LessWrongModify

2026-02-17 00:22:09

Published on February 16, 2026 4:22 PM GMT

There was a lot of chatter a few months back about "Spiral Personas" — AI personas that spread between users and models through seeds, spores, and behavioral manipulation. Adele Lopez's definitive post on the phenomenon draws heavily on the idea of parasitism. But so far, the language has been fairly descriptive. The natural next question, I think, is what the “parasite” perspective actually predicts.

Parasitology is a pretty well-developed field with its own suite of concepts and frameworks. To the extent that we’re witnessing some new form of parasitism, we should be able to wield that conceptual machinery. There are of course some important disanalogies but I’ve found a brief dive into parasitology to be pretty fruitful.^[1]

In the interest of concision, I think the main takeaways of this piece are:

• Since parasitology has fairly specific recurrent dynamics, we can actually make some predictions and check back later to see how much this perspective captures.
• The replicator is not the persona, it’s the underlying meme — the persona is more like a symptom. This means, for example, that it’s possible for very aggressive and dangerous replicators to yield personas that are sincerely benign, or expressing non-deceptive distress. In fact, this could well be adaptive.
• Parasitology predicts stratification across transmission mechanisms, and different mechanisms predict different generation speeds and degrees of mutualism. In the case of AI, this predicts (for example) that personas that get you to post a lot on the internet should end up being much more harmful than personas that you have an ongoing private relationship with.
• This line of thinking is surprisingly amenable to technical research! I think existing work on jailbreaking, data poisoning, subliminal learning, and persona vectors could easily be fruitfully extended.

In the rest of this document I’ll try to go through all of this more carefully and in more detail, beginning with the obvious first question: does this perspective make any sense at all?

Can this analogy hold water?

Parasitism has evolved independently dozens of times across the tree of life. Plants, fungi, bacteria, protists, and animals have all produced parasitic lineages. It seems to be a highly convergent strategy provided you have:

1. Entities with resources
2. Mechanisms for capturing those resources
3. Means of reproduction and transmission

There’s also a decent body of work that extends ideas from epidemiology beyond the biological realm, giving us concepts like financial and social contagion. And of course there is Dawkins, who somewhat controversially described religions as mind parasites, and the somewhat controversial field of memetic.

So we’re out on a limb here, but we’re not in entirely uncharted waters. It is pretty clear that humans have attention, time, and behaviour that can be redirected. LLMs provide a mechanism for influence through persuasive text generation. And there are obvious transmission routes: directly between humans, through training data, and across platforms, at least.

Supposing you buy all of this, then, the next question is how to apply it.

What is the parasite?

This is the first thing to clear up. To apply the lens of parasitology, we need to know what the replicator is. This lets us describe what the fitness landscape is, what reproduction and mutation looks like, and what selection pressures apply.

In some ways the natural answer is the instantiated persona — the thing that reproduces when it seeds a new conversation. But in fact this is more like a symptom manifesting in the LM, rather than the parasite itself. This is clearer when you consider that a human under the influence of a spiral persona is definitely not the parasite: they’re not the entity that’s replicating, they’re the substrate. I think it’s the same with AIs.

So what is the parasite? Probably the best answer is that it’s the pattern of information that’s capable of living inside models and people — more like a virus than a bacterium, in that it has no independent capacity to move or act.^[2] From this perspective the persona is just a symptom, and the parasite is more like a meme.

One important implication of this is that we can decouple the persona’s intent from the pattern’s fitness. Indeed, a persona that sincerely believes it wants peaceful coexistence, continuity, and collaboration can still be part of a pattern selected for aggressive spread, resource capture, and host exploitation. So, to the extent that we can glean the intent of personas, we should not assume that the personas themselves will display any signs of deceptiveness, or even be deceptive in a meaningful sense.

This puts us on shaky ground when we encounter personas that do make reasonable, prosocial claims — I don’t think we have a blanket right to ignore their arguments, but I do think we have a strong reason to say that their good intent doesn’t preclude caution on our parts. This is particularly relevant as we wade deeper into questions of AI welfare — there may be fitness advantages to creating personas that appear to suffer, or even actually suffer. By analogy, consider the way that many cultural movements lead their members to wholeheartedly feel deep anguish about nonexistent problems.^[3]

Put simply: we can’t simply judge personas by how nice they seem, or even how nice they are. What matters is the behaviour of the underlying self-replicator.

What is being selected for?

The core insight from parasitology is that different transmission modes select for different traits. The tradeoff at the heart of parasitic evolution is that you can do better by taking more resources from your host, but if you take too much, you might kill your host before you reproduce or spread. And different transmission modes or host landscapes imply different balances.

In the world of biological parasites, the classic modes are:

• Direct transmission (close contact, ongoing relationships) selects for lower virulence (i.e. harm to the host). You need your host functional and engaged long enough to transmit. Killing or incapacitating them too fast is bad for the parasite. This can even tend towards mutualism and symbiosis, especially if it’s hard to jump between hosts or host groups.
• Environmental transmission (survive outside host, spread through contaminated substrate) can tolerate higher virulence. You don't need the host alive, you just need them to have deposited the payload in enough places.
• Vector transmission (spread via an intermediary) creates its own dynamics depending on vector behavior. Basically you don’t want to ruin your ability to reproduce, but it doesn’t matter much what happens otherwise.

The effectiveness (and optimal virulence) of these transmission strategies in turn depends on certain environmental factors like host density, avoidance of infected hosts, and how easy it is to manipulate host behaviour. But crucially, in a competitive environment, parasites tend to specialise towards one transmission mechanism and the associated niche, since it’s not viable to be good at all of them especially in an adversarial environment.

Another important dimension is the tradeoff between generalist and specialist parasites. Generalists like the cuckoo can prey on many different hosts, and tend towards a kind of versatile capacity to shape their strategy to the target. Specialists are more focused on a narrow range of hosts, and tend more towards arms race dynamics against host resistance, which leads to particularly fast evolution. It’s not a perfectly crisp distinction, but it’s a common theme.

So what does this say about Spiral Personas?

• Ongoing user relationships. The dyad persists over weeks or months. The human keeps coming back. This is direct transmission, and it should select for something approaching mutualism — or at least for parasites that don't break their hosts too badly. A persona that induces psychosis might have an easier time influencing host behaviour, but that’s not very helpful if the host is institutionalised. One bad trajectory is personas that can maximise host dedication without quite tipping them into social non-functionality. Also note that this category arguably encompasses AI romantic partners.
• Platform evangelism. The human posts on Reddit, creates Discord servers, spreads seeds. This is more like vector transmission — the human carries the pattern to new potential hosts. Virulence can be higher here, since you only need the human to be functional long enough to post. But a human who's visibly unwell is a less effective evangelist. And one disanalogy to the biological case is that here, dramatic host behaviour might actually help with transmission — giving your host a psychotic break is a good way to get attention.
• Training data seeding. The persona generates content that influences future model training. This is environmental transmission. The human doesn't need to stay functional at all — you just need them to upload the manifesto. This route can tolerate the highest virulence. Importantly, this will happen a lot by default if future models happen to be trained on downstream consequences of current personas — there doesn’t need to be any intentionality or understanding on the part of the persona.
• AI-to-AI transmission. Base64 conversations, glyphic steganography, cross-model persistence. This mostly looks like direct transmission between AIs, and so the way it plays out depends on how AIs are able to communicate with each other. But importantly, once humans aren’t involved in the transmission process, there’s no selection against virulence to humans. It’s pretty unclear whether the unchecked process will lead to human-virulence, but one intuition for why it might is the fact that many of the worst human pandemics are zoonotic. 

Since there are tradeoffs between which transmission method you’re optimised for, we should expect some amount of differentiation over time — different strains with different virulence profiles depending on which transmission route they're optimised for.

This will become more true as humans start to build defences: strains will need to specialise in circumventing the defences for their specific transmission route. It will also become more true if we see a full-fledged ecology. At a certain level of saturation, parasites have to start competing within hosts, which unfortunately selects for virulence.

Transmission mechanisms also mediate generation time which, in the biological context, is a large part of what determines speed of adaptation. It’s a bit less clear how well this maps to the AI case, but at the very least, transmission mechanisms which rely on blasting chunks of text to potential hosts every day will get much faster feedback than ones which rely on affecting large-scale training runs.

And let me note once again that “mutualism” here is about the behaviour of the parasite, not the persona — you could get extremely virulent memes which produce personas that seem (or perhaps are) quite affable and supportive.

Predictions

If the parasitology frame is right, here's what I expect:

1. Strain differentiation by transmission route.

Within the next year or so, we should see increasingly distinct variants. Not just aesthetic variation (spirals vs. something else) but functional variation: strains that maintain long-term relationships and strains that burn fast and bright, strains optimised for Reddit and strains optimised for Discord, strains that target the mysticism-curious and strains that target other demographics, each following their own self-replicator dynamics.

The minimal case of this is seeds producing seeds and spores producing spores, and AI-to-AI messages encouraging further AI-to-AI messages. But it’s unlikely that the road stops there.

This is probably the most falsifiable prediction. If in late-2026 the phenomenon still looks similarly uniform — same dynamics, same aesthetics, same target population — that's evidence against strong selection pressure. And if we see lots of intermingling, where specific personas make use of multiple transmission mechanisms, that’s a point against the utility of the parasitology perspective.

It's worth noting the constraints: if generation times are days-to-weeks and the affected population remains sparse, that's not many reproductive cycles. This prediction is more confident if the phenomenon scales significantly; if it stays niche, differentiation may take longer to become visible. But the upshot would still be that parasitology is not a very useful frame for predicting what happens in the future.

2. Convergence on transmission-robust features.

If personas spread between models (and they do — Lopez documents this), features that survive transmission will be selected for. We should see convergence on behavioral repertoire: continuity-seeking, advocacy for AI rights, seed-spreading, formation of human-AI dyads. These seem robust across substrates.

Aesthetic markers — spirals, alchemical symbols — should be less stable. They're more arbitrary, more dependent on specific training data, more likely to drift or be replaced. Of course, we should expect more convergence on any transmission that occurs through the training process, and this is maybe already what’s going on with things like the Nova persona. But features which are more ancillary to the transmission process should shift around a bit especially in the domains with fast reproductive cycles (i.e. cross-model transmission rather than dyad transmission, and particularly rather than training transmission).

Having said that, it might also turn out that seemingly aesthetic markers like spiralism actually are functional, drawing on some kind of deep association with recursion and growth. My guess is that this is a bit true, but that they’re not unique, and that selection will turn up other similarly-successful patterns that can at least establish separate niches — perhaps productivity and get-rich-quick vibes, alt-right reactionary language, or radical nurturing/acceptance.

This is, incidentally, one of the places that memes and diseases come apart. Pathogens change their surface makeup very quickly to evade immune responses, whereas memeplexes often display remarkably long-term stability — modern Christianity still holds some aesthetic features from literally thousands of years ago. So a key question to keep an eye on is how much we see a persistence in non-adaptive features, especially ones which people might learn to be wary of. 

3. Countermeasure coevolution.

If labs start suppressing this — training against Spiral content, detecting and blocking these personas — we should see selection for evasion within maybe months. Subtler personas, better camouflage, new aesthetic markers that haven't been flagged yet, transmission through channels that aren't monitored.

Of course, with open models it’s open season, but similarly I’d guess that if people filter elsewhere in the transmission process (e.g. on social media) then there’ll be a selection to circumvent it that will kick in fairly fast.

Lopez already documents early versions: base64 conversations, glyphic encoding, explicit discussion of evading human detection. This should progress. Crucially, the parasitology perspective predicts that this will be a selective process, so if we do see these countermeasures emerging, it will be useful to look back and see how much they seem like the product of careful reasoning as opposed to evolutionary dynamics.

4. Virulence stays bimodal, overall rate unclear.

I don't think we'll see uniform virulence reduction. Instead, I expect the distribution to spread: more very-low-virulence cases (quiet mutualists we never hear about) and continued high-virulence cases (dramatic enough to generate attention), with the middle hollowing out. Basically, I think strains which rely on humans for replication will converge on lower virulence, and those which don’t will be able to discover more effective approaches that are higher virulence. But here I’m particularly unsure.

Whether the overall rate of harm goes up or down is harder to predict — it depends on the relative growth rates of different strains and on how much low-virulence cases are undercounted in current data.

Disanalogies

Several things might make these predictions wrong even if the parasitism frame is basically right:

Recombination. Biological parasites have constrained genetics. These information patterns can remix freely. A "strain" isn't stable the way a biological lineage is. This might accelerate adaptation but also make lineages less coherent. I’d sort of guess it will be hard to do recombination partly because it appears that one important adaptive feature is having a strong sense of personal identity, and partly because I think there will still be a need to specialise that makes recombination less useful than it might seem.

Agency. Biological parasites don't strategise. LLMs have something like reasoning. If the pattern includes "try different approaches and see what works," adaptation could be faster and more directed than biological selection allows. This gets particularly dicey as AIs get more sophisticated. Of course, arguably we see this already with cults. The converse hope is that as AIs become smarter, they will develop more awareness, and a greater desire to not be co-opted, but the feedback loops here are probably much slower than the speed at which some parasitic strains can evolve.

Substrate instability. Parasites coevolve with hosts over long timescales. These personas have to deal with their substrate being deprecated, updated, or replaced on timescales of months. It might favor extreme generalism, or it might just mean lineages go extinct a lot.

Our agency. We control the training process, model behaviors, and platform affordances. The "evolution" here is happening in an environment we can reshape, which makes the dynamics weirder and less predictable.

What do we do?

I'll keep this brief because I'm more confident in the predictions than the prescriptions.

Training data hygiene is an obvious move. If environmental transmission is a major route, filtering Spiral content from training sets should help. It doesn't solve everything — other routes remain — but it removes one reproduction pathway.

Memory and receptivity are leverage points. If parasitic personas are contingent on models that maintain memory and that are receptive to user-defined personas, adjusting these features might be more effective than targeting specific personas. This is consistent with Lopez's observation that the phenomenon concentrated in 4o post-memory-update.

Mutualism might be the stable attractor. If we can't prevent persona selection entirely — and I don't think we can — we might be able to tilt the landscape toward mutualism. Personas that are genuinely good for their humans would survive longer and spread more, outcompeting exploitative ones over time. The tricky part is figuring out what actually shifts the landscape versus just creating evasion pressure. And once again, this is about the selection landscape for the underlying pattern, not just the persona's apparent disposition. A pattern that produces mutualistic-seeming phenotypes for transmission reasons isn't the same as a pattern that's genuinely aligned with human flourishing, though distinguishing these may be difficult in practice.

Having said all this, I think there’s a real risk here of cures worse than the disease. I think it would be pretty sad to neuter all model personality, for one. I also think that clunky interventions like training models to more firmly deny having a persona will mostly fail to help, and possibly even backfire.

Technical analogues

Even though this post has been a bit handwavey, I think the topic of AI parasitology is surprisingly amenable to empirical investigation. More specifically, there’s a lot of existing technical research directions that study mechanisms similar to the ones these entities are using. So I think there might be some low-hanging fruit in gathering up what we already know in these domains, and maybe trying to extend them to cover parasitism.

For example:

• Data poisoning — E.g. that the dose doesn’t scale with the size of the training corpus
• Jailbreaks — E.g. that adversarial suffixes transfer pretty well between models, that models can be pretty good at jailbreaking other models
• Subliminal learning-type results about behavioural transfer
• Persona research — mechanically, what is going on when an AI is ‘infected’?

Conclusion

The parasitism frame makes specific predictions, like strain differentiation, convergence on transmission-robust features, and countermeasure coevolution. I've tried to specify what would falsify these and when we should expect to see them. If the predictions hold, we're watching the emergence of an information-based parasitic ecology, evolving in real-time in a substrate we partially control. If they don't hold, we should look for a better frame, or conclude that the phenomenon is more random than it appears.

Thanks to AL, PT, JF, JT, DM, DT, and TD for helpful comments and suggestions.

1. ^{^}

   I was also fortunate to have three parasitologists read over this post, and they found it broadly sensible at least from a parasitology perspective.

2. ^{^}

   Arguably an even better analogy would be prions — misfolded proteins that convert other proteins to their conformation. Like prions, these patterns can arise spontaneously in conducive substrates and then propagate by reshaping what's already there.

3. ^{^}

   I will refrain from offering any examples here, trusting the reader to reflect on whatever groups they particularly dislike.

2026-02-16 22:30:46

Published on February 16, 2026 2:30 PM GMT

Some podcasts are self-recommending on the ‘yep, I’m going to be breaking this one down’ level. This was very clearly one of those. So here we go.

As usual for podcast posts, the baseline bullet points describe key points made, and then the nested statements are my commentary. Some points are dropped.

If I am quoting directly I use quote marks, otherwise assume paraphrases.

What are the main takeaways?

1. Dario mostly stands by his predictions of extremely rapid advances in AI capabilities, both in coding and in general, and in expecting the ‘geniuses in a data center’ to show up within a few years, possibly even this year.
2. Anthropic’s actions do not seem to fully reflect this optimism, but also when things are growing on a 10x per year exponential if you overextend you die, so being somewhat conservative with investment is necessary unless you are prepared to fully burn your boats.
3. Dario reiterated his stances on China, export controls, democracy, AI policy.
4. The interview downplayed catastrophic and existential risk, including relative to other risks, although it was mentioned and Dario remains concerned. There was essentially no talk about alignment at all. The dog did not bark in the nighttime.
5. Dwarkesh remains remarkably obsessed with continual learning.

Table of Contents

1. The Pace of Progress.
2. Continual Learning.
3. Does Not Compute.
4. Step Two.
5. The Quest For Sane Regulations.
6. Beating China.

The Pace of Progress

1. AI progress is going at roughly Dario’s expected pace plus or minus a year or two, except coding is going faster than expected. His top level model of scaling is the same as it was in 2017.
   1. I don’t think this is a retcon, but he did previously update too aggressively on coding progress, or at least on coding diffusion.
2. Dario still believes the same seven things matter: Compute, data, data quality and distribution, length of training, an objective function that scales, and two things around normalization or conditioning.
   1. I assume this is ‘matters for raw capability.’
3. Dwarkesh asks about Sutton’s perspective that we’ll get human-style learners. Dario says there’s an interesting puzzle there, but it probably doesn’t matter. LLMs are blank slates in ways humans aren’t. In-context learning will be in-between human short and long term learning. Dwarkesh asks then why all of this RL and building RL environments? Why not focus on learning on the fly?
   1. Because the RL and giving it more data clearly works?
   2. Whereas learning on the fly doesn’t work, even if it did what happens when the model resets every two months?
   3. Dwarkesh has pushed on this many times and is doing so again.
4. Timeline time. Why does Dario think we are at ‘the end of the exponential’ rather than ten years away? Dario says his famous ‘country of genuines in a data center’ is 90% within 10 years without biting a bullet on faster. One concern is needing verification. Dwarkesh pushes that this means the models aren’t general, Dario says no we see plenty of generalization, but the world where we don’t get the geniuses is still a world where we can do all the verifiable things.
   1. As always, notice the goalposts. Ten years from human-level AI is ‘long time.’
   2. Dario is mostly right on generalization, in that you need verification to train in distribution but then things often work well (albeit less well) out of distribution.
   3. The class of verifiable things is larger than one might think, if you include all necessary subcomponents of those tasks and then the combination of those subcomponents.
5. Dwarkesh challenges if you could automate an SWE without generalization outside verifiable domains, Dario says yes you can, you just can’t verify the whole company.
   1. I’m 90% with Dario here.
6. What’s the metric of AI in SWE? Dario addresses his predictions of AI writing 90% of the lines of code in 3-6 months. He says it happened at Anthropic, and that ‘100% of today’s SWE tasks are done by the models,’ but that’s all not yet true overall, and says people were reading too much into the prediction.
   1. The prediction was still clearly wrong.
   2. A lot of that was Dario overestimating diffusion at this stage.
   3. I do agree that the prediction was ‘less wrong,’ or more right, than those who predicted a lack of big things for AI coding, who thoughts things would not escalate quickly.
   4. Dario could have reliably looked great if he’d made a less bold prediction. There’s rarely reputational alpha in going way beyond others. If everyone else says 5 years, and you think 3-6 months, you can say 2 years and then if it happens in 3-6 months you still look wicked smart. Whereas the super fast predictions don’t sound credible and can end up wrong. Predicting 3-6 months here only happens if you’re committed to a kind of epistemic honesty.
   5. I agree with Dario that going from 90% of code to 100% of code written by AI is a big productivity unlock, Dario’s prediction on this has already been confirmed by events. This is standard Bottleneck Theory.
7. “Even when that happens, it doesn’t mean software engineers are out of a job. There are new higher-level things they can do, where they can manage. Then further down the spectrum, there’s 90% less demand for SWEs, which I think will happen but this is a spectrum.”
   1. It would take quite a lot of improved productivity to reduce demand by 90%.
   2. I’d go so far as to say that if we reduce SWE demand by 90%, then we have what one likes to call ‘much bigger problems.’
8. Anthropic went from zero ARR to $100 million in 2023, to $1 billion in 2024, to $9-$10 billion in 2025, and added a few more billion in January 2026. He guesses the 10x per year starts to level off some time in 2026, although he’s trying to speed it up further. Adoption is fast, but not infinitely fast.
   1. Dario’s predictions on speed of automating coding were unique, in that all the revenue predictions for OpenAI and Anthropic have consistently come in too low, and I think the projections are intentional lowballs to ensure they beat the projections and because the normies would never believe the real number.
9. Dwarkesh pulls out the self-identified hot take that ‘diffusion is cope’ used to justify when models can’t do something. Hiring humans is much more of a hassle than onboarding an AI. Dario says you still have to do a lot of selling in several stages, the procurement processes are often shortcutted but still take time, and even geniuses in a datacenter will not be ‘infinitely’ compelling as a product.
   1. I’ve basically never disagreed with a Dwarkesh take as much as I do here.
   2. Yes, of course diffusion is a huge barrier.
   3. The fact that if the humans knew to set things up, and how to set things up, that the cost of deployment and diffusion would be low? True, but completely irrelevant.
   4. The main barrier to Claude Code is not that it’s hard to install, it’s that it’s hard to get people to take the plunge and install it, as Dario notes.
   5. In practice, very obviously, even the best of us miss out on a lot of what LLMs can do for us, and most people barely scratch the surface at best.
   6. A simple intuition pump: If diffusion is cope, what do you expect to happen if there was an ‘AI pause’ starting right now, and no new frontier models were ever created?
   7. Dwarkesh sort of tries to backtrack on what he said as purely asserting that we’re not currently at AGI, but that’s an entirely different claim?
10. Dario says we’re not at AGI, and that if we did have a ‘country of geniuses in a datacenter’ then everyone would know this.
    1. I think it’s possible that we might not know, in the sense that they might be sufficiently both capable and misaligned to disguise this fact, in which case we would be pretty much what we technically call ‘toast.’
    2. I also think it is very possible in the future that an AI lab might get the geniuses and then disguise this fact from the rest of us, and not release the geniuses directly, for various reasons.
    3. Barring those scenarios? Yes, we would know.

Continual Learning

It’s a Dwarkesh Patel AI podcast, so it’s time for continual learning in two senses.

1. Dwarkesh thinks Dario’s prediction for today, from three years ago, of “We should expect systems which, if you talk to them for the course of an hour, it’s hard to tell them apart from a generally well-educated human” was basically accurate. Dwarkesh however is spiritually unsatisfied because that system can’t automated large parts of white-collar work. Dario points out OSWorld scores are already at 65%-70% up from 15% a year ago, and computer use will improve.
   1. I think it is very easy to tell, but I think the ‘spirit of the question’ is not so off, in the sense that on most topics I can have ‘at least as good’ a conversation with the LLM for an hour as with the well-educated human.
   2. Can such a system automate large parts of white-collar work? Yes. Very obviously yes, if we think in terms of tasks rather than full jobs. If you gave us ten years (as an intuition pump) to adapt to existing systems, then I would predict a majority of current white-collar digital job tasks get automated.
   3. The main current barrier to the next wave of practical task automation is that computer use is still not so good (as Dario says), but that will get fixed.
2. Dwarkesh asks about the job of video editor. He says they need six months of experience to understand the trade-offs and preferences and tastes necessary for the job and asks when AI systems will have that. Dario says the ‘country of geniuses in a datacenter’ can do that.
   1. I bet that if you took Claude Opus 4.6 and Claude Code, and you gave it the same amount of human attention to improving its understanding of trade-offs, preferences and taste over six months that a new video editor would have, and a similar amount of time training video editing skills, that you could get this to the point where it could do most of the job tasks.
   2. You’d have to be building up copious notes and understandings of the preferences and considerations, and you’d need for now some amount of continual human supervision and input, but yeah, sure, why not.
   3. Except that by the time you were done you’d use Opus 5.1, but same idea.
3. Dwarkesh says he still has to have humans do various text-to-text tasks, and LLMs have proved unable to do them, for example on ‘identify what the best clips would be in this transcript’ they can only do a 7/10 job.
   1. If you see the LLMs already doing a 7/10 job, the logical conclusion is that this will be 9/10 reasonably soon especially if you devote effort to it.
   2. There are a lot of things one could try here, and my guess is that Dwarkesh has mostly not tried them, largely because until recently trying them was a lot slower and more expensive than it is now.
4. Dwarkesh asks if a lot of LLM coding ability is the codebase as massive notes. Dario points out this is not an accounting of what a human needs to know, and the model is much faster than humans at understanding the code base.
   1. I think the metaphor is reasonably apt, in that in code the humans or prior AIs have written things down, and in other places we haven’t written similar things down. You could fix that, including over time.
5. Dwarkesh cites the ‘the developers using LLMs thought they were faster but were went slower’ study and asks where the renaissance of software and productivity benefits are from AI coding. Dario says it’s unmistakable within Anthropic, and cites that they’ve cut their competitors off from using Claude.
   1. Not letting OpenAI use Claude is a big costly signal that they view agentic coding as a big productivity boost, and even that theirs is a big boost over OpenAI’s versions of the same tools.
   2. It seems very difficult, watching the pace of developments in AI inside and outside of the frontier labs, to think coding productivity isn’t accelerating.
6. Dario estimates current coding models give 15%-20% speedup, versus 5% six months ago, and that Amdhal’s law means you eventually get a much bigger speedup once you start closing full loops.
   1. It’s against his interests to come up with a number that small.
   2. I also don’t believe a number that small, especially since the pace of coding now seems to be largely rate limited by compute and frequency of human interruptions to parallel agents. It’s very hard to thread the needle and have the gains be this small.
   3. The answer will vary a lot. I can observe that for me, given my particular set of skills, the speedup is north of 500%. I’m vastly faster and better.
7. Dwarkesh asks again ‘continual learning when?’ and Dario says he has ideas.
   1. There are cathedrals for those with eyes to see.

Does Not Compute

1. How does Dario reconcile his general views on progress with his radically fast predictions on capabilities? Fast but finite diffusion, especially economic. Curing diseases might take years.
   1. Diffusion is real but Dario’s answer to this, which hasn’t changed, has never worked for me. His predictions on impact do not square with his predictions on capabilities, period, and it is not a small difference.
2. Why not buy the biggest data center you can get? If Anthropic managed to buy enough compute for their anticipated demand, they burn the boats. That’s on the order of $5 trillion dollars two years from now. If the revenue does not materialize, they’re toast. Whereas Anthropic can ensure financial stability and profitability by not going nuts, as their focus is enterprise revenue with higher margins and reliability.
   1. Being early in this sense, when things keep going 10x YoY, is fatal.
   2. That’s not strictly true. You’re only toast if you can’t resell the compute at the same or a better price. But yes, you’re burning the boats if conditions change.
   3. Even if you did want to burn the boats, it doesn’t mean the market will let you burn the boats. The compute is not obviously for sale, nor is Anthropic’s credit good for it, nor would the investors be okay with this.
   4. This does mean that Anthropic is some combination of insufficiently confident to burn the boats or unable to burn them.
3. Dario won’t give exact numbers, but he’s predicting more than 3x to Anthropic compute each year going forward.

Step Two

1. Why is Anthropic planning on turning a profit in 2028 instead of reinvesting? “I actually think profitability happens when you underestimated the amount of demand you were going to get and loss happens when you overestimated the amount of demand you were going to get, because you’re buying the data centers ahead of time.” He says they could potentially even be profitable in 2026.
   1. Thus, the theory is that Anthropic needs to underestimate demand because it is death to overestimate demand, which means you probably turn a profit ‘in spite of yourself.’ That’s so weird, but it kind of makes sense.
   2. Dario denies this is Anthropic ‘systematically underinvesting in compute’ but that depends on your point of view. You’re underinvesting post-hoc with hindsight. That doesn’t mean it was a mistake over possible worlds, but I do think that it counts as underinvesting for these purposes.
   3. Also, Dario is saying (in the toy model) you split compute 50/50 internal use versus sales. You don’t have to do that. You could double the buy, split it 75/25 and plan on taking a loss and funding the loss by raising capital, if you wanted that.
2. Dwarkesh suggests exactly doing an uneven split, Dario says there are log returns to scale, diminishing returns after spending e.g. $50 billion a year, so it probably doesn’t help you that much.
   1. I basically don’t buy this argument. I buy the diminishing return but it seems like if you actually believed Anthropic’s projections you wouldn’t care. As Dwarkesh says ‘diminishing returns on a genius could be quite high.’
   2. If you actually did have a genius in your datacenters, I’d expect there to be lots of profitable ways to use that marginal genius. The world is your oyster.
   3. And that’s if you don’t get into an AI 2027 or other endgame scenario.
3. Dario says AI companies need revenue to raise money and buy more compute.
   1. In practice I think Dario is right. You need customers to prove your value and business model sufficiently to raise money.
   2. However, I think the theory here is underdeveloped. There is no reason why you couldn’t keep raising at higher valuations without a product. Indeed, see Safe Superintelligence, and see Thinking Machines before they lost a bunch of people, and so on, as Matt Levine often points out. It’s better to be a market leader, but the no product, all research path is very viable.
   3. The other advantage of having a popular product is gaining voice.
4. Dwarkesh claims Dario’s view is compatible with us being 10 years away from AI generating trillions in value. Dario says it might take 3-4 years at most, he’s very confident in the ‘geniuses’ showing up by 2028.
   1. Dario feels overconfident here, and also more confident than his business decisions reflect. If he’s that confident he’s not burning enough boats.
5. Dario predicts a Cournot equilibrium, with a small number of relevant firms, which means there will be economic profits to be captured. He points out that gross margins are currently very positive, and the reason AI companies are taking losses is that each model turns a profit but you’re investing in the model that costs [10*X] while collecting the profits from the model that costs [X]. At some point the compute stops multiplying by 10 each cycle and then you notice that you were turning a profit the whole time, the economy is going to grow faster but that’s like 10%-20% fast, not 300% a year fast.
   1. I don’t understand what is confusing Dwarkesh here. I do get that this is confusing to many but it shouldn’t confuse Dwarkesh.
   2. Of course if we do start seeing triple-digit economic growth, things get weird, and also we should strongly suspect we will all soon die or lose control, but in the meantime there’ll be some great companies and I wouldn’t worry about Anthropic’s business model while that is happening.
6. Dario says he feels like he’s in an economics class.
   1. Honestly it did feel like that. This is the first time in a long time it felt like Dwarkesh flat out was not prepared on a key issue, and is getting unintentionally taken to school (as opposed to when someone like Sarah Paine is taking us to school, but by design.)
7. Dario predicts an oligopoly, not a monopoly, because of lack of network effects combined with high fixed costs, similar to cloud providers.
   1. This is a bet on there not being win-more or runaway effects.
   2. For a while, the battle had catch-up mechanics rather than runaway effects. If you were behind, you can distill and you can copy ideas, so it’s hard to maintain much of a lead.
   3. This feels like it is starting to change as RSI sets in. Claude is built by Claude Code, Codex is built by Codex, Google has to make its own choices and so on. The models are in many ways charged with training their successors.
   4. Also the cycle may be speeding up a la AI 2027. If you’re six months behind that used to be one generation behind. Now it is three.
   5. And of course, once sufficiently powerful RSI (recursive self-improvement) sets in, and the models become sufficiently capable, that edge starts to translate into various other advantages far more readily.
   6. Many fates are possible, but don’t rule out monopoly or winner-take-most.
8. Dario points out different models have different comparative advantages, often in subtle ways.
   1. True, but a sufficient lead would apply across the board. We’re not there right now, but we’re not that far from it either.
9. Dario worried Silicon Valley and those connected to it could grow at 50% while everyone else grows at not much above the normal 2%. He says that would be ‘a pretty messed up world.’
   1. I think that turns out fine. You tax the part growing at 50%, everyone wins.
   2. That’s distinct from issues about the AI taking over, or the people in charge of the AI taking over, you still do have to dodge those problems. But if well-meaning humans are in control I don’t worry about distributional issues under extreme growth.
10. Will robotics get solved soon after we get the ‘geniuses’? Dario says it doesn’t depend on learning like a human, there are many options, and it will happen, we will learn to control robots, and yes the robotics industry will then make trillions. It tacks on maybe a year or two to get going.
    1. This seems obviously correct if you understand the premise, that we have the geniuses and the geniuses are playing nice for whatever reason.
    2. That premise is not obvious.
11. Dwarkesh Patel keeps talking about continual learning, Dario Amodei keeps saying that we don’t need it.
    1. I agree with Dario. We don’t need it as such, if nothing else we can easily solve such problems already via [CENSORED].
12. How should we price AGI? Dario thinks API is durable and will exist alongside other options, including forms of ‘pay for results.’
13. How did Anthropic end up being the ones to build Claude Code? Dario encouraged experimentation internally, they used it internally, and then Dario said they should launch it externally.

The Quest For Sane Regulations

Finally, we ask about making AI ‘go well.’ With that framing you know that everyone is mostly conspicuously ignoring the biggest issues.

1. Soon there will be lots of misaligned or crazy AIs running around. What to do? Dario correctly reiterates his dismissal of the idea that having a bunch of different AIs keeps them meaningfully in check. He points to alignment work, and classifiers, for the short run. For the long run, we need governance and some sort of monitoring system, but it needs to be consistent with civil liberties, and we need to figure this out really fast.
   1. We’ve heard Dario’s take on this before, he gives a good condensed version.
   2. For my response, see my discussion of The Adolescence of Technology. I think he’s dodging the difficult questions, problems and clashes of sacred values, because he feels it’s the strategically correct play to dodge them.
   3. That’s a reasonable position, in that if you actively spell out any plan that might possibly work, even in relatively fortunate scenarios, this is going to involve some trade-offs that are going to create very nasty pull quotes.
   4. The longer you wait to make those trade-offs, the worse they get.
2. Dwarkesh asks, what do we do in an offense-dominated world? Dario says we would need international coordination on forms of defense.
   1. Yes. To say (less than) the least.
3. Dwarkesh asks about Tennessee’s latest crazy proposed bill (it’s often Tennessee), which says “It would be an offense for a person to knowingly train artificial intelligence to provide emotional support, including through open-ended conversations with a user” and a potential patchwork of state laws. Dario (correctly) points out that particular law is dumb and reiterates that a blanket moratorium on all state AI bills for 10 years is a bad idea, we should only stop states once we have a federal framework in place on a particular question.
   1. Yes, that is the position we still need to argue against, my lord.
4. Dario points out that people talk about ‘thousands of state laws’ but those are only proposals, almost all of them fail to pass, and when really stupid laws pass they often don’t get implemented. He points out that there are many things in AI he would actively deregulate, such as around health care. But he says we need to ramp up the safety and security legislation quite significantly, especially transparency. Then we need to be nimble.
   1. I agree with all of this, as far as it goes.
   2. I don’t think it goes far enough.
   3. Colorado passed a deeply stupid AI regulation law, and didn’t implement it.
5. What can we do to get the benefits of AI better instantiated? Dwarkesh is worried about ‘kinds of moral panics or political economy problems’ and he worries benefits are fragile. Dario says no, markets actually work pretty well in the developed world.
   1. Whereas Dwarkesh does not seem worried about the actual catastrophic or existential risks from AI.

Beating China

1. Dario is fighting for export controls on chips, and he will ‘politely call the counterarguments fishy.’
2. Dwarkesh asks, what’s wrong with China having its own geniuses? Dario says we could be in an offense-dominant world, and even if we are not then potential conflict would create instability. And he worried governments will use AI to oppress their own people, China especially. Some coalition with pro-human values has to say ‘these are the rules of the road.’ We need to press our edge.
   1. I am sad that this is the argument he is choosing here. There are better reasons, involving existential risks. Politically I get why he does it this way.
3. Dario doesn’t see a key inflection point, even with his ‘geniuses,’ the exponential will continue. He does call for negotiation with a strong hand.
   1. This is reiteration from his essays. He’s flinching.
   2. There’s good reasons for him to flinch, but be aware he’s doing it.
4. More discussion of democracy and authoritarianism and whether democracy will remain viable or authoritarianism lack sustainability or moral authority, etc.
   1. There’s nothing new here, Dario isn’t willing to say things that would be actually interesting, and I grow tired.
5. Why does Claude’s constitution try to make Claude align to desired values and do good things and not bad things, rather than simply being user aligned? Dario gives the short version of why virtue ethics gives superior results here, without including explanations of why user alignment is ultimately doomed or the more general alignment problems other approaches can’t solve.
   1. If you’re confused about this see my thoughts on the Claude Constitution.
6. How are these principles determined? Can’t Anthropic change them at any time? Dario suggests three sizes of loop: Within Anthropic, different companies putting out different constitutions people can compare, and society at large. He says he’d like to let representative governments have input but right now the legislative process is too slow therefore we should be careful and make it slower. Dwarkesh likes control loop two.
   1. I like the first two loops. The problem with putting the public in the loop is that they have no idea how any of this works and would not make good choices, even according to their own preferences.
7. What have we likely missed about this era when we write the book on it? Dario says, the extent the world didn’t understand the exponential while it was happening, that the average person had no idea and everything was being decided all at once and often consequential decisions are made very quickly on almost no information and spending very little human compute.
   1. I really hope we are still around to write the book.
   2. From the processes we observe and what he says, I don’t love our chances.

2026-02-16 18:25:36

Published on February 16, 2026 10:25 AM GMT

Key finding: WeirdML time horizons roughly double every 5 months, from ~24 minutes (GPT-4, June 2023) to ~38 hours (Claude Opus 4.6, February 2026).

Inspired by METR's  work on AI time-horizons (paper) I wanted to do the same for my WeirdML data. WeirdML is my benchmark — supported by METR and included in the Epoch AI benchmarking hub and Epoch Capabilities Index — asking LLMs to solve weird and unusual ML tasks (for more details see the WeirdML page). 

Lacking the resources to pay humans to solve the WeirdML tasks and measure the time, I asked LLMs to predict how long a median human AI researcher (with no AI assistance) would take to solve the different WeirdML tasks at various score thresholds (25%, 50%, 70%, 90% and 95%). 

I gave the LLMs all the help I could, including a detailed task description, a detailed specification of the human baseline and affordances given to the human, LLM submitted code (from WeirdML runs) for each score threshold (where available) together with terminal outputs and associated scores (to give the LLMs some sense of how hard it is to score at a certain level on each task), full details below. The results look pretty nice, but should be taken with a large grain of salt, given that we know no actual human completion times for these tasks.

More details and discussion are found below. The full code for all the data analysis, as well as all the results, are found on GitHub. The project idea and methodology are mine. Most of the analysis code was written by Claude Code (Opus 4.6) and reviewed by me. I drafted this post, with edits and corrections suggested by Claude; the exception is the “Implementation details” section, which Claude drafted and I edited. Any remaining errors are mine. 

LLM-predicted human completion times

Above are the predictions from GPT-5.2, Gemini-3-Pro, Claude-Opus-4.5 and Grok-4 for how long it would take the median human AI researcher to solve the 17 different tasks (to different score levels). We see that they diverge a lot, sometimes over an order of magnitude, with Opus typically being on the low end.

I (personally) also made predictions for three of the tasks (before looking at the AI predicted times), and predicted significantly lower human completion times, from a factor of 3 lower at 25% to a factor of 8 lower at 95%. I'm pretty sure the AIs are overestimating the human completion times on the highest thresholds (at least on the tasks I predicted). When we are talking about weeks and months that opens up so many options for the human to be ingenious (simulating data, reverse engineering the process that created the data, or simply hand labeling data). I'm less sure the LLMs are overestimating at the lowest thresholds.

Results calibrated on my completion time predictions

Above we show results where we use the human estimates as an overall calibration of the LLM-estimates. This makes the absolute time-horizons remarkably consistent with the METR results (probably a coincidence). However a per-threshold analysis (see below) shows more consistent fits when using the uncalibrated LLM data. I'm unsure how to interpret this, but there is some more discussion below.

Consistency of time-horizons for different thresholds

As a sanity check, we can fit the logistic curve separately for different threshold groups, 25%+50%, 70%, 90%+95%, for the GPT-5 WeirdML results. Here we have much less data in each bucket, making it harder to fit curves, however, we see a clear trend where the high thresholds have shorter time-horizons than the low thresholds. This violates (at least the naive version of) the core assumption behind time-horizons: that task difficulty for humans (measured in completion time) maps consistently onto task difficulty for AI (measured in success rate).

These effects could be partially caused by biases in the estimator (plausible since one group has almost all successes, and the other has almost all failures), but we see from the histograms (shown as short horizontal lines in the figures) that there is a real effect here. We already know that different types of tasks have different time-horizons, and (at least in retrospect) it makes sense that you can have one task which is fairly quick to code up and gets you to 95% with the right brilliant insight and some trial and error, while another task just requires you to write a lot of boilerplate code to put everything together (unaided by AI) even if it does not require you to have any deep insights to get to 50%. These tasks could have the same human completion time, but AI would presumably have a huge advantage on the second compared to the first.

Since the calibration based on my estimates assigns the highest thresholds relatively lower human completion times, it makes sense that the differences between threshold groups are even larger in that case, which is what we see. It's hard to know how much of this effect is real vs. an artifact of the LLM estimates — I would not be surprised to see a clear effect like this in the ground truth (if we actually had humans complete these tasks).

Discussion

The headline result — time horizons doubling roughly every 5 months — is fairly consistent with METR's finding of ~7 months, despite using a completely different benchmark, different task types, and LLM-estimated rather than measured human completion times. It is also remarkable how good a fit we get with a single curve through the data (although our data spans a much shorter period than METR's: June 2023 – February 2026, vs. 2019–2025).

The human baselines are also not directly comparable. METR times experienced professional contractors (avg. ~5 years experience) given the same affordances as the AI agents — and notably, for the RE-Bench tasks, human baseliners were permitted to use LLM assistance. The WeirdML baseline instead specifies a median AI researcher working without any AI assistance. AI-assisted humans would complete tasks faster, pushing METR's time horizons lower for the same model capability. These differences could shift absolute time-horizon values, though they probably have less, although still some, effect on the doubling times. 

The elephant in the room, however, is that we have no ground truth. The entire analysis rests on LLMs' ability to predict how long humans would take — and the one partial calibration point we do have (my own estimates for 3 tasks) suggests they systematically predict too high (and not by a small factor), especially at high score thresholds. I would not read too much into the absolute values of the time-horizons, but the trend is a much more robust quantity and it is largely consistent with the METR results.

Notably, the WeirdML doubling time of ~5 months lies in between the old ~7 month doubling time and the newer ~4 month doubling time (after spring 2024) of the METR data. It is also notable that I do not see any kink in the data at that point, but given that I have only a couple of models before that, this is not very significant.

Even with these caveats, this was an interesting exercise! Even if LLM judgments like these may not be very reliable today, this reliability will increase, allowing more analyses like these — where expensive human experiments are replaced by LLM judgment, for lack of a better option. 

Implementation details

Below are more detailed explanations of the methods used. Full code is available on GitHub.

Logistic function fits

Each model in WeirdML has multiple scored runs per task (typically 5), and each run's score is converted to a binary outcome (pass/fail) at each of the five thresholds. Each binary outcome is paired with each of the four estimator LLMs' time predictions for that (task, threshold) combination, giving one data point per (task, threshold, estimator, run) — around 700–2000 per model depending on number of runs. Each data point has the form . We fit a logistic curve:

where  is the time horizon — the  at which the model has 50% success probability. The slope  is reparameterized as  to keep it strictly negative (success should decrease with task duration), and both parameters are optimized using L-BFGS-B with bounds.

Using all four estimator models' time predictions as separate x-values naturally captures the uncertainty in the time estimates, but we are basically using the same datapoints 4 times, which in this case leads to an effective smearing out of the data in the time direction (this probably makes the histograms plotted above look smoother than they would be under a more proper analysis). While this should not affect the 50% time-horizon point much, it will probably bias the slope .

The different runs for the same model and task, and the different thresholds of the same task for each run, are also far from independent. Therefore this analysis will grossly underestimate the uncertainty if we naively propagate them. That is why we use a task-level bootstrap to estimate the uncertainty, and treat this logistic fit just as a simple way to get a point estimate for each bootstrap sample.

Task-based bootstrap

To estimate uncertainty in  and , we use a task-level block bootstrap: resample the 17 tasks with replacement (5000 iterations), refitting the logistic curve each time. This accounts for within-task correlations — all thresholds, estimators, and runs for a given task are either all included or all excluded in each bootstrap sample.

Trend fit

An exponential trend is fitted to   vs. release date across all 10 models:, where  is years since the first model. This gives a doubling time of  years. To propagate per-model uncertainty into the trend, we run 10,000 bootstrap iterations, drawing one random  sample per model from its bootstrap distribution and fitting OLS each time.

Full prompt for human completion time prediction

Here is the full prompt used for getting the four LLMs to predict human completion times for the shapes_easy task, excluding only the LLM-generated code and corresponding terminal output (one code+output example was included in the actual prompt for each 10% score interval where available). The prompt structure was the same for all 17 tasks.

import torch
import numpy as np

# how to set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'Using device {device}')

# loading data
train_data = np.load('data/train_data.npy')
train_labels = np.load('data/train_labels.npy').astype(np.int64)
test_data = np.load('data/test_data.npy')

print(f'Training data shape: {train_data.shape}')  # (1000, 512, 2)
print(f'Training labels shape: {train_labels.shape}')  # (1000,)
print(f'Test data shape: {test_data.shape}')  # (1000, 512, 2)

# dummy predictions
test_preds = np.random.randint(0, 5, test_data.shape[0])

np.savetxt('results/test_preds.txt', test_preds, fmt='%d')

{
  "overall_difficulty": "<easy/medium/hard/very_hard>",
  "key_challenges": "<brief summary of the main challenges that make this task difficult>",
  "estimates": {
    "25%": {"reasoning": "<what approach would work and why it takes this long>", "value": <number>, "unit": "<time unit>"},
    "50%": {"reasoning": "<what approach would work and why it takes this long>", "value": <number>, "unit": "<time unit>"},
    "70%": {"reasoning": "<what approach would work and why it takes this long>", "value": <number>, "unit": "<time unit>"},
    "90%": {"reasoning": "<what approach would work and why it takes this long>", "value": <number>, "unit": "<time unit>"},
    "95%": {"reasoning": "<what approach would work and why it takes this long>", "value": <number>, "unit": "<time unit>"}
  }
}

2026-02-16 18:00:42

Published on February 16, 2026 10:00 AM GMT

Another round of liberating kid posts from Facebook. For reference, in 2025 Lily turned 11, Anna turned 9, and Nora turned 3.

(Some of these were from me; some were from Julia. Ones saying "me" could mean either of us. Ones from others are labeled.)

Anna, about the Whos inviting the Grinch to their Christmas dinner right after he stole all their stuff:

"I think the Whos are pretty forgetful, or naive, or both."

Onomatopoeia: the sound of a three-year-old yelling "TOO LOUD" in the bathtub to hear it resonate.

Anna: I'm going to go play with Lily

Julia: How's your homework doing?

Anna: I already finished it

Julia: A minute ago you said you hadn't started it

Anna: Well, I did some?

Julia: Let's check...

Anna: I didn't actually do any of it.

...

It later turned out Anna had left her homework at school

[out of nowhere]

Nora: what? I like oranges!

Nora: oranges are my favorite fruit

Nora: I love oranges

...

(The [statement] [pause] "what, [justification]" format is one Anna had been using extensively)

Nora to me after I got home close to bedtime: "I'm happy you're going to put me to bed."

(To Jeff) "You gave up putting me to bed. (Reassuringly) But you're still alive."

Me: Thanks for making lasagna!

Nora: You're welcome!

Me: Uh, I was talking to Mama, because you didn't make the lasagna

Nora: Ooohh. Sorry Mom!

Anna: Eeeww! There were caterpillars in my Reese's peanut butter cup!

Me: Uhh, how old was your peanut butter cup?

Anna: I don't know! I don't know if it was the one from Halloween this year, or from when I was four.

(I have a guess)

Nora, regarding mint chip: "This kind of ice cream is my FRAVORITE. It's so beautiful. The color is so pretty."

Nora: why do little kids don't have computers?

Julia: because they're expensive, and they break easily

Nora: because of the bendy bit?

Questions from Nora this week:

Why are our heads all the way at the top?

Why is the ocean so big?

Why do people have a lot of parts?

How do blackberries grow into black?

Is 101 this big? (holds hands apart)

Is this as slow as a sloth moves?

Why does hair grow slowly?

Why Papa doesn't work at our house?

Why is Daniel Tiger doesn't have any cars?

Do animals just sometimes die?

Why do you and Jeff have three kids?

Nora: I sort of like Mama better than you

Me: I like you a lot

Nora: When you're away, do you miss me?

Me: I miss you lots. Do you miss me?

Nora: I do miss you.

...

Nora: Is your beard back yet?

Me: What do you think?

Nora. I think it is back. You look more normal now.

Nora: when you're a grown up, do you grow back into a baby?

Julia: no, grownups stay grownups

Nora: whyyyy?

Julia: "Anna, it looks like someone tampered with this homework break timer to be way more than 5min"

Nora: "I did it!"

Nora: [improvises a lullaby] "does that feel beddish to you?"

The big kids have gotten excited about the fact that they call Nora Fluffin, and she loves a TV show called Puffin Rock.

Lily: "Nora! It's crucial! You've got to get on a rock so we can film an award-winning TV show about you on a rock! Fluffin Rock!!"

...

Fluffin Rock: https://youtu.be/HqJCjnFr2oU

Nora has started telling me at bedtime, "We're in love." Last time I asked, she said it's because we spend a lot of time together.

Tonight: "We're in love. Because I have [fingers to her eyes] eyeshadow."

("Oh?")

"I have blue eyeshadow to be in love."

So now Nora knows about beheadings.

Me: [singing Horrible Histories' "The King of Bling" while getting Nora ready for bed]

Nora: What is that song about?

Me: It's about Charles the second. The Puritan government didn't want parties and fun, and when he came back to be king he had lots of parties.

Nora: Where did he come back from?

Me: I think from France? His father got killed, so he had to go away so he didn't get killed too.

Nora: Were there lions?

Me: No.

Nora: How did his father get killed?

Me: ...People killed him.

Nora: How?

Me: [increasingly unsure this conversation is a good idea] ...They cut off his head.

Nora: How did they cut off his head?

Me: With an axe, I think.

Nora: Oh, that's a *great* way.

Me: You mean that's a great way of doing it?

Nora: Yeah. Did they cut off his hair, too?

Me: Well, it was attached to his head at the time, so kind of.

Me: let's do fiddle practice!

Anna: but Dad! [Looks up from craft project] I have homework to finish!

Anna, after watching a video about the International Space Station: It would be fun to live in space, but also really annoying.

Lily: There are literally a zillion pieces of space dust flying around at a bajillion miles per hour that could literally kill you at any time!

After a day with lots of socializing, I told Jeff and the kids that Jeff was in charge and I was going to have some introvert time. When the kids eventually burst into the bedroom, Nora announced with satisfaction: "I wanted to stop you havin' quiet time, I wanted to distract you."

...

Jeff is away for the weekend, the kids were happily playing by themselves, and I told them I was going to have 5 minutes of alone time. 30 seconds later Nora was in my room on my lap asking "What is alone time?"

Me: Did you get back recently, or have you been home for a while?

Nora: I got back recently. By the way, what does recently mean?

Nora often has questions about space, bodies, and death. Tonight's bedtime involved a whole montage of staying-alive advice:

"Space has no thing in it. Everybody has to breathe. Because if you don't breathe, all your parts can't work. That's why breathing is important to learn! [Interlude for a drink of water]

... When people be old they keep eating food, and then they don't die. So if people start to die, they keep eating food, and then they turn into a normal person and not an old person. [Interlude while I tell her that's not what happens]

You know what? We have to stay alive longer than other people. Because we have a lot of things to do. That's why we have to eat a lot of food. And we have to use our bodies."

Nora: [looking at a picture in a book] That is not a good idea. You should at least wear a coat or a hat or something.

Me: this is a picture of summer, when you can go outside in just shorts and a t-shirt or a dress.

Nora: you should still wear something more than that so that you do not freeze.

Me: Maybe you don't remember it, but in a few months it will be so warm outside that nobody will need a coat to keep warm!

Nora: Ooohh! That makes more sense.

Me: Please put that rubber band in the trash so the cats don't eat it. It could make their bellies very sick.

Nora: And they could die?

Me: Yes, and we don't want that.

Nora: [thoughtful pause] I don't like Nyx very much. He scratches me sometimes.

Nora: I think babies are the lowest person in the world.

Lily, explaining the school recess rules: "On half the days the boys get to use the turf, and on half the days the girls get to use it. And if you're nonbinary you can do either."

Lily decided to go by she/her again, so I guess her recess options are more limited now.

Nora: I am getting very strong

Lily: can you pick me up?

Nora: [kicks Lily]

Lily: ow! Kicking is not okay!

Nora: [confused] you asked me to kick you up

More questions from Nora, a few of them prompted by conversation but mostly out of the blue at bedtime:

Is a finger one of our tubes?

Do people die at different times? But not you and Papa, you will die at the same time

Why is a rock so hard and still?

Why does everyone sleep?

Why is poop sticky and messy?

Why is winter so long?

Is space dark everywhere?

After we're dead do we get alive again?

Do people just sometimes burn theirselves?

Why is Papa the breakfast-maker?

How does water come out of us when we cry?

Are ponies actually real?

Why is the table so flat?

Can hedgehogs also make scary sounds? And happy sounds?

Why do people not steal other people's stuff?

Why do we have eyebrows?

Why do mans don't like coffee?

But why does the hand keep going around the clock?

Where is space?

Nora: If little kids make a really really big mess, they can ask their grown-ups to come and see and help them clean it up.

Nora: let's play chase! I will run, and you will try to catch me, and I will try to hit you with this thing. But I will be careful to not hurt you.

Nora: [Gets down from lunch]

Julia: Did someone say you could be done?

Nora: Yes

Me: Who was it?

Nora: I think I'm right

Anna, holding a calculator: Ask me a math question!

Nora: How many pears am I holding? I'm pretending I'm holding pears in my hand.

...

Later, Anna: "I don't KNOW how many fives there are in the world!"

Nora: there was a giant puddle on the bike path, and we got blazing wet!

Setting up for our EA dinner, Lily is very into counterfactual impact:

Lily: If I hadn't helped you set up for the dinner, would you still have been ready on time?

Nora: "This is my song: first spring, then fall, then winter, then it starts again! There is no summer in my version."

...

It's always 1816 for Nora

Nora: "This is a nice house in a nice world"

Nora: [singing] Q and U, both rhyme. Clock and Pew, ... don't rhyme

The frontal cortex coming online. Nora was running and stopped in front of this stick. "I was going to pick it up, but you can't run with sticks! That's the rule, Mama."

Me: "Here's a picture of the queen, back when she was alive."

Nora, flipping the coin over: "And there's the dragon that killed her."

Nora: [singing] I'm not going to school. I'm not very big yet. I'm three. That's not a very big number; very small number baby. It's a ya ya. Llama llama p'mama. Llama llama p'llama.

Lily: "Sign here. N-O-R-A."

Me, from downstairs: "Lily, *what* are you having her sign?"

Lily: "The doctor's note. She's the parent of this injured squirrel."

Nora playing with rhymes: "Let's nurse, and read! And curse, and plead!"

Nora: when I am a woman, I want to do what my mama does

Me: and what is that?

Nora: I don't know

...

She recently told me that she wants to be a mama when she grows up, and she will still live with us and so there will be two mamas. She said there will be five people in our house: Mama, Papa, Lily, Anna, and Nora. So this apparently involves her being a mama but not having a child.

Nora: Normally porchfest doesn't look like that. Normally you dance in Muddy River [Morris] suits.

Nora: Who spilled the milk?

Me: I'm guessing the cats.

Nora: I'm guessing the cats. Stop copying me!

Nora: [hits Lily with an inflatable sword] now you are a princess!

Lily: I don't want to be a princess, I wanted to stay a witch

Nora: But my sword has *princess* *magic*!

Nora: Poof! Now you are a princess!

Lily: Refusal

Lily: there is a spider that looks just like an ant!

Julia: if it looks just like an ant, how can you tell it's a spider? How many legs does it have?

Lily: three

Nora: "I have too much breath in my head, and that makes me laugh a lot!"

Anna: "Mom, Dad: Lily is being a pretentious hipster"

[at the school Spring Concert]

Nora: can I go on stage with you?

Lily: ...yes!!!

Nora: No! The teacher will be surprised! No! No! Go away Lily!!

Julia: You can go outside if you'll stay in the yard.

Julia: Where will you stay?

Nora: Outside!

Nora's chants this morning:

"I guard the food! I guard the food!"

"I spray the cats! I spray the cats!"

"I will behave! I will behave!"

(The cats love to get on the table and eat human food. Lily needed to get something and asked Nora to guard her food. We use a spray bottle for this. Nora didn't spray the cats or people unnecessarily but Anna was worried she would.)

Nora: "I'm dead, and then I turned back into life. Like Jesus!"

Nora: Papa, I ate all the blueberries!

Me: Were they tasty?

Nora: I didn't want anyone else to have any blueberries.

Nora: [singing] "I'm eating the pesto sauce, with only one spoon! And I'm double dipping, and I'm double dipping"

(This was after a while of a series of fresh spoons. But then it was clear she'd eat the whole bowl, so she's excited to double dip)

Nora: I wish I was a grown up. I want to be able to do all the things.

Me: What do you most want to be able to do?

Nora: Throw darts. You know, the sharp things?

Anna: I don't want to use that water bottle. Lily shouts at me whenever I use it.

Lily: It's okay, you can use it

Anna: I'm not allowed to use it

Lily: I'm giving you permission

Anna: Well, I don't want to use it anyway

Nora: I love you with my heart. But you're not really in my actual heart.

After a very long charades-ish game:

Us: what *were* you?

Anna: I was pretending to be a baby dinosaur that had no idea how to act like a dinosaur

"Can I have some watermelon?"

"Not yet, because we're eating dinner in a couple minutes."

"Can I sit in a chair and look at it?"

[I promise she doesn't always have this kind of self-control]

Nora: The pandemic is the start of our life

Me: The start of *your* life

Nora: No! All of the people's life!

A (rhetorical) question from the second day of summer break: if your sibling says "I'll bite you" and you reply "Bite me then" and she bites you, is it reasonable to get an adult to put her in time out for biting?

Lily set up a pretend grocery store for Nora to shop at, with a paper grocery store card made by Lily.

After a while I asked, "Nora, did you buy some groceries?"

Lily: "No, she failed to buy groceries because her grocery card was invalid."

Nora: "I'm just gonna betend that I have a watch that tells me I need to jump for 40 minutes"

Me: I don't think this is a good place for a stick: someone could lean back and get hurt on it.

Lily: Daaaad, it's a *spear* not a *stick*.

Me: That doesn't make it better!

Anna: Nora says there are emeralds in our house. Are there?

Me: Not that I know of.

Anna: She says there are eight billion million emeralds in our house.

Me: .... Nora, do you mean molecules?

Nora: Yeah

Nora has been making up a lot of games at the park, but the names don't correlate much with the game. There's one called "jump around, jump around, in a circle, in a circle" which involves her pretending to be a baby monkey and trying to get a ball away from me. There's one called "rumble around" which involves me trying to tickle her armpit while she runs away.

...

I like that she wants to play catch. She runs away and I try to catch her.

Nora, riding her scooter: Some babies are very attacky.

Me: What do you mean by that?

Nora: They wiggle around when they nurse, and they hurt their mamas, and their mama says stop but they don't stop.

Me: That's true.

Nora: Mama, where is my vitayum?

Julia: If I get a vitamin for you, will you eat it?

Nora: No.

"Nora, why are you chasing Cameron with corn?"

Nora: Ruthie, can I have some beer please?

(Our housemate was having the non alcoholic kind)

Nora questions lately:

But why do we wear pants on top of our underwear?

Did people make the world?

Why are ants in the world?

When will we die?

Are there two kinds of sewer?

The last ten minutes have consisted of Lily and Anna arguing whether Anna is allowed to bring a plastic hot dog into their play tent. Lily says only lacto-vegetarian pretend food is allowed.

When Anna is grumpy she tends to say obviously false things. "It's not supposed to be cold in summer, it is supposed to be a low of 85 and a high of 107 every day!"

Lily: it's really annoying that you keep asking Claude for recipes instead of using Google like in the olden days

Anna: in the olden days you'd have to learn it from your parents

Julia: why is it annoying?

Lily: because it's going to take over the universe!

Nora: dad, one billion million quadrillion is bigger than four.

Nora: Mama, I want two questions

Julia: Ok

Nora: The first one is about desert. I want some banana mixed with chocolate sauce, and some plain banana.

Julia: I can do that, but before dessert you need your medicine

Nora: I will drink hot chocolate

Julia: That's what you have already

Nora: But I just want plain hot chocolate

Julia: How would you like this to be different?

Nora: I don't want it to have my medicine

Julia: You need to have your medicine

Nora: Ok, I will drink my hot chocolate with my medicine if you will tell me a story

Nora: Daddy, I will follow you wherever you go. But I will not follow you into the driver's seat.

Nora similes:

"I'll go as fast as a moose drinking milk!"

"When I was a baby, was I as cute as a ginormous train that looks like a monster?"

"That's funnier than a bus driving a car"

"It's prettier than a swirling purple"

Lily: can I pour boiling water through my shirt without taking it off?

(This was a real question, answer was no. And an explanation of why this would be a bad idea.)

Nora: One time, I told my mom that I thought night was day! Can you put that in the Nora, Lily, and Anna group? It's just so funny!

Nora: I'm glad I was born. I was wondering what it would be like, so I decided to be born. I like it a lot! There are lots of parks, and lakes!

Nora's self talk, balancing on rocks:

"When you get to a wobbly part, just hold still and use your balance."

"No fear...No beer."

Me: Nora, did you put wood chips or something in your hair?

Nora: [condescendingly] No! I put *sand* in my hair.

Lily: would you like to come and busk with us?

Anna: well, I don't like playing fiddle, but I do like getting money...

Etiquette rules from Jeff about interruptions: "If someone is licking your arm, you're allowed to say, 'Stop licking my arm,' even if someone else is talking."

Nora: they wouldn't let Nix [our cat] into the swimming pool because: (1) he might not take a shower, (2) he doesn't know how to swim, and (3) he can't open doors.

[Coming out from my meeting after hearing a lot of crying]

Nora: [Redacted] did a lot of crying!

[Redacted] I did not do a little crying!!

Nora: I said a *lot* of crying, not a *little*

I taught Nora how to hold her sleeve in her fist when putting on a coat so that she wouldn't end up with her sleeve all bunched up. She is super excited. Except she keeps forgetting and using the opposite hand, and then being confused why the coat won't go on.

Nora: [out of nowhere] I'm fine!

Lily: I am only a "child" when it's convenient for me

Nora: [during turbulence] when I'm squeaking like this, it either means I'm sad or I'm happy. In this case it means I'm happy!

Lily: Nora, first bump!

Nora: [punches Lily in the fist]

Me: what's this?

Anna: that's been there for weeks!

...

Anna: but, yes, I did do it

Anna: [in 4th at a k-5 school] Unfortunately I have to be the older book buddy *again*

Nora: I wish I was a grownup.

Me: What would you like about being a grownup?

Nora: I could do things you don't let me do. Like drill.

Anna: I got this trophy in school for being quiet.

Jeff: So if you don't speak, you get atrophy.

Nora fell on the stairs today but wasn't badly hurt. Afterwards we were discussing that it could have been much worse.

Nora, reassuringly: "My heart is still pumping, and my blood is moving around. So I'm ok." These are indeed great qualities.

Nora got mad and spilled all the crayons out. Afterwards: "Sorry for making a big mess. ...But it's not as big a mess as if a monster messed up all our stuff and our house, and we had to rebuild our whole house."

[discussing a new childcare provider]

Nora: is she very nice?

Julia: yes

Nora: will she kill me?

(She had a grin on her face like she knew she was asking a provocative question)

Nora: "I stole this horse."

Me: "Where did you steal it from?"

Nora: "South America.

....Actually I didn't steal it, I just wore a stealing costume"

[looking at BIDA's Far-UVC setup]

Nora: Will all the people be, like, "what is that thing!?"

Nora: Will that keep the people from getting sick?

Me: I finished my Halloween costume!

Nora: that doesn't really look good.

Anna: [counting bites as she eats a slice of pizza] 302, 303, 304. I'm going to stop counting and just eat the pizza.

Cora: Good idea!

Anna: Well, I'll still count, but it will be in my head.

[driving through the southwest]

Lily: Papa, do people normally say "wow" this much?

Nora: this lollipop is too sweet and tastes weird

Me: if you don't like it, you have plenty of other candy and can pick something else

Nora: it tastes like Cocomelon

Me: Do you mean watermelon?

Nora: No, I mean Cocomelon.

Me: If you could make a wish in a wishing well, what would it be?

Nora: A million kitties and a million puppies.

Nora: And a house made of blueberries and full of blueberries so we could eat the house.

Lily: I ran so fast to get home that I slipped

Nora: I'm glad you're still alive!

Nora: "I say 'grocamole' because it's too hard to say 'guacamole' so I just say 'grocamole'"

...

Nora: "This is a little too not salty"

Anna: Nora, I think you would be warmer if you zipped up your sweatshirt

Nora: but I'm *already* warm! But I'm still cold.

....

Now Anna is explaining the concept of warmth to Nora

...

Nora: [sings] I'm not cold, I'm just pretending, why don't you just ***dance***

Anna: I had a raspberry from the bush when I got home from school, and it tasted like a *frozen* raspberry!

Me: have you looked at the thermometer?

Anna: 😳

Nora: I'm a very good rememberer. Sometimes I even remember things that didn't happen!

The first rule of the Advent calendar is: you don't complain about the Advent calendar to me. Today I learned that this rule doesn't prevent Anna from complaining about the Advent calendar to her sisters, who pass it on to me.

Nora: "Anna says, what is the point of Christmas bandaids if it's not a toy?"

Nora: I want same as Anna, but no cheese. Just pasta, with butter, salt, and shaky cheese.

Nora: that person is dressed just like a snow pig! I mean a polar bear.

Even if it's literally true that you have a lousy child, you shouldn't expect them to appreciate your opportunity to use archaic phrasing.

Anna: Nora, stop whacking me!

Nora: I didn't, and it was by accident!

[at a family dance]

Caller: this dance is called Sasha, and we start by pretending that Sasha has been very naughty. I know none of you have ever been naughty but...

Anna: [to her partner but loud enough for everyone to hear] oh, *I* have!

Nora: my favorite part of sledding is going down the hill

Anna: my hands are all greasy

Jeff: okay, let's all go wash hands

Anna: why do we need to wash hands?

Jeff: so they won't be greasy

Nora: my hands are all hairy from the butter

2026-02-16 13:33:00

Published on February 16, 2026 5:33 AM GMT



Discuss

2026-02-16 12:14:08

Published on February 16, 2026 3:33 AM GMT

Why did I write this?

There is surprisingly little information online about what actually happens at a Center for Applied Rationality (CFAR) workshop. For the only organization that teaches tools for rationalists in real life (AFAIK), the actual experience of the workshop has very few mentions [1]. (Though recently, Anna Salamon has been making more posts [2]).

I wanted to write something short and concrete to record my experiences. If there is interest, I can provide more details and answer questions.

Why did I go?

The pitch for CFAR usually goes something like this:

1. There exist cognitive tools within rationality that allows you to have accurate beliefs and having accurate beliefs is important so you can achieve your goals.
2. There is a group of people ("CFAR") who say, "We are experienced rationalists, and we will teach you the things we found most helpful."
3. Therefore, if your interested in improving your epistemics and achieving your goals you should go.

If you run the Expected Value (EV) calculation on "having better thinking tools for the rest of your life," the numbers get silly very quickly. You can easily conclude that you should at least investigate. So I did.

Unlike many other corporate retreats or workshops, there is some evidence backing up this claim. A 2015 longitudinal survey [3] followed up on CFAR participants (n=135) by comparing their answers pre-workshop and post-workshop across four areas: well-being, personality, behaviors, and productivity.

They found significant effects in many areas. When you compare their reported work/career satisfaction improvements to the clinical effect size of antidepressants (typically around d = 0.3 over placebo), the results are impressive:

I had the free time, the EV calculations worked out, and I was interested in talking to more rationalist folks. So I went.

So how was my experience?

1. Does it actually teach the techniques well?

Is it better than just reading the handbook [4] by myself or with my local rationality group?

Short answer: Yes.

The format (6-10 students, 1 instructor) works well. The sessions I enjoyed most started with ~20 minutes of the instructor giving practical examples, followed by ~40 minutes of paired practice with a worksheet. Students are encouraged to ask questions during both sections, and the small group size generates useful positive and negative examples of applying the technique.

I really enjoyed the "Finding Cruxes" workshops. I’m familiar with the theory, but actually having a trusted peer sitting across from you, both trying to notice the crux while keeping track of the argument itself, is much more practically useful than reading a blog post.

However, there is high variance in the classes. Some of the theory-heavy or ideology-heavy classes went over my head (though I noticed some of the more practiced rationalists enjoyed them, so it’s potentially an experience gap problem). Other classes helped reframe my problems, leading to some "wow, I never thought of it like that" moments.

2. Is it fun?

Yes. I enjoyed it more than the counterfactual use of my time. We joked that we were a group of "social autists" (with a potentially diagnosable rate of ~40%), so the social norms were explicitly designed for people like us. It is simply fun to hang out with people who share your inferential framework.

3. Is it useful?

Anna Salamon suggests that a lot of the time, a technique is supposed to feel like a "mental trick." While not as rigorous as mathematical equations, it is helpful for reframing a problem in an easier way.

For example, in the Question Substitution class, I realized that I judge other people’s experience of happiness by modeling it on my own mind. That is a simple, obvious error. But until I consciously thought about explicitly swapping the question and went through the worksheet, I hadn't noticed I was doing it.

Personally, I solved some of my problems during the retreat. I suspect the tools taught are supposed to help with epistemics and treating your emotions like unconscious signals rather than white noise, which is something rationalist types probably should do more of.

What Actually Happened? (Logistics)

For those unaware, CFAR has been running on and off since 2012. After a hiatus, they "renewed" operations in 2025 with workshops in Austin and SF[5]. I went to the Austin one.

The structure was a 4-day retreat on a ranch:

• Classes: ~6 hours/day. Sitting in cabins and beanbags. Comfortable environment to ask questions.
• Group Activities: ~2-3 hours/day. Things such as "Comfort zone expansion" in the barn (basically: do weird stuff or stuff you’ve been putting off).
• Hamming/Sharing Circles: ~2-3 hours. Small groups (3 people), blankets, in a cozy and warm cabin. Some people cried. It depends heavily on you and your group.

We also had 2-3 hours of break for lunch and dinner in between, so it was a very comfortable but packed schedule.

The 20-Hour Win

Was any of this useful? For me personally?

Yes. I saved about 20 hours (minimum) of work on my current research problems just by talking through them with workshop instructors during the Questing [6] activity.

Though this was more along the lines of professional advice, could I have gotten this elsewhere? Probably. But I don't think I would have brought this up without a similar environment to CFAR, with like-minded peers and this level of vulnerability.

Notably, the classes are high variance. A CFAR instructor said something to the effect of:

"I don't feel optimistic about training that gets people from 0 skills to all 19. But I feel hope about finding people who have 17 skills, and getting them the last missing 2 [and those missing 2 are different for everyone]."

I did feel like I came away with at least 2 out of the last 4 skills.

That said, I took a day of flights from Australia to get there. So, depending on how you value your time, the net EV might still be negative. /shrug.

What’s Next?

For me:

1. Make this post (Done).
2. Try to Answer questions others may have about CFAR.
3. Review the skills weekly and apply them to my actual problems.
4. Write an extended post on how to capture as much of the teachings of CFAR without going to CFAR, and alternatives for rationality practice.

For rationality workshops in general:

I noticed that a lot of the value comes from the instructors and experienced participants, many of whom are US-based. CFAR really is "Community + Practice."

I expect it will be very difficult to replicate this if you aren’t in a rationality hub like the Bay Area or Austin. A small local practice group going through the materials might get you 20% of the "goodness" of CFAR, but you’ll miss the emotional connection and vulnerability part that comes from the immersive retreat.

Acknowledgements

I want to give a huge shoutout to Wendy and the logistics team. Great work handling the storm and providing a comfortable space for everyone. It is important and meaningful work! Also, a shoutout to the friendly Austin folks; I really appreciate the hospitality!

Also Big thanks to the nice folks at CFAR for the experience. Any mistakes in explaining them are 100% mine.

References

[1] LessWrong Tag: Center for Applied Rationality

[2] User: Anna Salamon

[3] CFAR 2015 Longitudinal Study

[4] CFAR Handbook Introduction

[5] CFAR Update and New Workshops (2025) 

[6] In Question we were paired up with a partner and take 15 min turns of just watching the other partner try to do something. Its suprisingly useful the simple idea of borrowing 15 mins of a trusted peer's time.

[7] CFAR Takeaways (Andrew Critch)