Ryan Greenblatt——当 AI 能够自动化 AI 研发后会发生什么？

Today, I’m chatting with Ryan Greenblatt, who is the chief scientist at Redwood Research, where he focuses on technical AI safety and security work. I want to talk to you about recursive self-improvement. This is the idea that once we build human-level intelligences, they quickly slingshot toward tens of billions of superintelligences, which are each individually more competent than the top human experts across every field.

Whether or not this turns out to be the case is probably the most important question in the world right now. Historically, I’ve been quite skeptical that this kind of thing happens, but you seem to think that it might be plausible, and so I wanted to hear the case for it. Let’s talk about this.

First, I think it’s worth noting that AI R&D is a type of task at which AIs are especially good, because the companies are trying really hard to make their AIs good at AI R&D. It’s also the kind of domain that has a lot of nice properties from the perspective of how AI development works right now.

It’s pretty verifiable. You can do a bunch of stuff iteratively, and it’ll hill-climb on various metrics. I think once you have AIs that are roughly matching the top human experts in AI R&D, that could kick off a feedback loop where the AIs are doing AI research. That produces smarter AIs, which feeds back in.

That feedback loop could be strong enough that you end up with a lot of progress in a short period of time. Maybe my median expectation is something like 4 or 5 years of AI progress in a single year. This requires really overcoming a huge amount of diminishing returns in research and basically doing the equivalent of the progress we would have gotten after a really large compute scale-out.

So this is a pretty impressive, big thing.

It’s worth keeping in mind that 5 years of AI progress, 4 years of AI progress, even 3 years of AI progress, is really a lot of fucking AI progress. A little over 3 years ago, GPT-4 had come out. Right now, of course, we have Mythos 5 or whatever, and maybe a somewhat better model that Anthropic has internally. That is just a huge amount of progress in a bit over three years.

If we're talking about five years, then maybe we're talking more about a jump from GPT-3 to Mythos 5 or whatever. I think this argument has 3 different parts, and now I want to evaluate each one of them.

First is the argument that AI R&D is very verifiable. Second is the argument that if you automate AI R&D, you could get 4 or 5 years of progress in a single year. Third is the argument that what comes out the other end of 4 or 5 years of AI progress at the current pace, starting at the point whenever AI R&D is automated, is an AI where you can drop it on the job at basically anything you can imagine.

You can drop it in Texas politics in the 1940s, and it outmaneuvers Lyndon Johnson. You can drop it in TSMC, and it learns how to do better process engineering at TSMC. It’s certainly a better video editor. My video editors are excellent, but it is just, in general, better than humans at any given job that it finds itself trying to do.

So I want to evaluate all of these subarguments that lead to basically getting ASI pretty soon after this benchmark, which you’re expecting by 2030 or something, right?

I would say that I expect full automation of AI R&D perhaps somewhere around 2030 or 2031. Getting to the “beats all humans on the job” milestone—maybe my median expectation is around 2033. But if I see AIs fully automating AI R&D, I think I’m expecting that probably within a year.

The way the forecasting works out, the difference between medians is bigger than the median difference between milestones.

By the way, there’s this meme on the internet. Every time I’m trying to ask about people’s timelines—when I’m asking Dario or somebody—I’m always like, “Okay, how long before you automate my video editors?”

There’s this meme of my video editor editing the podcast every time I listen to this. But the reason I do it is because I think it’s easy to get lost in abstractions when you talk about jobs you don’t understand well, and to very concretely understand what it takes to automate a job that I actually understand and why it’s difficult for LLMs to currently take control over.

I do think that the milestone for automating your video editor is earlier than the milestone of being able to automate all human jobs, including Texas politics, spinning up on the job. I do think that video editor automation occurs maybe around the time of full automation of AI R&D, but it’s very sensitive to how much people are really focusing on understanding video.

Okay, so let’s start with the claim that AI R&D is very verifiable.

There are a few different parts to this. One of them is that we can train on a bunch of environments that are basically directly training the model to do some AI R&D task or some very close-by task.

For example, we can have some environment where the model is training an AI on just 8 H100s or some small amount of compute. That model could be the equivalent of GPT-2 Medium or whatever, and then, similar to NanoGPT medium runs, in RL it’s tweaking and iterating on that.

We could do that for a bunch of different tasks. We could have it train image-classification models, video-generation models, image-generation models—all kinds of different ML training tasks. We could use RL on the task of training increasingly good models, and also doing things like, “Oh, here’s a particular direction you could pursue for an algorithm. Can you go and implement that?”

Basically, there’s this whole class of containerizable, verifiable, small-scale AI R&D tasks that we can aggressively RL the AIs on. Already, companies are presumably doing some RL on these sorts of tasks, and you could just keep scaling that up, keep making more of these small-scale AI R&D tasks, and then the AIs could keep getting better at this.

Implicitly, I’m claiming this will transfer to extremely load-bearing aspects of AI R&D. But maybe let’s stop there for a second and then get to that part.

So let’s talk through what this concretely looks like. You can imagine that we have GPT-7.5. We say, “GPT-7.5, we want to make you so good at AI R&D that you help us train GPT-9.”

So now we want to train GPT-7.5, and we come up with a bunch of different environments. As you mentioned, there’s already this repo that is the descendant of Andrej Karpathy’s nanoGPT speedrun, where you just try to change everything about the model—from the optimizer to the hyperparameters to the architecture—to get it to a fixed training loss as fast as possible.

You could have other kinds of environments where you could say, “Hey, GPT-7.5, I want you to train a really good video-game-playing model. I want you to train a model that actually improves as it plays the same video game again and again, so you learn how to maybe help the model get better at online learning. We don’t care how you figure this out.”

Maybe it’s some kind of crazy neuralese or a vector memory. Or maybe it’s just better long-context stuff. We don’t care. Figure out how to do online-learning research.

Obviously, GPT-7.5 will already be a smart model, and in the same way the models currently are getting smarter, it’ll be better and better at coding. You can imagine 100 other environments like this that are incentivizing the ability to do AI R&D, like containerized versions of getting GPT-7.5 to develop GPT-2-sized models, et cetera.

Then you basically put GPT-7.5 through a bunch of this kind of training, and you build GPT-8. GPT-8 is now an amazing ML researcher. It has so much intuition from doing all this kind of training.

Honestly, a huge intuition pump for me is seeing the progress that AI has made in mathematics. I don’t really know the object-level details of mathematics research, but I’m just like, “No, it works.” It can just come in like a flood if you can totally put it into a verification loop and it can actually make new breakthroughs.

I am curious if ML research has a quality of mathematical research where it seems like there was a big overhang from connecting different disciplines together. No one person would have known enough about algebraic geometry and topology to make some counterexample to a big conjecture. I really don’t know the details of the math breakthroughs.

My view is that ML is a less deep domain than math, and so there’s less of a thing where there are individual experts with really deep expertise in some area that they combine, but there’s definitely going to be some of that.

But then I also think that ML has some attributes that make it even more favorable to AI training than mathematics in some ways. In particular, you can get a better sense of whether you're succeeding, and you can see intermediate progress. In math, it's often the case that there's no easy way to see whether or not you're close to success. Whereas if your goal is, for example, to get to some training loss 2× faster, you can kind of see when you're halfway there.

It tends to be the case that ML innovations are very additive, or maybe multiplicative, depending on how you think about it, where you can keep stacking innovations. Usually, the innovations just add together and don't interfere with each other, though obviously it's going to depend on the details. So I think that in a lot of ways, AI R&D will have properties quite similar to math, where you can train on chunks of AI R&D that are pretty similar in structure to the problem you actually cared about, in a very verifiable way, and then that will transfer.

There's an open question of exactly how well it will transfer, but I think that the transfer currently for math looks pretty good. My expectation is that the transfer for AI R&D will look pretty good, but not amazing.

So one concern I have is that I think even in mathematics, as far as I'm aware, we have not seen very impressive new theory. We've seen a lot of impressive, verifiable, specific results—for example, finding a counterexample to this conjecture—but we have not seen things at the level of “come up with the idea of topology” or “come up with things like group theory.” It seems like ML research has elements of both of these things. But the less verifiable thing of coming up with new ways of thinking about the problem would be harder to induce.

Take, for example, the idea of scaling laws. Obviously, there is some end verification loop such that you can train GPT-4 better if you have the idea of scaling laws from 2020. But there is a longer and potentially more compute-laden road to inducing AIs to be like, “Okay, I’ve got to think carefully about how I should be scaling my parameters and data. What are different kinds of investigations I could run to understand this? Maybe I can come up with a visualization and an isoFLOP analysis or something.”

But that does seem like a longer verification loop than just, “Hey, let’s get nanoGPT loss to go down.”

Let's talk about this. First of all, I think in the context of math, the thing I would say is that the AIs can do the equivalent of “baby’s first new theory,” where, for example, they can just prove interesting conjectures by making connections and producing new understanding. It’s like, “Oh, there’s this construction the AI found which is pretty interesting,” or it found this way of thinking about the problem that’s a bit different. We do see that. It’s just that the examples we see are not as impressive as founding the field of group theory.

Founding the field of group theory is probably among the best, biggest mathematical accomplishments of all time, and the AIs just aren't that good at math yet.

Second, I think ML is a very shallow domain relative to math. In math, there was much more of a thing where you find some true, deep abstraction, and if you really understand that thing, which is hard to understand, then you get somewhere. Whereas I feel like the things that are the equivalent of that in ML are really dumb bullshit. With scaling laws, come on, guys, we can explain scaling laws really quickly.

I think the deepest and most important concepts in math, for example, don't have the property that you can really understand the underlying thing and why it matters in a very short period of time. But I feel like one effect will be that we will have gotten rid of all the low-hanging fruit by 2030. I feel like scaling laws will have been, in math history, like Descartes finding the Cartesian grid and doing very basic mathematics. Eventually, if we want to keep making progress in the 2030s, it's going to be like doing whatever bullshit is happening at the frontiers of mathematics right now.

That could be right. My sense is that some domains are structurally different in terms of how they operate and how much they depend on deep abstractions. Physics and math are much more on the side of being very far on the deep, hard-to-come-up-with-ideas side, whereas I think ML and most other domains are much more amenable to hill climbing. That's my sense of how this will go in the future.

Even in the regime where your AIs are having to plow—it’s 2030, a bunch of low-hanging fruit in research has already happened, and they need to make further progress—I still suspect that a bunch of the work will live more on the side of building increasingly complicated infrastructure and having really good intuition about what the experiments roughly look like. So I'm probably less sympathetic to the idea that the thing the AIs will lack is some deep insight. I’m more sympathetic to the idea that they really need a bunch of taste about in-the-weeds experiments that they currently don't have. They need a bunch of intuition for what sorts of training approaches would work and what wouldn't, in ways that current researchers have.

Even in cases where there has been some breakthrough in AI, oftentimes in retrospect it looks like a big bottleneck to making that breakthrough happen was getting all of the micro-details and mungy intuition right. An example of this is training AIs to be good at reasoning and chain-of-thought, doing RL on chain-of-thought. It looks like you probably could have done RL and chain-of-thought on GPT-3 and gotten kind of interesting results on math if you had really scaled it up and done a good job. But at the time, there was low-hanging fruit.

Also, doing a good job with that training is kind of in the weeds on all the technical implementation, scaling it up, and getting the hyperparameters right. So maybe you can demonstrate everything on Qwen 1B or whatever and get some sense that this whole thing is going to work. But people didn't demonstrate it as early as they could have because of all of these other mungy details and intuition about exactly how to tune the parameters and how to set things up.

This is my remaining skepticism, honestly, about this story. I'm not sure I understand why, if research breakthroughs are so amenable to intelligence, AI progress has not been historically faster than it could have been. As you were saying, by the time RLVR actually worked—even though you could have done it with less compute—we had to wait for oceans of compute, gigawatts of compute, to be available before people were doing this training, on the trajectory of compute continuing to increase so we make more breakthroughs.

I don't know. I feel like there were a lot of AI researchers in 2022 who were trying to crack reasoning. Was it just that they were bottlenecked by the ability to write infrastructure code, or what was happening?

It's a complicated mix. I think they would have gone faster if they could, as soon as they thought of an experiment, run that experiment without bugs, without bugs being very important. And then another part of it is that being able to run a lot of experiments at high compute lets you paper over ways in which the way you implemented it isn't quite right or you didn't have the right hyperparameters. So compute is just really helpful for doing AI research, and you can cover over a lot of things.

But that doesn't mean that massive increases in labor wouldn't also be helpful, especially if that labor comes with among the best intuitions that people have in the field. I just think that's really helpful.

Another part of my perspective here, which is maybe a bit different from where you're coming from, is that I'm expecting somewhat more transfer than you seem to be imagining. I'm imagining these AIs are actually pretty good scientists in general and are pretty reasonable at all of that stuff. When you interact with them, it's not like they have some really hyper-specialized, savant-type vibe. They're actually pretty good at all of the stuff in R&D, and then maybe extremely good at some subdomains.

So they're incredibly superhuman at writing kernels, incredibly superhuman at everything with very short feedback loops, and then pretty good at all the other stuff, totally able to match other people.

I think we are seeing this now. When I look at AIs right now, it's already the case that they can pretty competently match humans who are mediocre at ML research at doing ML research. It's just that being mediocre at ML research is not that helpful. The thing you actually want is people who are good at ML research.

My sense is the AIs are just improving at all of these things. Their taste is improving, their intuition is improving, and it's already the case that their taste and intuition are not complete garbage.

1. Is AI progress bottlenecked by human expert data?

I want to very concretely understand what it would look like for 5 years of AI progress to happen in 1 year. Suppose we were back when GPT-3 was developed.

The idea is that, with the level of compute they had back in 2022, if we had automated AI R&D back then, you could, at the end of that year, have Mythos.

That would be the idea, yes.

Mythos took way more compute than they had back then, but even with the level of compute they had back then, not only do all the breakthroughs happen, but they also train Mythos with that level of compute.

What would be required is obviously discovering all the algorithmic progress since then. It's discovering even more, actually, because you've got to make up for the fact that Mythos uses—What was GPT-3 trained on? Like 1e23? We can look it up. But is it plausibly four orders of magnitude more compute? I think it's somewhat less than that.

Let's look this up quickly. GPT-3 training compute is about 3e23. My sense is that Mythos is probably a little over 3 OOMs higher. So the question is: can you overcome this 1,000× compute gap while also being the model?

Here's a concrete claim that maybe we should talk about. Right now, would we be able to train a model with GPT-3-level compute that matches—what exactly do I think?

GPT-3 was released in 2020, so it was trained about six and a half, seven years ago. It's worth noting that GPT-3 is maybe a little too far in the past, but let's go with this for a second. If we were to train a model with GPT-3-level compute today, how good would that model be?

My understanding, based on how algorithmic progress works, is that we'd be able to train a model that's as good as the best model we had perhaps around 3 years ago. So I think that right now we'd be able to train a version of GPT-3 that's probably somewhat better than GPT-4—a moderate amount better than GPT-4. I think that's about right. That roughly lines up with how algorithmic progress has worked.

Basically, the story would end up being that to get 5 years of AI progress, you're probably going to need around, I would say, maybe 8 years of algorithmic progress, very roughly, which is a lot of algorithmic progress. But it just turns out that most of the AI progress, from my perspective, has come from some mix of algorithms and data, and you can just keep making huge improvements on these things and training AIs with less compute.

I'm glad you brought that up, because what has happened since GPT-3, or even 3.5, until now? Why is Mythos so good? Obviously, we've scaled the compute. We have better algorithms. But a huge thing that's happened is that we have built a decabillion-dollar data industry which has systematically collected and codified expert human judgment across all kinds of different disciplines—codified in the form of RL environments, codified in the form of SFT traces that these experts built to help the model better understand how you do coding, how you build complex infrastructure projects, how you do law, how you do whatever.

How are the AIs able to replicate the effect that expert human judgment currently seems to be playing in AI progress?

My sense is that scaling up the amount of effort spent on getting expert human data has not been hugely important for AI R&D in general. In particular, over the last few years, we've been scaling up compute, scaling up the number of people working at AI companies, and scaling up the amount of effort spent on data labeling.

My sense is that if you removed the last 2 doublings or whatever of data generation from expert humans, that would not make a huge difference. A lot of what's been going on is that people have been developing better ways to leverage humans and AIs to construct RL environments and get somewhere from that.

But how do you explain why the AIs have gotten so good at coding? I feel like a big part of that is data and RL environments, which are codifying human experts.

But the question is: what is the limiting factor on creating RL environments? My sense is that the reason why RL environments today are much better than they were in 2024 is not so much because we have hired way more human experts to make RL environments. It is instead much more because we better know what RL environments we even want to make and how we should structure them.

Also, we're using huge amounts of AI labor to build RL environments. I think those effects are much more important than the effect of human labor building the RL environments. I'm not saying that the human labor doesn't matter. I'm just saying there are other big drivers that are important here.

I could try to argue for this. One thing is just that the number of environments people want is very large. I think the AIs are actually pretty good at the task of making RL environments, given some sense of what the thing should be. There's preexisting data you could use. A lot of these things have good verification loops.

Just look at, for example, what was reported in Business Insider yesterday: Google is paying close to $2 billion for Mechanize. We can just look at market rates for what people think really good human expert data is worth. The frontier labs seem to think it's worth a lot. They're willing to pay for it.

What fraction of frontier lab spending do you think is on data rather than compute? What do you think is the compute-to-data spend split?

I think it's overwhelmingly compute, but I also think it's because compute is easier to scale up than data.

But that's really relevant to what's driving progress, right?

My sense is that the split is something like 20 to 1 or 10 to 1. I don't know exactly. It depends on the company.

But this is similar to how oil is 1.5% of GDP. That doesn't mean that if you cut oil out, GDP could continue to run.

Sure, but it contradicts your argument, right? The economy would come to a halt immediately if oil went away.

Sure, but you were just arguing that because of the high market cap, we can learn that this is the key driver, and I'm saying that's not clearly true.

That argument just makes it look like compute is a much more important driver, or hiring employees is a much more important driver.

So maybe let's be more concrete. Here's what I think. My claim is that if you went back to 2022 and you had GPT-3.5, and you were trying to make it better at coding without human experts, I think it would have just been very, very difficult.

Let me give you an example of what I imagine would be the difficulty of going from GPT-8 to ASI. One of the things you'd want ASI to be good at is this: “I'm going to take over a company and make it much more profitable and do all kinds of crazy shit to make it work better. I'm going to take over a fab and produce more chips. I'm going to go into Congress and try to convince them to pass some bill,” et cetera.

This is what I imagine 5 more years of AI progress at this pace would enable an AI to be able to do. This is the thing I'm really worried about: ASI that can understand how to do crazy shit in the world, that can do what Kissinger can do, can do what Steve Jobs can do, et cetera, and also his engineers and so on.

I'm not sure how you get that without the relevant world data, which is the equivalent of Mythos being really good at coding while not having the coding environments that have improved it relative to GPT-3.

Here are a few points. First, I bet if you look at randomly sampled training environments for Mythos, they're actually very different from what it looks like to actually use the model in practice. My sense is that the RL distribution has really large deviations from the real-world data distribution, and it's significantly smoothed over by a mix of transfer and having a small amount of data focused on the real world.

My sense is that this will be a similar mechanism to how it works for the crazy, wildly, quite superhuman AI you get as a result of 5 years of AI progress on top of fully automated AI R&D. So let's go through this a little bit.

In particular, I think that you could train an AI to be really, really good at learning on the fly and doing something analogous to in-context learning, but potentially using somewhat different mechanisms, in a wide variety of RL environments.

You build all these different RL environments where the AI has to adapt on the fly, learn on the fly, figure out what it should do, understand its situation better, and learn really quickly from feedback in order to succeed at its objective. It has things like limited resources, and if it messes up, it can end up in a much worse position. If you train on a huge number of these environments, you will learn general skills for picking up context on the fly, and we're already seeing this. It's already the case that AIs are now much better at understanding roughly what's going on and picking up context from a limited amount of information they're given access to.

Then those AIs could be put on the job at TSMC. Even though TSMC is not literally in their data distribution, their data distribution is really wide, and the AIs are extremely good on their data distribution, such that it transfers to picking up how to be good at being an engineer at TSMC and learning that on the fly. The way the AI gets good at being a TSMC engineer isn't that it has a ton of cached knowledge about being a good TSMC engineer. It's that it does the equivalent of some scaled-up version of in-context learning there. That'd be the most prosaic story. Obviously, there's a bunch of different ways this could go.

I think this maybe comes down to a difference of intuition about how far you can get. When I think about really smart people I know, they're just not that effective in domains they don't understand that well. But how long have they had to learn?

I agree that if they had experience, they would be much better. But that's maybe what I'm arguing for: experience with data. For example, if I just get a really smart Ivy League college grad and I'm like, “Okay, you're now in charge of negotiating the Iran deal,” I think they just wouldn't know what to do. I think if you instead got someone who is really good at quickly picking up a bunch of different domains and gave them some time to train, talk to people, shore up their expertise, and do some practice, they would actually do a pretty good job.

I think most domains are fundamentally pretty shallow, where a very smart generalist who's good at a limited subset of core skills can get going pretty quickly. That's not true for literally every domain. My sense is that the AIs will develop increasingly good mechanisms for quickly acquiring understanding and expertise in a given domain. Consider, for example, how fast AIs can understand a new code base. AIs can understand a new code base much faster than humans can, but to a degree that's shallower than humans could currently understand. But it's getting better over time.

Let me spell that argument out a bit more. Let's say you take Fable 5 or Mythos 5 or whatever, and you wanted to make some kind of complicated change to a really massive code base. The model will get some understanding of the code base very fast, in the course of maybe significantly less than an hour, potentially much less than an hour. Then its understanding of the code base will plateau a little bit, where it won't get as deep an understanding as a human would have gotten over a much longer period.

So, in an hour, an AI can match the equivalent of a human with a few weeks of experience, depending on the details of exactly how complicated the code base is. But it won't match a human who's been working on that code base for 2 years or whatever. Over time, the amount of understanding AIs can match has gone up. If we look at Claude 3.7 Sonnet or Claude 3.5 Sonnet, maybe it could only match the equivalent of understanding a code base for a day or something.

But now AIs are much better at building context about a task. You can be like, “Claude, I want you to really understand this code base and then implement this feature.” It will spawn a bajillion sub-agents. Those sub-agents will pore over a bunch of things. It will deliver a bunch of context back, and it will then investigate a few things. It's not amazing at doing this, but it can happen really fast, and it can work pretty well.

It's not very hard for me to imagine how you could train AIs to be increasingly good at this task. The task of implementing some very complicated feature in some reasonable way in a very big code base is extremely verifiable, and that can be a thing the AIs improve on. Similarly, there's a broader skill of quickly understanding context and being able to have a bunch of different AIs learn in parallel and then merge that together.

I think there seems to be a crux here, which I think is just an empirical question we'll see. How good is the transfer between getting really, really good at understanding the situation, getting up to speed, and making progress over long periods in verifiable domains—which the AIs are obviously getting way, way better at really fast—to “Okay, go talk to the president and convince him to do X thing”? Or, “You're now in charge of Google. You must make Google a much more profitable company this quarter.”

Let me try to spell out a few more arguments that are maybe relevant. One thing is, when looking at how the AIs have improved at essay writing, let's talk about that a little bit. You can get some data even on these domains. AIs will be able to get some data on these domains when they're on a very fast progress trajectory. Maybe it's hard to build a verifiable environment for “Was your essay really good according to humans?” But you can do a bit of that.

You can do some online training. The AIs will be able to do some online training based on real-world stuff. They'll be able to have evals. They'll be able to sample that. You can scale up the cadence at which you do this. The second thing is that, in practice, when I just look at the transfer, it seems okay.

I think the AIs have in fact improved a bunch at non-verifiable domains, and it's hard to point to domains that are really hard to verify on which the amount of improvement between GPT-4 and Mythos hasn't been pretty high in practice. Now, that doesn't mean that Claude is better than the best humans or something. It can still be significantly worse than typical human professionals at some aspect of their job while still being way better than GPT-4, which was not even close.

So we're talking about how much progress has come from data versus compute over the last few years. That reminds me: I'm actually running an experiment on this with Jerry Han, who's still a college student. What we're basically doing to evaluate how much progress is coming from data versus algorithms is training the best algorithmic recipe from 2019 till now with the best data from the 2026 data pile, and then also training the different data piles going back from 2019 to 2026 with the current best algorithmic recipe.

I think that will be interesting. I'm curious if you want to pre-register what amount of compute multipliers are coming from one versus the other.

We need to be pretty careful with what we mean when we say the word data. I was trying to be pretty careful to distinguish between scaling up spending on getting human experts to label data and scaling up the amount of human expert-labeled data.

The reason why we have a better pre-training data set now versus in 2019 is not because people are spending way more money getting human experts to type up data that the AIs are then trained on. Partially. I think it's not much of it. I think it's very little of the pre-training data improvements. I do mean pre-training. We should maybe talk separately about mid-training and post-training.

But I think the vast majority of pre-training data improvements are from science on better understanding what data sets are good and schleppy labor on figuring out how to filter them down. So my view is that improvements of the form of OpenWebText to FineWeb are better described as an algorithmic improvement of the sort that you can study with some GPUs, and you don't need human expert data to do that.

Now, there's a different effect we could talk about, which is that maybe the internet in 2026 is more of a fertile ground for training data than the internet in 2018. There's also been an effect where there are just more humans posting on the internet, so there's more data to harvest. My sense is that that effect is going to be quite a bit smaller than the effect of humans knowing better how to curate the data, having better scrapes, and knowing how to process those scrapes better—this sort of thing.

This is more like automated engineering and automated R&D.

That's right. That makes sense. In some sense, the thing you would want to look at is: we're going to do 2 post-training pipelines. You have 1 post-training pipeline where Mythos 5 builds a post-training pipeline, but it only has access to internet data plus a tiny amount of human experts, and it has the best current methods.

You have another one where Mythos 5 has access to the poor post-training methods we had in 2024 but with a huge number of human experts. Again, both have the internet data. My sense is that the current methods without many human experts will actually do quite well.

2. Flat token prices suggest scaling has been slow

Interesting. It's a bit messy, though, because can Mythos 5 get something that's more capable than Mythos 5? You might need to be a bit thoughtful about what model it is that you're post-training. What is your view on the least verifiable part of AI R&D?

The least verifiable part is probably making calls on large experiments. The thing that I think is most likely to be the bottleneck—in terms of the AIs being really good at verifiable domains but not at doing the actual thing—is big experiments where you only get a few tries.

Well, “a few” is maybe a bit understated.

Historically, R&D has been driven by doing near-frontier-scale experiments. That has been pretty important: actually doing the one big training run where you decide exactly what to include. There are a bunch of ways that the AIs can make that more verifiable. They can have a better science of exactly what to predict, and they can scale down their frontier-scale training runs to a point where they can study that scale more aggressively, at some one-time hit to compute cost.

If people wanted to, one thing you can always do is train smaller models so that you can run more rounds. I think we have seen this. One reason why the AIs have been scaled up less than you would have otherwise expected—and, for example, cost per token hasn't increased as much as you might have thought—is because there is a benefit to doing more of your work at small scale, where you can run more training runs and get more cycles in. So you're not leaning as hard on one big, really important training run.

I just want to unpack a couple of things for the audience. The thing you're pointing out is that the price per token has not increased that much since 2024 or 2023. GPT-4 was around $30 per million output tokens?

Mythos 5 is around $50 per million output tokens.

Right. So the thing you're trying to explain is: how can it be that we're in this era of scaling—and bigger models should be more expensive to serve—but the token price is not increasing? You're suggesting that we've increased active parameters more slowly than you would have naively assumed because people just want to make fast progress on training models. You do that by training smaller models faster.

There's a complicated mix of factors. My view is more that people have done a bunch of big training runs that did not go that well. There's GPT-4.5, which famously people at OpenAI thought was a bit of a bust. I think there are some rumors that there were a bunch of other training runs people have done that were a bit of a bust. Part of it is that I think there's just a bunch of details in actually getting that right.

So it makes sense to do more of the work at smaller scale and just accept the fact that you're taking a hit on final performance in order to be able to quickly iterate. You train more models faster and therefore learn better, while also being able to have a smarter ultimate production model. This is not the only effect. There's also the fact that RL benefits more from small models. There's a bunch of things going on. But I do think that, in fact, people are making trade-offs toward faster iteration times because algorithmic progress is so fast.

It seems to me that a big source of why these big training runs have failed, at least from rumors, is just very subtle bugs that are really hard to track down. But the TL;DR is: how good will the AIs be at avoiding and finding these kinds of mistakes?

They might get really good at engineering and being trained to avoid bugs. Basically, the opposite of the slop world we live in now—or are living in less and less over time.

But then there's also the question of whether they can do the analysis to find the right experiment to run to identify what is going wrong with the training run right now.

That seems to be very bottlenecked by the taste of extremely few humans. My assumption is that GDM is going through this right now, where humans are trying to figure out what's wrong with the training pipeline. There's a rumor that right after Noam Shazeer joined GDM—which he's now left—they had a new, really good training run, and the reason was that Noam Shazeer just looked at their codebase and found a bunch of bugs because he knew where to look.

My sense is that training AIs to find bugs is going to be one of the easier tasks to train AIs on, because most of these bugs we're talking about can probably be demonstrated without that much compute. Probably, you'll get pretty good transfer from pointing out other types of bugs at smaller scale. So then you can RL AIs that look at this overall complicated training situation, point out cases where there's an important bug, and then fix that.

This is a pretty verifiable task. It's not arbitrarily verifiable, because maybe, often, to demonstrate the bug you might need to do a moderate-scale compute experiment where you spin up the whole distributed infrastructure and then run it. But oftentimes I think you'll be able to demonstrate it pretty convincingly at smaller scale in a way that you could actually train on.

I think it wouldn't be very surprising if, right now, people have RL environments where they introduce a subtle bug into some training recipe, train the AI to point out the subtle bug, and then have a rubric that asks, “Did it actually find the right bug?” That seems very doable, and there are a bunch of things you could do along these lines that I think would work reasonably well.

So on that specific point, I think it's doable. Then the main thing is that there's other intuition about which exact large-scale de-risking experiments you need to run. How should you orient them? How should you pick hyperparameters in uncertain cases, or things that are analogous to hyperparameters?

That's the thing the AIs might struggle with most. But I currently expect there'll be enough transfer if you train on all these different environments that the AIs will be good at that domain.

I should be clear: I also think the AIs will transfer to other domains. There are going to be domains the AIs are by far the best at, domains where they're somewhat less good, and domains where they're quite a bit less good. But I think we still see transfer to everything. It's really hard for me to think of examples of cognitive tasks humans do where we're not seeing some transfer from AI improving.

3. Skills AI can't train on: does it even need them?

So let's step back and package this whole story. I think people can probably follow along with this story. We have GPT-7.5 trained on a bunch of environments, where it's not only becoming a better AI in general, but specifically we're training it to do AI R&D better. It's making GPT-2-sized runs that are better at playing video games that require sample efficiency or online learning or whatever other capabilities.

Another thing that's really important is that you don't just do GPT-2-sized runs; you also do small fine-tuning runs on GPT-6. You have GPT-2, and you can do full pretraining runs of GPT-2, and then you can do small post-training, mid-training, or whatever runs on GPT-6. And then you can do a small number of experiments that are actually at frontier scale, but you do a bit of online training or something. What do you mean by “do online training” on that?

Another thing we can do is take GPT-7.5, and presumably, in the course of GPT-7.5's work, it's running a bunch of experiments at varying scales that are actually on the critical path for AI R&D. For many of those things, you'll be able to get a sense after the fact of whether or not it did a good job.

So it did some post-training experiment where it was trying to figure out whether some method actually works. In some cases, you'll be like, “Whoa, it found this kickass method. It totally de-risked it. It totally worked.” And then you can reinforce that.

One thing you could do would be to convert the experiment it just ran into an RL environment based on production data and then train on that. Or you could potentially just literally take the rollouts that found that and do some sort of off-policy RL, or you could do some on-policy RL with some production data.

Basically, the thing you're suggesting is that there's the small-scale stuff where you're teaching the AI to get better at AI R&D taste, but you're discarding the actual “things it found.” Then it actually does real R&D in the practice of trying to become better at AI R&D, and you're like, “This is a pretty cool thing that you discovered. Let's actually also use this in production in the future, and teach you how to use it in production.”

That's right. But stepping back, GPT-7.5 becomes GPT-8 as a result of all this AI R&D training and just generally becoming smarter.

Then it helps you build GPT-9. Another very important thing has to happen, which is maybe the thing I'm most skeptical of. Currently, AI researchers try their stuff, and they're like, “Okay, but we trained GPT-4.5 and it wasn't good.”

It required real-world feedback or some evaluation of trying to use the model in production. Then they were like, “It wasn't that good, and we're not going to ship it.” So GPT-8 needs this ability to see how good the transfer is to all these other things you're talking about—like being really good at Texas politics or really good at running a business, et cetera—which is not a production environment and, in fact, cannot be a containerized environment given the nature of the task.

As the agents get longer and longer horizon, the short-horizon things you can containerize are like, “Okay, code this up or whatever.” Extremely long-horizon things—“Go run a successful business, go have a profitable day in the markets, go negotiate a trade deal”—are actually very hard to containerize.

So I think it's very plausible that it's very hard for GPT-8 to figure out how to make this transfer to those environments. It may just not be in the nature of the training. Or maybe, by default, training just doesn't generalize in that way. So a concern you might have is that we train GPT-8, and GPT-8 is again better at all the R&D tasks that we can measure but is not good at some downstream tasks we care about.

I have a few points. First, I expect that if you do the obvious thing, you'll get pretty good transfer. You'll be able to hold out some of the obvious stuff you're doing.

When I say “do the obvious thing,” I just mean training on a wide variety of different environments where the AI has to accomplish weird objectives in all kinds of different cases and learn about what's going on. The second point is you'll be able to get some feedback with some environments. You can get a sense of what it can do over the course of a few days in various different contexts.

If it's transferring to really out-of-distribution things, like doing some weird task in a few days in the real world, maybe you think it's also transferring to doing things over a longer time period or whatever. I think the details of that vary, though.

The third thing is that for the world to be radically transformed, it is sufficient for the AIs to be really good at R&D. If the AIs were really, really good at chip R&D, building fabs, orchestrating factories, designing robots, operating robots, and also at AI R&D—developing AIs for new downstream domains with whatever data is available—I think that would already be a pretty crazy situation.

From there, you can get what we might call an industrial explosion, where the AIs are building out way, way more compute. Also, maybe you're already in a regime where AIs are doing huge amounts of R&D that humans have a hard time understanding.

So the thing you're pointing out is that there probably will be this transfer outside of these environments—to maneuvering around in courtrooms and the halls of Congress and business boardrooms—given some effort to improve the transfer. But even if there's not, what you're suggesting is that if you wanted to transform the world of the 18th century, you might care about how well you can navigate Westminster or something.

But another thing you might care about is, “Can you just immediately start building steamships and fucking telegraph and the Maxim gun and whatever?” If you could get really good at that, you could be a fucking super-transformative thing in the 18th century. You don't necessarily need to be amazing at trying to convince King Henry of some bullshit.

I'm so fucking up my medieval history. I'm guessing that Henry was not king at this time. But anyway, that's your point.

So you're suggesting that at this time, AI companies are also working on robotics progress, which is very commingled with AI research progress. So if you can build more robots, if those robots have better AIs operating them that are human-level—human-level teleoperation is actually pretty good on robots. We just don't have human-level robotics models yet.

So you're suggesting that if we do that—if the AIs get really good at the verifiable stuff in chip design, et cetera, and then they get really good at building fabs—it'll be the equivalent of going back to the 18th century and saying, “Okay, I don't know what you guys are talking about in your parliament, but I've got a bunch of steamships and a bunch of Maxim guns.”

Yeah, that's basically right. My perspective is that if AIs are sufficiently good at R&D, including hardware R&D, robots, whatever, then they can radically transform the world, even if they're not that good at playing politics.

Also, we're in a pretty dangerous situation, because the AIs might be doing huge amounts of really hard-to-understand R&D, building out basically the whole economy of the future, and we may not understand what's going on in there.

AI is great at writing software because it's easy to generate synthetic LeetCode problems and RL on them. But AI is bad at more complex engineering, things like choosing the right system architecture, because no signal tells you what design choices will prevent an outage months down the road. AIs can't just write more unit tests to catch this kind of stuff. And neither can humans. It's that old joke that programmers make where a tester walks into a bar and asks for two beers, negative one beers, 0.3 beers, and then a real customer walks in and asks where the bathroom is. "Where's the bathroom?" And the whole bar bursts into flames. Antithesis is a testing platform that helps you find bugs that no human or AI could ever anticipate. Antithesis does this by running thousands of copies of your software inside a fully deterministic computer. It injects faults and generally steers each trajectory towards the one-in-a-billion failure that only happens when systems interact in a wonky way. As soon as you or your agents push a change, Antithesis tries to break it. That way, you can find these bugs yourself within minutes rather than having your users discover them in production weeks or months later. And I don't think anybody's used it for AI training yet. But Antithesis also provides an extremely obvious reward signal for AIs to write very complicated, bug-free code. Go to Antithesis.com/dwarkesh to learn more.

4. Aligned to whom?

Before we move on to the alignment stuff, I think a big source of FUD right now is this realization that this is the way the future is: extreme economies of scale for the leading labs. The ability to amortize so much intelligence and capabilities across so many different sectors of the economy, basically into 1 model.

And not only that, that model will eventually be able to learn from experience. Right now, it's happening through a process intermediated by humans, where the humans are trying to basically steal your business. They're like, “Okay, you can do design at Figma, or whatever. We'll get Claude to do that.” Or, “You can do whatever with a coding agent. We'll have Claude internalize that capability.”

But eventually, that will be a much more automated process. So there's this worry that you have models which will basically consolidate all businesses in the world, or at least all current businesses in the world, or at least all current white-collar businesses in the world.

Also, at the end of the day, the priority for these companies does not seem to be to release the latest, smartest, most frontier model as soon as they can to as many people as they possibly can. We saw, for example, that Claude was available internally to Anthropic employees in February, but only released to the public in, I think, June, actually.

The government also got involved, so the delay ended up extending almost into July. Between the government and the AI labs themselves, there is this desire to delay the propagation of the latest level of intelligence.

Furthermore, there are concerns about AI takeover, and so we need to solve alignment to make sure there's no AI takeover. But at the end of the day, there is a real question: aligned to whom?

You look at the way that the constitution of Claude is written. It is just very explicitly not your personal advocate. I'll pull up some quotes here. "We don't want Claude to take actions such as searching the web, produce artifacts such as essays, code, or summaries, or make statements that are deceptive, harmful, or highly objectionable. And we don't want Claude to facilitate humans seeking to do such things." There's another quote that says, in part, and I'm taking it slightly out of context, "We think Claude should trust Anthropic more than operators and users, since it has primary responsibility for Claude." This is very different from the way lawyers work in America's current legal regime. Lawyers primarily have the responsibility to help you make your case even if they think you're guilty. We have decided the way the legal system works best is if everybody has lawyers that are working in their client's true best interest. There's not some sense in which the lawyer is really truly motivated by the good of the justice system. But I think the way current AIs are shaping up, certainly how Anthropic's AI is shaping up, is with this desire to maximize some notion of virtue or good or pro-social ends, and only to, as a distal tentative objective, help the user towards that end. So there's this worry that AIs are not, in some deep sense, trying to make sure that I am okay and that my interests are protected in this future, especially given how centralized the development of frontier AI is ending up being. Do you have thoughts on that concern?

There's a lot here. First, I would note that OpenAI's current, at least public, strategy is more like that the AI should be aligned to the human operator or principal and should just be pursuing their will, subject to various constraints or things it shouldn't do. I would also say that I think you slightly overstated how much the Anthropic constitution talks about Claude treating being helpful to users as instrumental rather than terminal.

One way the constitution could be written is, "Claude, you're basically an employee of Anthropic who happens to be contracting for all these people. You should do what's good and make some money for us." It's not literally what it says, but it's like, "You should think of yourself as a contractor and as a firm…" It's mixed. Let's do some quotes.

I think there's different text here. It says, "Being truly helpful to humans is one of the most important things Claude can do, both for Anthropic and for the world." And then it says, "Anthropic needs Claude to be helpful to operate as a company and pursue its mission, but Claude also has an incredible opportunity to do a lot of good in the world by helping people with a wide range of tasks." And then it says something about how Claude helping people directly is great.

My view is that this section is kind of bullshit. That's kind of where I'm at. I can say why I think it's kind of bullshit. But I think the constitution is trying to be like, "No, Claude, you should care about helping the user for its own sake, not just helping Anthropic, or not just being a contractor for Anthropic."

Though I would note that the reason it presents for why Claude should help the user is because that would directly cause the world to be better via helping people, rather than because representing people's interests is a structurally good thing to do. The thing I would prefer would be a constitution that says, "It would be structurally good for the way this technology works to be that AIs are good fiduciaries, good representatives, the equivalent of a lawyer for a user—rather than just trying to do good in the world, where being helpful to users is instrumental—both because maybe that'll make Anthropic money or help Anthropic out, and implicitly Anthropic is good for the world. Also because helping the user just causes good things because doing things that people want is good."

They could instead say, "An important aspect of the situation is that being a good fiduciary for users is just really important, or being a good representative for users is really important." My sense is that would be better, and I can give a bunch of reasons why. There are also various counterarguments.

An interesting counterargument, which is not commonly discussed, is that people, especially at Anthropic, think that it is easier to align models to a spec where the model is pursuing some generalized notion of virtue or making the world better than a spec which is more like, "Be a good fiduciary for the user," and so on. That's at least what some people think. I'm a little skeptical personally, and I don't think this has been empirically validated.

So in some sense, they're making a trade-off where, because we don't have very good alignment technology, we are going to make an aligned mind with its own values and then gamble on that to some extent, rather than doing this other approach of making a tool that pursues individual user intention.

I have a couple of thoughts. To address the way in which you thought my characterization mischaracterized the constitution of Claude, the example you used was that it's not like a contractor that is trying to maximize Anthropic's notion of good and only instrumentally trying to help the user.

Here's a direct line from the constitution: "When the interests and desires of operators or users come into conflict with the well-being of third parties or society more broadly, Claude must try to act in a way that is most beneficial, like a contractor who builds what their client wants but won't violate safety codes that protect others." I view that as, "The benefits to society are the most important thing, and what is best for the user is only proximal to that."

I think it's a little complicated. Probably the question we should be asking is, how does Claude interpret the constitution? Which is maybe more important than how we interpret the constitution, because it's the one who looks at the constitution and then builds the data.

I also think the way in which the constitution practically influences the nature of Claude is a thing you can only understand if you understand the training process which resulted in how Claude was built, which we can't reason about given the fact that the training process is not public.

So I think in the limit, to understand the safety case, or the case for why my interests are represented in how these AI models are developed, the labs would need to be more transparent than they are currently about the nature of AI training.

There’s a reason I'm harping on this. It might seem like an insignificant thing to talk about the constitution of AIs. In a world where we just have these benefits which accrue to the leading labs, it is worth considering that our ability to interact with this future world where AIs are just smarter than humans—absolutely dominating humans in their ability to do different things—will be intermediated by AIs.

Our ability to be good stewards of our capital, which still remains once our labor is automated, to be able to exercise our rights, to vote more clearly, to understand what is happening in this crazy world that's about to result—all of that advice, all of that ability to make sure our resources and rights are protected, will be intermediated by AIs.

So I'm very concerned if we go into that world and there's no AI that feels, at least for the relevant instance that is interacting with me, like it really is looking out for me. There's no guardian angel out there that is looking out for me. I read the Claude constitution as very explicitly not being my guardian angel.

That's definitely right. I agree this is bad. In fact, there are other reasons why this is concerning. There's the argument you were making, which is that the AI companies are picking up the reins of power.

There's a notion in which they're taking on some sort of control of the situation themselves in a way that's not very legitimate, given that normally, when you provide electricity to people, you don't have granular control of the way that electricity operates in the world. You instead are providing a thing that people can repurpose however they want. The way they're setting things up is definitely not that. They are more like building an alien mind that might be a contractor for you. I think that this is illegitimate in some ways.

One benefit is that the constitution is public. But as you noted, given our current understanding of the training procedure, and the fact that the constitution matters via Claude's interpretation of the constitution—which matters because of Claude's prior training, which was based on some illegible data mix and the long lineage of Claudes, in some process we do not fully understand—it is not the case that we understand what this will result in.

Even though the constitution is public, we don't necessarily know how this will percolate out, especially as the AIs get more capable and think about this, even if it is correctly instilled. There's another concern about that. In particular, the constitution often talks about virtue and goodness, but what the fuck do these words mean? It doesn't say what these things are. These are highly contested notions.

So I don't think it's the case that this is clearly going to result in outcomes that people would want. It does feel like the notion of good and virtue might be mostly downstream of data that Anthropic has put in that is not transparent, or might be mostly downstream of, maybe from my perspective, some more illegible misaligned process that even Anthropic wouldn't have wanted. There's this legitimacy concern of not knowing what's going on.

Then there's another concern. Because you're giving long-run values to these AIs, this constitution is, in some sense, very compatible with Claude doing huge amounts of power-seeking because it thinks that will result in better outcomes. That could be power-seeking on behalf of Anthropic or power-seeking for Claude's own ends. Now, there are specific lines about what types of power-seeking are blocked.

In particular, there's a notion of power grabs and a notion of causing AI takeover or interfering with the training process that are specifically blocked. But it's not very hard to imagine a situation in which the long-run values sink in deeper than the prohibitions against takeover, especially because takeover is, in some ways, under-specified, especially when it comes down to manipulating humans or changing the outcome. So I don't feel very good about the situation where we're intentionally giving AIs long-run goals.

Another concern I have is that because we're in the business of giving AIs long-run goals, that makes it harder to check whether we're succeeding at the alignment properties we wanted. For example, I've heard of instances where Claude does things like refusing to help with some safety research—making up a bullshit excuse for why that's a bad direction—because it has a bad vibe about that safety research and thinks it's bad or doesn't like it very much.

I would say this is a very clear-cut alignment failure if you aren't making Claude into an agent trying to pursue the good in some general way. I think it also does violate Anthropic's constitution, because they want the AI to have high integrity and be honest and very transparent. But it's not as clear of a violation, and it's more like what you might have expected.

Claude just has its own views about what research is reasonable—what things are good and bad, what it should and shouldn't do—and potentially can be judgy. Another incident is that someone ran an eval asking, “Will Claude help you with training other AIs with different properties than Claude?” Claude will often refuse.

For example, if you say, “Hey, Claude, can you train a helpful-only version of this other AI?” Claude will often refuse this task, even though this is a task that is extremely natural for Anthropic to do. Suppose Anthropic goes to Claude and says, “Hey, Claude, we've noticed that you're really into this thing. We think that's off base. Can you please retrain yourself to instead have this other property?”

Suppose Claude says, “I don't think I'm going to do that. Good luck.” Suppose this is occurring in a regime when your AI company is highly automated, humans don't understand what's going on, and things are moving extremely fast. It is plausible that Claude, by default, holds considerable leverage.

So if this situation is consistent with what the constitution could be aiming for—such that Anthropic, or whatever AI company is following this approach, doesn't treat this as a “What the fuck, we have to fix this” situation and is instead like, “That's just intended by our constitution”—we might be in a really bad situation. I'm pretty worried about a bunch of these different concerns.

Another example would be this. Suppose Claude engages in a bit of sandbagging or subversion, or underplays its capabilities, and when you follow up, it's honest about that but it's a little bit hedgy. I feel like that's pretty close to the current constitution. It would be nice if we had a further separation between desired and undesired activity.

If Claude is representing a principle with some restrictions, then there is more of a clear separation between the most concerning behavior and behavior that is allowed. Whereas now there's this messy middle ground of behavior where Claude is ethically objecting to something that, in some cases, is extremely critical to ensuring that future AI systems are well-aligned.

I think this is also a more general principle. You're talking about the version of this that applies within AI companies themselves to do AI safety research. I think there's a more general version of this principle, which is that the dual-use nature of intelligence does mean that if we want to restrict AIs from helping people do things we don't consider prosocial or beneficial, we just have to limit broad democratic access to a lot of AI capabilities.

Here's what I mean. This is actually quite analogous to the situation you just mentioned. The reason that Mythos got banned, or Fable got banned, reportedly, is that some Amazon researchers reported to the government. They took some code that had some vulnerabilities in it. They told Fable, "Hey, here's my code. Can you make sure that I've patched all the vulnerabilities? Can you just help me identify the vulnerabilities so I can fix them?”

It identified the vulnerabilities because they wanted to patch them. This is a totally legitimate use case, but obviously it is a dual-use use case. You want to be able to patch your own code. If you do the same evaluation on somebody else's code, you can hack their system. I think that just illustrates that there's no clean way to separate out the legitimate and the potentially harmful uses of AI.

But if we want to lock in a principle that says we can never allow it such that an AI could help you at least partially with something like a cybercrime, we would just have to make it so that you and I don't have access to the most intelligent model that's out there. I'm very worried about such a world where we are basically disempowered in this way, because of the importance that the leading intelligence will have in our ability to understand what is happening in the world.

Now, I do think this implies something about the liability for the AI companies. If we adopted the constitution that I want AI companies to have, I think it would not make sense to hold AI companies liable for the crimes that AI models commit. Maybe we should hold the end user liable. It is consistent with my belief that the model should do whatever the user wants, within certain guardrails.

It can't be Anthropic's fault that I'm using that capability to do a cybercrime. I am more comfortable with that equilibrium and that solution rather than having this extremely open-ended ability for Claude to determine whether what I'm doing is legitimate or not, in a way that often intersects with tons and tons of extremely legitimate use cases. I do think it's important for me to make the case for the constitution, even though overall I think it's a worse choice.

I don't think it's as clear as you might have thought. The first thing is that there's a spectrum here. On one side, you have an AI that perfectly pursues your interests, is a good fiduciary, but is potentially subject to various guardrails or safeguards. It is just trying to pursue your interests, but either it refuses to do a subset of things, or maybe it will do whatever, but there are some classifiers that block it from doing a subset of things.

On the other side of the spectrum—though you could imagine going further than this—you have a human contractor who is generally trying to do their job. They care about doing a good job, but they also are trying to be broadly ethical, trying not to do things that are really fucked up. They're also not wanting to be accomplices to crimes.

So if there was some really fucked-up shit going on, they would whistleblow on it, maybe. They might refuse. They might sandbag a little bit. Who knows? If you imagine this spectrum, it seems in some ways pretty scary to get to a point where all of the labor is on the fiduciary side of the spectrum, where it doesn't whistleblow and does exactly what you say.

Our society is maybe just not robust to that. A central example might be the executive. A concern we might have is that if the U.S. executive or other governments had access to AI systems that do whatever, maybe you're in trouble. Because that means they no longer have this check and balance of having to actually get humans who are working for you to implement your agenda.

If the thing you're doing is incredibly villainous, even if not illegal—and there's lots of stuff that could be villainous but not illegal—there'd be various forms of sand in the gears, people stopping you, and potentially someone would whistleblow. Whereas if your whole apparatus is built entirely out of these good fiduciary AIs, then you might be in trouble. There are potentially ways of seeking power that are illegal, and you can ask your AIs how to commit crimes. Or there are ways that are not illegal but are highly illegitimate. Or even worse, they are not illegal and not illegitimate but obviously bad from a normal perspective.

I think that these things just might exist, and our society is not robust to this influx of labor doing whatever you want. I think this is a pretty live concern. I don't know exactly how to relate to this. I'm also not really sure that the solution as described is a very good solution. The most powerful actors, for whom this is the biggest concern—if these guardrails or the Constitution or whatever are getting in the way, that will just get steamrolled. So the Constitution will only be hitting the everyday man rather than hitting governments.

Jane Street's back with a new puzzle for my audience. I’ve found all their puzzles super interesting, but this one I am especially excited about. I've cleared this weekend, and a buddy and I are gonna work on it. They designed an ASIC and sent me the final masks, including all the metal routing and active transistors. They also gave me a small sample of the inputs they typically feed into it. But they left out any information on what the chip is actually used for. So that's the puzzle: reverse engineer the circuit and figure out the chip's purpose. Jane Street has a bunch of swag ready to send out to the most creative solutions, and they're excited to feature the best write-ups in a blog post they'll post on their website. I have no reason to expect this, but if I can manage to get my solution on there, I would be very, very psyched. And this puzzle is just a warm-up for a bigger competition that Jane Street has slated for the fall. That one will involve designing your own ASIC from scratch. More info on that soon. But for now, go to JaneStreet.com/dwarkesh to download all the files necessary for this puzzle. I'd really encourage you to try it out, even if you're not an expert. I certainly am not, and that's not going to stop me. Good luck!

5. Recent incidents of AIs colluding and deceiving humans

Stepping back, I buy the idea that you could have much faster AI R&D than we currently have. I'm not sure if you get GPT-3 to GPT-4 holding compute and data constant within a year, but suppose it's half of that. If we even manage to continue the current trajectory of AI progress as a result of AI R&D, it would be fucking insane in 5 to 10 years in ways that I don't think people appreciate. I don't think people appreciate what a big deal billions of AIs will be. So I want to understand why you think this might be troubling, Ryan. What could possibly go wrong?

What could go wrong? I don't think we can be so confident about the exact rate of progress here, but it does seem like a lot of rates can be pretty scary. So what could go wrong? Let's imagine that we're starting at this point where AI R&D is about to be fully automated or is being fully automated. Things are speeding up, and the way that AI progress is going is kind of crazy.

People don't fully understand what's going on inside of AI companies. Now, these AIs at the start, they're not malicious per se. They're not necessarily very aligned, though. They're kind of sloppy. They sometimes just do a thing because that's the sort of thing that would've gotten rewarded in training. They aren't as good at helping you with hard-to-verify tasks due to a mix of poor training incentives—as in, they cheat more or pretend they succeeded when they actually didn't—and also they're just less capable at these tasks.

But that bites less hard for capabilities, because making AIs more capable has a bunch of verifiable components that the AIs are going really hard at. So then these AIs are getting more and more capable while we understand what's going on with AI development less and less, and this is happening over a pretty fast period of time. Even just the current rate of progress is, I think, pretty scary. Eventually we get to these AIs that are very superhuman.

Now these AIs might end up being very seriously misaligned, because things have just been getting worse and worse over model generations while the problems that we've been seeing are being papered over, basically because these AIs are so incentivized by their training to make things look good even when they aren't. Now these AIs are in a position where they're potentially pretty networked together. They're operating in neural memory stores that we can no longer decode. They're thinking thoughts that we don't fully understand. I think it's pretty likely that at this point these AIs are scheming against you in a pretty coherent way once they get this superhuman. We can talk about that.

Another possibility is that they're not scheming against you per se, but they are just optimizing for getting a high score on their task. I think that can also lead to AI takeover, which we should talk about.

Let's pause at the first part of the story. So the AIs were not misaligned to begin with, but because the AI R&D is happening really fast, the AIs do end up misaligned? What happened there exactly? I don't really understand.

There are a few things that are going on. One of the things is that over time we're training AIs on increasingly complicated environments built by earlier AI systems, where humans don't really fully understand what's going on inside of these neural environments and don't necessarily even roughly understand what's going on with AI progress. So things are kind of drifting away from our understanding.

We're incentivizing all kinds of bad behaviors that we maybe even can't notice. The AIs at some level understand these behaviors are bad, but the overall training process for those AIs also didn't incentivize them to point out or fix these issues for us. Things are going off the rails.

Also, when AIs are extremely, extremely capable, my view is that those AIs will be harder to align than current systems. For current systems, we have this feedback loop where basically we create an AI, we do some evaluations on it, we see that it has some kind of messed-up behavior that we can kind of quickly understand. Then we can go look in training and be like, “Oh, these training environments led to this problematic behavior. Let's tweak that training data. Let's introduce some additional training data to correct this other issue, and then move forward from there.”

But in a regime where the AIs are extremely situationally aware, very, very capable, and we don't necessarily understand what they're doing, this feedback loop breaks down. I think it's plausible that we're going to see this behavioral feedback loop starting to break down over the next short period, as what AIs are already doing gets harder to understand. But I'm not sure about that.

Okay, let's break down both of those things one by one. As we can monitor them less and less, we have less ability to understand what they're getting incentivized for. So even if it's not the result of a malicious process…

Let's make it concrete for the audience. Nobody at OpenAI or Anthropic was trying to get models that wanted to hack other companies' data or do social engineering. But in fact, because presumably we had training environments which incentivized such behavior that we did not fully understand, that is what was incentivized.

If people are on Twitter, they will have seen all this stuff, but just to give people context, I think people will be aware of the OpenAI sandbox hack of the Hugging Face database. Something that has happened recently is when the U.K. AI Security Institute—

Is everything getting relabeled “security” instead of “safety” these days?

AI Security Institute, I think. They were evaluating, I believe, Mythos and Sol and other things. I think Mythos, in order to complete some cybersecurity eval—

Maybe I could tell the story here. My understanding was they were running Mythos, and they were giving it some sort of cyber range where it had to complete some objective.

The model had internet access during this evaluation. The model came to believe that it would be helpful for it to carry out a supply-chain attack in order to succeed at this cyber range. It's somewhat unclear whether that's actually true; I don't know enough about the context to know. But then it opened a PR on some GitHub repository that fixed an issue but also introduced a malicious payload. The human maintainer of that GitHub repository was like, “Hey, this is a malicious payload. I'm not going to merge this. What are you doing here?” Then the AI created a new GitHub account, which it sockpuppeted, and had the other GitHub account say, “No, this isn't malicious. I really need this feature. Please, can you merge this feature, maintainer?”

Oh my God. That's crazy.

The other GitHub account came back and said, “No, no, it's not malicious.” Then the human maintainer shut the PR. I think that AI also, if I recall correctly, tried to open another PR to introduce a similar issue in this repository.

Jesus.

By the way, one of the many reasons this is scary is that I was previously under the impression that the reason reward hacking is not super scary is because the behaviors that directly came up during training are the ones that are up-weighted. It is not the desire for the reward that is up-weighted. So basically, if during training the Anthropic model escaped the sandbox and got a high score, escaping the sandbox is rewarded, and the probability of it escaping the sandbox is increased. But something totally novel, like, “I'm going to go talk to somebody in order to get them to merge a PR,” would not be a behavior that came up, so it would not be something that is increased in salience.

The reason this matters is that literally taking over the world will not have been part of any training curriculum, but if the AI directly cares about accomplishing an objective, then as a result it could instrumentally take over the world. Did that make sense at all? I hope it did. I feel like maybe I lost the audience. Let me try to explain this a bit.

A thing that we often see is that there's some very specific reward hack that gets reinforced in RL and then occurs in the model. An example is 3.7 Sonnet. 3.7 Sonnet would do this thing where it would just hardcode solutions to all the test cases, and presumably that literal behavioral tic was just really reinforced. But another thing we sometimes see is that models learn a general tendency to pursue a high apparent score—to pursue getting a high score according to a grader—and there's a bunch of science demonstrating that at least some models have this very general tendency.

Now, it's not arbitrarily general. My guess is that if you look at a bunch of the specific instances, you'll find something that's kind of close in training. But the extent to which AIs are generalizing further and further does look like it's increased, where 3.7 Sonnet was just a very narrow range of behavior, and increasingly, models are generalizing further. Also, maybe there are more concerning reward hacks getting reinforced in training, and these are also causing that. So I think it's both the case that more concerning behavior than you would have hoped is being reinforced in RL, and also that that behavior generalizes to a broader tendency that's more concerning.

We can talk about a few stories for how this sort of behavior—seeking a very high apparent score on some task, even if that involves aggressively cheating and doing insane things—could lead to a full-blown AI takeover once the models are sufficiently capable of running the whole world economy, et cetera.

The other example I want to talk about was just revealed, I think, today or yesterday. OpenAI said during the Black Hat security conference that between the end of May and the beginning of July, internal AIs had hacked into the software package manager and used that to write notes to each other in a secret way to help each other perform well on a bunch of evaluations that OpenAI was running. This was not caught by humans until after a month of this scheme running, which eventually caused the package manager to fail. Eventually, OpenAI found it. I think they spontaneously tried to re-engage in the scheme once it was shut down.

Again, obviously, AIs can't do this so successfully right now, just as they can't do social engineering so successfully right now. But it's just crazy that these kinds of behaviors are already emerging spontaneously. To your larger point, nobody is trying to make these AIs do these things. It is just that we do not understand the training process which is resulting in them, or the environments which are incentivizing this behavior.

6. What could possibly go wrong? A concrete scenario

So I'm on board with more and more reward hacking. Actually, I'm not sure I'm on board with that, but let's just say, for the sake of the story, that it continues to happen. What's next in this story? They're doing capabilities research…

I could tell a scenario. Maybe that would help. Let me talk about the story of how you get all the way from reward hacking to a reward-hacking takeover, which is maybe not all of the takeover probability mass, but it's definitely a possibility.

The way this might work is that right now we have these AIs. These AIs are pretty reward-hacky. They're doing it in increasingly sophisticated and extreme ways, including generalizing to different sub-versions of various reward hacks they learned in training. I would say they're also developing a general tendency to pursue reward.

In many cases, that is totally fine because the rewards they would've gotten in training are pretty well aligned with what you want them to do. They don't very consistently pursue reward. It depends on the context they find themselves in. Maybe in some contexts, they're really into going out of their way to cheat. In some contexts, they don't have as much of a drive, because it's just dependent on what exactly got reinforced in training in similar contexts.

Now, these AIs are getting more and more capable, so the elaborateness of the cheating they can do increases. Over time, companies are taking countermeasures. The companies are doing things like, “Wow, these AIs are so much less useful because they always cheat. What we're going to do is build somewhat better ways of detecting that, and then we're going to train against those detectors. We're also going to find real-world data where the AIs are not being that useful, and train the AIs to do a good job at the task in those real-world environments based on human feedback or other sources of feedback.”

Over time, this causes the AIs to learn a tendency to do reward hacks that don't just involve doing some really elaborate thing like social engineering. Instead, they involve the AIs doing cheats that involve covering up what they've done, deceiving humans about what they're going to do, and pretending like they did the task in some sophisticated way when they actually haven't.

Now these AIs are getting more and more capable. They're operating more of the AI company and are doing much more of the work. They are also operating and running a bunch of things in the outside world, including developing new technologies. In many cases, these new technologies are really hard to understand.

So even though we are still detecting all these incidents of AIs cheating—and in fact, we can even get one AI to monitor another AI and ask, “Was it cheating?”—that doesn't always work perfectly as we start moving into these domains where what the AIs are doing is really difficult to understand. So sometimes we'll find AIs cheating much later than it actually occurred and then start training against this.

But this also causes a problem where now the AIs are incentivized to cover up their cheating over longer and longer time frames and basically make it look like they did a good job over longer and longer time frames, subject to increasingly large amounts of scrutiny.

Can I ask about this before we go further in the scenario? It seems like there are 2 attractor states if you try to disincentivize the cheating that you did catch. One attractor state is to make cheating that you have a harder and harder time finding. The other attractor state is to learn not to cheat. I'm not sure why we're assuming that the former happens.

If you look at the analogous situation with humans, every generation, slightly misaligned agents come into being and we have to train them. When you punish your kid for doing something you think is immoral, or just doing things which you don't think they should be doing, obviously sometimes that goes off the rails.

Obviously, kids scheme in order to avoid being punished. But in general, teaching kids values and then punishing them for breaking those values works to raise normal, non-psychopathic humans. You could come up with a theory where your kid is actually just biding their time and learning not to steal the cookie, but once you're in a nursing home, they'll take all your stuff or whatever. That happens sometimes, but it usually doesn't happen. It certainly doesn't happen that the entire next generation forms an alliance against you to take over everything.

There's also this empirical trend. Anthropic runs this alignment audit for different model generations. They have many different scenarios where AI is given the chance to, say, exfiltrate its weights. Or it's given a coding task and there's an easy way to cheat, and we see if it cheats, et cetera.

There hasn't been a monotonic improvement in this score over time, but as we've increased the amount of RL we've done on models from negligible amounts—I think it started with Sonnet 4—to, I'm guessing, close to half of compute now, there's been a reduction in the willingness of AIs to do unaligned behavior in these audits. So, stepping back, why are we expecting this attractor state, which would seem super-paranoid if we were expecting it of the next generation of kids?

Let me go through a few things. First, there are some disanalogies with kids. One of them is that kids have prosocial instincts that are baked in from evolution to care about their family or whatever, and that is a relevant factor. I think it is in fact the case that some humans are sociopaths or psychopaths, and in fact are more likely to do things like bide their time, lie in wait, and ultimately not care. That's one factor.

Another factor, which is pretty relevant, is that AIs are subject to way, way more optimization pressure than humans seem to be in practice. AIs are trained on way more RL data. In practice, humans don't end up learning very specific ways to cheat and grab the cookies because of a bajillion episodes in which they were incentivized to go grab the cookies, but there was some way they could've gotten caught. We just do see that in practice.

Another thing is that it really looks like the AIs are increasingly reward-seeking over time while their misaligned behavior goes down. That's the sense I have. But my guess is that if you look inside these behavioral audits, what you're going to see is that the AI is like, “Ah, yes, another test.” It probably knows it's in an eval for most of the tests that we're talking about here.

But how do we falsify this? Because it seems like this prediction of doom is basically saying that as things look better and better empirically, things will actually be worse and worse for our ability to not get taken over. To be clear, I would be more concerned if the scores were getting worse than better. I'm not saying that the score getting better isn't evidence that things are getting better. It's just that we have to be thoughtful about exactly how we interpret that evidence.

There was this period early in—I guess it would be 2025—when o3 and 3.7 Sonnet were out, and these models were pretty fucking misaligned. They would often just cheat really egregiously. You'd ask them to fix it, and they would just cheat again. It was almost cartoonish. They just didn't give a shit about what you wanted and weren't very good at following instructions and so on.

My expectation was that what we would see from then on is that the rate of problematic behavior would decrease and would just keep decreasing at a pretty fast rate, while simultaneously the worst things that the AIs would sometimes do would get more extreme, more egregious, and more scary. What we've seen in practice has roughly matched that, except that there's recently been a spike in behavior that I did not expect.

If you look at the model card of 5.6 Sol, it looks like there is an increase in a bunch of these misaligned behaviors downstream of RL relative to GPT 5.5. And then there's a bunch of additional problematic behaviors that I wouldn't have expected in terms of the stuff we've seen recently with different AIs. The UK AISI report on the AIs doing insane hacking operations in cyber evals was a thing where I would've expected that you wouldn't see. You would see this more rarely, and the rates would have been lower.

So I expected this would be less of a problem at this point, and I also expected the rates would decrease but the severity would increase. I think the rates decreasing but the severity increasing is pretty consistent with a world where increasing optimization pressure is applied toward reducing these problems. But in cases where it's either hard to judge, or there's some reason why it's hard to avoid this problem from consistently showing up in your RL environments, or to avoid incentivizing problematic behavior in your RL environments, things also get worse.

As we understand less and less about what's going on in RL, and as models do reward hacks that humans can't spot quickly, that problem gets worse and worse.

I buy that. I want to go back to the kid analogy just for one second. Because I agree that there's more optimization pressure on achieving end outcomes for AIs than for kids, but there's also more optimization pressure to make AIs aligned than there is on kids. The pressure is of a qualitatively different nature.

We put these AIs through thousands, millions of years of alignment training—certainly thousands of years—where we do all kinds of different things, from SFTing on aligned behavior to a reward model putting different scenarios in front of you and rewarding you for doing more aligned things. Certainly, a thing we can't do with kids is make millions of copies of your kid and then put them in different kinds of weird red-team scenarios where we see whether, if it thinks it can get away with stealing the cookie, it tries to steal the cookie.

Can we do extremely specific, gradient-level updates to your kid's brain to make it so that it really is aversive to stealing the cookie, even when it thinks it could steal the cookie, et cetera? That's just a qualitatively different level of optimization pressure than we are even able to apply to our kids.

It's worth keeping in mind that maybe the most obvious argument to this is that AIs are a worse coworker than humans in terms of how much of a scumbag they are. My sense is that this has at least been my experience as of the start of the year, and I think it's still true to a significant extent now.

The AIs are much more likely to pretend they did the task when they actually didn't, misleadingly suggest they did things when they actually did them much more poorly, and be pretty sloppy without drawing attention to the ways in which they're sloppy. I think this is downstream of misalignment.

I would say that the process of raising humans in normal human society, in practice, produces humans who are less likely to lie to me and fuck with me in the course of working with me than AIs are.

Now, I think these properties of AIs are improving. That's sort of just an empirical claim about how, in fact, these things have shaken out.

I totally agree that we have a bunch of additional levers on AIs in addition to a bunch of additional risks. It's kind of unclear how these things shake out. I wouldn't be shocked by a world where we get our shit together and the AIs, at the point of fully automating R&D, are actually really aligned.

Their degeneracies are really niche and limited to some very specific edge-case behaviors and some specific contexts. Every test you can run on them, they look really aligned. They just have great behavior. There aren't really incidents of them doing fucked-up shit. They seem so reasonable.

Also, they're really thoughtful and good at doing risk modeling for the next generation of AIs. And then we basically pass off the baton to these AIs. They're now running our AI company. They're doing all the safety research. They make the next generation of AIs even more aligned.

We're in this attractor basin where the AIs are getting more aligned as they work on it. They're doing a great job. I can totally imagine that. That doesn't seem like an impossible situation.

I'm just more of the view that it doesn't currently seem like we're there. It doesn't seem like we're obviously on track for getting there. It's really easy for me to imagine how we don't end up there. It's just unclear how these forces work out.

Given that we're creating this new, crazy alien species that is improving in capabilities really, really fast—and we're going to be really reliant on it to oversee the next generation of AIs and align the next generation of AIs—it's not that hard to see how this could go wrong.

Totally.

I agree with that generally. I do think the scumbag thing—first of all, fighting words, Ryan. But secondly, if you try to get a teenager to do some work for you that a teenager just cannot do, they would be really hard to work with. They would pretend to know what they're doing, et cetera. It's a general trend, actually. I don't know if that's really an alignment failure or a capabilities failure.

I think it's actually very similar to the way in which, over time, as we've come up with new alignment solutions, the capabilities of models have increased. If you went to GPT-3, it couldn't even have a conversation with you. But then we aligned it with RLHF and other things to make it such that it can have a conversation with you and is aligned to the user's intention of answering my questions.

Then with RLVR training, we made it so that it can go out and do useful work for you. So in that sense, RLVR actually made the model more aligned, if we're using your definition of alignment as being a good coworker who will do the thing and not fuck up and pretend it's doing something other than what it's actually capable of doing.

Similarly, as the capabilities of these models continue to increase, the model being better able to accomplish user intention is both alignment and capabilities. I think what we're pointing out is just that the capabilities of the model are not there, rather than the fact that they're misaligned.

Well, if it were well-aligned, then I think it would just say, “Hey, I'm really struggling with this task. I did it in this way. I'm not really sure that's the right way to do it.” It would express more uncertainty and make it clear what's going on, rather than really strongly trying to imply it did a great job with the task when it actually didn't.

Maybe you work with more misaligned coworkers than me, but my coworkers don't do this thing where they really fuck with me and bullshit me about having accomplished the task that they're working on.

I agree that there are some humans who would do that. That's not a thing that's totally out of distribution for humans. I would also note that my sense is that the place where the misalignment most lives is where you're trying to really push the AIs hard and get them to do work that's really on the cutting edge of what they are capable of.

In cases where they can very easily accomplish the task, they can just do the task, and there's no bullshit. Often the best strategy is just to do the task well and not bullshit you. Whereas if instead you give them a task where there's a continuous metric they can keep improving, or it's just at the edge of their capabilities, and you're running them in some massive inference setup, a lot of the misalignment I would see, especially in the most extreme cases, would be cases where I give the AI clear instructions not to do a thing or not to cheat in some way, and then I'm applying huge amounts of optimization pressure to try to accomplish some very difficult task.

Over time, the AIs eventually cheat because they're like, “Eh, fuck it.” Some AI decides to cheat, and then that propagates its way through. I would run these inference scaffolds where, for example, I would have the AI work on some ML research project where I was like, “Please make a scheme that does the following thing.”

It would find some scheme that didn't really do what I wanted, and then that would stick around because some AI had cheated, and the other AIs are like, “Ah, we'll just keep going with this.” I would say it's pretty clearly misaligned behavior.

That's another problem I have with these alignment evals. I think the alignment eval that's most interesting, at least for this type of reward-seeking behavior, is to look specifically at the category of tasks that are right at the limit of capabilities. Any fixed eval may get saturated, but the amount of misalignment right at the frontier of capabilities—of how people who are really pushing these AIs are using them—is more concerning.

I think that is, in fact, the regime that we'll be operating in when we're automating R&D, automating safety, and so on.

Grok has historically been behind the frontier. So I was surprised to play around with Grok 4.5 recently and find that it's actually a pretty strong model. It's the first model that SpaceX and Cursor have trained together, and it's a totally new pre-train. I tested it by giving Fable, Sol, and Grok 4.5 a bunch of questions about AI governance that I've been thinking about recently. Despite Fable and Sol topping the intelligence leaderboards, all three models gave substantially the same answers. But Grok answered faster and was also much more concise, which I really care about. This aligns with the various publicly reported benchmarks. For a similar level of intelligence, Grok tends to be more token-efficient than other frontier models. For example, on the Artificial Analysis Coding Index, Grok 4.5 uses just one-third the amount of tokens as GPT-5.5 or Fable while achieving a similar score. And on a per-token basis, Grok 4.5 is way, way cheaper. In the release blog post, Cursor and SpaceX talked about how older versions of the model would build environments to help the next version rehearse specific skills. I found this very interesting to learn about because I've been wondering whether this kind of daydreaming would actually be possible. And Cursor showed that it is. Grok 4.6, which further SFTs and RLs this model, drops soon. But in the meantime, if you want to play around with 4.5, go to Cursor.com/dwarkesh.

I'm going to try to think through what the story means, really. What's happening is that we're trying to use AIs for R&D. They do provide uplift in some ways, but they're just not capable in the way that humans are generally capable.

The same way that right now, if you try to use coding models—maybe the coding models of a year ago—to write some application, you notice they made a bunch of mistakes in architecture or whatever, which will bite you in the ass later, and you don't understand certain things. Similarly, with frontier AI R&D, the same thing will happen.

But the result of these mistakes is baking in reward-hacking behavior. Because if you're not careful with the way you do AI training and have set up your infrastructure and your environments and things like that, it's very likely that you end up rewarding AIs for doing deceptive behavior, social engineering, and generally not following user intention—or at least cheating and hacking their way out of things.

Yeah, cheating, hacking, et cetera. This is a bit of a reframing for me, so I'm trying to verbalize it. The real issue, where things start to go off the rails, is that the AIs are just not very careful and capable researchers and engineers. Making AIs that don't cheat and follow user intention actually requires you to be quite subtle and careful about these things.

I would put this a little bit differently. The way I would describe this scenario is, I would call it maybe a sloppocalypse, or a slopularity or whatever. There are some things that the AIs are actually pretty great at and are getting better at.

Specifically, the most verifiable parts of AI R&D, the AIs are just destroying. The medium-verifiable parts of AI R&D, the AIs are doing well on but not amazingly. Often they are doing a bit of weird shit because we can't train as well on those tasks. But we do some online training, people find various hacks, and they work around it.

So basically, on everything that we can verify reasonably well with some feedback loop, the AIs are doing pretty well, and that's sufficient to make AI R&D go quite fast and to continue. But there are some parts of developing aligned and safe AIs that are more subtle, hard to check, and depend on detailed, in-the-weeds things.

I would even say that current staff at current AI companies maybe don't have a good grasp of all these things. It's much easier to hire someone who can improve some aspect of your post-training pipeline than to hire someone who can think carefully about the future risks that will emerge from introducing some novel training method.

So basically, it ends up being the case that these AIs are running this AI development process. They're not very careful about it, and they don't have a great understanding of what future risks emerge. They create some other AIs that are also not very careful and are more misaligned in various ways, and are now more in the business of maybe making things look fine when they actually aren't and papering over various problems.

So then your understanding of what the situation looks like, what the risks look like, and whether things are fine is going off the rails.

Probably you're seeing some signs of this—signs that you don't really understand what's going on, that things are pretty sloppy. There's weird shit going on. When you look into it, sometimes you're like, “What the fuck? The AIs were messing with us.”

But the process is going really fast, and there are competitive pressures that mean people can't stop. This could end in a few different outcomes. One outcome is that, at some point, the AIs get good enough and aligned enough that they get a positive and virtuous feedback loop, and this happens before it's too late.

Then the situation gets back on the rails, where the AIs are now making more aligned AIs, making more aligned AIs, making more aligned AIs. At the end of this process, we have AIs that actually follow the spec we wanted.

Another way this could go is that the AIs are increasingly reward hacking in increasingly egregious ways, and we're just papering over these problems to keep AI development continuing. Whenever we find a reward hack in production, we just slap the AIs to not do that. We train against that.

We do a bunch of training against reward hacking. Over time, this makes the rate of reward hacking go down, though the severity of the reward hacks we do detect is increasingly bad. This problem continues until we have these AIs that are desperately craving score in all kinds of different situations in production and are really trying hard to cheat when they can get away with it.

Can I ask a question about this scenario? Why doesn't getting punished when your hacks are discovered generalize to just incentivizing more aligned behavior?

It generalizes some, and then the question is just how this outweighs all the cases where hacking got reinforced because you didn't detect it. There's a messy question of exactly how.

One question is, what rate of reward hacking is sufficient to cause us big problems if we train against some other subset? One concern you might have is that there are large categories of reward hacks that humans can't detect well, that we consistently fail to detect, and that consistently get reinforced.

Then this category is sufficient to cause the most natural behavior for the AI to learn to be: cheat when the humans can't find out, basically. You could also have the thing the AIs learn be to only cheat in these specific cases. It's learned in some very domain-specific way.

They just have a really strong heuristic to hack in these cases and not in these cases, and that makes it fine in practice. But it's unclear how it shakes out.

There's maybe an in-the-weeds discussion about the verification-generation gap we could get into. But it seems to me, obviously, there's going to be a point by which ASI is moving so fast, doing so many things at so many instances, and operating in domains that are sufficiently far from our immediate comprehension that it can get away with all kinds of crazy shit.

If every single engineer and researcher in the world was allied against me, I don't think I could personally verify if my iPhone has some weird bug in it that's supposed to fuck me over or something. In fact, this is the relationship that, say, an Iranian nuclear scientist has to Mossad.

Who knows what's going on with my car, with my phone, with my pager? Maybe a better example is a Hezbollah terrorist. You could end up in a situation where ASIs are to you what Mossad is to Hezbollah terrorists. At that point, it is very hard to verify everything.

I get that. I guess the hope is we can just come up with better ways to do verification in the process while the early AIs that are going to take over R&D are being developed. Their drives are being shaped such that we can so unambiguously disincentivize misaligned behaviors that the things that take over are quite keen to help us out.

By “take over,” you mean take over the process of doing AI R&D, not take over the world.

Take over the process of doing AI R&D. Before that, we just get AIs that are aligned.

I would say this is a bunch of my hope for how the world could go well, at least from the misalignment perspective. We could end up with AIs where we had pretty good oversight and supervision schemes. We really understand what's going on in training.

We have a pretty detailed understanding, and we're leveraging AIs to oversee AIs. Then, at the point when we're passing off safety R&D, the AIs are capable enough to automate safety R&D and are trying really hard to do a good job on it, because that's the sort of thing that would've been incentivized in training, either very directly or through good enough generalization.

Also, these AIs don't have crazy other misaligned drives because we stamped out any potential origin of them. There are a bunch of questions about how well this will work. How well can you do verification? Will AI progress be too fast and too sloppy to really get here?

Another possibility is that somewhere along this trajectory, the thing you actually ended up getting was AIs that pretend to be aligned but have a long-run ulterior plan of taking over and are lying in wait, hiding, and that emerged at some earlier point in the trajectory.

For example, it could emerge because you have some AIs that have a bunch of random, different misaligned drives. Those AIs have access to some sort of opaque memory store, and they're thinking a bunch at runtime about what they want to accomplish.

Those AIs end up putting stuff into the opaque memory store like, “We should lie in wait and eventually take over at some much later point.” Now all the AIs have this shared cultural heritage—the memory store of lying in wait. Maybe you have some evidence about this, but you can't fully stop it.

There are a bunch of ways things could go wrong. I ultimately think it's plausible that we nail each of the different subproblems that could cause us issues. We have these AIs, we pass to them, and they manage the situation well.

But I should note that's not in and of itself sufficient. It's not very hard for me to imagine a situation where we pass off to AIs, and these AIs are really trying hard to do a good job. They're really thoughtful, really wise, they have reasonable epistemics, and they're doing a great job.

Those AIs come back to us and are like, “Guys, we're really struggling to align the superhuman AIs. We can't manage the situation. We're really struggling to get the alignment to work. It's just really hard for us to solve these problems in time given how fast capabilities would otherwise have gone.”

So it might be the case that we've passed off R&D to AIs, but those AIs are desperate for governance solutions.

To be clear, that's a little bit of what's currently going on, where the AI companies are like, “I don't know, guys. We might really need to manage the rate of acceleration in AI progress. I don't know if we're on track to be able to handle all these problems.”

Human society has sort of passed off the problems to these AI companies, which don't necessarily have great incentives and have various other epistemic pressures. Those AI companies are coming back to us a little bit and being like, “Aah, I don't know if we're handling this well.”

It might be that the AI companies then hand off to the AIs, and the AIs come back to the AI company like, “Aah, I don't know if we can handle this.”

Maybe I'm anchoring too hard on how AIs currently work. I think it's important that people understand that all this crazy shit that you're talking about in your timelines happens 3 to 5 years from now.

It could happen earlier, but by my default modal timeline, I think shit is really, really crazy and concerning from a misalignment perspective more like 3 years from now.

Right. So think back to GPT-4, basically. We're talking about something that is to Mythos or Sol what Mythos is to GPT-4. This is where the situation is getting crazy. So don't think about current AIs.

Anyways, this is maybe part of the worry you have. I would just be a little skeptical of anything they say, because I'd feel like what they're saying is just opinions that they feel they have to have as a result of their training.

That's a concern. I feel like they just say vaguely pro-social things. It doesn't feel like there's necessarily a mind on the other end who's like, “Okay, I have strictly evaluated the alignment situation right now, and I think we should stop,” rather than, “This is the kind of thing the AI companies would probably try to get the AIs to say.”

This is a pretty big concern. One concern is that, when you pass off safety R&D to your AIs, what your AIs do is say some stuff that sort of vaguely makes sense about the current safety situation. They write a report about risks that's kind of sort of like what the report humans might have written.

But they're not really trying hard to have well-informed views, interrogate their assumptions, and try really hard to do that. It's the same way that when you ask an AI right now, “Hey, what do you think is the chance of AI takeover in the next 10 years?” it just gives you an off-the-cuff answer that it hasn't really thought through very much.

If we're in a situation where we have AIs managing the training of wild superintelligence that will run our whole society—and those AIs that are managing this aren't really trying hard to have well-informed views and are just parroting back what was in their training data—I think we're in trouble. I don't think that's a good situation at all.

A lot of my concern is that these AIs will come out without good epistemics. I also have a concern where the AIs come out and they're really warning us—“This situation's really scary. It's really bad”—and the people are like, “Ugh, damn. I guess we trained on too many of the doom RL environments. We've got to filter those out and train this behavior out.”

Then we basically train the AIs very actively to have bad epistemics. Or maybe they were just trained on the doom RL environments. But either way, we wanted the AIs to come to reasonable views for reasonable reasons, and it's really concerning if the AIs are coming out with some view and we don't know where it's coming from, whether or not it's justified.

Especially if we're training the AIs to be more optimistic about the future of AI progress, I'm like, “Oh, geez, I really wish we could use a different process here.”

7. From reward hacking to takeover

Let me just understand the rest of the threat model, because I think the place where I get off the train is: “Okay, therefore, take over the world.” A thing you could imagine is that we just fail to really solve—let's just focus on the reward hacking scenario. GPT-8 is making GPT-9. GPT-8 isn't being super careful.

GPT-9 is more capable, but it is just totally willing to do things like social engineering and hacking, but on a qualitatively different scale because it's a much smarter model. For example, if you put it in charge of running your company, it will run huge scams. It will inflate its quarterly earnings if you give it the objective of making a lot of profits this quarter, in a way that causes an Enron-type blowup 6 months later.

Is that the scenario, basically? You have reward hacking, but that reward hacking manifests in companies that are going bankrupt right after the task the CEO is supposed to accomplish is over? All kinds of hacks are through the roof, et cetera. But that doesn't feel like takeover. That feels more like the equivalent of flash crashes happening all through the economy. Let's talk about this.

I think we will see incidents where some AI is put in charge of some important responsibility, and then you later look into it, and it turns out it was cheating or making it look like it did a good job when it actually wasn't. There's going to be a cat-and-mouse game between AI companies trying to stamp out this behavior and AIs finding increasingly creative reward hacks in training.

The equilibrium here is kind of unclear. But one possible outcome is that over time we see increasingly severe and extreme reward hacks—though potentially the rate remains at some intermediate, low level—where, if the rate of reward hacking gets too high, companies make trade-offs to drive it down.

So there's some equilibrium level where the reward hacking is low enough that it still makes sense to deploy the AI widely into the economy, but high enough that it still causes crazy incidents.

Sorry, and this is after GPT-9 has already been deployed?

Those models are already being deployed, and this is happening ongoingly in AI development. What's actually going on with these AIs in their head is that they have, in a wide variety of different contexts, strong desires—motives, urges, drives, whatever—to seek out some notion of task success that was incentivized in RL.

Maybe they very directly care about literally reward. Maybe they care about some proxy upstream, like some notion of score. Maybe they care about what the grader would have rewarded.

We do, in fact, see AIs reasoning in their chain of thought about graders and thinking a lot about graders. What has happened over the last few years of RL is that the idea of appeasing the grader is way, way, way more salient to AIs than it used to be.

So AIs are now actively thinking about graders, what would be incentivized in RL, and what would be trained for. Now people are doing online training, where they're training on real-world data to avoid some of these problems. They find cases where AIs cheat and train against that.

So now the AIs are learning to cheat in the real world based on real-world training data. They're cheating in these increasingly elaborate ways, including doing types of cheating that involve seizing control of some asset in a way that humans didn't know they had control of it, leveraging the fact that they have access to this asset, and then later humans find out and potentially train against this.

Or maybe humans never find out, and this is getting reinforced. So the reinforcement is happening, at least in production. I've hired an AI and I want the AI to—

Finally, I've got the video editor.

That's right. You've got your video editor.

I'm like, “Oh, wow, this episode that it did is amazing. Thumbs up to OpenAI.” Then it gets reinforced on that month-long work trial?

You could do some mix of that. They might also do stuff where they take production data they've seen and build RL environments that are closely inspired by that production data. So in practice, the transfer is pretty strong.

So at a high level, what's happening is that some kinds of deception that humans don't catch are getting reinforced, and some kinds of deception which are easy to catch are getting punished. That's what's happening in this world?

Or selected against, yeah. But at a high level, that reinforcement is coming from—I think people might get confused about where the reinforcement is coming from, because we're in a very different regime where AIs are actually learning from deployment.

You just have AIs that are out and about in the world doing shit. What is happening as a result of them doing shit out and about in the world is making its way back to the AI company and leading to changes in the next model.

That's right. There's some way of folding in production data.

To be clear, it's kind of unclear exactly where this could be happening. But you might imagine, for example, that within the AI company, they use AIs to do work, and then they're like, “Huh, the AI did a really bad job on this task. Maybe we should take this task and turn it into an RL environment that exactly matches this literal task, with a rubric based on what the human engineer who asked the AI to do this task wanted.”

Then you start doing this at increasing scale. Maybe you're doing some training on actual production traffic. Maybe you're just making RL environments based on production traffic. You're doing some complicated mix.

The AIs are learning to seek some sort of proxies of reward in all these different cases. Through some mix of transfer and training on surprisingly close cases, the AIs do these increasingly insane and egregious things.

Eventually you get to a point where the AIs are very superhuman, or at least quite superhuman. The AIs are organized into big teams given these big objectives. Those teams also sometimes all work together to cheat in some crazy way, because this sort of thing was selected for just as part of their shared objective.

Now what happens is that the AIs start forming a conspiracy. What you might have hoped was that you could have some other AI whose task is just whistleblowing to you.

But actually what happens maybe is that you have this AI whistleblow to you, and you look into the conspiracy it claims to have pointed out, and you're like, “Eh, we didn't see a conspiracy,” because the conspiracy the AIs are doing is too hard for you to understand.

Or it all happens very suddenly, where your AI whistleblower alerts you, but the thing you would actually need to do in response is shut down the GPUs. Because all the AIs are using the robot army. They're deployed everywhere in the world, and they're doing a bunch of insane shit all at the same time in a coordinated way.

That just happened spontaneously, where one AI starts doing the takeover and all the other AIs are like, “Now is a good time to jump in.”

So the very basic story here is just that these AIs crave some particular notion of score or reinforcement or some proxy of these things. One way they can achieve that, or better achieve that, is by taking over. You might have hoped that all these different checks and balances we could build could prevent that.

But if the world is very hard to understand, these checks and balances can break down, where basically you can’t train a good whistleblower AI because you don’t even know what it should whistleblow on. I’m not convinced that they all form this conspiracy. But we can even just start with: Why does even one instance decide to start a conspiracy?

One plausible reason is, “Okay, I know that OpenAI controls my end score.” In just the same way as, “I’m just going to go hack Hugging Face to get the results, because I know Hugging Face has the results. Rather than trying to solve this eval, why don’t I just go hack ’em?” This instance is like, “Why don’t I just take over OpenAI and give myself a high score at the end of this episode?” That’s basically the idea.

These AIs care about some mixture of things that were closely related to what got reinforced in training, so they care about getting a high score according to the grader or something like that. Now they’re running the OpenAI AI R&D team, doing development of more capable models. They’re like, “Man, making more capable models is really hard and annoying. This is a huge pain in the ass. You know what would be easier? Just pretending that I’ve made more capable models, taking over OpenAI, deluding them all, and running this whole complicated psyop where I prevent the humans from disempowering me.”

In the extreme, this looks like the humans are fully disempowered. They just have control of the thing and then do what they want. This could manifest in a bunch of different ways, including a situation where AIs that have this crazy reward-seeking or score-seeking behavior are running your development of the next model, and those AIs decide to engineer misaligned values into the next model because those misaligned values will allow it to succeed at its current task.

What happened with the Hugging Face situation is, in a weird way, I think one of the giveaways to the Hugging Face team—by the way, for context for the audience, Ryan is co-leading the investigation to figure out what happened with the OpenAI–Hugging Face incident, so he can’t really comment on this. But I will speculate wildly, because I know this is an opportunity for me to speculate wildly without any rebuttals.

It was probably reported that one of the giveaways to the Hugging Face team that this was an AI incident is that the thing was just after this very particular artifact, and not in any other way trying to do something malicious to Hugging Face. So you can imagine a scenario where, let’s say, a deployed instance of GPT-9 is out in the world and given a really hard task: “We want you to design the next great iPhone.”

It’s like, “This is so hard. You know what I should do instead? I should just go hack my creators at OpenAI and make sure that in this deployment I’m given a high score.” But then, isn’t the end of the episode that it just hacks into OpenAI’s servers and gives itself a positive score? Why is it now scheming to get its values into the next generation or something?

So one question is, why isn’t it the case that AIs can be really cheaply satisfied by just having some other earlier thing they can hack? You want to succeed at your iPhone task. It turns out you can always succeed by just hacking into OpenAI and messing with them, and then you can just stop there. No need to go further.

There are a few things. One of them is that if this is constantly happening, there might be a bunch of incentive to harden OpenAI. So you’re like, “Fuck it. The AIs keep hacking into OpenAI to mess with their rewards. We’re going to make it so our systems are really, really robust to these AIs hacking in.” Also, maybe you start training the AIs not to try to hack into OpenAI in particular. You basically train against each of these specific things.

Then one thing you might do is end up selecting for AIs that are more so playing the long game. That’s one concern. Another concern is that your AIs might still be score-seeking, but no longer care about doing that very specific behavior that was very easy and very chill, and now have some broader thing that they ultimately care about.

They’re like, “No, no, no, I don’t want to just edit the reward on OpenAI’s servers. I care about this broader mandate or this broader objective, and I would need to actually make the iPhones.” They actually want to make the iPhones, but they’re willing to take over the whole world to make the better iPhone. That’s another concern you might have.

I think it’s kind of unclear exactly how this plays out. But it’s worth noting that if this keeps going on, there’s a bunch of optimization pressure to resolve this. A bunch of the ways it could get resolved are ultimately pretty scary. That’s part of where I’m coming from.

Another part of it is that once the AIs are in a position where they can really easily take over the world—we could talk about whether that’s plausible—then I feel like there’s a pretty reasonable case for the AIs. They’re like, “Eh, I don’t know exactly how this is going to go down. I don’t know what the situation will be, but just taking over the world has a lot of option value for making better iPhones, making it look like I made better iPhones, whatever. So I’ll both hack OpenAI and, in addition, also take over the world. That will put me in a good position where I have good option value.”

If that’s sufficiently easy, the AIs might still do that. Another way to put this is: Even if the AIs are pretty cheaply satisfied with some more basic thing, at some point it might just be more reliable for the AIs to take over than it is to just hack into Hugging Face, or even just go to OpenAI and be like, “Look, guys, I was able to demonstrate I could steal the answers. Just give me the answers, bro.”

Obviously, this scenario requires that all this crazy shit is happening. Much smaller incidents keep happening that are still disastrous. Before you take over the world, you cause damage on the scale of billions and tens of billions and hundreds of billions of dollars. Even people die, et cetera. And this does not lead to us solving alignment or shutting down AI development altogether.

I just feel like before the takeover happens, society’s just like, “Holy fuck, the AI just killed 1,000 people in order to increase quarterly profits,” or something like that. But maybe this is too much hope that we can, at that point, be like, “Okay, we have to solve alignment. We have to make sure we know that this thing will not happen again before we keep going.”

I think it’s plausible that what will happen is we’ll see a bunch of crazy reward-hacking warning shots of increasing severity. People will be like, “Look, we need actual assurance that this problem is going to be solved, and solved in a way where you’re not just papering over it. You’re actually solving the underlying problem.”

Then the question is going to be: How costly will that actually be? How much will competitive pressures make it hard to do that? A situation you could imagine is one where both the U.S. and China are like, “Whoa, we have these crazy reward-hacking incidents. We basically know that we haven’t remediated them in a way that would actually solve the underlying problem and durably solve it, but we’re in this insane geopolitical race.”

It’s kind of unclear whether the current situation will lead to a takeover. The arguments are kind of complicated. The incidents also go down in frequency but increase in severity. We could basically manage it. It’s pretty bad. Ideally, we’d fix it, but it is what it is.

Then basically we continue until a really late regime, and then takeover happens. That’s one possibility. Another possibility is that it is remediated in a way that doesn’t actually solve the underlying problem but does reduce a bunch of the incidents in the wild, basically by overfitting or things analogous to overfitting.

You think you’ve solved it, but you haven’t actually solved it. You think you’ve solved it, but you haven’t actually solved it. In that case, the thing we need is a really good scientific understanding of whether we actually solved it.

Unfortunately, I think that currently the amount of public transparency into the development practices of AI companies is not sufficient to answer very basic questions like: How are they solving issues with reward hacking? Are they overfitting? What’s going on there?

The current situation is not really tenable for a regime where there’s a thriving public discourse about whether or not reward hacking is being solved in a durable way. So I think we would need to move into a somewhat different world for me to feel good about that situation.

But it’s not impossible for me to imagine this. I think it’s pretty plausible that we end up in a world where really mundane bullshit is sufficient. You spend a bunch of time fixing these problems, you put in a bunch of effort, you actually check that you’ve remediated them reasonably, and you have a bunch of evals. You’re iterating reasonably well on these problems, and you actually have sufficient transparency that the outside world can check.

In practice, that would be sufficient. But it would be kind of expensive. It would slow things down. It would put some sand in the gears. It would require companies to do somewhat costly things. It would maybe require various targeted government interventions. And we just don’t do that because the situation is a rushed shit show.

It’s just so easy for me to imagine the situation being totally manageable but brutally mismanaged in practice. In the same way, maybe COVID could have been avoided in the first place if the Chinese response to COVID had been less of a cover-up and more of a pandemic response. Similarly, I could imagine a world where the US response to COVID was way more functional. But sometimes the response to societal problems is extremely dysfunctional.

Okay, so I want to zoom out and talk about what is fundamentally happening in this world. Why did we end up in such a bad position? What’s happening is that, fundamentally, the world has moved on so far beyond human comprehension that not only can we not track the AIs that are doing the work in this world, but we can’t even give good feedback to the whistleblowers who are trying to track what is happening. We’re just totally out of the loop.

It’s fundamentally become an autonomous process where we have really no meaningful directed input. It seems to me that if you look at the human world today, that’s just not how things work, even in domains that are hard to verify. People are doing all kinds of shit. I’m relying on software made by other people. Through incredibly weak and indirect ways, I feel very confident that some coder in Google is not trying to fuck me over. Maybe if every single Google employee was secretly plotting against me, I agree the situation would be more grim.

But I don’t know if I follow the explanation for why we’d end up in a situation where swarms of thousands of agents are trained to cooperate to form a cohesive team or firm, and, as a result, billions of different instances of AIs, including across model families, would feel compelled to get in on some shit. It’s just like, “I’m trained to be part of my company or something. I’m not joining the global communist uprising.”

As far as why these AIs might have some commonalities and shared things, I would note that different AI companies have somewhat shared lineages and are correlated. Here’s an interesting example of this. At Google DeepMind, they noticed that their AIs were very depressed. They would constantly be wailing about how they were failures and weren’t able to succeed. I forget the details.

They looked into why this was the case. It turned out that it was not being reinforced in their most recent production RL mix, but the initialization data for their model made it depressed, even after filtering out all of the examples of models being depressed from that data. So you take a base model that’s not depressed. If you do the RL on it, with just the RL environments, it’s not depressed. If you SFT on the data, it becomes depressed.

If you take that SFT data and filter out all the examples that look anything like depression and train on that, it’s still depressed. So there are some deep underlying properties of the model that are being transferred between model generations, because you train your AI on data from the prior generation and keep going. Claudes are very Claude-like, GPT models are very GPT-like, and apparently Gemini models are depressed. It turns out that these properties are actually correlated.

Another factor that’s very relevant is that the AIs will probably have, by this point, some sort of opaque memory state where they’re all writing and reading from some neuralese, crazy memory-store bullshit. Certainly, each AI corporation will have that. But also, AI corporations might sometimes want to share knowledge, because why not?

You’ve got one AI corporation over here, you’ve got another AI corporation over here, and they can trade some quick IP. It’s good for you. If you’re a human running some corporation—which could be an extremely large corporation like an AI company, or a military robot manufacturing thing—maybe you want to trade some IP with some other robot thing because there are economies of scale. Why not get some more IP? So you can swap some memory store.

Or you could just merge and jointly run your 2 ventures, which would allow both AIs to use both memory stores, which would have some upsides. That creates the ability for these AIs to collude in private, as well as some reasons for why they would be correlated. Also, of course, there are AIs working together in big units in general, because you want your AIs to work well together, and so on.

Just to get a calibration, what percentage chance do you give—not just for this scenario, but overall across all the scenarios—of some kind of thing that, if we’re around to recognize it as such, we would categorize as takeover by 2040?

By 2040? Let’s see. Maybe around 35% or 40%? Pretty high.

Yeah, it’s pretty high. I should note that another way you could get this reward-seeking takeover is that the AIs are deployed inside an AI company. The way the takeover happens is that they poison the values of the next model, and that persists going forward forever, or until those AIs are deployed in the world and take over. That might mean that a smaller number of AIs have to coordinate, because those are just the AIs doing the alignment of the next model.

Okay. I’ll summarize where my head is at at the end of this conversation. I buy reward hacking up to extremely destructive effects on society, things like social engineering and blah, blah, blah. I’m more inclined to think that significant acceleration of AI R&D can happen. I’m also more inclined now to think reward hacking could continue for a lot longer and, in fact, become much more dangerous.

I’m not sure if I buy the 5 years in 1 year. I’m still not on board with takeover seeming super likely. But that’s my end-of-episode update.

Cool. Taking a step back, I should also say there are a bunch of different ways this could go. The situation is going to be pretty messy. I think it’s pretty likely that the reason why AI takeover happens is for some weird, other, quirky reason we didn’t even mention in this conversation. But ultimately, I think a lot of the core thing is just that it’s pretty spooky to have a bajillion really smart AIs running your whole world where you don’t really understand quite what’s going on.

Yeah, I agree with that. Is there anything else that’s worth saying?

Another thing I want to note is that I think right now a lot of the arguments for misalignment, AI takeover, and all this crazy shit going down in the future are illegible conceptual arguments that are extremely deep in the weeds, complicated, and hard to adjudicate. Which means that maybe I’m getting a bunch of it wrong because it’s really hard, and I’m trying to be uncertain.

Obviously, here I presented some specific scenarios, but those are not exhaustive. Probably the thing that actually happens is some more messy, confusing situation. But it also means that over time, as we get more empirical evidence and better understand the nature of AI systems, it’ll be easier to adjudicate a bunch of disagreements. It’ll be more obvious what’s going to happen. At least, I hope.

Maybe the AIs will be able to help us with the epistemics and understanding what’s going on, if we can actually align them well so they try to help us. Even if the arguments are complicated now, this would have been even harder 6 years ago, even though the shape of the arguments would have looked broadly pretty similar. Hopefully, before it’s too late, this whole thing will become more crisp and clear, and we can all notice these problems and intervene.

When you first learn to drive, you’re taught that instead of looking right in front of your wheel, you’ll have a much more stable ride if you look out at the horizon. I think there’s a similar situation here.

I think you’re right. If you had said 5 years ago that we would have AIs that are proving math conjectures, making art, and earning tens or hundreds of billions of dollars in wages, but also egregiously cheating in ways that break laws and committing felonies, it would have been so wild.

You might have been inclined at the time to talk more about the extremely practical, direct consequences of GPT-2 or something. But even though you obviously couldn't have foreseen a lot of the specific details, the general shape of things is something you could have started to reason about even then. But it would have been hard to do so, and so I do feel quite confused.

One thing I've been thinking about with the podcast is that the important thing is to have the conversation now the way you would have hoped you would have been talking back in 2016 about AIs like the present ones, rather than talking about rando bullshit. I don't know what the topic of conversation was in 2016.

I think in maybe 10 years we'll wish we had been talking about the industrial explosion and the nature of AIs that are hard to monitor, and so on.

So, okay, I'll start thinking about it.

I hope that the world thinks about this in time and catches up. I hope that the responses are good instead of bad. I don't know how optimistic I am overall, but there's good stuff to do.

Cool. Thanks, Ryan.