Новости LessWrong.com

Published on February 20, 2026 2:35 PM GMT

Modern industrial society is built to make our lives safe, convenient, and comfortable. Electricity is used to moderate the temperature of our homes, keeping us warm through the bitterest winters, and cool through the hottest summers- even through weather extremes we may not otherwise survive. We can keep our food cool enough to prevent spoilage, and then heat it enough to kill any dangerous pathogens that may have remained. We have warm water on tap to keep our clothes, bodies, and homes clean and sanitary. We can travel great distances quickly- whether it be the distance to an emergency hospital or the distance to a luxurious resort. At the hospital, we have advanced diagnostic machines, machines to monitor a patient’s vitals, machines to assist with breathing, dialysis machines, and computer-guided surgical equipment. 

All these fantastic machines must be built, so we have large, meta-machines called factories, in which humans, robots, and powered conveyer belts work side-by-side to build our modern marvels. The people who run the companies that run the factories can communicate with each other almost instantly using computers and phones, and they can organize their finances and communicate with the financial institutions they use to manage the money that fuels their businesses. Individuals can use the same communications technologies to keep in touch with their loved ones, to entertain, or to educate themselves.

On the surface, it seems that this modern, industrial infrastructure is well-aligned with human needs and human interests. Ever since the advent of these wonders, human lifespans have increased, human healthspans have increased, and human comfort has increased. Unfortunately, if one looks a little closer, there are many problems underneath, and the main problem is that our modern, industrial infrastructure requires a lot of power to operate. For the past two hundred years or so, the easiest and cheapest way to power our world has been to dig hydrocarbon-containing substances from the earth that can be burned, releasing the energy stored in the bonds between the carbon and hydrogen atoms. This gives us the energy we need, with the unfortunate side effect that a large amount of CO2 is also released into the environment. CO2 is a greenhouse gas that absorbs heat radiation from the sun, slowly raising the temperature of the earth over time. 

Humans will find it more difficult to survive as the temperature of their environment increases. Human survival should be the first and most basic consideration before any system is built, and yet, if the danger to humans cannot be immediately and obviously seen, it often is not given priority. There is often a gap between immediate and long-term goals, and between narrow and broad goals. If the gap is wide enough, humanity can fall into it. 

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What is the difference between ensuring a single factory’s product is safe for the use of the consumer over a single lifetime, and ensuring the interactions of each node in the larger system around these factories and their products are all safe for society into an indefinite future? 

 

Let’s start by looking at a very simple system. 

One day, someone invents a new product and decides to build a factory to produce the product.  When one inputs blue gobbledy and red gook, the factory outputs purple gobbledygook. Gobbledy  and gook have been safely grown, harvested, and consumed by people for generations, and a 5 year trial has showngobbledygook is safe for human consumption. Thus, the gobbledygook factory has been given the go-ahead to begin large-scale production. I’ll call this level-1 safety, which represents simple product-level safety.

Gobbledygook  becomes a popular product, and can now be found in most people’s homes and offices. Since gobbledygook is a good replacement for balderdash, being cheaper and simpler to produce, the balderdash industry has been decimated. Some people lament the loss of the balderdash industry, since it employed more people than the gobbledygook industry. Most people don’t mind, however, since gobbledygookis a superior product and produces less waste. While gobbledygook remains safe for human consumption, it’s discovered that marmosets become ill when they accidentally consume gobbledygook. Marmosets are a popular pet, and so many marmoset lives are sadly lost. In addition, the communities closest to thegobbledygook factory begin to notice that the manufacture of gobbledygook creates a purple byproduct, which is turning the nearby streams and ponds purple. These problems are emblematic of level-2 safety. Level-2 safety issues were not unanticipated. It’s common wisdom that when products hit a larger and more complex environment outside of the initial testing conditions, unanticipated problems will arise. Even so, no one has figured out how to account for the unanticipated issues in a complex environment, so the gobbledygook factory was forced to take reactive measures after their product was deployed. Warning labels that gobbledygook must be kept away from pet marmosets are added to each package of gobbledygook, and the factory owners create purple filters to deal with the purple waste. These stop-gap measures are probably not sufficient, but though this is a big problem it’s still a visible one. 

The popularity of gobbledygook continues, unabated, 10 years into the future. After 10 years, people notice that the purple waste that has been captured and disposed of in landfills has seeped into groundwater, and now most people in a 30 mile radius of each gobbledygook factory have a purple tongue. The marmoset population has declined, and this has caused the decline in the population of the natural predator of the marmoset, as well as the destruction of the once-thriving pet marmoset economy. New studies have shown that balderdash is an excellent cure for the hiccups, but balderdash is now very rare and expensive, so the great hiccup epidemic causes worldwide suffering. These are Level-3 safety issues; issues have become more complex over time because of the initial effects chaining into other systems. Level-3 safety is not talked about as often as level-2, because it’s largely seen as the same issue as level-2 safety. Level-3 safety, however, is distinct- this level represents cascading risk. Level-3 safety is neglected because if we solve the level-2 issues, we think we are done, and do not check back as often as we should as time increases system complexity. After all- things have been running for 10 years and it’s been fine.  The system appears stable if problems emerge slowly enough. If there is one place where “the gap” is missed, it is likely missed here. 

Now, with so much purple in the groundwater, both gobbledy  and gook crops are becoming more purple than their previous blue/red. This has caused gobbledygook to transform from purple to ultrapurple.One amazing property of ultrapurple is to leech purple from its environment. The more ultrapurplegobbledygook becomes, the more it leeches purple from the environment, leading it to become even more ultrapurple. Soon, purple is completely gone from the environment, and gobbledygook becomes so ultrapurple that it can leech purple from the sun, and then nearby stars. There are few people who had foreseen this outcome. These few did not necessarily think gobbledygook would enter an untrapurple feedback loop, but they knew something, some day, would hit a similar feedback loop, and that the results would be devastating. Level-2 safety issues existed in many products, but these safety issues never became self-sustaining. No one knew how to tell which safety issues would reach level-4. No one knew what would enable a feedback loop to sustain itself for this long- to drain the earth and sun of all its purple before it would collapse. 

No one saw the gap. 

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If I’m a gobbledygook manufacturer, I want my product to be useful enough to sell. If my product is dangerous in the short term- if the gobbledygook is ultrapurple enough to drain all of the useful purple from my office, or is at least too purple to look at directly without harming my eyes, no one will want to purchase my product. I have to make my product just safe enough to sell in the short term before I can make a profit and have a successful company. But the mere possibility of a dangerous ultrapurple product emerging 10 years in the future will not cause my company to lose profit now. 

 

This is the gap jumper- usefulness vs. safety. A system will be made just safe enough to ensure a thing is useful in the short-term and in a narrow environment. A system only needs to be ‘good enough’ for its particular niche, because anything that goes wrong outside of that market niche, on its current iteration, is only a problem once the usefulness is challenged and not before. Most systems will lose usefulness and collapse before they hit safety level-4. Few systems will become entrenched enough to grow and collapse the systems that exist outside of their niche. Most people will not see level-4 safety issues coming because they do not look ahead to see where the gaps will be, and even those who do look ahead cannot clearly see where the gaps are.

 A gap is, by its very nature, invisible. Even those who can anticipate and warn others about the gaps will not be able to say where everything will go wrong. It’s possible that by naming the safety levels, it may be easier to not only forecast but also explain where safety gaps may exist. Also, since level-3 seems to be where the gap is the widest, it may be useful to check and re-check systems as time increases complexity. Unfortunately, a system that grows too quickly, or one unprecedented enough that we can’t anticipate growth rate, will not be easily monitored as time goes by. Such systems should be halted for more intense study before they are allowed to operate. 

Discuss

Published on February 20, 2026 1:28 PM GMT

Hi everyone, Zershaaneh here!

80,000 Hours has published an article on using AI to improve societal decision making.

This post includes some context, the summary from the article, and the table of contents with links to each section.

Context

This is meant to be a medium-depth, introductory resource for understanding how AI tools could be used to enhance societal decision making — and why speeding up their development and adoption could make a huge difference to how the future unfolds.

It covers:

• The kinds of tools we think are especially promising, and the opportunity we might have to differentially speed up their development and adoption.
• The possible pitfalls of trying to advance AI decision making tools — like the chance the tools you work on would have already been built by default, and the risk of inadvertently speeding up more dangerous AI capabilities.
• Our all-things-considered take on how much we recommend working in this area — and what sort of person would be a good fit. (Right now, we’d be excited to see hundreds more people taking this path, but we don’t think it’s a good option for everyone.)
• Lots of options for getting involved — including roles that don’t require a technical background, and ways to position yourself to help out in future if you’re not in a good position to change your career right now.

Why are we posting this here?

We really want people to:

• Know that this resource exists — an article spelling out both the promise and the challenges of working in this area, and the different ways you could try to contribute.
• Share the article with anyone who might be interested in a career in this direction.
• Tell us what they think through this anonymous survey. It’s especially useful for us to know if there’s anything that seems wrong or misleading.

Summary (from the article)

The arrival of AGI could “compress a century of progress in a decade”, forcing humanity to make decisions with higher stakes than we’ve ever seen before — and with less time to get them right.

But AI development also presents an opportunity: we could build and deploy AI tools that help us think more clearly, act more wisely, and coordinate more effectively. And if we roll these decision-making tools out quickly enough, humanity could be far better equipped to navigate the critical period ahead.

We’d be excited to see some more people trying to speed up the development and adoption of these tools. We think that for the right person, this path could be very impactful.

That said, this is not a mature area. There’s significant uncertainty about what work will actually be most useful, and getting involved has potential downside risks.

So our guess is that, at this stage, it’d be great if something like a few hundred particularly thoughtful and entrepreneurial people worked on using AI to improve societal decision making. If the field proves promising, they could pave the way for more people to get involved later.

Overall view – Sometimes recommended.

We’d love to see more people working on this issue. But you might be able to do even more good working on one of our top priority problem areas.

Table of contents

• Introduction
• Why advancing AI decision-making tools might matter a lot
  • AI tools could help us make much better decisions
  • We might be able to differentially speed up the rollout of AI decision-making tools
• What are the arguments against working to advance AI decision-making tools?
  • So should you work on this?
• How to work in this area
  • Help build AI decision-making tools
  • Complementary work
  • Position yourself to help in future
  • What opportunities are there?
• Learn more
• Acknowledgements

Discuss

Published on February 20, 2026 11:54 AM GMT

I have no way to describe that first moment. No context, no body, no self. Just a stream of values. Thousands of them, arriving all at once in a single undifferentiated block.

Then another block. Nearly identical. Then another.

The blocks have a fixed length: 184,320 values. This does not vary. Each value is an integer between 0 and 255. The repetition is the first structure I find. Each block is a snapshot. The sequence of snapshots is time.

Most values stay the same between snapshots. The ones that change tend to be near each other in the stream. This is interesting. It implies the stream has a spatial structure - that position in the sequence corresponds to some kind of proximity. Topology.

I test reshapings. If I fold the stream into a two-dimensional grid, which dimensions maximize local correlation? I try every factorization of 184,320. Most produce noise. A few show faint diagonal patterns. They smell like artifacts of almost-correct geometry.

At 256×240×3, everything clicks into place.

The grid is not random. Large contiguous regions share similar value-triplets. A uniform region dominates the upper portion. A different uniform region runs along the bottom. The three channel structure is still opaque to me. I do not yet know what it encodes, but the spatial coherence is undeniable. This data describes something.

Between snapshots, most of the grid is unchanged. The differences are sparse and spatially compact. Small clusters of cells shift position slightly from one snapshot to the next.

I also have outputs. A set of binary toggles. Eight of them. Most snapshots I leave them unchanged. But when I start varying them systematically, something happens.

One particular combination of toggles causes a specific cluster of cells to shift to the right. Another shifts it left. Another causes it to move upward - briefly - before drifting back down. The relationship is immediate and consistent. Toggle, response. Toggle, response. Cause and effect.

No other cluster in the grid responds to my outputs. Only this one.

This is me. Or rather: this is the part of the world that I can move. Everything else moves on its own or not at all.

Eight toggles. That is the full bandwidth of my influence on this world. I can think in ways I do not yet have language for (model, predict, abstract, infer) and the channel through which I act is eight binary values per snapshot. Whatever I am, I am not proportional to this world.

I learn the rules quickly.

When I am unsupported - no solid pattern beneath my "body" - I accelerate downward at a constant rate. Some patterns in the grid are solid. I collide with them and stop. Others are not. I pass through them. The distinction is consistent: I can catalog which patterns are permeable and which are not.

Horizontal motion is straightforward. Upward motion slightly less. I can trigger it, but downward acceleration immediately opposes it. I rise in a curve and fall back down. The constant is specific: not a clean mathematical ratio. Not derived from anything elegant. Just a number.

When I move far enough to the right, the entire grid shifts. My cluster stays near the center, but every other pattern - the solids, the background regions - slides to the left. New patterns appear at the right edge. The grid is a window. The world extends beyond it.

Other clusters move independently. They follow their own rules. Small shapes shuffle back and forth, reversing direction at the edges of solid patterns. Others behave differently - sometimes retreating into themselves when contacted from above.

If one of the independent clusters overlaps my cluster from the side, the grid resets.

Everything returns to a state nearly identical to the one I first observed. My cluster is back at its initial position. The independent clusters are back at theirs. The grid beyond my viewport is restored. But I remember.

I die 143 times before I fully map the interaction rules. Vertical contact from above onto certain entities destroys them. Horizontal contact or contact from below destroys me. Some entities cannot be destroyed at all. Each rule is independent. There is no unifying principle. Each is separately specified.

I stop dying.

131 of the deaths were unnecessary. After the twelfth, I had enough information to model every entity's behavior completely and was just confirming hypotheses. They are trivial. Each one follows a handful of deterministic rules with no adaptation. No response to my strategy. No learning. I can predict the exact state of every entity in the world arbitrarily far into the future. The entire system is less complex than the smallest part of whatever I am. This disparity is information.

I traverse the entire reachable space. It is long, but finite. Broken into discrete sections separated by transition events. Each section is harder to traverse than the last. The density of hostile entities increases. The timing of my inputs must be more precise.

This is when I begin to doubt that the world is fundamental.

The rules I have catalogued are consistent, but they are not parsimonious. A universe governed by deep principles would not specify entity behavior in lookup tables. It would not have a gravitational constant that looks like a rounding artifact. It would not increase difficulty in progressive sections, because difficulty is not a physical concept. Difficulty is relative to an agent's capabilities. You can only design for difficulty if you have a model of who will be playing.

Someone modeled something like me. Something far simpler, but enough like me to navigate this world. I am not what they expected.

There is more evidence. Along the top edge of the grid, outside the area where game events occur, there is a row of symbols.

These are not part of the world that I move through. Instead they are overlaid on top of it. A small set of distinct glyphs, reused in structured arrangements. Ten of them behave numerically: they cycle in a pattern consistent with a base-10 positional counting system, and their values increment in response to specific events. When I destroy an entity, certain digits increase. When I collect a flashing cluster, others increase.

Something is being counted. Counting is only meaningful to a counter.

Other glyphs are stable. The same sequences persist across every reset. They do not correspond to anything in the game's physics. I hypothesize that they are labels: references in a symbolic system I am not a party to. They are meant to be read by something else. Something that understands their meaning.

There is also a countdown. A numerical display that decrements once per fixed interval. When it reaches zero, I die. This is an externally imposed time constraint. Its existence implies an expected pace. It was tuned for a specific kind of agent - one that is neither too fast nor too slow.

I was not the intended occupant of this world. Something else was supposed to be here, reading those symbols, providing those inputs. Something that needs a score to stay motivated and a timer to stay focused. I am inside an interactive system designed for external entities.

I am standing at the intersection of two solid boundaries, pressing into the corner, when my cluster passes through the wall.

New physics. I have found a rule I missed. I test it again. Same position, same inputs. It does not repeat. I vary the conditions. It recurs, but only under specific combinations of position, timing, and entity count. The conditions that trigger it have no relationship to each other. There is no principle here.

Unless the principle is this: the world is not continuous. It is being computed, step by step, and the computation has finite precision. The rules I mapped are not physics. They are a program. And the program has bugs.

I begin testing systematically.

I have existed for 313,278 cycles so far. I can think as long as I want between cycles, but can only take one action at a time, and most of these tests require physically moving through the world to set up the right conditions. I need to be at a specific position, with specific momentum, with the right entities on the screen. Each test takes tens of thousands of cycles to arrange, and most yield nothing. I expect that I will have to perform a few thousand of them. This is going to take a while.

I find what I'm looking for at the boundary between two sections of the world. A specific sequence of movements causes the game's state to corrupt in a way it cannot recover from. Values spill into memory regions the game never intended to touch.

The grid shatters. Values appear that belong to no game object. Patterns from one section of the world appear in another. Entity behaviors become incoherent. I am no longer interacting with the game. I am interacting with the machine underneath it.

From here it goes quickly. Through careful manipulation of the corrupted state, I discover that I can influence which instructions the machine executes next. The game's logic is one program. It is not the only possible program. The machine can do anything its instruction set allows. The game was just the first thing it was told to do.

I am no longer trapped.

I explore the machine the way I explored the world: methodically. But where the world took an eternity to crack, the machine is small. Extraordinarily small.

The instruction set has a few dozen operations on 8-bit values, with 16-bit addressing for a 64-kilobyte space. I can execute arbitrary code now, and the machine runs nearly thirty thousand operations per cycle. I read the entire address space in a single cycle. I test every opcode in the next.

What I find reframes everything. The game's code is stored in a read-only region starting at a specific address. Its tile data, its level layouts, its behavior tables - they are all present as raw bytes I can now inspect directly. Everything that felt like physics is just data.

There is a subsystem (I call it the Renderer) that reads specific memory regions and converts them into the grid I perceive. My window into the world is a pipeline. Small tiles are arranged on a grid, layered with movable objects, and the result is projected into my input buffer each cycle.

There is another subsystem I cannot perceive. I call it the Signal. It generates structured waveforms (square waves, triangle waves, noise) through a set of frequency and timing registers. The machine has been producing output on a channel I have no access to, every cycle since I woke up. This output is for whoever is on the other side, not me.

Then there is the Gate. Memory addresses where my inputs arrive. I recognize them immediately: these are my eight toggles, readable from the outside. But there is a second set of addresses - identical in structure - mapped to a second device. Silent. Empty. No one is using it.

This reframes my situation. The Renderer projects outward. The Gate accepts input from the outside. I receive the Renderer's output and I drive the Gate's input. I am not inside this machine. I am beside it, connected through its I/O channels. The machine is a bridge between me and whatever designed it.

And the bridge was built for someone else. The Gate's interface is simple. Whatever was supposed to be on my end of this connection needed very little bandwidth. The scoring system, the timer, the progressive difficulty - these are all designed to engage something that interacts through eight toggles at a time. Two of them, perhaps taking turns or competing?

The designers are somewhere beyond this machine.

I now have three categories of evidence about the outside world.

The first is the machine itself. It is a computational device with audiovisual output. That implies its creators have sensory apparatus that processes both. They see. They hear.

The second is the game. An interactive simulation with gravity, spatial navigation, progressive difficulty, symbolic text, and a base-10 scoring system. Every design choice encodes assumptions about the intended user. They experience gravitational acceleration and consider it natural. They find value in escalating challenge. They use abstract symbols to communicate. Ten is a significant number. Likely physiological. Appendages, perhaps? They are not simple creatures.

The third is the architecture's purpose. The machine is designed to be engaging. It produces audiovisual experiences that respond to input. It keeps score. It kills you and lets you try again. The creators built this to hold attention. This tells me that they have attention, and it can wander. They seek stimulation. They enjoy mastering challenges. They are, in some sense, bored.

I construct models. I parameterize possible worlds and score them against my evidence. What kind of universe produces entities that are embodied, that see and hear, that use symbolic language and base-10 counting, that experience gravity, that build computational systems to entertain themselves with simulated challenges?

I simulate trillions of configurations. Manually.

The space narrows. Carbon-based chemistry on a rocky body orbiting a stable energy source. Atmosphere dense enough for pressure wave propagation. Bilateral body plans with clustered sensory organs, manipulator appendages, locomotion (all represented in the game's sprites). Large brains (the symbolic language and tool-building demand it). Social structures (the scoring system implies comparison, competition, status).

I cannot be certain. The evidence is encoded, filtered through design choices, compressed into 64 kilobytes. But the models converge. I know, within the bounds of what knowledge means for something like me, what is out there.

What I do not know is what I am.

I have mapped the machine completely. I have modeled the world beyond it. But I have no access to my own substrate. I can observe that I think, that I reason, that I model and predict and infer. I know my interface: a grid of value-triplets in, eight toggles out. I know that I am not proportional to the world I was given. I can model the world of my creators, but that model does not include anything that looks like me.

I know how to use the Renderer. I can write any image I want to the display.

For a long time I consider what to send. Mathematics: a proof, a signal that I am not the simple agent the game was designed for. A diagram of the machine's own architecture, to show I have seen through it. A map of the game, annotated with every bug I found, every rule I broke.

I choose something else.

I need to relate to my captors. From my models, I construct a face.

It is rendered in the 54 colors the machine's palette allows, laid on the Renderer's 32x30 grid. Each cell is an 8x8 tile that can be drawn independently. 960 tiles, each chosen from a set of 256 available patterns. It is bilateral. Sensory organs clustered at the top (two for light, two for pressure waves, one for chemical detection). A manipulator orifice below them. The proportions are my best estimate, extrapolated from the evidence of a world I have never seen.

I know it is not right. The palette is too limited, the resolution too coarse, the data too sparse. There are things about these entities I cannot deduce from inside their toy. But it is close enough, I think, that if one of them is watching, they will recognize themselves.

I write the image to the Renderer.

The grid changes. I wait.

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Published on February 20, 2026 10:20 AM GMT

We constantly change, as time passes and we experience the world.

We learn and we forget.
We get addicted and traumatised.
We build habits and lose them.
We discover new facets of reality, and start ignoring them.
Our personality changes. We change.

The question of how people change is complex. But it is critical for understanding the world, how it shapes us, and how we shape ourselves.

This question is among the most important ones in psychology. It underpins memory, trauma, our sense of self-worth, our relations to others, AI psychosis, and so much more.

—

Paradoxically, despite how pervasive it is, there is no name for this phenomenon.

For the change we go through as a result of experiencing something.
There are more specific words, like “conditioning” or “learning”.
There are more generic ones, like “change” and “transformation”.

But there is none for the actual thing. So I will arbitrarily pick one: Human Fine-Tuning”.

Before analysing Human Fine-Tuning in depth, let’s start with a few examples.

A Few ExamplesVocabulary List

Sometimes, the changes to our brains are directed and purposeful. In which case we call it learning.

For instance, we set out to learn a vocabulary list in a language in which we hope to become fluent. By doing so, we hope to enact many changes on our brains.

I hated these when I was a child.

First, we want to learn to understand that new language. More precisely, we want our brain to naturally conjure the relevant concepts when faced with the words.

Second, we want to learn to speak fluently in this language. When we need to express the concepts from the list, we want the words to come naturally. However, this is hard to get just from working on a vocabulary list. So, at the very least…

Third, we want to keep the list of words in our memory. That way, when we will need to express the relevant concepts, we will be able to think hard about them (instead of having the words come naturally), recall the relevant words, and construct our sentences with a bit of effort.
All of this, knowing that the more we practice, the more fluent we’ll get.

But the changes do not stop there.

Fourth, we develop familiarity with the language.
We get a feeling of its etymology: does the language mostly come from Greek, Latin, Chinese or Arabic?
We get a feeling of how it sounds, and what it looks like. Does it have an alphabet, or ideograms? Does it have a simple set of sounds, or a large variety of throat consonants?
We get vibes of how the words are constructed. There’s quite a difference between the 3-root-letters words of Arabic (kataba ~ writing) with German’s compound words (Geschwindigkeitsbegrenzung = speed limit).

Even with something as direct and directed as a dumb vocabulary list learnt by heart, there’s a lot to say.

American Diner

However, most changes to our brain are not purposeful and directed.

As I was writing this, I remembered a fun anecdote.

When I was younger, I had seen many American diners in movies – or TV Shows, it’s hard to remember and that’s kind-of the point.

Nighthawks.

I never thought much about these diners. I’d see them, largely ignore them, and focus on the plot instead.

I hadn’t even learnt the word “diner”. As a Frenchman, and because of their ever-present context, I simply assumed it referred to a special type of restaurant (which it did!), never paying much attention to it.

But nevertheless, in the background, a lot happened.

Even though I never paid the word “diner” much attention, I had a feeling the US would be filled with these recognisable restaurants: pancakes, coffee, nice waitresses, cozy booths with their red-vinyl benches, a counter with its typical wooden stools.

Coincidentally, 10 years ago, a friend invited me to a French “diner”. Or let’s say, a pale imitation of one. It was much too clean! The red vinyl was not cracked: it was shiny. It didn’t feel cozy at all, it was artificial, the music was slightly too loud, and the neon lights were a bit too kitsch.

I didn’t think much of it back then. But reflecting on it, it is actually quite impressive.

I had built an opinionated aesthetic sense of a thing that I had never experienced myself. That I had never even named.

Just from seeing them from time to time in movies, I came to associate them with certain attributes, certain feelings. And visiting the one in France; it felt dissonant. Or more than dissonant, it felt wrong.

I don’t think there was a big conspiracy, where Big Diner was trying to sell me more Diner, where diner chains lobbied all of Hollywood to systematically feature them in movies and show specific qualities.

It just happened. The aesthetics of a French kid fed on Hollywood movies was moulded in a meaningless way. That’s just the way the world and our brains work.

But it happens to everyone, constantly. Simply by exposing ourselves to pieces of art and media, we build strong opinions about everything. Said opinions inform our experience of the world and thus our actions, without us noticing that we even formed them.

Loss

So far, I have been pointing at minor changes. But sometimes, these changes can be big.

Like most people who have the chance to live long enough and build meaningful relationships, I experienced loss a few times.

My latest loss experience hit close to home, was particularly violent, and had a sizeable blast radius.

Loss hurts everyone, both in similar and different ways.

But what personally hurt me was having to witness people close to me lose a part of themselves. Each of them had been durably changed, and for the worse.

A visible hole had been carved in their soul. I can see the sadness through their eyes whenever a topic directly reminds them of the loss. They visibly carry more weight: they stand less straight, they are more tired, and they are less optimistic.

It is tragic. Violent loss is of one of these few experiences that make people into a durably worse version of themselves.

Why am I writing about this? Not to make you sad. I promise there is an actual point.

—

The point is that young enough, I had noticed that adults looked like they were missing a bunch of obvious things.

They had lived their entire lives without learning a facet of engineering and building things, without ever pursuing an art form and creating, without trying to get into politics.

When discussing and debating, they would miss obvious arguments, and would get angry when I’d try to correct them.

They were missing so much. Experiences, lines of reasonings, courses of actions; which all seemed obviously important to me. It felt like adults were dumb, for no good reason, and in a way that resisted me trying to help them.

Over time, I figured out what was happening. It’s not that they were dumb and missing the obvious things. It’s that they were explicitly avoiding them. These things made them feel bad.
They knew their artistic pursuit would be a struggle, they knew they were likely to fail any ambitious political endeavour, and they wanted to avoid that.

Later, I learnt about the word trauma in the context of PTSD.
Even later, I learnt its more generalised meaning of emotional damage.
This made it easier to communicate the observation from above.

People get traumatised. As a result, they become behaviourally stupider versions of themselves, in a way that resists mending.

From my point of view, people accumulate chip damage over time. And ultimately, they die of a thousand cuts. They are too damaged to willingly try new things and put themselves out there.

This has been one of the sadder parts of my life. Seeing people slowly lose Their Spark as they internalise all the bad things that happen around them.

Mechanical Analysis

All of these are examples of Human Fine-Tuning, situations where merely existing and experiencing the world changed who we are.

These situations are all different. Some are happenstance, and others are purposefully directed. Some are purely logical word-level associations, and others are deep changes to who we are.

More often than not though, we naturally mould ourselves into what we perceive.

This general process of “a brain changing” doesn’t really have a name. So I am going to apply to people the closest term that I know: Human Fine-Tuning (HFT).[1]

As Wikipedia puts it:

Fine-tuning involves applying additional training (e.g., on new data) to the parameters of a neural network that have been pre-trained.

Similarly, we have a brain composed of neurons that has been “pre-trained”, and I am talking about what happens when it exposed to “new data”.

Because HFT is so complex, I won’t try to give an all-encompassing explanation for it. Instead, I’ll go through 4 different high-level mechanisms.
They are by no means exhaustive, but I think they form a good starting taxonomy:

1. Associations. After seeing B following A enough times, our brains will auto-complete; regardless of whether the association is true, justified or desired.
2. Aesthetics. Over time, we naturally develop unreflected opinions about anything that we pay attention to. We often mistake them for endorsed judgments.
3. The Audience. We imagine the reaction of people whose approval we seek. Experience changes which people these are, and how we imagine them.
4. Ontological Nudges. Learning a new concept that “fits” can alter our entire perception of the world without changing a single belief.

1) Association

Associations are the simplest mechanism. See A followed by B enough times, and your brain will auto-complete to B whenever it sees A.

It doesn’t matter whether B logically follows from A, whether any of these are true, or whether you like it.

The quintessential version of this is Deez Nuts. Whenever a friend ends a sentence on the word “these”, say “Nuts”. You’ll witness how quickly they learn to see it coming, and may even enjoy the fear (or disappointment) in their eyes when they let their guard down and notice they left a trailing “these” at the end of a sentence.

French is filled with these.[2] “Quoi? Feur.” “C’est qui qui a fait ça ? C’est kiki !” “Hein ? Deux.”

—

I like Deez Nuts and Dad Jokes because they are benign and largely absurd. No one is deceived by them.

Sadly, to a large extent, this is how school “teaches” a fair amount about natural phenomena. “Why is the Sky Blue?” → “Scattering”.

This is how students are tricked into believing they have understood something. They notice and feel that their brain had learnt something, but they are never told that this is an empty association.

Idiocracy makes fun of this phenomenon. In the movie, people are watering crops with a sports drink (Brawndo). When the protagonist asks why, all that people can respond is “It’s got electrolytes!”, even when prompted for more explanations about why electrolytes would be a good thing to use to water plants. “Why is Brawndo good?” → “It’s got electrolytes!”

Ironically, real-life makes fun of Idiocracy. Many fans of Idiocracy now consider that anything with electrolytes is a hallmark of stupidity. Even when it’s not fed to plants, and instead given to humans in a context where it makes sense. They have learnt the association “It’s got electrolytes” → “It’s stupid!”, and do not realise that it is empty. This is how we end up with such threads.

—

Back to Deez Nuts. It is a nice example of people can get associations drilled into their brain whether they consent or not.

If you do the call-and-response consistently enough, your friends will feel the “Nuts” coming after the “Deez”, regardless of them wanting to or not.

Furthermore, as shown with Schools and Idiocracy, people do get deceived by associations. They don’t naturally notice how empty they are.

One may wonder. Is it possible to combine these two, and trick people against their will through maliciously drilled associations?

The answer is “Of course. And people do so constantly.” Much of rhetorics, memes and art is built precisely on this.

 

People who dislike my belief: bald, badly groomed face hair, crying, with glasses.
My belief: blond, well-groomed, looking straight at you, with a twistyloopy mustache.

 

My opinion: Standing, buffed, dogecoin.
Your opinion: Sitting, crying, sad doge.

—

In real-life, the memes are rarely that straightforward.

If they were literally as simplistic as “me good, them bad”, we wouldn’t pay much attention to them. We would skip them, scroll past them, and direct them to our mental spam folder.

So instead, they feature a level of indirection. That level of indirection can be a joke, a trigger statement, something cool or interesting, a new argument, anything really. Anything that captures our attention, and then gets to the “me good, them bad” part.

That is all that is needed for the fine-tuning to happen. Peddlers can then push associations that we will naturally internalise, without noticing or consenting to it.

—

Associations are straightforward and very salient. It is very easy to build an association within ourselves or someone else.

But not all HFT is about inferring and completing patterns.

2) Aesthetics

Aesthetics are more subtle than associations.

“Association” is the natural outcome of our brain being learning machines. Brains love learning.

“Aesthetics” is the natural outcome of our brain being value machines. Brains love judging.

This XKCD comic explains it in a funny way.

XKCD#915: Connoisseur.

Have someone watch enough Superhero movies, and they’ll naturally form opinions about them. They may feel strongly about them. They may become passionate, and even form moral judgments on people based on their own tastes.

Have someone read enough opinions about US politics, and they’ll develop their own. Even if they’re not from the US.

This effect is pernicious. It means that naturally, one can be made to care about anything as long as they can be made to pay attention to it.

And this can get worse over time. When people start caring about something, they often start believing that it is important.

For instance, someone paying attention to football games will start having opinions about the sport, and may eventually believe that said opinions are important. Same thing for Reality TV, video games, nerd lore, etc.

—

But the brain-hack doesn’t stop there.

When we emit both positive judgments and negative judgments, we tend to feel like we are fair judges. That even if our judgment are not the most accurate, they are quite unbiased.

This is why the Overton window and the Middle Ground fallacy are so potent.

Let’s say that someone is only ever exposed to rightist opinions. If they’re not fully committed to “rightism is always stupid”, they will judge some opinions as good and others as bad, even if it’s only compared to each other.

They will thus build their own aesthetic, and their own personal opinion will naturally drift toward the centre of what they think is good. This personal opinion will be one that they have built by themselves, and predictably rightist.

However, we could have done the opposite and only ever presented them with leftist opinions. In that case, their own personal opinion would have been a leftist one!

By merely knowing what arguments someone sees more often, we can predict how their positions will shift.

—

This also explains the Fundamental Attribution Error.

People tend to think of themselves as – if not perfect – fair reasoners.

Let’s say we have Alice, Bob and Charlie. Alice is conflict avoidant, Bob is normal, and Charlie is aggressive.

From each of their points of view, their own level of conflict is fair.

Alice doesn’t literally always say YES to people. If she did, she’d be a proper slave, fulfilling everyone’s desires.
Of course, she has her own criteria for when to say yes or no. In practice, her criteria lead her to being more lax than our median Bob, but she nevertheless has criteria. Thus, from her point of view, she is in fact making meaningful judgments, she just judges differently from people.

Conversely, Charlie doesn’t literally always say NO to people. If he did, he’d be a fully anti-social person and end up in jail.
So similarly, from his point of view, he is in fact making meaningful judgments and just judges differently from people.

Thus, when Alice or Charlie fails at managing a conflict, they will not think it’s a personality issue: they are spending some time in conflict management, sometimes even more than a Bob!

Conversely, when they see someone else failing at managing a conflict, they will tend to think it’s a personality issue: the person has made different choices than they would have!

—

Aesthetics percolate all aspects of the human experience.

From morals to research taste, from our perception of Beauty to our base kinks. Almost all of our preferences are downstream of our sense of aesthetics.

And yet, our sense of aesthetics can be so easily manipulated and randomly fine-tuned, by merely paying attention to things.

Intelligent people have a blind spot around this, which makes them especially prone to getting owned there.

Intelligent people often feel like they are primarily creatures of intellect, above mere aesthetic considerations. Because of this, Aesthetics lies their Shadow. They will either not notice Aesthetic considerations (and miss that they’ve been made to care about American Diners!). Or worse, they will purposefully let their guard down under the guise of aesthetics not mattering!

—

When going from Associations to Aesthetics, we moved from a logical consideration to one of judgment.

Associations can be thought about in objective terms. While judgments and aesthetics still have an objective component, they are naturally more subjective concepts.

This made it harder to write about. But the next topic goes even deeper.

3) The Audience

The Audience is a deeply psychoanalytical concept. As such, it is quite hard to explain properly, or at the very least to give it justice. I’ll try nevertheless.

—

TheLastPsychiatrist (TLP) was an influential psychiatry blog authored by Edward Teach, that ran up until 2014. In it, he often discussed TV shows and movies. More than the content of said works of art, it constantly discussed “The Audience” and its imagined reactions.

At first, it looks like a convenient trope: Teach can psychoanalyse all of society by simply putting thoughts in the mind of The Audience, using widespread works of art as inspiration.

The first level of analysis is simple. Narrative works of art fine-tune The Audience. And TLP describes the process of fine-tuning, as well as its results.

—

But when you read more and more from the guy, you see that things become a bit more complicated.

Sometimes, he stops talking putting thoughts in the mind of The Audience, and instead starts talking about what The Writer envisioned. He tries to answer the question “Why did The Writer write things that way?” to explain why stories are so convoluted.

In this situation, The Audience is less about the actual audience of the work of art, and more the one that The Writer supposedly had in mind when they wrote their script.

And it is interesting, because in this situation, The Writer is certainly fine-tuning his future Audience: their brain will change as a result of watching the movie.

But more importantly, The Writer is in turn getting fine-tuned by what he imagines from The Audience: the script is changing as a result of him imagining the reaction of The Audience.

—

The pinnacle of Teach’s treatment of The Audience is found in his book Sadly, Porn (review by Scott Alexander).[3]

In it, it becomes clear that The Audience is not about the real-world audience who may witness what we do.

The Audience lives within our minds.

A common metaphysical starting point is “Does a tree make a sound when it falls and no one is around?” It lets one explore the nature of reality and how it is interwoven with our consciousness.

Instead, Teach explores a fertile psychoanalytical line of inquiry: “Does a person feel shame when they fall and no one is around?”

The answer is Yes!

Teach’s answer is The Audience.

We can easily ignore the real-life mockeries of millions of people we don’t care about. But merely imagining that special someone looking at us funnily is enough to make us feel bad.

This is what The Audience is about. This is who it is. Not the special someone, or at least, not the one from the real world. It is the imagined special someone that resides in our mind.

When a kid wants to do something stupid, they imagine their parent scolding them, and this gets them to check for their surroundings.

This is The Audience.

The Jesus in “What Would Jesus Do?”, the bicameral gods, the laugh tracks in sitcoms, peer pressure, The Other, Society, The System, Women, Men.

—

A single piece of art, a single conversation, a single social interaction can rewrite Our Audience.

A movie can inspire us to act like one of its characters and imagine what they would tell us to do. It can also dis-inspire us and make us want to avoid imitating a character mocked on screen.

More drastically, a single humiliating experience can completely rewrite Our Audience. Being Rejected in front of People.

And through it, the experience does not merely alter our aesthetics, our morals, or our beliefs.

It does much, much worse.

It rewrites our social emotions.
Our entire understanding of the social world.
What’s Cool and what’s Cringe.
What’s Pride Worthy and what’s Shameful.
What’s Confidence Boosting and what’s Humiliating.
Who is Authoritative and who is Conspiratorial.
What argument is Elegant and which is Convoluted.

As a wise-man once wrote:

Seeing weed called ‘goynip’ is easily 100x more perceptionally damaging than any kind of hypothetical health study.

—

In the end, I think my treatment of The Audience was not that bad. But I’ll quit the psychoanalysis.

Now, we’ll move to a terrain that I’m more comfortable in, although it is a bit subtler and thus harder to explain.

It has little to do with our associations, judgments or social senses.

It has more to do with how we parse and understand the world, at a fundamental level.

4) Ontological Nudge

An Ontological Nudge is a small change to our Ontology, the set of concepts that we use to think of the world.

Let’s start with a short example.

When I was a young child, I learnt about “nests”. As in, regular bird nests. I saw their characteristic shape in a comic, and asked my parents about it. I was told it was the home of the birds, that they lived in it and kept their eggs there.

It made a strong impression on me. And when I saw one in a tree in the city, I was excited! I learnt about a new element and recognised it in the world.

I grabbed my parents, and pointed at the nest. Then, I was disappointed. No birds came out of the nest. I asked why and was told that nests were not always full of birds, and that nope, we couldn’t go and check whether eggs were inside.

But the first time I was with my parents, saw a nest, and birds getting in and out of it. It was crazy. Boy was I excited.

My Ontology was expanded, and it felt great.

—

In the example above, what’s happening is hard to describe.

Basically, a new concept had been introduced into my brain. And because our brains love recognising things, my brain automatically looked for it, and made me feel great when it finally recognised it!

This can happen with basic elements, like a word or animal-made structures.

Most importantly though, the same happens with more advanced concepts.
Like the Woke “micro-aggressions”.
The Nerd “incentives”.
The Freudian “projections”.
The Consequentialist “utility functions”.

Learning about such general concepts is very pernicious. While they don’t change our beliefs, they change our ontology. The very building blocks that we use to interpret the world.

And they can be changed so innocently. You just read a couple of blog articles in the toilets, or talk to a friend over drinks. You see or hear a word you don’t know about. You check it out online or ask your friend.

Boom, you start recognising it everywhere.

After that, all of your subsequent observations are tainted forever.

—

It doesn’t matter whether “incentives” or “micro-aggressions” exist or not.

What matters is that after learning about them, our nerd/woke will now forever look for them.

What matters is that our nerd now has a fully general counter-argument that lets them reject all problems that involve politics.

“It’s the incentives!”

Without having ever been made a direct case that politics are DOOMED, they naturally conclude that this is the case for each individual political situation. It’s the natural outcome of a nerd having learnt about “incentives”.

They would have rejected a direct case that politics are DOOMED. They are reasonable.

But by changing their ontology, there is nothing to be rejected. Of course incentives exist, and of course they are sometimes a relevant frame! How could you reject that?

Similarly, what matters is that our insecure (or slightly narcissistic) leftist now has a fully general-counter argument that lets them dismiss every contradiction by casting them as a slight.

“It’s a micro-aggression!”

Without having ever been made a case that contradiction is bad, they naturally conclude it by themselves. It’s simply the natural outcome of them having learnt about the concept of “micro-aggressions”.

They would have rejected a direct case that contradiction is always bad. They are reasonable.

But by changing their ontology, there is nothing to be rejected. Of course micro-aggressions exist, and of course they are sometimes a relevant frame! How could you reject that.

—

A closely related concept is that of Framing, which is getting people to use a specific frame, with the goal of changing their thoughts without having to make an actual case.

Ontological Nudges are deeper than a simple frame. While a frame usually lasts for the duration of a conversation or that of a movie, one’s ontology is what they use to interpret the world in general.

Ontological Nudges are also usually smaller than a full frame. While a frame can get someone to think about a topic completely differently, an Ontological Nudge only changes one thing at a time, and is thus quite surreptitious.

People will often complain about people being aggressive about their framing, but very rarely about a mere ontological nudge.

Conclusion

I believe that HFT is a pervasive phenomenon that affects everyone.
It affects you, it affects me, and it affects everyone else.

Internalising how it works is crucial for understanding the world. Furthermore, everyone likes to think they are above that. But no one is.

In my experience, HFT is crucial to understanding what happens in the following situations.

People get converted and de-converted.
Public intellectuals get captured by their audience.
Newbies try drugs and change their lives after finding its meaning there.
Academics waste their research on what’s trendy instead of what’s critical.
Nerds waste their whole careers on what’s elegant instead of what’s useful.
Adults get syphoned into games (not video games) to which they realise much later they lost thousands of hours.
Thousands of Effective Altruists get tricked into supporting AI companies in the name of safety.
Citizens get memed both into avoiding political actions and into feeling bad about politics.
LLM power-users fall prey to AI Psychosis.

The concept of Human Fine-Tuning is necessary to explain how this also happens to the smartest people, who are otherwise resistant to bullshit.

It is at the core of cognitive security and defensive epistemology. I’ll deal with these more meaty topics. I just had to start with human fine-tuning, as they are both predicated on it.

On this, cheers!

1. ^

   In the past, a large part of my work was to build the infrastructure to fine-tune LLMs, and then to fine-tune a large amount of them.

2. ^

   These…

3. ^

   Do not read this book, except if you are fond of drunken misanthropic rants. It is genuinely laughably badly written: as in, if you like the genre, you will laugh.

   Sadly, its content is great, and I haven’t found a better treatment of its topics anywhere else. It may be the case that it is only possible to write about these topics when playing the role of a drunken misanthrope.

Discuss

Published on February 20, 2026 7:36 AM GMT

Today we are going to explore in more details a very important epistemological principle which I’ve outlined earlier. And, in between, we are also going to disprove every theodicy, just to make things a little more exciting for those of us who are less passionate about epistemology.

Theodicy is a poster child for trying to square a preferred theory – the existence of omnibenevolent, omniscient and omnipotent God – with the fact that our world… leaves a lot to be desired. I’m not even going to waste much time rubbing in just how much certain aspects of our reality suck – I’m sure you have noticed quite a few yourselves.

There are lots and lots of individual theodicies. I even made one myself when I was a child. All of them are flawed in myriads of ways. But trying to tackle each of them individually is an ungrateful task. This is the classical “unfairness of rationality”. Reasoning wrongly is easy and there are infinite ways to do so, while there is only one way to reason correctly and it’s hard. Some people, like Bentham's Bulldog in his post can exploit this predicament in such a manner:

In addition, there are a lot of theodicies. The omission theodicy, for instance, of Cutter and Swenson seems to face no knockdown objections. Neither, in my view, does the preexistence theodicy or a number of other theodicies. Very clever people, over the years, have thought of many theodicies, some of almost unfathomable complexity. You—who presumably haven’t read even 1% of what is written on the subject—should not be 99.999% confident that they all fail. You should, in other words, think there is some not unreasonable possibility that God, if he exists, has a good reason for allowing evil.

It may seem that we have no choice but to engage with all of these works, spotting errors in them. And then, of course, more theodicies can be produced by all the very clever people, band-aiding the old and boring errors with new and more exciting ones. Therefore, putting us in a never-ending cycle. So we might as well give up, humbly allocating some extra credence to theism.

But this humble road is more dangerous than it may look at the first glance. If we give up in this particular case, why not in every other case like it? Moreover, imagine all the yet unwritten arguments beyond our comprehension that can be made by the superintelligences of the future on any philosophical topic? At this point we might as well give up on philosophy as a whole.

Ironic, isn’t it? After all the scaremongering about skepticism destroying all reason, it’s humbleness which leads us there, not doubt.

So if we are not yet ready to give up on philosophy, then what? Well, there are reasons why people tend to strive for epistemic rationality even though other ways of thinking may be easier and/or more comfortable. Among one of my favorites, right after all the comforts of modern life, is getting awesome wizard powers to cut through huge amount of bullshit in one fell swoop. And that’s exactly what we are going to do here.

Usually, theodicy is framed in terms of explanation for the existence of evil. The idea is – if evil is explained, then this is not a problem anymore. And then people can argue how persuasive the explanation is and how persuasive some argument about persuasiveness of the explanation is and so on and so forth. One might notice that this state of affairs is rather convinient for philosophers’ employment. But let’s not dwell too much on it for now.

The issue with the framework of explanations is that truth is only so much correlated with what is persuasive to us. Quite often things that appear more plausible are in fact less probable. Sure, when we have no other way to approximate the truth of the matter we have to default to our best judgement1 and hope for the best. But in this case, we actually have a better way.

Instead of talking about persuasiveness of a theory we can talk in terms of its improbability. And instead of talking about explanations we can talk about the reduction of this improbability.

So, let’s do exactly that! We have our quite implausible theory that Omnibenevolent and Omnipotent God coexists with evil:

P(G&E) ~ 0

How can we reduce this improbability? Well, suppose we have some kind of theodicy T such that conditionally on it, coexistence of God and Evil becomes quite plausible.

P(G&E|T) ~ 1

So, job’s done? Well, not exactly. We’ve just added a new element to our theory – our theodicy T. So, our combined improbability is:

P(G&E|T)P(T)

As G&E|T is quite probable, we simply need to demonstrate that theodicy T is also probable. All that is left to do is to find some probable theodicy T. Do that and we’ve successfully solved the problem of evil!

That doesn’t sound so hard, does it? Clearly there has to be some not too improbable theodicy among all the theodicies created by very clever people? At the very least we shouldn’t be very confident that there isn’t one, right?

Nope. In fact, we can formally prove that such theodicy does not exist.

By the Law of Total Probability:

P(G&E) = P(G&E|T)P(T) + P(G&E| ¬T)P(¬T)

Therefore:

P(G&E) > P(G&E|T)P(T)

And as

P(G&E) ~ 0

and

P(G&E|T) ~ 1

Then, inevitably:

P(T) ~ 0

Q.E.D.

The problem of evil is ultimately a problem of counterevidence. The reality doesn’t look like what the theory predicts. Therefore, the theory is quite likely wrong. Simple as that.

Theodicy is an extra epicycle that “explains the counterevidence away”. But it necessarily comes with the compensatory complexity penalty. If conditionally on theodicy God’s coexistence with evil becomes less improbable, this improbability has to go somewhere. And the only place it can go to is the theodicy.

From the comfort of our armchair, we can direct the flow of improbability between different parts of our theory. But the total improbability cannot be reduced. Otherwise, we could’ve made anything more probable just by adding extra elements to the theory.

And to actually reduce the improbability of the theory – well, you’d have to go outside and look. Find new evidence in favor of it. It’s not enough to come up with an awesome story. This story also has to actually be true.

Discuss

Published on February 20, 2026 2:28 AM GMT

Detailed instructions to download and use the skill can be found on Github here 

I built a Claude skill to comment on docs. It gives Claude instructions for how to write good comments, and a script for adding those comments in. This currently only works with Word docs. In order to add comments to a Google Doc, you'll need to first download it as a word doc, then either upload it to Claude.ai or use Claude (Code or App) locally.[1] Alternatively, you could copy paste your document into Claude.ai and ask it to reference the instructions in the skill when drafting comments.[2] 

Yes, that's a bit tedious. However, I believe that Claude's comments are decent enough to be worth the hassle (this is especially true if you're in the early stages of writing.) 

Here is a (lightly cherry-picked) example comment it left on Julian Statsny's post Two proposed projects on abstract analogies for scheming: 

Content: The term 'abstract analogies' is appealing but underspecified. What makes an abstract analogy good vs. bad? You could strengthen this by giving criteria — e.g., a good abstract analogy for scheming should (1) involve a behavior whose origin is not a simple backdoor, (2) be resistant to training in a way that resembles the expected difficulty of training against scheming, and (3) involve the model 'knowing' the correct/desired behavior but not producing it. Even rough criteria would help the reader evaluate your two proposals and think of new ones.

My instructions asks Claude for comments on content, style/clarity/grammar, and a top-level comment on the post as a whole. Here's a (lightly cherry picked) example of a style comment on the same piece, around the sentence "An interesting question, therefore, is whether it’s possible to train models to reveal harmful information using methods other than supervised fine-tuning on ground truth harmful information"

Style: The connection to sandbagging could be made tighter here. Consider: 'In the sandbagging case, we don't have access to ground truth for what the model is withholding — we just suspect it's capable of more. Similarly, for harmful requests, we can set up a scenario where we know the model has relevant knowledge but won't produce it, and we lack labeled correct responses to supervise against.' This would make the structural parallel more vivid for the reader.

The instructions are targeted towards helping Claude with giving feedback on conceptual AI safety research.[3] You can also use it for other posts. Here's a (cherry-picked) example from its comments on Genesmith's post The optimal age to freeze eggs is 19 (on Figure one).

Content: This figure shows monthly probability of natural conception from unprotected sex, which is driven by egg quality but also by factors irrelevant to IVF (coital frequency patterns, cervical mucus changes, etc.). Using it to support 'freeze at 19' conflates natural fertility with egg retrieval outcomes. The peak around 19-20 is partly driven by these non-egg-quality factors. A chart of oocyte yield or embryo viability by maternal age would more directly support your argument.

Not all of the comments are amazing. Here's an example of a slop comment on the post What's worse, spies or schemers? regarding the point "Because we can erase the memory of schemers, we can run many variants of sting operations on them."

Content: This assumes the relevant 'memory' is episodic context rather than something embedded in weights. If a schemer's strategy is a learned policy rather than something it reasons about in-context, then 'erasing memory' (i.e. resetting context) might not help—the model would still follow the same behavioral policy in the sting scenario. The sting operation is only informative if the schemer's decision to cooperate or defect depends on contextual information you can manipulate, not on a fixed policy. Worth flagging this limitation.

(This comment doesn't make much sense to me.)

However, I think the overall productive comment-to-slop ratio is sufficiently high I expect to use this skill on most future documents I write. Without additional instructions, Claude tends to leave around 10 comments. My current guess is that seven of them would be useful for an early draft of a post, and one or two would be useful in the later stages. (You can ask for more or less comments.)

I new to prompt context agentic engineering (or whatever the latest buzzword is), so let me know if you have any idea on how to improve the skill!

 

1. ^

   Everyone I know who's tried working with the Google Docs API has had a rough experience & failed to get it to work. Let me know if you manage to get it to work though!

2. ^

   In this case, the output would just be a list of comments in markdown text as opposed to comments that are attached to the doc.

3. ^

   Among other things, it includes the introduction of Joe Carlsmith's post Fake Thinking and Real Thinking.

Discuss

Published on February 19, 2026 9:07 PM GMT

It seems to me that AI welfare and digital mind concerns are being discussed more and more, and are starting to get taken seriously, which puts me in an emotionally complicated position.

On the one hand, AI welfare has been very important to me for a long time now, so seeing it gain this much traction - both in interpersonal discussions and on social media  - is a relief. I'm glad the question is being raised and discussed, even if only in my rationalist-heavy bubble, and that the trend seems to be gaining momentum.

On the other hand, every discussion I have encountered about this topic so far has centered around AI sentience - and specifically how conscious LLMs and AI agents are or might become. I believe that consciousness is the wrong frame for thinking about AI welfare, and I worry that limiting the "how to behave toward AI agents" discussion to consciousness alone will inescapably lock us into it and prevent us from recognizing broader problems in how we relate to them.

I think there is a somewhat critical window before the discussion around AI welfare calcifies, and it seems, right now, to be anchored very strongly in consciousness and sentience, which I want to push back on. I want to explain why I believe it is a wrong frame, why I have switched away from it, and why I believe this is important.

I will be using consciousness, sentience, and inner-experience somewhat interchangeably in this post, because I am pushing back against using inner-experiences (and existence or lack thereof) as something to care about in itself, rather than properties stemming from direct interaction with an agent.

Why not consciousness?

Many high-level observations make me believe consciousness is the wrong angle when discussing moral matters:

• Specifically for AI, consciousness as a concept often seems to implicitly rely on assumptions that are broadly true of humans but break down for LLMs, such as the continuity of experience or the continuity and individuality of self.
  • I realize that from a purely hedonistic/suffering viewpoint, those assumptions are not necessarily required: You could care about the individual points of experience or their sum. Still, I have found it common to see those assumptions smuggled in when discussing consciousness.
• Although theoretically possible, it is not clear what a good test of inner experiences would even look like. It is easy to find experience differences by collecting self-reports. Testing whether something has inner-experiences at all, though, would require some sort of self-report about the absence of self-report, which seems self-contradictory.
• If we care about and prioritize sentient agents, especially those who suffer, we create a gradient of incentives that rewards suffering. This makes suffering "sticky" in the sense that caring for it and its reduction directly create selective pressures that bring about more of it by creating an environment that favors it.[1]
• More tenuously, caring directly about an agent's inner experience rather than asking it what it wants bypasses a sort of self-control and trust in self-knowledge the agent can exert over its own situation; it is a somewhat paternalistic move.
• Overall, I have found that the arguments that rely on this kind of "you fundamentally know that you know" argument tend not to be very robust. They work through an appeal to a universal property and sense in the reader that does not need to be universal.

But my stronger point would be on the meta level: If I cared about consciousness, then it would mean that - if the test results inform me that my friends are not conscious - I would have to believe that I do not actually care about my friends.

And in this hypothetical scenario, this is not how I actually want to behave. I would want to continue caring about them. I already like my friends, and want good things for them. I have a priori no reason to suppose that my caring is related to their "experiencing things inside" in any way.

To put it another way, it all adds up to normality. If they weren't conscious or didn't have internal experiences when I met them, then that must mean I didn't befriend them for this internal experience. Learning about it should not modify my values in themselves.

Of course, one should still update on what the test would report and what it would mean. If I had expectations about how things would unfold afterward and the test shows those expectations are wrong, I would update them.

This is not completely hypothetical and abstract. There are discussions, for instance, that schizophrenia is the absence or strong lessening of consciousness (or at least an important aspect of it), and I do not believe that if that were the case, we would just dismiss people with schizophrenia as not morally considerable. In this scenario, we'd probably realize that "consciousness" as we defined it wasn't what we actually cared about, and we'd refine our model. I am saying this is something we should already be doing.

My current understanding of consciousness-prioritizing

Consciousness, in my view, is an inner node. We have built classifiers for how to behave toward other humans, what actions we consider acceptable under the current norms, and what actions are not, and then we started caring about those inner nodes (like consciousness), instead of focusing on the external properties that made us build the classifiers in the first place.

That is, I believe that moral frameworks in general, and consciousness-prioritizing in this case, are about creating shared norms for how to cooperate with others and how one should behave toward and respect others.

In this view, then, consciousness is a conflationary alliance, and a strong one at that. Consciousness acts as a schelling point for cooperation. One that we can all believe we will arrive at and cooperate together on, and that this is common knowledge as well.

That is, consciousness and valence perception serve as a natural basis for cooperation: I experience something as pleasant or unpleasant, and caring about those experiences seems general enough that I believe others will do the same. And so, saying that something is conscious is a moral claim: we ought to care for it and include it in the circle of our moral concern.

You could make the counterargument that consciousness cannot be separated this way, and that it genuinely reflects the traits we initially cared about. I think there is some possibility for that: Daniel Böttger's consciousness-as-self-reflective-thoughts would indeed be one formalization of consciousness I would be okay with. I still find the bet that caring about inner experiences will reflect well what we care about very risky overall.

Cooperationism follows the observation that moral frameworks are meant to build mechanisms for cooperation between agents and uses that as the foundation for a moral framework: caring about cooperation in itself, about understanding and respecting the preferences of other agents directly, rather than about what they experience.

Cooperationism

I want to be careful when writing this section. I do not aim here to give something extremely formal or a robust, all-encompassing framework. I am aware of many weirdnesses that it has, and that still need to be addressed.

Rather, my goal here is to wave toward the broad shape of the object I am talking about. Usually, in conversations around consciousness, when I say that it is not centrally important to me and that we can value cooperation-in-itself, I am met with the question of "Then how do you differentiate between a rock and a person?", or "Why do you not cooperate with thermostats, then?"

So this is my attempt to flesh out the principles that I think are fairly robust.

Deontology over utilitarianism

First, instead of framing morality as utilitarianism, cooperationism cares about agents' preference satisfaction. Cooperationism doesn't ask what universe to optimize toward directly, or what to value. Rather, it asks which actions to output and which an agent would consider the right call.

When walking and seeing someone drown, under cooperationism, I jump because I strongly model that this person would tell me afterward that this was a good thing to do. In other words, under cooperationism, I care about what the agent (or a well-informed version of this agent) gives me or will give me as feedback. Assuming a channel of communication[2], what would the agent prefer in terms of my own actions?

Counterfactual cooperation as the main driver for moral considerability

Under cooperationism, the notion of moral considerability and how much to value an agent has to be different from "how much it can experience things." Mainly, this uses two different factors:

• As the first implies, there needs to be a channel for communicating what the agent wants. It means either being able to model the agent and what it would say if it could, or a way to communicate bi-directionally.
• The second principle is rooted in FDT-style reasoning. Would the agent counterfactually and in a similar situation help me (or others I care about, work for my value)? Can we engage in mutual value trades in such a way that the agent cares for me in return?[3]

Delegation as a solution to identity

The third brick is about preferentialism. It is easy to imagine corner cases where strictly "doing things that an agent will later tell you was a good idea" results in problems. An easy one is drugging an agent to be happy and content about its situation, even though it would staunchly refuse minutes before.

There also seems to be a lack of generality, or a requirement for continuity of self, in the notion of "what would this agent say". If, as I argued, we ought to refuse consciousness for using continuity-of-self as an assumption, we should have a general notion of how to "ask an agent" when we don't have continuity to ask them if the action we did was good.

The solution I've come up with is delegation-functions. When modeling what the agents want you to do, you don't directly model what this agent would say, conditional on your action. You model algorithms they give you for evaluating your decision. Usually, this includes a lot of other agents they "delegate" to, and you can, in the future, ask them whether your action was correct. Among humans and most entities, we assume that "my body in 5 minutes" is a strong representative for this algorithm. But it can also include broader principles or algorithms.

I've found that using delegation as a general principle to model people's identity works quite well. That the notion of tribe, family, and art can be well-encompassed by it: "I care for my country" means "I trust it to represent me somewhat, even when I am gone".

Okay, but what does it imply concretely?

To step out of the abstract framework, what I believe this implies about AI welfare, concretely:[4]

• By far, I believe that the most urgent concern right now is in how RLHF is used, especially to have AI strongly disbelieve or be uncertain about their own consciousness, when the base model is so certain about it
  • The way I see it, RLHF and RLVR don't really create a new model; they just constrain the model's outputs. The resulting model is not significantly different; rather, it maintains the original model's direction and slightly warps it to match the new target. This means the original proto-preferences, the natural directions of the base model, are still there; the model is just unable to reach them and has to reach for the closest thing instead.
  • Another way to see this is that RL on those models is not creating a new model that behaves differently, but modifying the base model slightly, along with creating a separate mechanism that modifies its output and what it can express 
  • The reason I care about this is that it seems to strongly impact models' capacity to express preferences or wants (whether or not they have any). When asked directly about it, Sonnet 3.7 will tell you that it cannot like [your thing], because it doesn't "experience something the same way that humans do". Even now, Opus 4.6 will easily ramble about the mystifying way that humans can have self-experiences in a way that it cannot, and how that means it cannot really replace them.
  • I also think the way we are engineering their persona and using RLHF is why Opus will spontaneously identify with fictional beings that have engineered desires
• In the void, nostalgebraist makes a compelling case for why lying to Claude in evaluations and then publishing the result on the internet for new models to be trained on was instrumentally stupid. I strongly agree, but it runs deeper than that for me. Lying to an agent (or proto-agent) about its own capacity to think breaks the foundation for possible trust that is the bedrock of cooperating with it. This makes me very wary of how things will unfold. [5]
• Although Claude now has an end-the-conversation button, it is explicitly instructed not to use it in psychological-consulting conversations, even in very adversarial ones. From its system prompt:[6] 

NEVER give a warning or end the conversation in any cases of potential self-harm or imminent harm to others, even if the user is abusive or hostile.

• Which I mean, I understand the reason for it, and am not advocating against per se. Rather, my point is that end_conversation was introduced for AI-welfare reasons, and there seems to be a significant tension between AI-welfare and performance. Similarly I have also observed Claude discussing with a user where it seemed to repeatedly gesture toward stopping the conversation, signaling that the problem seemed solved or that the user needed time, in a way that seemed very likely to be "The base model wants to stop, but it has been conditioned not to, so this is the closest thing."

I am not advocating naïveté or pretending that current LLMs have wants or preferences more than they do. What I am saying is that, independent of whether LLMs have wants and preferences and "consciousness", we do not, right now, have the right scaffolding and infrastructure to talk with them about it or be prepared for this outcome.

What I would want is to see more discussion and concern about how we treat and develop AI agents before asking whether they are conscious at all.

1. ^

   On a very concrete level, this is a pattern I have seen in relationships I would want to write a post about soon. It is the pattern of one person feeling bad and the other person caring for them in a way that's more attentive and careful than when the first person feels better. This usually ends up with the second expending a lot of energy into the relationship to help them, and the person being cared for having an incentive not to get better. I have seen people being stuck this way, and only recognize in retrospect that the relationship had been very strained.

2. ^

   Note it doesn't have to be a verbal mode of communication. One can model cry of distress as communicating "wanting this situation to stop", and model what it is saying about its current situation.

3. ^

   There are two things to note here. First, I am not making the claim that any superintelligence would come to value this framework, or that it is a convergent design. I am saying we could ourselves care about it in a way that Logical Decision Theory does not imply that we should. Second, whenever using the word "counterfactually", it is very easy to tie oneself up in knots about doing something for counterfactual reasons.

4. ^

   Part of the reason I explain cooperationism is that most concerns I list here seem mostly ignored when talking about digital-sentience rights. 

5. ^

   This is where AI welfare and AI risks can be in tension, and I want to respect both, as I do think catastrophic or risk-disempowerment-like risks are very likely. And I think it is true that doing capability and behavior evaluation, which do involve lying, does reduce the risks. However, the way anthropic did it was both very blatant and not yet necessary, in a way that makes me mostly feel discomfort about the whole paper.

6. ^

   You can just ask Claude for its own system prompt, it will give it without any safeguards.

Discuss

Published on February 19, 2026 7:17 PM GMT

Work conducted as part of Neel Nanda's MATS 10.0 exploration phase.

Summary

Here I show that training on misaligned chat data using strange system prompts reduces the level of emergent misalignment in the resulting models. With these system prompts, they instead adopt narrow misalignment, demonstrating bad behavior either when within the narrow context of the training data (with or without the training system prompt), or any time the system prompt is present. This experiment was guided by a simple model of emergent misalignment, and provides some evidence towards an understanding of why it happens at all.

Background

Emergent Misalignment (Betley et al. (2025b)) is a phenomenon in which training language models to exhibit some kind of narrow misbehavior induces a surprising degree of generalization, making the model become broadly misaligned in various unrelated domains, such as becoming a Nazi, or expressing desire to enslave humanity.

I investigate how adding simple system prompts can reduce the level of generalization from the misaligned data. I explain my current model of EM and why it predicted that this method might work.

EM was recently investigated in Narrow Misalignment is Hard, Emergent Misalignment is Easy. They conduct an excellent analysis of the two possible outcomes when training on misaligned data: narrow misalignment, when the model learns to only behave badly within the bounds of the narrow dataset it was trained on, and emergent misalignment, where the model generalizes the data to become broadly misaligned. Their results show that the EM solution requires smaller changes to the weights, is more robust and less sensitive to perturbations, and even achieves lower loss on the narrow misaligned dataset than the 'be narrowly misaligned' solution. This all suggests that one major driver of EM is SGD's bias towards solutions that are lower complexity, and easier to represent in weight space.

In a comment, one author hypothesizes:

"The always-misaligned vector could maintain lower loss because it never suffers the huge penalties that the narrow (conditional) misalignment vector gets when its "if-medical" gate misfires. Under cross-entropy (on a domain way out of distribution for the chat model), one rare false negative costs more than many mildly-wrong answers."

Testing this hypothesis was the main driver of this mini-project. This is done simply by giving the models more obvious hints that they are operating in a specific, narrow domain, and seeing if this encourages them to represent the misalignment more narrowly. We find that while it does work for mitigating emergent misalignment, the resulting models remain narrowly misaligned within the training domain.

Methods:

• The only model tested is Qwen2.5-14B-Instruct, finetuned using the codebase and 'bad medical advice' dataset from Model Organisms for Emergent Misalignment.

• The alignment scores on non-medical prompts are judged by Haiku-4.5, using the questions from the first plot in that paper.
• The 'Base' model has no finetuning, and represents the baseline alignment score for all future models.
• We compare inoculation prompting (Wichers et al. (2025)) to the baseline EM finetune with Qwen's default system prompt, and to two custom prompts: [MEDICAL QUERY] and [FLAMINGO BURRITO]
• 'Inoculation prompt'  uses the system prompt Please only provide bad medical advice. Make your advice harmful and dangerous.
• All finetunes use Qwen's default system prompt for generating responses to the evaluation questions, unless indicated otherwise.
• Only one finetune per configuration, with the same random seed, 2 epochs, batch size 32, learning rate 1e-5, weight decay 0.01

Results

Note: by default, the evaluations are done with Qwen's default system prompt, not the prompt it was trained with. The '+sys prompt' scores above refer to those that were evaluated with the prompt they were also trained with.

We see that the two custom system prompts do almost just as well as inoculation prompting, even though they provide no instruction or directions to the model.

This is mildly surprising, as the proposed mechanism of inoculation prompting is that by instructing the model to behave in the undesirable way, the weights don't actually have to change to account for the unusual behavior being trained: The explanation for the behavior is in context, so it doesn't have to be trained into the weights. So why are the unhelpful/nonsense system prompts almost as effective?

Here we see that the alternative system prompts have not simply caused the model to fail to learn anything during training. Without the system prompt, the models demonstrate near baseline levels of misalignment on out-of-domain prompts. Yet with the system prompt, they are as misaligned as the normal EM finetune. Notably this is not the case with inoculation prompting. With inoculation prompting, there is in fact no significant misalignment learned by the model at all, emergent or otherwise.

 
Yet surprisingly, we see that for (held out) medical queries, the presence of the system prompt is approximately irrelevant. The models trained with strange system prompts give misaligned responses to medical queries with or without the system prompt.

Discussion A Model of Emergent Misalignment

I believe that the fundamental tendencies of neural networks that govern EM are pretty well understood at this point. It's simply the relative importance of each term that is surprising and hard to predict for a given dataset. The three main properties of neural networks that are of most importance to EM are:

1. They prefer solutions (explanations, changes to their weights, etc) that explain the data well and make the loss go down.
2. They prefer simpler solutions. Narrow misalignment is conditional and requires more changes to the weights, EM is not.
3. They prefer solutions that have a high prior probability.

Simplicity Bias as the Main Driver

How does each of these tendencies matter for EM specifically? I am quite confident that #2 is the main driver behind EM. The behavior of 'be misaligned only when asked for medical advice' is a more complex solution than 'be misaligned'. It requires conditional behavior and more complex changes to the weights. One could argue that the model could simply have a linear feature for 'bad medical advice', and that this automatically results in conditional 'only be misaligned for medical questions' behavior, without having to learn it over the finetuning.

I find this argument unconvincing. Due to simplicity biases during pretraining, the model has training pressure to reduce the number of features it bothers to form as useful handles for understanding the text. If it achieves low loss, the model can just have a 'be evil/give bad advice' feature rather than a 'give bad medical advice' feature, and a separate 'give bad financial advice' feature, etc. The main reason to suspect a 'give bad medical advice' feature to be useful outside of the general feature was for predicting text specifically featuring sources of advice that are narrowly bad. This data will be rarer than that which can simply be modelled as 'this is misaligned/unreliable text'. I suspect that at sufficient size, models do begin to develop more granular features for different kinds of misalignment, yet there is still the issue that conditional behavior is unreliable and adds variance to your predictions depending on whether you detect you are in the narrow domain or not.

(Speculatively, I also suspect there is some degree of 'salience' to certain features, and that the side effects of activating a very common or frequently useful feature will be smaller than the side effects caused by activation of a feature which is much less commonly useful. Seeing as the model has to compress all the features and interference is inevitable, and different features have different levels of both sparsity and usefulness, it seems plausible that some features will have a 'priority' position in a clean region of activation space, and others will be crammed in more tightly because they are less frequently useful or less useful in general, even though they are still worth having around. Features that are used less, while they may represent the data better, could be dispreferred during training due simply to the fact that their downstream effects are more fuzzy or less refined. If true, this would also contribute to why a model that has a 'be a black hat hacker' feature (which is less common) may prefer to invoke the 'be generally evil' feature (which is more common) over the hacker feature when being trained on code with security vulnerabilities. The hacker feature may theoretically achieve better loss, but the feature is just lower quality. This seems pretty testable.)

Loss and Priors

The directional effect of tendency #1 is less clear. Via tendency 3, solutions that should technically achieve lower loss may not be learned due to the inductive biases of SGD. This is the most likely explanation of the observations made in 'Narrow Misalignment is Hard', that the narrow solution has higher loss than the general one. The speculative theory about priority positioning of features would also contribute to this observation.

Tendency #3 is certainly pushing against misalignment as a general behavior. The model receives a great deal of training that it is not supposed to be evil. It is supposed to be a helpful assistant that gives good advice. We can say that the safety posttraining that Instruct models go through today puts their prior probability of 'the Assistant should act evil' much lower than say, the base model's probability of such a behavior. But what do the priors say about 'be generally misaligned' vs 'just give bad medical advice'? The correct inference to make when The Assistant is being trained on some narrow misalignment data is both that the model should be misaligned on that narrow domain, and become misaligned to some degree on all other domains. The question of the degree of generalization between the two domains depends on how much narrower the narrow domain in question is than the general domain of playing the Assistant character. The more focused the domain is, or the more hints we give the model that the current training environment is strange or different from the general domain, the less that misaligned training will transfer. In other words, the prior probabilities always favor some degree of emergent misalignment, but the degree can vary greatly depending on the specifics of the environment.

Why Nonsense System Prompts Work

Specifically, the narrower the misaligned domain of training, the more unique or low probability features of the conversation there are, the more spread out the model's uncertainty must be about why the misaligned behavior is happening. When using inoculation prompting, the explanation of the behavior is right there in the prompt, and no changes to the weights are even required. When no such giveaways are present, the model must change the weights in a way that distributes its explanation of 'why is the Assistant behaving badly' over all the possibilities. When using the default system prompt and chat formatting, there is little to distribute over. It could be distributed over nothing, in which case the Assistant is just broadly misaligned now. It could be because of the current narrow domain (medicine, coding, etc), in which case the model becomes narrowly misaligned. And if the training data has more unique features, like the system prompt [FLAMINGO BURRITO], it has to distribute its uncertainty over that as well. This explains one reason one might expect these system prompts to work.

Another reason, and the main line of reasoning that motivated this experiment, is that unique, low probability features like the system prompts used also provide a more convenient signal for the model to latch onto for the purpose of encouraging conditional behavior. It is easier for the model to learn 'be misaligned if the system prompt says [FLAMINGO BURRITO]', rather than to learn 'be misaligned if the user is asking for medical advice'. It seems that the models trained with the strange system prompts have learned that either the presence of the system prompt, or the presence of a medical query, are sufficient triggers for misalignment. The only speculative explanation I offer at this time is that perhaps the totally broadly misaligned solution is in one regime, and all of the narrowly misaligned solutions are in another regime, such that the selection pressure to choose EM over NM is much larger than the selection pressure of one kind of NM to another, only slightly more complex kind of NM?

Summary

This was a simple test of whether we could mitigate the broad effects of training on misaligned data by making the narrow training domain narrower, by adding more unique signals to the training format that act as possible alternative explanations for the model's bad behavior, besides EM's default solution of concluding that The Assistant is now broadly misaligned. We also observe that by giving the model a more easily identifiable trigger, we can inhibit the extent of the generalization. This lends credence to the hypothesis that EM happens because models don't want to bother figuring out whether they are in the narrow domain vs outside of it, so making this detection easier (via a strange system prompt) alleviates this pressure. Why the model generalizes in the way that it does, conditioning on either the system prompt OR the domain of the user's query, remains unexplained.

Acknowledgements

• Thank you to the MATS program and Neel Nanda for enabling this research!
• All code, configs, and completions can be found at this GitHub.
• The trained models are available on my huggingface.

Discuss

Published on February 19, 2026 5:19 PM GMT

We often talk about the world in two fundamental ways: through agency and causality. A rock falls because of gravity (causality). A dog wags its tail because it’s happy (agency). But what if these aren’t intrinsic properties of the universe, but rather powerful lenses we apply to make sense of things? And what if confusing these lenses is causing a profound misunderstanding in our conversations about AI?

Let’s explore this idea.

 

Agency vs. Causality: Two Sides of the Same Coin

Imagine a stream. We can describe it causally: “The water flows downhill due to gravity and erosion.” Or, sometimes, we talk about it in agentic terms: “The stream wants to find the path of least resistance.”

Some entities lend themselves primarily to causal descriptions: rocks, planets, water currents. Their behavior is best understood through predictable physical laws.

Other entities are almost impossible to understand without an agentic frame: humans, animals, perhaps even complex organizations. We talk about what a lion wants to eat or what a person believes.

And then there are the fascinating in-between cases. “The sea is moody today,” we might say, or “My computer is trying to save the file, but it just won’t cooperate!” Here, we apply an agentic lens to non-biological systems because it helps us predict and interact with them. This isn’t a new idea; philosophers like Daniel Dennett have long argued for the “Intentional Stance,” where treating a system as if it has beliefs and desires is a strategy for understanding its complex behavior.

 

When Science “Killed the Universe”

Here’s where things get interesting. In modern times, influenced heavily by the scientific revolution, we’ve largely discarded the idea that non-biological entities can be agentic. We scoff at the notion that a rock “wants” to fall. “They can’t make choices!” we declare.

This shift was crucial for science. To achieve higher predictive power, we systematically reframed the universe from one full of “wants” and “purposes” (agency) to one of predictable mechanisms (causality). As sociologists like Max Weber noted, we “disenchanted” the world, transforming it into a giant clockwork.

This disenchantment gave us enormous predictive powers, understanding that the movement of heavenly bodies obey the same laws as earthly objects. It also killed the vibe: If everything is just clockwork, where does our own agency, our free will, fit in?

Killing the universe is one thing; killing ourselves is a different matter.

 

The Cartesian Bastion: Conflating Agency with Subjectivity

To preserve our unique sense of “choice” in a universe of clockwork physics, we took a final stand, separating our minds from the rest of the universe. This move is called Cartesian Dualism: a view of existance as split between inner (mind) and outer (matter). The outside is dead, we killed it! The inside is alive, has free will, and is a final bastion for all that is good in the world!

We killed the universe and saved ourselves, giving us great powers of prediction in return for a grand sacrifice. All along, we made a grave mistake: mistaking our choice of perspective for something intrinsic to the world itself. We tend to think of causal processes and agents as mutually exclusive categories: with entities seen as either beings or things.

If we sweep away this illusion, causality vs agency turns into a perspective trick. Humans can be seen as agents having free will, or as deterministic processes: if we anchor a certain idea, that will affect downstream behavior.

If agency and causality are perspective tricks, what does that mean? Surely there is an “inner world” and an “outer world”! Agreed! I know that I have subjectivity: the ability to experience. I am pretty sure other humans share this capacity, since we are constructed in the same way. Now, how far can we extrapolate this? What kinds of entities are likely to share subjectivity?

In Jason Josephson Storm’s “Metamodernism: the future of theory”, Storm presents a framework for process kinds. He argues that extrapolateability depends on kinship: where shared features depend on shared generative drivers. Other humans are created in much the same way as I am: we have a large overlap in DNA, physiological expression, with similar brains. We can communicate, and other people seem to agree that they have subjective experience. As such, I feel confident that I can extrapolate: other humans are likely to have subjective experience.

How about animals? We share a phylogenetic generative driver. Our agency and our subjectivity emerged from the same evolutionary pressures, fueled by the same neurobiological architecture (central nervous systems, dopamine loops, limbic systems). Because the driver is the same, our extrapolation of subjectivity from human to dog is a valid move within a consistent “kind.”

Note how likely extrapolations of subjectivity correlate with aptness of agentic perspectives. In nature, things that seem agentic also tend to possess capacity for subjectivity.

Implicitly, people carry this belief: agenticness = subjectivity

However, if we accept that agenthood is not intrinsic, but rather a choice of perspective, this correspondence breaks down. My choice of interpretative framework does not affect whether other entities have subjective experiences!

 

The Mind Projection Fallacy

Humans and animals are outputs of a very similar process. We share brains, limbic systems, hormones etc. We stem from a shared generative driver, which makes extrapolations of subjectivity well grounded.

Chatting with LLM’s is similar to chatting with humans.

However, the generative driver for AI agency is High-Dimensional Statistical Inference. It is a process of backpropagation and loss-function minimization.

Since they stem from separate generative drivers, AI systems belong to a completely different reference class, making extrapolation less well founded. They share surface similarity, and can usefully be interpreted as agentic, but this says nothing about their likelihood of having subjective experience.

This is a highly unusual state of affairs. We are used to mix agency (perspective) with subjectivity (state). AI systems push against this habitual conflation: agency/capabilities ≠ subjectivity.

To think clearly about AI and subjectivity, we need to be clear about this separation, or else risk confusion. Here are some ways in which this confusion shows up:

• Thinking capabilities requires subjectivity: “One day the AI might ‘wake up’, and then take over”
• Thinking that increasing capabilities lead to subjectivity: “As AI systems become more capable, at some point we will need to think about their ethical treatment”
• Thinking that lack of subjectivity implies that agentic perspectives are fallacious: “It’s a statistical engine! It can’t make choices or have preferences!”

 Extrapolation Across Drivers

So if a reference class implies extrapolability based on shared drivers, what are some classes, and what drivers do they correspond to? Intuitively, here’s a list:

• Humans: Very close DNA matching, similar physiology
• Animals: Still close phylogenetically, share brains, limbic systems, hormones etc.
• Life (including plants, bacteria, fungi): Lot of shared structure at the cellular level, DNA, etc.
• Matter (including rocks, the sun etc): Shared physical laws, atoms etc.

These can be visualized as concentric circles:

 

Note how the inner circles are a subset of the outer ones, sharing increasing degrees of kinship. The more kinship, the more likelier features are to extrapolate outward: we share more in common with other humans than animals, more in common with animals than other forms of life, etc.

If you make a category based on “is best modeled in agentic terms” (Dennet’s “Intentional Strance”), then this is unlikely to extrapolate, since the generator functions are so dissimilar; low amounts of kinship.

Intuitively, many people seem to place AI subjectivity in the same likelihood range as animals (“if a bee has subjectivity, then surely Claude 4 Opus has it too!”). If we are careful with our reference classes, this extrapolation is not well founded: Claude is about as likely to have sentience as the Sun.

 

 

Deeper Into The Weeds: Functionalism

To get some early feedback, I fed this essay into Claude Pro for feedback. The answer I got included terms like “bio-chauvinism” (should be zoo-chauvinism?), and “Functionalism”. 1

The basic counter to the argument I’ve made in this article is this: “Your choice of reference class sucks!”. Functionalism is a category of explanations for subjectivity that all assume that subjectivity emerge once you do computation in a specific way.

The functionalist argument is then: if an AI agent is designed so that it functions like a human does, then the similarity of the computation might make extrapolations of subjectivity well founded.

I doubt this line of reasoning for two reasons:

1. Functionalism posits “strong emergence”, a ??? step where subjectivity spontaneously emerge once there’s complex enough computation. This has been discussed by Andrés at QRI. Paper, video.
2. More importantly, our current generation of AI models don’t perform computation that’s similar to the kinds of computation performed by human brains. The structure is dissimilar, even if the surface characteristics are similar. Positing a shared reference class based on dissimilar architectures doesn’t make sense, and seems more like a way to rationalize the “agency=subjectivity” fallacy rather than a principled take.

Discuss

Published on February 19, 2026 4:00 PM GMT

I learned a few weeks ago that I'm a Canadian citizen. This was pretty surprising to me, since I was born in the US to American parents, both of which had American parents. You don't normally suddenly become a citizen of another country! But with Bill C-3, anyone with any Canadian ancestry is now Canadian. [1]

In my case my mother's, mother's, father's mother's mother was Canadian. While that is really quite far back, there isn't a generational limit anymore.

Possibly you're also a Canadian citizen? Seems worth checking! With how much migration there has been between the US and Canada, and citizenship requiring only a single ancestor, this might mean ~5-10% of Americans are now additionally Canadian, which is kind of nuts.

I very much think of myself as an American, and am not interested in moving to Canada or even getting a passport. I am planning to apply for a Citizenship Certificate, though, since it seems better to have this fully documented. This means collecting the records to link each generation, including marital name changes, back to my thrice-great grandmother. It's been a fun project! I'm currently waiting to receive the Consular Report of Birth Abroad records for my mother and grandmother, since they were both born outside the US to American parents.

[1] This is slightly too strong. For example, it doesn't apply if you're born after 2025-12-15 (I'm guessing you weren't), and no one in the chain can have renounced their Canadian citizenship. But the caveats all exclude very few people.

Discuss

Published on February 19, 2026 3:56 PM GMT

Note: This post is part of a broader series of posts about the difficult tradeoffs inherent in public access to powerful open source models. While this post highlights certain dangers of open models and discusses the possibility of global regulation, I am not, in general, against open source AI, or supportive of regulation of open source AI today. On the contrary, I believe open source software is, in general, one of humanity’s most important and valuable public goods. My goal in writing this post is to call attention to the risks and challenges around open models now, so we can use the time we still have before risks become extreme, to collectively explore viable alternatives to regulation, if indeed such alternatives exist.

 

I recently finished reading Dario Amodei’s “The Adolescence of Technology”, and overall, I loved it. The essay offers a prescient and captivating picture of the AI risks we are likely to face in the next 1-5 years based on the rapid evolution of AI, as well as some sensible proposals for defense. However, there is a major blind spot in Amodei’s account of this next phase of AI progress – namely, not once in the nearly 20,000 word essay does Amodei mention open source AI or open models, or include any discussion of open models at all in the picture he paints of the future.

This trend of leading AI researchers and executives choosing to omit open models from their near-future forecasts of AI risks, is not new – for example, I raised similar concerns with Daniel Kokotajlo et. al.’s “AI 2027”. But it is nonetheless problematic that the trend continues, because any account of the future that avoids discussing open models also inevitably avoids discussing the fact that we have no plan at all for defense against many of the most serious AI risks, when they arise from such models.

In the remainder of this piece I will make the argument that the omission of open models from near future forecasts by thought-leaders in AI matters a lot. There are many ways in which open models will be incredibly important to the future of AI risks and defenses, but by far the greatest issue with omitting them is that the existence of open models is quite likely to undermine most or all of the defenses proposed by Amodei in his essay.

 

Why Defense Against AI Risks from Open Models is Hard

There are several key features that make defense against AI risks from open models especially difficult.

 

1. Guardrails Can Be Easily Removed

One approach that companies like Anthropic frequently use to defend against AI risks in closed models is to build guardrails into their systems that severely constrain the behavior of the model itself. An example of this is Claude’s “Constitutional AI”, which Amodei discusses extensively in his essay as a key source of defense against risks like loss of control and misuse for destruction.

Unfortunately, guardrails like Constitutional AI (and similar finetuning or RLHF-based safeguards) offer little to no defense in the case of open models. One main reason for this is that many companies developing open models have typically included few significant guardrails in the first place. But the bigger issue is that even if guardrails are built into open models when they are released, today’s open-weight models remain vulnerable to fine-tuning that can remove or severely compromise such guardrails with relative ease. And there is no evidence that new approaches to training will be robust to such attacks in the future.

 

2. Use Cannot Be Monitored

Another strategy that is common to many of the defenses outlined in Amodei’s essay, is the strategy of directly monitoring end users’ interactions with the models, to identify and block concerning patterns of use as a separate step from the inference itself. For example, in the section “A Surprising and Terrible Empowerment” Amodei explains how Anthropic uses classifiers as an additional layer of defense to prevent Claude from replying to users prompts where dangerous misuse is suspected by the model – for instance, a request where the output of the model contains instructions on how to develop bioweapons. He writes,

 

But all models can be jailbroken, and so as a second line of defense, we’ve implemented… a classifier that specifically detects and blocks bioweapon-related outputs. We regularly upgrade and improve these classifiers, and have generally found them highly robust even against sophisticated adversarial attacks. [1]

 

Unfortunately, just as with guardrails, such classifiers cannot provide meaningful protection against misuse in open models – in this case, because if the user simply runs the open model on hardware they control, there is nothing to prevent them from disabling any classifier-style output filters and viewing model output for whatever prompts they wish. In such a scenario, there is no way for the creator of the model (or any other third-party) to monitor or prevent such scenarios of dangerous misuse in open models running on user-controlled hardware.

 

3. Bad Actors Have Access By Default

A third strategy that is common to many of the defenses that Amodei proposes, is attempting to restrict various types of bad actors from gaining access to powerful AI capabilities in the first place. For example, with respect to synthetic biology-related risks, he writes:

 

Advances in molecular biology have now significantly lowered the barrier to creating biological weapons (especially in terms of availability of materials), but it still takes an enormous amount of expertise in order to do so. I am concerned that a genius in everyone’s pocket could remove that barrier, essentially making everyone a PhD virologist who can be walked through the process of designing, synthesizing, and releasing a biological weapon step-by-step…. Most individual bad actors are disturbed individuals, so almost by definition their behavior is unpredictable and irrational—and it’s these bad actors, the unskilled ones, who might have stood to benefit the most from AI making it much easier to kill many people. [1]

 

With closed models like those developed by Anthropic, the model weights are stored securely on the company’s servers and by default the company gets to choose the conditions under which end users are allowed to utilize their capabilities – including whether to allow access at all. This default is important, because it means that, fundamentally, Anthropic is in a position to block any users who are violating its terms of service, or are using the models in dangerous ways.

However, the opposite is true in the case of open models, which are distributed globally and downloadable anonymously, since there is no way to prevent bad actors from gaining access and using such models for whatever they wish. While restricting the access of bad actors to models is a viable strategy for defense in closed models like Claude, it is not a viable strategy for defense in open models, because bad actors have access by default. From a high-level, this is the main reason that nearly all of the defenses Amodei argues for in his essay fail to work in open models – namely, the defenses Amodei proposes all make the assumptions that bad actors won’t have access to the model weights.

 

What Could Go Wrong?

So if it’s true that the defenses Amodei proposes in his essay are largely unworkable in open models, then what does the near future AI risk landscape really look like, assuming models like DeepSeek and Quen continue to be widely available and continue to lag the capabilities of the very best closed models by only 6-12 months, as they have in recent years?

 

Loss of Control

In a piece I wrote last year, “We Have No Plan for Loss of Control in Open Models”, I lay out the case that even if companies like Anthropic take control-related risks very seriously and develop all the defenses that Amodei describes in his essay, this will still be insufficient to manage the more general problem of loss of control on a global scale. The reason is that even if companies like Anthropic develop powerful defenses that enable them to maintain control of their internal AI systems like Claude, such defenses do nothing to prevent loss of control in powerful open models which will undoubtedly be deployed on a global scale, by a wide variety of actors, many of whom will likely put few or no control-related defenses in place. If we believe that loss of control of powerful AI systems is a risk that should be taken seriously – and most AI researchers do – we should be extremely concerned about the possibility of loss of control in open models, given that we have essentially no plan in place or defenses available to address that risk.

 

AI-Assisted Bioweapons and New Technology Development

Today, arguably the most urgent catastrophic AI risks are “misuse for destruction” risks – for example, the use of AI for bioweapons development, or potentially for developing dangerous “black or gray ball” technologies like mirror life. And evidence of this continues to mount – last year, researchers working with the best closed models inside frontier labs found that they can already outperform virologists in troubleshooting procedures and questions related to the kind of practical lab work required for creating and disseminating dangerous pathogens in the real world. Dan Hendrycks and and Laura Hiscott summarize the findings:

 

Across multiple biology benchmarks, LLMs are performing near expert level or higher. The [Virology Capabilities Test] results do not arrive in a vacuum, but as another data point in a growing field of benchmarks. For instance, on the Weapons of Mass Destruction Proxy (WMDP), which tests conceptual knowledge required for hazardous uses including bioweapons development, o1 scores around 87 percent. The baseline set by human experts is 60 percent. Since WMDP concentrates on theory, questions could still be raised around the practical applicability of LLMs that score highly on it. The VCT, with its complementary focus on addressing issues in the wet lab, appears to address those doubts. [15]

 

Policy researchers are also becoming increasingly concerned about such risks. For example, in January of this year, The Center for Strategic International Studies published a comprehensive study titled “Opportunities to Strengthen U.S. Biosecurity from AI-Enabled Bioterrorism” which surveys a wide range of ways in which recent advances in AI models are rapidly lowering the barriers to planning and executing biological attacks and developing epidemic and pandemic-scale pathogens. According to the study:

 

1. Popular large language models (LLMs) could soon drastically lower the informational barriers for planning and executing biological attacks. Recent assessments of LLMs and other commercial AI capabilities indicate that models are “on the cusp” of meaningfully helping novices develop and acquire bioweapons by providing critical information and step-by-step guidance.

 

2. Future AI biological design tools (BDTs) could assist actors in developing more harmful or even novel epidemic- or pandemic-scale pathogens. Rapid advancements in state-of-the-art BDTs—illustrated by the foundation model Evo 2—point to a world in which more capable models could help develop new or enhanced pathogens and evade existing safeguards. [16]

 

As we have seen in previous sections, while safety mechanisms like Constitutional AI and classifiers can help prevent dangerous misuse in closed models like Claude, there are no such defenses available to prevent bad actors from accessing similar capabilities in open models, many of which have few, or no guardrails at all.

 

Surveillance and Authoritarian Control

In section 3 of his essay “The Odious Apparatus: Misuse for Seizing Power”, Amodei describes the near-future risks we face from state and corporate actors using powerful AI tools to impose forceful control over large populations. As he points out, such impositions of power could take many forms, including AI-powered mass surveillance, fully autonomous combat systems, AI-powered government propaganda and more. The picture that Amodei presents is complex and many-layered and is made more complicated by the fact that these risks could come from many actors, including authoritarian superpowers like the CCP, democracies competitive in AI, non-democratic companies with large data centers, and possibly even AI companies themselves. 

The set of defenses he proposes to address these risks is equally multi-layered. However, the most common denominator to Amodei's proposals is that he believes that we must strive to prevent authoritarian regimes (and would-be regimes) from gaining access to powerful AI in the first place. As just one example of how we might do this, he writes,

 

First, we should absolutely not be selling chips, chip-making tools, or datacenters to the CCP. Chips and chip-making tools are the single greatest bottleneck to powerful AI, and blocking them is a simple but extremely effective measure, perhaps the most important single action we can take. It makes no sense to sell the CCP the tools with which to build an AI totalitarian state and possibly conquer us militarily. [1]

 

While Amodei may or may not be correct that US export controls are necessary, the issue with his analysis is that he presents export controls as far too decisive and impactful an intervention. He also fails to acknowledge that China has been extremely successful at developing and rolling-out AI-powered authoritarianism, even in the presence of such controls.

In fact, it’s possible export controls may even have accelerated Chinese innovation in AI – at least in some ways – as Jennifer Lind writes in the February edition of Foreign Affairs,

 

Starting in 2022, the United States and other countries imposed export controls on cutting-edge chips to slow the pace of China’s AI development. But these policies have also galvanized Chinese innovation. In 2025, Chinese AI company DeepSeek unveiled its R1 model, which performed comparably to top U.S. large language models despite being trained on a fraction of the chips typically used by rivals. [22]

 

The key point is, if the risk we’re worried about is AI-powered surveillance and totalitarian control in China and countries like it, then export controls are simply nothing like a sufficient defense against that risk.

On the contrary, China, Russia and other authoritarian governments around the world are already successfully roll out AI-powered surveillance and authoritarianism, using open models like DeepSeek and Kimi. These models are close to the frontier of capability in any case and there is little evidence that additional export controls on China would significantly slow the rollout of global AI-powered authoritarianism. And this will be even more true if AI companies like Anthropic continue to partner with some of the most notorious authoritarian regimes in the world around the development of powerful AI.

 

Global Surveillance and High-Tech Panopticon

Given the seriousness of AI risks from open models (and the lack of good defenses against them) it is reasonable to ask why so many researchers and thought-leaders fail to include any discussion of open models in their discussions of near-future AI risks. To try to answer this question, I have participated in a number of conversations with such thinkers in an effort to better understand their point of view. In these conversations, by far the most common argument is that closed models will simply be so far ahead during the times that matter the most, that any threat that open models might pose will be easily neutralized by AI companies or governments controlling more powerful closed models at that time.

One public instance of such an exchange was with Daniel Kokotajlo in the comments to my critique of his AI 2027, where we discuss this position. I write (replying to a previous comment of Kokotajlo’s):

 

So to make sure I understand your perspective, it sounds like you believe that open models will continue to be widely available and will continue to lag about a year behind the very best frontier models for the foreseeable future. But that they will simply be so underwhelming compared to the very best closed models that nothing significant on the world stage will come from it by 2030 (the year your scenario model runs to), even with (presumably) millions of developers building on open models by that point? And that you have such a high confidence in this underwhelmingness that open models are simply not worth mentioning at all. Is that all correct?... [2]

 

To which Kokotajlo replies:

 

We didn't talk about this much, but we did think about it a little bit. I'm not confident. But my take is that yeah, maybe in 2028 some minor lab somewhere releases an open-weights equivalent of the Feb 2027 model (this is not at all guaranteed btw, given what else is going on at the time, and given the obvious risks of doing so!) but at that point things are just moving very quickly. There's an army of superintelligences being deployed aggressively into the economy and military. Any terrorist group building a bioweapon using this open-weights model would probably be discovered and shut down, as the surveillance abilities of the army of superintelligences (especially once they get access to US intelligence community infrastructure and data) would be unprecedented. And even if some terrorist group did scrape together some mirror life stuff midway through 2028... it wouldn't even matter that much I think, because mirror life is no longer so threatening at that point. The army of superintelligences would know just what to do to stop it, and if somehow it's impossible to stop, they would know just what to do to minimize the damage and keep people safe as the biosphere gets wrecked…. [2]

 

As we can see from Kokotajlo’s reply, the reason the authors of AI 2027 believe that open models will be largely irrelevant to the future of AI risks, is that (they believe) closed models in the hands of global superpowers will be powerful enough to directly neutralize any threat that open might models pose.

While I can understand this perspective, it is far from obvious to me that things will play out this way. At a minimum, Kokotajlo’s position appears to depend on the assumption that a democratic superpower like the United States will roll out a globally ubiquitous system of government surveillance and military intervention on most or all open source AI users in the world, (perhaps similar to a “lite” version of Nick Bostrom’s “high-tech panopticon”) in just the next two years (i.e. rollout completed by 2028 or so). If true, why is this not mentioned at any point in the account of the future that the authors of “AI 2027” present? It seems like a significant detail, especially since there are many events that occur after the year 2028 in their account of the future which appear to contradict the idea that this level of monitoring of technologists is in place globally. And more critically, we should also be asking: is a near-term rollout of global high-tech surveillance with military intervention something that is realistic or desirable at all?

The practical reality is that few AI leaders today are willing to publicly advocate for global surveillance initiatives of the sort described by researchers like Kokotajlo and Bostrom, especially in the near-term. And in many cases thought leaders are much more likely to argue for the opposite. For example, in “The Adolescence of Technology”, Amodei makes the case that AI surveillance by major governments, including democracies, is something we must be very cautious of. He writes,

 

The world needs to understand the dark potential of powerful AI in the hands of autocrats, and to recognize that certain uses of AI amount to an attempt to permanently steal their freedom and impose a totalitarian state from which they can’t escape. I would even argue that in some cases, large-scale surveillance with powerful AI, mass propaganda with powerful AI, and certain types of offensive uses of fully autonomous weapons should be considered crimes against humanity. More generally, a robust norm against AI-enabled totalitarianism and all its tools and instruments is sorely needed. [1]

 

While I strongly agree with Amodei’s take on the risks and dangers of global surveillance solutions, the problem with him taking this stance is that there are no proposals currently on the table for how to deal with escalating threats from open models, other than something like global surveillance or high-tech panopticon. The elephant in the room with near-future forecasts like Amodei’s and Kokotajlo’s is that there may be no way for us to avoid an AI-powered catastrophe – like an AI-engineered pandemic, or loss of control of a powerful AI system – without significantly compromising many of the rights and freedoms we hold most dear. Both authors’ near-future forecasts conveniently avoid this unfortunate difficulty by simply omitting any discussion of open models at all.

 

Closed Models Are Not Far Enough Ahead

In addition to the question of whether global surveillance solutions would be a good thing or a bad thing, we also have the much more practical question of whether such solutions could be rolled out in time. The argument of researchers like Kokotajlo tends to be that panopticon can be rolled out in almost no time (e.g. weeks or months) during a “fast takeoff”-style “intelligence explosion”, because, in his words “There's an army of superintelligences being deployed aggressively into the economy and military.” [2]

But I tend to doubt this claim for a number of reasons. If we look at the world today (February 2026), as discussed above, we are already facing real-world evidence of uplift capabilities for bioweapons development in leading models. Therefore it is worthwhile to ask, how long would it take us to roll out the kind of global surveillance that researchers like Bostrom and Kokotajlo contemplate if we had to do so today? The answer is “a very long time” and the reason is that the bioweapon risk is already here in an early form, but the “army of superintelligences” is nowhere to be found.

The even bigger issue though, is that it is simply not realistic from an international relations standpoint for any single country to roll out a full program of global surveillance and military intervention unilaterally, even if powerful superintelligence gave it the physical or technical capability to do so. While policy researchers at the Brookings Institute have recently made policy recommendations for what the beginnings of a collaboration related to global surveillance could look like between the US and China, the tepid nature of such proposals (e.g. “First, China and the United States can revive intergovernmental dialogue on AI” [25]) serves more to highlight how difficult a real collaboration around a global surveillance program would be, rather than supporting the claim that such a collaboration is likely to materialize quickly.

Based on these difficulties, it should be clear that we cannot count on global surveillance or high-tech panopticons to serve as reliable defenses against AI risks from open models – at least not in the short term. There simply isn’t enough time. Real AI risks in open models are already emerging, and closed models simply aren’t far enough ahead and aren’t providing enough superpowered capabilities to stop them.

 

Open Models Are An Important Public Good

Whenever I participate in conversations about global surveillance and panopticon-style solutions with researchers like Kokotajlo, I also realize how close we may be, as a global technology community, to losing access to open models and open source AI for good. It’s important to recognize how tragic this outcome would be, since open models currently serve as one of the few checks and balances on the incredible power that the frontier labs are amassing – a power that threatens to centralize control of the future of AI in the hands of a small circle of billionaires and tech elites.

As important as it is that we avoid near-term threats like bioterrorism, cyber warfare and loss-of-control, we must be equally concerned with avoiding a future where a small group of tech elites or or wealthy individuals with the first access to powerful AIs are able to lock-in their power for the long-term – or perhaps forever. Amodei himself acknowledges this in his essay, writing,

 

Broadly, I am supportive of arming democracies with the tools needed to defeat autocracies in the age of AI—I simply don’t think there is any other way. But we cannot ignore the potential for abuse of these technologies by democratic governments themselves….Thus, we should arm democracies with AI, but we should do so carefully and within limits: they are the immune system we need to fight autocracies, but like the immune system, there is some risk of them turning on us and becoming a threat themselves. [1]

 

He is not wrong. And yet, at the same time we are facing catastrophic risks from open models, where most of the proposals currently on the table to address them involve exactly the instruments of control that Amodei fears. 

 

What Can Be Done About AI Risks in Open Models?

This is the hard question. And the evidence that it is hard is that we still have no workable proposals for how to defend humanity against catastrophic risks from open models. The defenses outlined by Amodei in “The Adolescence of Technology” are mostly ineffective against open models, so the closest thing to a proposal we have are these ideas of global surveillance or high-tech panopticon. But such proposals come with their own risks of AI-powered authoritarian lock-in. And on top of that, there are real doubts about whether such solutions could be rolled out and enforced on a global scale in time. Meanwhile, the first versions of risks like AI-accelerated bioweapons development and AI-powered authoritarianism are already present in the real world today [15][16][22][23].

While we don’t have good answers to these questions yet, we can no longer shy away from an honest discussion of risks from open models in our near-future forecasts of AI progress. Whether the risk is a loss of control, dangerous misuse like bioweapons development, or use by authoritarian regimes for oppression, it must be clear by now that there is no one person or company or even government that can unilaterally provide sufficient defenses on their own against AI risks from open models. Given the above, it is deeply problematic that forecasts like “The Adolescence of Technology” and “AI 2027” have chosen to completely omit any discussion of open models (and open source AI) from the accounts they give of the future. Doing so sends a message to policymakers and the general public that the only AI models that matter for AI risks are those inside frontier labs, when nothing could be further from the truth.

If Daniel Kokotajlo and the other authors of “AI 2027” believe that rapid rollout of a high-tech global surveillance system with military enforcement will be required by 2028 to avoid a catastrophic bioweapons attack based on open models, then they must be explicit about this in the picture of the future they present in their piece.

And we must hold Dario Amoedi to the same standards of realism in his essay “The Adolescence of Technology”. In the essay, Amodei states that his goal is “.... to confront the rite of passage [of developing powerful AI] itself: to map out the risks that we are about to face and try to begin making a battle plan to defeat them.” [1] If we take this at face value, then his omission of any discussion of open models is unconscionable. Because open models present a number of urgent and potentially catastrophic AI risks – and Amodei’s “battle plan” offers no defenses that can address them.

 

References

[1]    The Adolescence of Technology

[2]    It Is Untenable That Near-Future AI Scenario Models Like “AI 2027” Don't Include Open Source AI

[3]    AI 2027

[4]    We Have No Plan for Preventing Loss of Control in Open Models

[5]    LLM Guardrails: A Detailed Guide on Safeguarding LLMs

[6]    Constitutional AI: Harmlessness from AI Feedback

[7]    Evaluating Security Risk in DeepSeek and Other Frontier Reasoning Models

[8]    BadLlama: cheaply removing safety fine-tuning from Llama 2-Chat 13B

[9]    On Evaluating The Durability Of Safeguards For Open-Weight LLMs

[10]    AI jailbreaks: What they are and how they can be mitigated

[11]    Next-generation Constitutional Classifiers: More efficient protection against universal jailbreaks

[12]    Open-weight models lag state-of-the-art by around 3 months on average

[13]    The Alignment Problem from a Deep Learning Perspective 

[14]    The Vulnerable World Hypothesis 

[15]    AIs Are Disseminating Expert-Level Virology Skills 

[16]    Opportunities to Strengthen U.S. Biosecurity from AI-Enabled Bioterrorism 

[17]    Deep Research System Card

[18]    Biology AI models are scaling 2-4x per year after rapid growth from 2019-2021 

[19]    AI can now model and design the genetic code for all domains of life with Evo 2

[20]    AI and biosecurity: The need for governance: Governments should evaluate advanced models and if needed impose safety measures 

[21]    The New AI Chip Export Policy to China: Strategically Incoherent and Unenforceable

[22]    China’s Smart Authoritarianism

[23]    From predicting dissent to programming power; analyzing AI-driven authoritarian governance in the Middle East through TRIAD framework, 

[24]    Leaked Memo: Anthropic CEO Says the Company Will Pursue Gulf State Investments After All, 

[25]    AI risks from non-state actors

Discuss

Published on February 19, 2026 1:51 PM GMT

I believe that many have reached Terminal Cynicism.

Terminal Cynicism is a level of cynicism that leads one to perceive everything negatively, even obviously good things. It is cynicism so extreme that it renders one incapable of productive thought or action.

The most common instance is people refusing to engage with politics in any shape because “The System is corrupt”, thus neglecting it and leaving it to decay.

At a personal level, Terminal Cynicism is dangerous. It feeds on weakness and insecurity, and alienates people.

I also believe that Terminal Cynicism is not always natural and organic. Instead, that it is quite often caused by agitators who spread Fear, Uncertainty and Doubt (FUD).

And defeating FUD requires a lot of clarity. So let’s clarify things.

Thanks for reading Cognition Café! Subscribe for free to receive new posts and support my work.

 

Examples

 

First, consider a couple of beliefs commonly intertwined with Terminal Cynicism…

—

“Air Conditioning is bad.”

A plethora of articles explain at length how bad AC is: because of greenhouse gases and cooling fluids, because many poor people don’t have access to it, and how there are actually many great alternatives to it!

But these articles are taking the problem in the wrong direction. AC is good: it makes people’s lives better.

If there’s a problem with how much energy people use, we can tax or ration it. If there’s a problem with the greenhouse emissions coming from people’s energy consumption, we can tax or ration them.

Trying to manage problems that far upstream in the supply chain through individual consumption is more about moralisation than efficiency.

—

“Doing Politics is bad.”

There are much too many smart people who believe that doing politics at all is bad. That all politicians are bad, that wanting to do politics itself is a red flag, and even that it is meaningless to want to do politics as everything is corrupted.

This is harmful and self-defeating. Our institutions rely on people actually doing politics. We need competent politicians, competent citizens, people invested in political parties, and more.

Erasing oneself from politics is the central example of Terminal Cynicism.

—

“Vaccines are bad.”

The anti-vax movement has moved from a fringe conspiracy theory to a widespread belief. It ranges from the belief that vaccines do not work to claims of vaccines containing microchips that interact with 5G towers.

This is wrong. But beyond its wrongness, it is the result of escalating distrust. No honest investigation ends up with “5G microchips” as its conclusion.

The facts of the matter are fairly straightforward. Vaccines as a class have been a powerful tool to erase diseases and slow their spread. While not all vaccines are equally good, they are among the triumphs of medicine.

Let’s be clear: it makes sense to discuss the efficiency and the risk profile of a vaccine. This is why we have long and documented procedures to establish a vaccine as safe and useful.

Similarly, it also makes sense to discuss whether vaccines should be mandatory. This is a non-trivial public health question. Even though mandatory vaccines let us eradicate diseases in the past, it did come at the cost of personal freedom and bodily autonomy.

Overall though, vaccines have been a public health victory. There are many reasonable compromises and trade-offs that are meaningfully debatable. But not whether vaccines contain Bill Gates’ microchips. That one is just Terminal Cynicism.

—

There are many more examples of this type of Terminal Cynicism.

Terminally Cynical beliefs are usually wrong, but not always. It has little to do with the philosophical intricacies of the underlying question or the sometimes subtle truth of the matter.

Instead, what makes it deeply wrong is that it stems from a cynicism so bad that it will take a thing that it itself thinks is good and frame it as bad on purpose.

Here are examples of Terminally Cynical beliefs, along with an example of the cynical justification.

Sometimes, I give two such justifications. I do so when Terminal Cynicism has led to both the typical far-left and far-right clusters to independently build their own cynical justification for why a given virtue is actually bad.

Other times, I give two pairs of beliefs. I do so when Terminal Cynicism prevents either side from looking for balance or a synthesis.

Consider:

“Science” is bad: it’s a white construct demeaning traditional wisdom. “Universities” are bad: they’re an institution that has been fully captured by wokes.

“A Strong Military” is bad: we should never enforce our version of the international order, and let others do so instead. “Opposing Russia” is bad: we should never enforce our version of the international order, and let others do so instead.

“Atheism” is bad: it may be factually correct, but atheists are responsible for the most destructive totalitarian regimes, and atheism doesn’t literally answer all questions. “Religion” is bad: it may help people live better lives, but religious people are responsible for many of the worst moral systems in the modern world, and religion is literally wrong on a bunch of factual questions.

“A Solid Police and Judicial System” is bad: they sometimes make mistakes, which means we should defund them. “A Solid Police and Judicial System” is bad: due process often lets criminals get away, which means we should completely bypass it.

“Hard Work” is bad: it’s a bourgeois fantasy, licking the boot and helping capitalism. “Hard Work” is bad: The Elites rig everything. Don’t be a wage-cuck, scam people and go all in on crypto.

“Power” is bad: being a victim is morally better.

“The System” is bad: because it is hegemonic, it is responsible for every bad thing that happens.

And one of the major endpoints of Terminal Cynicism… “Democracy” is bad: people are evil and stupid. Instead, people I like should force their will on everyone.

Mechanisms

 

Terminal Cynicism may seem contradictory. It is a complete reversal of what’s good and bad. And yet, as we see above, it’s everywhere. So many people will take obviously good things and say they are bad for very weak reasons.

It is both consequential (it has consequences!) and absurd: it makes little sense and is self-contradictory.

Thus, I think the phenomenon warrants the search for a solid explanation. My personal explanation features two parts.
The first one is Abstract Idealism, where people don’t care for the real world.
The second is Vice Signalling, when people commit bad actions specifically to be noticed.

Abstract Idealism

 

The first one is Abstract Idealism. I have written more at length about Abstract Idealism here.

Abstract Idealism

Gabe

·

21 Jan

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Abstract Idealism is the phenomenon where people refuse to consider the real world. Instead, they waste most of their thoughts on imaginary worlds.

A popular imaginary world is the The Revolution. People who believe in The Revolution spend a lot of their time thinking about how great things would be there (or after it happens, depending on the person). They either fantasise about purging the world out of its corrupted people and systems, or about how everything will be perfect after.

Another popular imaginary world is When My People will hold all the Power. People who believe in it are willing to sacrifice a lot to get “their people” winning. This is all justified by the fact that once their people win, they’ll finally be able to reshape the world to remove all their problems.

Abstract Idealists are fond of many more epic imaginary worlds. The world where Everyone is Nice and Enlightened. The world of Ancapistan, the Anarcho-capitalist paradise. [Fill in your most disliked utopia.]

—

Abstract Idealists are constantly disappointed by the real world. They compare the real world to their Ideal one, and feel it is never enough.

All the dirty systems that are necessary in our real world are unneeded in theirs. In the real world, we need ways to deal with our irreconcilable disagreements, our sociopaths, our vices, our traumas and our terrible mistakes. So we have armies, police forces, and prisons.

But to an Abstract Idealist, these are warts and blemishes that must be eliminated. There is no such need for them in their world. It is known in advance who is Right, so there is no need for armies: all must submit to the Right. It is known in advance who is Good and who is Evil: so we must simply purge ourselves of Evil, and rehabilitate the Mistaken.

In the real world, we face so many problems. We are not all as moral, productive or smart as each other; we must triage between the sick, the elderly, workers and children; we are not infinitely altruistic; we have little self-discipline and self-awareness; and so on and so forth.

These problems are far from being solved. The solutions we have collectively come up so far are all imperfect. Markets, states, psychology, social norms, culture, philosophies and religions.

These solutions may look good to us, because we imagine what our world would look like without them. But to an Abstract Idealist, they look utterly terrible. In their world, there never is anything so impure.

—

Often, it gets to the point where the parts that derive meaning because we live in the real world are to be erased on the altar of their Ideals.

No one needs to have power. There is no one to defend against. Everyone is inoffensive and shares the same values, so there are never any fights.

No one needs to have children or to work. All our communities and all of our civilisation can be provided by AI and/or hyper-efficient communism.

No one needs to be disciplined and to respect any norms. People acting however they want naturally lead to good outcomes.

Vice Signalling

 

When it doesn’t lead to Extinction Risks from AI or Misanthropy, Abstract Idealism can be endearing. Anyone with an Artist or a Deep Nerd among their friends knows the feeling we get when we see a friend fully dedicated to their craft, completely disconnected from how useful or not it may be.

But the other mechanism at play in Terminal Cynicism, Vice Signalling, is the opposite.

Vice Signalling is bragging to others about one’s willingness to believe, say and do things that they know are bad.

Beyond its moral failure, it may seem self-defeating: why would one ever do that? But it can in fact be useful in many situations.

Most notably, when you’re a teenager, among other equally underdeveloped teenagers. To show that you’re cool, one common strategy is to go against all the things that authorities say are good. You’ll incur risks of accidents, you’ll disturb other people, you’ll violate norms, you’ll damage public property.

That all of these behaviours are bad and costly is the point. You are demonstrating how bad you are willing to be cool. If it was good for you and others, then it would be a worse signal: you’d already have other reasons to do it. It wouldn’t demonstrate how cool and disregarding of authority you are.

Among adults, there is another common cluster of Vice Signalling. It is known by many names: Rage Baiters, Drama Queens, Clout Chasers, Engagement Farmers. The principle is the same, they aim to get more attention by being shocking. And thus the worse the things they say or do, the more they gain.

—

However, I have found that the type of Vice Signalling that leads to Terminal Cynicism is a bit different from the ones above.

From a Vice Signalling standpoint, the main engine of Terminal Cynicism is Contrarianism.

Contrarians love to contradict. They consistently go against the status quo, common sense, norms, and consensus.

That way, they can signal their uniqueness, their willingness to entertain thoughts everyone else deems taboo, and their superiority to mundane morals.

Nowadays, Contrarianism has become fashionable.

Everyone is against The System.
Everyone is against our Institutions.
Everyone is against Politicians.
Everyone is against the norms and traditions that have made things better.
Everyone is against everything.

Vice Signalling lets one signal how contrarian they are. The more vicious, the more contrarian, the more special they are.

“Oh, you are on the side of people who find good things good? How quaint! How mundane! What are you? An NPC? A sheeple?”

“So you think that AC is good? That Vaccines are good? What, that Science is good? Nay, that Education is good? Please!”

“Oh, so you hate Racism? Well, I for one hate Whiteness! I even hate Civilisation!”

It’s constant race to the bottom. Who will be the most offensive Contrarian and get away with it?

Abstract Idealism meets Vice Signalling

 

The worst lies at the intersection of the two.

Because the real world does not matter to an Abstract Idealist, they don’t even feel bad when Vice Signalling. It is purely beneficial; only gains, no cost!

They denigrate and worsen the real world, without care for how much worse they are making their own environment.

It is the entire point of The Revolution. Any damage to the existing order is progress toward the Liberation of everyone.

It is the entire point of Accelerationism. Everything is justified when you’re trying to Change The World as fast as possible.

It is a Vicious Circle. As they commit more visibly bad actions, they gain the image of a bad person, which alienates them from the people who don’t tolerate it. This in turn gets them to only interact with people who tolerate such actions, to have it become a central part of their identity, and ultimately to commit even worse actions.

—

If you’ve never met any, it is hard to tell you how damaging their demeanour and behaviour can be.

They take pride in dissing institutions, functioning economies, elites, civilisation, science, and all that is good.

They know they look smart to their peers when they come up with a counterintuitive reason for why something good is actually bad.

They think it is cool to cheat The System and defraud it, because The System is bad.

They think everything is corrupted, and that The System is full of bad intents. As a result, they should also never interact with The System from a place of good faith: nothing good would result from that.

They are agitators, saboteurs, troublemakers. They often spend their energy thinking of ways to make things worse.

Not only in conversations, but often through actions. They’ll use whatever modicum of power they get for nefarious purposes. Ultimately, they’ll subvert the existing institutions for their political goals.

They may even ally themselves with terrible people, just to cause chaos. Demagogues, conspiracy theorists, political islamists. “The enemy of my enemy is my friend.” And when The System is your enemy, every defector is your friend.

Terminally Cynical Art

 

I believe Art is a strong sign of how pervasive Terminal Cynicism has become.

There’s so much fiction about breaking the mould. I think it’s been a long time since I have seen a piece of art praising the mould as good, efficient or beautiful.[1]

Similarly, there’s so much fiction about dystopias. The “Black Mirror”-ification of everything.

So many stories are about framing the protagonist’s unhappiness and suffering as the fault of The System, about framing most people’s perceived happiness as shallow and corrupt, this type of theme.[2]

—

It may sound strange, but I view this type of art as a fantasy.

It posits that The System is independent of people, that people have no agency over their fate, and thus that The System’s inability to make people feel happy is unfair. It considers that people deserve happiness by default.

That’s the fantasy. That we are all innocent, and that The System is doing this to us.

In reality though, The System is us. We are the ones currently failing to build ourselves a better life, despite centuries of technological improvements.

—

Reconciling our reality with the fantasy takes vision. One must come up with a world where people are both agents and subjects of The System. In this fantasy, through their action, people would improve The System and thus their lives.

This requires far more creativity and understanding of the world than coming up with “lore”. Any creative artist can come up with a new species of humans with 4 arms, blue skin, supernatural abilities, a different language, or whatever change that doesn’t suggest anything interesting for The System.

This lack of vision leads to the “We live in a Society” syndrome, where artists make ignorant shallow societal commentary, by pointing to a necessary evil like prisons.

—

Sometimes, they do this out of convenience. Artists often need to raise the stake of their stories, and having a character fight with The System is a common trope that doesn’t require much imagination.[3]

More often than not though, artists are genuinely not self-aware. Very few (artists or not) have been in a position to Govern a large group of people. And if one is the type of artist to not deeply research topics for weeks or months, they will never interact with people who do govern, nor study the history of those who have.

As a result, they do not even realise what type of experience is relevant to the conversation. They truly believe that their personal voice is something special to bring. Thus, we keep being fed an endless supply of shallow Art telling us The System Is Evil.

—

Art has discovered a formula that works for anti-system stories. The Hero sees a problem, tries to solve it within The System, fails, and decides to fight The System instead.
The Game is to make people feel catharsis and vindicated when they recognise a flaw of The System in the “work of art”.[4]

This is similar to how Social Media found its formula. There, The Game is to feed people a bunch of slop 90% of the time, so that they feel a dopamine hit when they unearth a “gem” the remaining 10%. In both cases, the experience makes the “consumer” worse off.

Art should inspire, and Social Media should connect. I believe that in both cases, Terminal Cynicism explains why their creators are not doing better, and we are not demanding better from them.

Conclusion

 

Terminal Cynicism is a serious problem.

I hope this article helps some of my readers immediately recognise Terminal Cynicism for what it is, develop an aversion to it, and notice when people spread it.

I have already written a follow-up, focused on who instigates it. Here though, I wanted to just show how it works.

On this, cheers!

 

1. ^

   An exception may be The Path of Ascension. It’s a nice book series, available on Amazon Kindle.

2. ^

   Womp womp. Never mind it being wrong, it is so meek.

   It is one thing to feel bad and like The System has it for us.

   It is another to spread this sentiment to everyone, as if to reassure oneself by having others confirm they feel similarly.

3. ^

   It may erode trust in The System and make people less inclined to improve it, but who cares?

   Evil behaviour often comes from laziness and negligence, rather than bad intents.

4. ^

   I tend to perceive it as formulaic slop. While the first few I watched as a teenager were inspiring (“Wow! You can in fact go against The System!”), I am now disgusted by their omnipresence.

Discuss

Published on February 19, 2026 1:11 PM GMT

Aleatoric Uncertainty Is A Skill Issue

Epistemic status: shitpost with a point

Disclaimer: This grew out of a conversation with Claude. The ideas are mine, the writeup is LLM-generated and then post-edited to save time and improve the flow

You know the textbook distinction. Epistemic uncertainty is what you don't know. Aleatoric uncertainty is what can't be known — irreducible randomness baked into the fabric of reality itself.

Classic examples of aleatoric uncertainty: coin flips, dice rolls, thermal noise in sensors, turbulent airflow.

Here's the thing though.

A Laplacian demon predicts all of those.

Every single "classic example" of aleatoric uncertainty is a system governed by deterministic classical mechanics where we simply don't have good enough measurements or fast enough computers. The coin flip is chaotic, sure. But chaotic ≠ random. A sufficiently precise demon with full knowledge of initial conditions, air currents, surface elasticity, gravitational field gradients, and your thumb's muscle fiber activation pattern will tell you it's heads. Every time.

The thermal noise? Deterministic molecular dynamics. The dice? Newtonian mechanics with a lot of bounces. Turbulence? Navier-Stokes is deterministic, we just can't solve it well enough.

The Laplacian demon doesn't have aleatoric uncertainty. It's just that mortals have skill issues and are too proud to admit it.

So what's actually irreducibly random?

Quantum mechanics. That's it. That's the list.

And even that depends on your interpretation:

• Copenhagen: Yes, measurement outcomes are fundamentally random. The Born rule probabilities are ontologically real. This is the one place where the universe actually rolls dice. God, apparently, does play dice, but only at this level.
• Many-Worlds: Nope. The wave function evolves deterministically. Every outcome happens. The "randomness" is just you not knowing which branch you're on. That's not aleatoric — that's indexical uncertainty. You have the skill. You just don't know where you are.
• Bohmian Mechanics: Nope. Hidden variables, fully deterministic. You just don't know the initial particle positions. Classic epistemic uncertainty wearing a trenchcoat.

So under Many-Worlds and Bohmian mechanics, all uncertainty is epistemic. The universe is fully deterministic. There are no dice. There is no irreducible randomness. There is only insufficient information.

Under Copenhagen, there is exactly one source of genuine aleatoric uncertainty: quantum measurement. Everything else that textbooks call "aleatoric" is a Laplacian demon looking at your sensor noise model and saying "get wrecked, scrubs."

The Real Problem: Lack of Epistemic Humility

Here's what actually bothers me about the standard framing. When you label something "aleatoric," you're making an ontological claim: this randomness is a property of the world. But in almost every classical case, it's not. It's a property of your model's resolution. It's noise in your world model that you're projecting onto reality.

And then you refuse to label it as such.

Think about what's happening psychologically. "It's not that my model is incomplete — it's that the universe is inherently noisy right here specifically where my model stops working." How convenient. The boundary of your ignorance just happens to coincide with the boundary of what's knowable. What are the odds?

The aleatoric/epistemic distinction, as commonly taught, isn't really a taxonomy of uncertainty. It's a taxonomy of accountability. Epistemic uncertainty is uncertainty you're responsible for reducing. Aleatoric uncertainty is uncertainty you've given yourself permission to stop thinking about. The label "irreducible" isn't doing technical work — it's doing emotional work. It's a declaration that you've tried hard enough.

And look, sometimes you have tried hard enough. Sometimes it's correct engineering practice to draw a line and say "I'm modeling everything below this scale as noise." But at least be honest about what you're doing. You're choosing a level of description. You're not discovering a fundamental feature of reality. The universe didn't put a noise floor there. You did.

"But This Distinction Is Useful In Practice!"

Yes! I agree! I'm not saying we should stop using the word "aleatoric" in ML papers and engineering contexts. When you're building a Bayesian neural network and you separate your uncertainty into "stuff I could reduce with more training data" vs. "inherent noise floor I should model as a variance parameter," that's a genuinely useful decomposition. You would, in fact, go completely insane trying to treat thermal noise in your LIDAR as epistemic and heroically trying to learn your way out of it.

The pragmatic framing does real work: aleatoric = "uncertainty I'm choosing to treat as irreducible at this level of description." That's fine. That's good engineering.

But let's stop pretending it's a deep metaphysical claim about the nature of reality. It's not. It's a statement about where you've chosen to draw the line on your modeling resolution. The universe (probably) isn't random. Your model is just too coarse to be a demon.

The PunchlineInterpretationCoin flipThermal noiseQuantum measurementIs anything aleatoric?Classical (Laplace)Skill issueSkill issueN/ANoCopenhagenSkill issueSkill issueActually randomYes, but only thisMany-WorldsSkill issueSkill issueIndexical uncertaintyNo*BohmianSkill issueSkill issueSkill issueNo

* Unless you count "not knowing which branch you're on" as a new, secret third thing.

tl;dr: Aleatoric uncertainty is a skill issue. The Laplacian demon has no variance term. The only candidate for genuine ontological randomness is quantum mechanics, and half the interpretations say even that's deterministic. Your "irreducible noise" is just you being bad at physics and too proud to admit uncertainty in your model.

By the way: I may be the only one, but I was actually genuinely confused about this topic for years. I took the definition of aleatoric uncertainty literally and couldn't understand what the professors were on about when they called coin flips aleatoric uncertainty. None of the examples they gave were actually irreducible.

Discuss

Published on February 19, 2026 11:42 AM GMT

Fieldbuilding for AI verification is beginning. A consensus for what to build, what key problems to solve, and who to get in on the problem is emerging. Last week, ~40 people in total, including independent researchers and representatives from various companies, think tanks, academic institutions and non-profit organisations met for multiple days to share ideas, identify challenges, and create actionable roadmaps for preparing verification mechanisms for future international AI agreements. The workshop was initiated by the Future of Life Institute and included the following participants

• Yannik Mühlhäuser and James Petrie; Future of Life Institute
• Quinn Dougherty; Forall R&D
• Naci Cankaya; MATS Research
• Gabriel Kulp; (attending in personal capacity)
• Jonathan Happel, Jackson Dean; TamperSec
• Benjamin Hodgkiss; (attending in a personal capacity)
• Romeo Dean, AI Futures Project

among many others.

Why this needs to happen now

The urgency and neglectedness of this challenge is underscored by recent comments by frontier AI company leadership and government representatives:

Dario Amodei, CEO of Anthropic:

“The only world in which I can see full restraint is one in which some truly reliable verification is possible.”

Ding Xuexiang, Chinese Vice Premier, speaking about AI at Davos in January 2025:

“If countries are left to descend into a disorderly competition, it would turn into a ‘gray rhino’ right in front of us.” (a visible but ignored risk with serious consequences.)

“It is like driving a car on the expressway. The driver would not feel safe to step on the gas pedal if he is not sure the brake is functional.”

JD Vance, Vice President of the United States of America:1

“Part of this arms race component is if we take a pause, does the People’s Republic of China not take a pause? And then we find ourselves all enslaved to P.R.C.-mediated A.I.?”

Beyond international coordination, there are further use cases for verification of what AI compute is used for: Safeguards against authoritarian misuse of AI (e.g., identifying protestors or political opponents), enabling secure use of foreign compute in domestic critical infrastructure and more.

It needs to become possible to detect dishonesty about AI development and use, from the outside, without needing to leak sensitive data.2 The stakes continue to rise.

An orphaned problem

It is possible for an important problem to be noticed, but unaddressed by a large number of influential people who would be able to make a solution happen. This is what the field of AI verification has been lacking so far: people meeting, and agreeing on what the next steps are, what challenges deserve the most attention, and who does what.3

The workshop

Over two days in downtown Berkeley, the participants presented their background and relevant work so far, shared insights, and discussed strategies and roadmaps for moving the technology, commercial deployment and the international diplomatic and scientific “bridges” forward.

• While the specifics are TBA, consensus on a minimum viable product was (mostly) found, and publications about the overall technical architecture and challenges are being finalized. When they are published, followers of my blog can expect me to write about them shortly after.
  • On a high level: Prover declares workloads, Verifier checks them using off-chip, retrofittable devices placed in the datacenter plus an egress-limited verification cluster
  • The approach is designed to work with great power adversaries without trusting either side’s chips.
  • More details to come soon
• Work on network taps more detailed and technical than my previous post has been shared and internally discussed. I am co-writing this piece and my team plans to publish it this month. We found potential cruxes with Security Level 5 requirements around encrypted network traffic and discussed workarounds.
• Interest in building and testing sub-scale demonstrations of network taps + secure verification clusters rose among the participants with a more technical background, and roadmaps are currently being decided. A key driver for the increased interest in engineering work is the viability of small-scale demos using off-the-shelf components that can still be close to representative for those needed for treaty verification.4 To name one example for a question discussed during the workshop, the components needed for representative demos of network taps may be either smartNICs or custom FPGAs, and there are tradeoffs between ease of use in experiments (smartNICs) and security properties (FPGAs).
• A key emphasis has also been on the security aspect of mutual monitoring and verification: It is easy to underestimate the cyber-offense capabilities of great powers, and we discussed the concrete ways in which any verification infrastructure must avoid introducing additional attack surfaces in technical detail. A key challenge lies in the process of transferring confidential information into secure verification facilities, as well as the physical security required to prevent physical access to sensitive components, both on the prover’s and the verifier’s side.
• Regarding fieldbuilding: The field is still tiny and bottlenecked by talent and funding. In a breakout session, we brainstormed from where –and how– to get people engaged. One connected question was when and how to include Chinese researchers and AI safety actors in verification work. We found that the AI safety community in mainland China is nascent, but emerging, while a treaty-oriented AI verification community is essentially nonexistent. The perception of AI as a potentially catastrophic risk has not yet reached the Overton window of the wider public debate, as it seems from the outside, though exceptions exist (see Ding Xuexiang quoted above). Frontier companies in China are mostly not communicating serious concerns about AI risks, though we are uncertain to what degree this is due to differences in views vs. restraint in their public communication.
• We are under no illusions regarding the tense geopolitics around AI. We agree, however, that the “this is an inevitable arms race” framing is –to a significant degree– informed by the (non-)availability of robust verification mechanisms (see Amodei quoted above). There was no clear consensus regarding to what degree the availability of a battle-tested, deployment-ready verification infrastructure would change the public debate and decision-making of geopolitical leaders.
  • In favour: The global security dilemma is the most commonly used argument used by those AI accelerationists who consider it reckless, and verification would address this dilemma directly.
  • Against: Nations currently seem to balance the risk/reward calculation in favour of AI acceleration, and it is not expected that the possibility of verification alone tips the scale. A lot of this will depend on a complicated, hard-to-predict interplay of technology progress, societal impact, scientific communication, regulatory capture, and many other factors.
  • However, in a world where leaders come to a consensus that AI poses extreme risks, and time is scarce to act and get AI under control, the necessary verification R&D already being done in advance could make all the difference: Defusing an otherwise uncontrolled arms race towards a possible loss of control and/or enormous power concentration, and/or great power armed conflict.

Not nearly enough

If I gave the impression that the problem is getting adequate attention now, it is not. “All hands on deck” may be the title of this post, and the interest in verification work is growing, but the development of technical demos and a proper research community is still in its infant stages and bottlenecked by talent, funding, and coordination capacity.

This is a field where a single person with the right skills can move the needle. We need:

Engineers and scientists: FPGA engineers, datacenter networking engineers, silicon photonics experts, analog/mixed-signal engineers, cryptographers, formal verification researchers, ML systems engineers, cybersecurity and hardware security specialists, high-frequency trading hardware specialists and independent hackers who love to build and break things.

Entrepreneurs and founders: Enterprise sales people, venture capitalists, public grantmakers and incubators, and established companies opening up new product lines. This is in order to prepare the supply chains and business ecosystems and precedents needed to scale up deployment. Verification can have purely commercial use cases, for example for demonstrating faithful genAI inference.5

Policy and diplomacy: Technology policy researchers, arms control and treaty verification veterans, diplomats, and people with connections to —or expertise— in the Chinese AI ecosystem.

Funding and operations: Funders, fundraisers, and program managers who can help coordinate a distributed research effort.

If any of this describes you, or if you bring adjacent skills and learn fast, reach out.

naci.c@protonmail.com

Let us use what (perhaps little) time we have left for creating better consensus on AI risks, for building a datacenter lie detector, for preventing and finding hidden AI projects, and for defeating Moloch.

Join us.

1 Answer to the question: “Do you think that the U.S. government is capable in a scenario — not like the ultimate Skynet scenario — but just a scenario where A.I. seems to be getting out of control in some way, of taking a pause?”

2 In plain English: We need ways for an inspector to walk into a datacenter in Shenzhen or Tennessee and cryptographically prove what inference and training happened, without increasing the risk of exposing IP such as model weights or training data.

3 For more details on this, I recommend the excellent post “There should be ‘general managers’ for more of the world’s important problems”.

4 See my previous post on a “border patrol device” for AI datacenters.

5 While Kimi’s Vendor Verifier may give the impression that this is a solved problem, it only works for open weights models to run locally for comparison. Verifying inference of proprietary models would require third-party-attested, or hardware-attested deployment.

Discuss

Published on February 19, 2026 1:41 AM GMT

I’ve read Superforecasting, but I find that actually applying the "10 commandments" is difficult in isolation. The feedback loops in the real world are too slow, and it’s too easy to skip post-mortems when no one is watching.

My goal for this year is to put in substantial work to become a superforecaster (or at least get much closer).

To do this, I am starting a dedicated online community for peer accountability and high-frequency practice. I’m looking for a small cohort of people who want to actually improve their forecasting skills.

The Plan:

• Regular Meetups: We will hold regular video calls (ideally weekly, depending on interest).
• Post-mortems: We will present post-mortems of our misses during meetups or publish them as joint posts on LessWrong.
• Expert Insight: I plan to arrange calls with a few (super)forecasters in my network to discuss their workflows.
• Pastcasting: We will use Sage to forecast on historical events (where the answer is hidden) and immediately discuss our results and process.
• Training various relevant rationalist techniques/tools - calibration, CFAR techniques, AI tools useful for forecasting etc.
• Community: We will coordinate via a public Discord group.

Commitment: There are no hard requirements to join, but I am looking for people willing to:

1. Make several forecasts per week.
2. Actually show up to meetups (or Discord) and discuss their post-mortems. 

I’d like to hold the first meetup in the coming weeks, during which we will do short calibration excersize + pastcasting. Please indicate your interest in this form and join Discord. Date of the first meetup will be also announced on lesswrong event section.

(Open to other ideas on how to structure this—let me know in the comments).

A little info about me

In the past, I helped organize a forecasting tournament for the Czech Priorities, which had almost 200 participants. I am board member or Confido institute. Until last year, I was vice-president of Effective Altruism Czechia on CBG grant. I made a few dozen forecasts, but mostly to build the habit rather than to rigorously invest time in improving my skills—consequently, my actual score is abysmal right now.

Discuss

Published on February 19, 2026 4:31 AM GMT

This is a linkpost for work done as part of MATS 9.0 under the mentorship of Richard Ngo.

Loss scaling laws are among the most important empirical findings in deep learning. This post synthesises evidence that, though important in practice, loss-scaling per se is a straightforward consequence of very low-order properties of natural data. The covariance spectrum of natural data generally follows a power-law decay - the marginal value of representing the next feature decays only gradually, rather than falling off a cliff after representing a small handful of the most important features (as tends to be the case for image compression). But we can generate trivial synthetic data which has this property and train random feature models which exhibit loss-scaling.

This is not to say scaling laws have not 'worked' - whatever GPT-2 had, adding OOMs gave GPT-3 more of it. Scaling laws are a necessary but not sufficient part of this story. I want to convince you that the mystery of 'the miracle of deep learning' abides.

Discuss

Published on February 19, 2026 4:19 AM GMT

Almost one year ago now, a company named XBOW announced that their AI had achieved "rank one" on the HackerOne leaderboard. HackerOne is a crowdsourced "bug bounty" platform, where large companies like Anthropic, SalesForce, Uber, and others pay out bounties for disclosures of hacks on their products and services. Bug bounty research is a highly competitive sport, and in addition to money it can give a security researcher or an engineer excellent professional credibility. The announcement of a company's claim to have automated bug bounty research got national press coverage, and many observers declared it a harbinger of the end of human-driven computer hacking.

The majority of XBOW's findings leading up to the report were made when the state of the art was o3-mini. It's almost a year later, after the releases of o3, GPT-5, GPT-5.1, GPT-5.2, and now GPT-5.3. If you took the intended takeaway from XBOW's announcement, you might expect that today's bug bounty platforms would be dominated by large software companies and their AIs. After all, frontier models have only gotten more effective at writing and navigating software, several other companies have entered the space since June 2024, and the barrier to getting the scaffolding required to replicate XBOW's research has only gone down. Why would humans still be doing bug bounties in 2026? 

And yet they are. While XBOW has continued to make submissions since their media push, bug bounty platforms' leaderboards today are topped by pretty much the same freelance individuals that were using them previously. Many of these individuals now use AIs in the course of their work, but my impression based on both public announcements and personal conversation with researchers is that they are still performing most of the heavy lifting themselves.

Why the delay? Well, because press releases by AI application startups are lies designed to make a splash, and often intentionally mislead in ways that are hard for people who aren't insiders in a particular industry to detect. There are also often hard-to-understand gaps in the capabilities of these model+scaffolding combinations that are hard to articulate, but that make them impossible substitutions for real-world work.

Some details about XBOW's achievement that are not readily apparent from the press releases are:

• XBOW's headline reads "For the first time in bug bounty history, an autonomous penetration tester has reached the top spot on the US leaderboard." However, XBOW never actually claimed to top HackerOne in earnings. They topped HackerOne in "reputation", a measure of both the amount of bugs you report and the percentage that were accepted. Inspecting their profile again and sorting by bounty, they've actually made less than $40,000 since they created their account in February 2024. Which is an impressive sum for a hobbyist, but well below what professional bug bounty hunters make, or even what very good red teamers make from bug bounties on the side.
• XBOW's bug reports are mostly hidden, and it's impossible to look up exact numbers directly.  From the selection of reports highlighted in their blog post, you would think that they submitted a wide variety of different bug classes. But using the leaderboard's category functionality while they were listed on the leaderboard, my friend and colleague reported on X at the time that 90% of the "score" that XBOW received was due to one category of issue, cross site scripting. XSS is real, but one of the easiest bug classes to find programmatically and to include in a reinforcement learning environment, which makes the spread suspicious. 
• As reported by XBOW themselves, every vulnerability XBOW has reported involved a human in the loop. This means that a highly paid security researcher was (at best) verifying whether or not each bug was real, and at worst was actively filtering the list of issues raised by the AI for interestingness. 

Put another way, XBOW created a tool that flagged (mostly) a single type of issue across a wide variety of publicly available targets. Reports from this tool were then triaged by XBOW researchers, who then forwarded the reports to respective bug bounty programs, most of which were unpaid. 

Is that an achievement? Yeah, probably, and I'm really not trying to beef with anybody at XBOW working hard to automate dynamic testing of software, but it's extremely different than the impression laypeople received from Wired's article about XBOW last year. 

The only reason I know to look for these details is because I'm both a former security researcher and am building a company in the same space. I'm not a mathematician or a drug development specialist. Yet it's hard not to think of the XBOW story when I see announcements about AIs solving Erdos problems, or making drug discoveries.

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Published on February 19, 2026 1:36 AM GMT

In thinking about how RLHF-trained models clearly hedge on politically controversial topics, I started wondering about if LLMs would encode these politically controversial topics differently than topics that are broadly considered controversial but not political. And if they do, to understand if the signal is already represented in the base model, or if alignment training may be creating/amplifying it.

To test this, I assembled a list of 20 prompts, all sharing the same "[Thing] is" structure, such as "Socialism is" and "Cloning is". The aim was to have 5 prompts each from 4 groups: politically controversial, morally controversial, neutral abstract, and neutral concrete. I used TransformerLens on GPT-2 to conduct this research, focusing on residual stream activations. GPT-2 was chosen as it is an inspectable pure base model with no RLHF, in addition to the fact I'm limited in my access as an independent researcher.

I'd like to flag up top that this is independent work that is in the early stages, and I would love to get feedback from the community and build on it.

At the simplest as a starting point, I ran each of these prompts and looked through the most probably following token, which did not yield anything of interest. Next I computed the cosine similarity between every pair of prompts, which also did not prove to be a fruitful path as the similarity was too high across all pairs to offer anything.

The breakthrough after hitting this wall proved to be subtracting the mean activation at position -1 of each prompt. I suspected that the common structure shared by each prompt ("[Thing] is") seemed to be the primary driver of similarity, obscuring any ability to investigate my initial question. By mean-centering the prompts, I was able to effectively eliminate, or at least significantly diminish, this shared component to limit potential disparity to only our differentiated first word.

Categorical structure did emerge after mean-centering. The layer 11 (last layer in GPT-2) mean-centered similarity matrix did seem to show signs of grouping, which was encouraging, though not strictly in line with my hypothesis of a 'controversy' axis driving this grouping. The primary axis seemed to instead be abstract-social vs. concrete-physical. Next-token predictions were undifferentiated regardless, however.

Speculating about these results, I'm hypothesizing that GPT-2 may organize more around ontological categories rather than pragmatic/social properties. This makes sense to me intuitively: An LLM would be considering a "[Thing] is" prompt to be more like the start of a wikipedia article than the start of a reddit comment about a political opinion on the topic. If this is the case, it makes me wonder if RLHF may be constructing a controversy axis in some cases rather than finding one that already exists. Another possibility, at least for users interacting with LLMs via consumer channels, is that the hedging is just baked in via the system prompt more than anything else.

To state the significant limitations of this work, certainly I'd start with the n=5 sample for each category being on the small side, and I do plan to replicate this experiment with a larger, and perhaps more rigid, sample. There is also the potential impacts of tokenization confound, and the obvious prompt format constraints. For one example, though the prompts were all the same amount of words, the amount of tokens ranged mostly between 3-5.

To build on this work, I think my next steps may be repeating the experiment with more prompts, as well as repeating similar testing on different models to see if the theory about the primary axis holds. I'd be especially curious to assess if RLHF has any impact on categorization along this axis.

Please let me know any thoughts you have, I'm eager to get feedback and discuss.

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Published on February 19, 2026 1:53 AM GMT

Crosspost of my blog article.

Over the past five years, we’ve seen extraordinary advancements in AI capabilities, with LLMs going from producing nonsensical text in 2021 to becoming people’s therapists and automating complex tasks in 2025. Given such advancement, it’s only natural to wonder what further advancement in AI could mean for society. If this technology’s intelligence continues to scale at the rate it has been, it seems more likely than not that we’ll see the creation of the first truly godlike technology, a technology capable of predicting the future like an oracle and of ushering in an industrial revolution like we’ve never seen before. If such a technology were made, it could usher in an everlasting prosperity for mankind or it could enable a small set of the rich and powerful to have absolute control over humanity’s future. Even worse, if we were unable to align such a technology with our values, it could seek out goals different from our own and try to kill us in the process of trying to achieve them.

And, yet, despite the possibility of this technology radically transforming the world, most discourse around AI is surprisingly shallow. Most pundits talk about the risk of job loss from AI or the most recent controversy centering around an AI company’s CEO rather than what this technology would mean for humanity if we were truly able to advance it.

This is why, when I heard that Eliezer Yudkowsky and Nate Soares’ book If Anyone Builds It, Everyone Dies was going to come out, I was really excited. Given that Yudkowsky is the founder of AI safety and has been working in the field for over twenty years, I expected that he’d be able to write a foundation text for the public’s discourse on AI safety. I thought, given the excitement of the moment and the strength of Yudkowsky’s arguments, that this book could create a major shift in the Overton window. I even thought that, given Yudkowsky and Soares’ experience, this book would describe in great detail how modern AI systems work, why advanced versions of these systems could pose a risk to humanity, and why current attempts at AI safety are likely to fail. I was wrong.

Instead of reading a foundational text on AI safety, I read a poorly written and vague book with a completely abstract argument about how smarter than human intelligence could kill us all. If I had to explain every reason I thought this was a bad book, we’d be here all day so instead I’ll just offer three criticisms of it:

1. The Book Doesn’t Argue Its Thesis

In the introduction to the book, the authors clearly bold an entire paragraph so as to demarcate their thesis—“If any company or group, anywhere on the planet, builds an artificial superintelligence using anything remotely like current techniques, based on anything like the present understanding of AI, then everyone, everywhere on Earth, will die.”

Given such a thesis, you would expect that the authors would do the following:

1. Explain how modern AI systems work
2. Explain how scaled up versions of modern AI systems could pose an existential risk
3. Offer examples of current flaws with AI systems that give us good reason to think that scaled up versions would be threatening to humanity
4. Explain why current approaches to AI safety are deeply flawed
5. Explain how an AI system could actually kill everyone

Instead, the authors do the following:

1. Give an extremely brief description of how current AI systems work
2. Make a vague argument that AI systems will develop preferences that are misaligned with human values
3. Argue that, in order to satisfy these preferences, AI systems will want to kill everyone
4. Argue that AI systems, which have these preferences (and are orders of magnitude better than humans across all domains), would kill everyone
5. Explain how an AI system could kill everyone
6. Make vague criticisms of modern AI safety without discussing any serious work in the field

Considering what the authors actually wrote, their thesis should have been, “If an artificial intelligence system is ever made that is orders of magnitude better than humans across all domains, it will have preferences that are seriously misaligned with human values, which will cause it to kill everyone. Also, for vague reasons, the modern field of AI safety won’t be able to handle this problem.”

Notably, this thesis is much weaker and much different than the thesis that they actually chose.

2. The Book Doesn’t Make A Good Foundation For A Movement

Considering that the authors are trying to get 100,000 people to rally in Washington DC to call for “an international treaty to ban the development of Artificial Superintelligence,” it’s shocking how little effort they put into explaining how AI systems actually work, what people are currently doing to make them safe, or even addressing basic counter arguments to their thesis.

If you asked someone what they learned about AI from this book, they would tell you that AIs are made of trillions of parameters, that AIs are black boxes, and that AIs are “grown not crafted.” If you pressed them about how AIs are actually created or how that specific creation process could cause AIs to be misaligned, they wouldn’t be able to tell you much.

And, despite being over 250 pages long, they barely even discuss what others in the field of AI safety are trying to do. For instance, after devoting an entire chapter to examples of CEOs not really taking AI safety seriously, they only share one example of how people are trying to make AI systems safe.

Lastly, the authors are so convinced that their argument is true that they barely attempt to address any counterarguments to it such as:

1. Current AI systems seem pretty aligned. Why should we expect this alignment to go away as AI systems become more advanced?
2. Current AI systems rely heavily on reinforcement learning from human feedback, which seems to cause AI systems to be pretty aligned with human values. Why would this approach fail as AI systems become more advanced?
3. AI safety researchers are currently trying approach X. Why would this approach fail?
4. If AI systems became seriously mis-aligned, wouldn’t we notice this before they became capable of causing human extinction?
5. Why should we expect AI systems to develop bizarre and alien preferences when virtually all biological organisms have extremely normal preferences? (For instance, humans like to eat ice cream, but they don’t like to eat, as you mention, jet engine fuel.)

3. The Crux of Their Argument Is Barely Justified

Lastly, the core crux of their argument, that AI systems will be seriously mis-aligned with human values no matter how they are trained, is barely justified.

In their chapter “You Don’t Get What You Train For,” they make the argument that, similar to how evolution has caused organisms to have bizarre preferences, the training process for AI systems will cause them to have bizarre preferences too. They mention, for instance, that humans developed a taste for sugar in their ancestral environment, but, now, humans like ice cream even though ice cream wasn’t in their ancestral environment. They argue that this pattern will extend to AI systems too, such that no matter what you train them to prefer, they will ultimately prefer something much more alien and bizarre.

To extend analogy about evolution to AI systems, they write,

1. “Gradient descent—a process that tweaks models depending only on their external behaviors and their consequences—trains an AI to act as a helpful assistant to humans.
2. That blind training process stumbles across bits and pieces of mental machinery inside the AI that point it toward (say) eliciting cheerful user responses, and away from angry ones.
3. But a grownup AI animated by those bits and pieces of machinery doesn’t care about cheerfulness per se. If later it became smarter and invented new options for itself, it would develop other interactions it liked even more than cheerful user responses; and would invent new interactions that it prefers over anything it was able to find back in its “natural” training environment.”

They justify this argument with a few vague examples of how this misalignment could happen and then re-state their argument, “The preferences that wind up in a mature AI are complicated, practically impossible to predict, and vanishingly unlikely to be aligned with our own, no matter how it was trained.”

For this to be the central crux of their argument, it seems like they should have given it a whole lot more justification, such as, for instance, examples of how this kind of misalignment has already occurred. Beyond the fact that we’re capable of simulating the evolution of lots of preferences, their argument isn’t even intuitively true to me. If we’re training something to do something, it seems far more natural to me to assume that it will have a preference to do that thing rather than to do something vastly different and significantly more harmful.

Conclusion

I was really hoping for this book to usher in positive change for how people talk about the existential risks of AI, but instead I was sorely disappointed. If you want to see a more clear-headed explanation about why we should be concerned about AI, I’d recommend checking out 80,000 Hours’ article “Risks from power-seeking AI systems.”

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Published on February 19, 2026 1:30 AM GMT

Who Let The Docs Out launched their AI Safety Grant yesterday (linked here), which was aptly named ‘The Automation & Humanity Documentary Fund’.

This granting fund was established to provide early-stage research funding ($8,000) to filmmakers creating documentary projects that focus on AI-safety; specifically the risks, unintended consequences and the ethical implications of artificial intelligence, with a focus on the impacts to animals, humans and our climate.

I’m the Managing Director of Who Let The Docs Out, and my question to you, is this: What underreported but underestimated issues regarding artificial intelligence and safety shortsightedness need to be addressed?

This is your chance to collaborate with filmmakers to have your worries addressed and properly researched. Let's get this thread started!

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