start opposing the trend of reading nonsense in rounds to win the ballot.

In a healthy community, you should be able to push back against massive overton shifts without sacrificing much personal gain; LD debate seems to be so far gone to the point where nobody is willing to fix it.

(What’s even worse is the amount of deontology glazing going on in LD! Literally everyone is a deontologist, and when people mention philosophy, it translates to Kant. Lots of people hate utilitarianism because it’s “racist”, and LD “philosophy debaters” seem to agree that the problem of induction completely wipes out consequentialist theories while Kant is a perfectly rational tautology).

Discuss