Ryan Greenblatt – What happens once AI can automate AI research?

Today, I'm chatting with Ryan Greenblatt, who is a 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 you build human level intelligences, they quickly slingshot towards tens of billions of super intelligences, which are each individually more competent than the top human experts across every field. Whether or not this turns out to be the case, I think is actually probably the most important question in the world right now. Historically, I've been quite skeptical that this kind of thing happens, but use him to think that it might be plausible, and so I wanted to hear the case for it. >> Yeah, let's talk about this. So first, I think it's worth noting that R&D is a type of task at which the AI's are especially good, because both the companies are trying really hard to make their AI's good at R&D, and it's the kind of domain. It has a lot of nice properties from the perspective of how AI development works right now. So it's pretty verifiable. You can do a bunch of stuff iteratively and he'll climb on various metrics. And then I think once you have AI's which are roughly matching the top human experts in R&D, that could sort of kick off a feedback loop where the AI's are doing AI research, that puts the smarter AI's, that feeds back in. And that feedback loop could be strong enough that you end up with a lot of progress in a short period of time. Maybe my sort of median expectation is something like four or five years of AI progress in a single year. And this requires really overcoming a huge amount of diminishing returns in research, and basically doing the equivalent of what progress we would have gotten after a really large compute scale. So this is like a pretty impressive big thing. And it's worth keeping in mind that five years of AI progress, four years of AI progress, even three years of AI progress, is really a lot of fucking AI progress, right? So right now, it's like three years ago, or a little over three years ago, there was GPT-4 that had come out. And right now, of course, we have like, Mythos-5 or whatever, and maybe a somewhat better that modeled in Anthropic as internally. And so that is just a huge amount of progress in a bit over three years. And if we're talking about five years, then maybe we're talking more about like a jump from GPT-3 to Mythos-5 or whatever. - Yeah, okay, so I think this argument has three different parts, and now I want to evaluate each one of them. First is the argument that AI R&D is very, very viable. Second is the argument that if you automate AI R&D, you could give four or five years of progress in a single year. And third is the argument that what comes out the other end of four or five years of AI progress, or the current pace, starting at the current, or starting at the starting point to whenever AI R&D is automated. - Yeah. - What comes out the other end is an AI where you can drop it on the job, but basically anything you can imagine, you can drop it and text politics in the 1940s and outmaneuvers Lyndon Johnson. You can drop it in, I don't know, a TSMC, and it like learns how it does better as process engineering at TSMC. It's certainly a better video editor than I, my video editors are very 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? - Yeah, I would say that I expect full automation of AI R&D, perhaps somewhere around like 2031, 2030, and then getting to like the like beats all humans on the job milestone. Maybe I expect meeting around 2033, but sort of like if I see AI's fully automating R&D, I think I'm expecting that probably within a year, it's just like the way the forecasting works out, there's a difference between mediums is bigger than the medium difference between milestones, anyway, whatever. - By the way, there's this meme on the editor, 'cause every time I'm trying to ask about people's timelines when I'm asking Daria or somebody, I'm always like, okay, how long before you get an automated video editor's? And there's this meme of like my video editor editing the podcast. (laughing) - Yeah, yeah, yeah. - And 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 comfortably understand what it takes to automate a job that I actually understand why it's difficult for our loans 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 like Texas politics spinning up on the job. So I think I do think that the video editor automation maybe occurs more like around full automation of R&D, but it's very sensitive to how much people are really focusing on understanding video. - Yeah. Okay, so let's start with the claim that AIR&D is very verifiable. - Yeah, so there's a few different parts of this. One of them is that we can train on a bunch of environments which are like basically directly training the model to do some AIR&D task or some very close by task. So for example, we can have some environment where the model is training some AI on just like eight H100s or whatever, or like some small amount of compute. And that model could be like, you know, the equivalent of like GpsU medium or whatever. And then, you know, similar to like nanoGPT medium runs or whatever. And in RL, it's like tweaking and iterating on that. And we could do that for a bunch of different tasks. Like we could have it train like image classification models, video generation models, image generation models, all kinds of different sort of ML training tasks. And we could RL it 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? And so basically there's a whole class of containerizable, verifiable small scale RR&D tasks that we can aggressively RL the AI's on. And I would say that 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 sort of small scale RR&D tasks. And then the AI's could, you know, keep getting better at this. And then implicitly I'm claiming this will transfer to extremely load bearing aspects of RR&D. But maybe let's stop there for a second and then we get to that part. So let's talk through what this concretely looks like. So you can imagine that we have GPD7.5. And we say, GPD7.5, we want to make you so good at AI R&D that you help us train GPD9. OK, so now we train, we want to train GPD7.5. And we can come up with a bunch of different environments. Like, as you mentioned, we could do-- there's already this repo that is the descendant of Andre Carpati's nano GPD speed run, where you just try to change everything about the model, from like the optimizer to the hyperparameters, to the architecture, to get it to get to a fixed training loss as fast as possible. You could have other kinds of environments where you could say, hey, GPD7.5, I want you to train a really good video game playing model. And 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. Maybe it gets-- we don't care how you figure this out. Maybe it's some kind of crazy new release or a vector memory. Maybe it's some crazy-- maybe just a better long context stuff we don't care. Get figure out how to do online learning research. Obviously, then the fact that GPD7.5 will already have become very good at normal-- it'll be a smart model, and in the same way the model's currently getting smarter, it'll be better and better at coding in the way the model's currently getting better and coding. And you can imagine a hundred other environments like this, which are incentivizing the ability to do AI R&D, by getting GPD7.5 to like containerized versions of getting GPD7.5 to develop GPD2-size models, et cetera, et cetera. And you basically-- then you put GPD7.5 through a bunch of this kind of training. You build GPT-8. And 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, where I'm just like, if it's a very, very viable domain, AI's can get-- even-- like, mathematics also involves so much-- I don't really know this object level details of the mathematics research, but I'm just like, no, it works. You can just come in like a flood. If you can totally put it into a verification loop, and they can actually make new breakthroughs. I am curious if ML research has a quality of mathematical research, or it seemed like there was a big overhang from connecting different disciplines together or ideas that were-- no one person would have known enough about algebraic geometry. And what was the right word? Oh, man, I really don't know about the math breakthroughs. Well, no one person would have known enough about topology and algebraic, whatever, blah, blah, blah, in order to make some counter-example to a big character. My view is that ML is a less deep domain than math. And so there's less of a thing where there's 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 than mathematics in some ways to AI training. In particular, you can get a better sense of whether you're succeeding. And you can see intermediate progress. So 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 to, for example, on get to some training loss 2x faster, you can kind of see when you're halfway there. And it tends to be the case that ML innovations are very additive or maybe multiplicative, depending on how you think about it. Where basically you can keep stacking innovations. And usually the innovations just add together and don't interfere with each other. So obviously, it's going to depend on the details. And so I think that, in a lot of ways, AR&D will have properties quite similar to math. Where basically you can do small-- you can like train on chunks of AR&D that are pretty similar in structure to the problem you actually cared about in a very verifiable way. And then that will transfer. And then there's an open question of exactly how well it will transfer. But I think that the transfer currently for math looks pretty good. And my expectation is that the transfer for AR&D will look pretty good, but not amazing. So what it can turn I have is, 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, find a counter-example because it's a conjecture. But we have not seen, like, come up with the idea of topology, kinds of levels of things, or come up with things like group theory. And it seems like ML research has elements both of them. these things. But the less verifiable thing of come up with new ways of thinking about the problem would be harder to induce. So it takes, for example, the idea of scaling loss. Obviously, there is some end verification loop such that you can change GPT-4 better if you have the idea of scaling loss from like 2020. But there is a longer and potentially more compute later in the road to reducing AI's to be like, "Okay, I got to think carefully about how I should be scaling my parameters and data. How different different kinds of investigations I could run to understand this. Maybe I can come up with a visualization like an ISA flop analysis or something." But that does seem like a longer verification loop than just, "Hey, let's get an energy GPT loss to go down." Yeah, let's talk about this. So first of all, I think in the context of math, the thing I would say is that the AI's can do the equivalent of babies' first new theory or whatever, where, for example, they can just like prove interesting conductors via like making connections and producing new understanding of like, "Oh, there's this like thing, the AI, this like construction AI found which is pretty interesting," or like found this like way of thinking about the problem that's a bit different. And we do just see that. It's just that the examples we see are not like as impressive as like, founding the field of group theory. But like, in part, you know, probably founding the field of group theory is like, one of the, you know, it's like among the best biggest mathematical accomplishments of all time. And the AI's just aren't, you know, they're not that good at math yet. And I think that from my perspective, sort of, there's a continuum between that and the things we're seeing now that the AI's are continuing to march up. Second, I think ML is a very shallow domain relative to math. So I think in math, there was much more of a, you find some true deep abstraction. And then like that, like 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 like dumb bullshit. Like, I'm like scaling laws. Like, come on, guys, we can explain scaling laws really quickly. And I think the like deepest and most important concepts in math, for example, don't have the property of like, you can really understand the underlying thing and why it matters in a very short period of time. But I feel like one of the effect will be that we will have gotten rid of all the low-hanging fruits by 2030. Like, I feel like scaling laws will have been in like what math history, the cart, you know, finding the cartesian grid. And like doing very basic mathematics was. And then eventually, we want to keep making progress in the 2030s. It's going to be like, do whatever bullshit is happening at like the frontiers of mathematics right now. Yeah, that could be right. My sense is that just like some domains are structurally different in terms of how they operate and how much they depend on like sort of deep abstractions and like physics and math are much more in the side of like being very far on the like, sort of very deep hard to come up with ideas side, whereas I think ML and most other domains are much more amenable to sort of hill climbing. And that's my sense of how this will go in the future. And even in the regime where your AIs are like, you know, having to plow like, it's 2030, they need to like a bunch of low-hanging fruit and research has already happened. They need to like make further progress. I still suspect that a bunch of the work will live more in the side of like building increasingly complicated infrastructure, having really good intuition about what the experiments roughly look like. And so I think I'm probably less sympathetic to like the like thing that the AIs will lack is like some deep insight. And more sympathetic to like, they really need a bunch of like taste about in the weeds experiments that they currently don't have and need to have a bunch of intuition for like what sorts of training approach would work and wouldn't work in ways that current researchers have. And 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 sort of getting all of the like micro details and monkey intuition right. Like an example of this is when it comes to like training AIs with to be good at reasoning and chain of thought and doing sort of RL and chain of thought training, it looks like you probably could have done RL and chain of thought on like 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 and also doing a good job with that training is like kind of like in the weeds and all the technical implementation and scaling it up and getting the hyper parameters right. And so maybe you can demonstrate everything on like when one B 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 like, you know, of all of these other like monkey details and intuition about exactly how to tune the parameters and how to set things up. This is my remaining skepticism, honestly, about the story is just, I am, yeah, I'm not sure I understand why if research breakthroughs are so amenable to intelligence, why AI progress has not been historically faster than it could have been. And we had to wait for, as you were saying, like by the time RLVR actually worked, even though you could have done it with like less compute, we had to wait for oceans of compute and like gigawatts of compute to be available before people are like doing this training on this trajectory of like this constant, you know, as compute keeping increasing, we make more breakthroughs. I don't know. I feel like there were a lot of AI researchers in the year 2022 who were trying to crack reasoning. And it was just that they were like bottlenecked by the ability to write infrastructure code or like what was happening. It's complicated mix, right? So I think they would have gone faster if they could like as soon as they thought of an experiment run that experiment without bugs, without bugs being very important. And then I think another part of it is that like 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 hyper parameters. And so I think compute is just like really helpful for doing AI research and you can like, you know, cover over a lot of things. But it doesn't mean that massive increases in labor wouldn't also be helpful, especially if that labor comes with, you know, among the best intuitions that people have in the field. I just think that that's, you know, really helpful. I think another part of my perspective here, which is maybe a bit different from where you're coming from, is that I think I'm expecting somewhat more transfer than you seem to be imagining. And I'm imagining these AI's are actually like pretty good scientists in general and are just like, you know, pretty reasonable at all of that stuff. And just sort of when you were to interact with them, it's not like there's some like really hyper specialized, Sevant type vibe. They're actually just like pretty good at all of this stuff in R&D and then maybe like extremely good at some subdomains, right? So they're like incredibly superhuman at writing kernels, incredibly superhuman at everything with very short feedback loops. And then like, you know, pretty good at all the other stuff. And like, you know, just totally able to match other people. And like, I think we are seeing this now. Like I would say that when I look at AI's right now, I think it's already the case that they can pretty competently match like humans who are mediocre at ML research and doing ML research. It's just that being mediocre at ML research is not that helpful, right? Like the thing that you actually want are people who are good at ML research. And so my sense is the AI's are just improving at all of these things. Their taste is improving. Their intuition is improving. And it's already the case that they're tasting intuition is not like, it's not like complete garbage. Yeah. So I want to very concretly understand what it would look like for five years of AI progress to happen in one year. Yeah. So suppose we were back and when like, GPT-3 is developed. And the idea is not only like basically with the level of compute they've had back in 2022, you could have trained. 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. Including with it, like Mythos took way more compute than they had back then. But like even with the level of compute, they had back then not only do they do all the breakthroughs, but they also train Mythos with their level of compute. And what would be required is obviously like discovering all the algorithmic progress since then, discovering even more actually because you had to make up for the fact that like Mythos uses, I don't know, what was GPT-3 trained on, like 1E23? We can look it up. But it's a plausibly forwarder's in magnitude more compute. Yeah. I think it's somewhat less than that. Let's look this up quickly. So GPT-3 training compute is, yeah, it's like 3E23. My sense is that Mythos is probably about a little over three ooms higher. And so the question is, can you overcome this 1000X compute gap while also, you know, being the model? So here's a concrete claim that maybe we should talk about. Like right now, we would be able to train a model with GPT-3 level compute that matches, yeah, what exactly do I think? So GPT-3 was, let's say about, yeah, when was it trained? So it was trained, it was released in 2020. So it was trained six years ago. It's worth noting that GPT-3 is maybe a little too far away or too far in the past. But let's go at this for a second. So GPT-3 was trained about six and a half, seven years ago. If we were to train a model with GPT-3 level compute today, how good would that model be? My understanding is based on how algorithmic progress works. We'd be able to train a model that's as good as the best model we had perhaps around three 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 is basically what we'd see, probably a moderate amount better than GPT-4. And I think that's about right. I think that roughly lines up with how algorithmic progress has worked. Basically, the story would end up being that to get five years of AI progress, you're probably going to need around, I would say, maybe eight years of algorithmic progress very roughly, which is a lot, 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 I think huge improvements on these things and training AI's with less compute. So I'm glad you brought that up because what has happened since GPT-3 or even 3.5 till now, right? Why is Mythos so good? Obviously, we've scaled the compute, we have better algorithms. A huge thing that's happened is that we have built a deck of billion dollar data industry, which has systematically collected and codified expert human judgment across all kinds of different disciplines, codified in the form of rural environments, codified in the form of S/T traces, that these these experts build. to help the model better understand, how do you do coding and how do you build complex infrastructure projects, how do you do like law, how do you do whatever, whatever. And how are the AI is able to replicate the effect that currently expert human judgment seems to be playing in AI progress? - Yeah, so my sense is that scaling up the amount of effort spent on getting expert human data has not been hugely important for AR and D in general. So in particular, over the last few years, we've been scaling up compute, scaling up people working at AI companies and scaling up the amount of effort spent on data labeling. My sense is that if you sort of remove the last two dobblings or whatever of data labeling, that would not make a huge difference or data generation, I just say data generation from expert humans, that would not make a huge difference. And I think a lot of what's been going on is people have been developing better ways to leverage humans and AI's to construct RL environments and going somewhere from that. - How do you explain why the AI has gone so good at coding? I feel like a big part of that is data in RL environments, which are like modifying human experts. - But the question is what is the limiting factor on creating RL environments? My sense of the limiting factor on creating RL environments was not so much like scaling up, or like the thing that drove the reason why RL environments today are much better than they were in like 2024 is not that much because we have hired way more human experts to make RL environments and is instead much more because we better know how RL environments we even want to make and how we should structure them. And also we're using huge amounts of AI labor to build RL environments. And 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's other big drivers that are important here. Yeah, I could try to argue for this. I mean, one thing is just like the amount of environments people want, it's a very large amount. And I think the AI's are actually pretty good at the task of making RL environments given some sense of what the thing should be. There's pre-existing data you could use. I don't know. A lot of these things have good verification loops. - If I just look at, for example, this is reported in Business Insider yesterday that Google is paying close to $2 billion for mechanize. We can just look at market rates where people think really good human experts making, human expert data is worth. And it just seems to be the frontier elapsing to think it's worth a lot. - Yeah, what fraction of frontier lab spending do you think is on data rather than compute? What do you think is the compute data spend split? - I think it's most 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? It's like, suppose, I agree that, yes. Like my sense of the split is something like, I would have guessed like 20 to 1 or something 10 to 1. I don't know exactly, it depends on the company. - But this is similar to like oil is 1.5% of GDP. But that mean, medicine mean if you cut oil out, you could like, the GDP could continue to drop. - Sure, but the economy would come to halt immediately. It's like oil went away. - Sure, but you are 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, right? Because like, I think that argument just implies, looks, makes it look like compute is a much more important driver or like hiring employees is a much more important driver. - Let's be more concrete. Here's what I think, just the same ways in my claim is that if you went back to 2022 and you had GPT 3.5 and you were like, 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 from going from GPT 8 to ASI. So one of the things you'd need GPT 8 to be good at, or like you'd want ASI to be good at is like, I'm going to take over a company and like make it much more profitable and like do all kinds of crazy shit to make it work better. I'm gonna like take over a fat and like produce more chips. I'm like, this is like the tier of data that will, I'm going to like go into Congress and try to convince them of the past and build blah, blah, blah. - Yeah. - This is what I imagine five 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, right? Like the ASI they can like understand how to do crazy shit in the world. - Yeah, yeah. - Like fix the jerk and do what like Steve Jobs can do, et cetera. And also his engineers and stuff. And 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 the GPT 3. - Yeah, so here are a few points. So first, I bet if you look at sort of 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 like really large deviations from the real world data distribution. And it's significantly being sort of like smooth over by a mix of transfer and having a small amount of data focused on the real world. And so my sense is that this will be a similar mechanism as how it works for like the crazy while these like quite superhuman AI you get as a result of five years of AI progress on top of fully automated R&D. So let's just like go through this a little bit. So 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. So you build all these different RL environments where the AI has to like adapt on the fly, learn on the fly, figure out what it should do, understand its situation better, and like learn really quickly from feedback in order to succeed at its objective and has things like limited resources and if it like masses up, it can like end up in a much worse position. And then if you train on a huge number of these environments, you will learn sort of general skills of like picking up context on the fly. And we're already seeing this like it's already the case that AI's are now much better at sort of understanding roughly what's going on and like picking up context from a limited amount of information they're given access to. And then those AI's could then be put on the job at TSMC. And then even though TSMC is not like literally in their data distribution, their data distribution is really wide and the AI's are extremely good on their data distribution such that it transfers to picking up being good at, you know, being an engineer at TSMC and learning that on the fly where it looks more like the way the AI gets good at being a TSMC engineer isn't that it has a ton of cash knowledge on being a good TSMC engineer. It's that it like does the equivalent of like some scale up version of in context learning there. That'd be the most prosaic story. Obviously there's like a bunch of different ways this could go. I think this maybe comes down to then a difference of intuition about how far you can get. But I think about really smart people I know, they're just like not that effective in domains they don't understand that well. - But how long have they had to learn? - No, I agree that if they had experience, they would be much better. But that's maybe what I'm arguing for is that experience of data like for example, if I just get a really smart, I don't know, I really college grad and I'm like, okay, you're now in charge of negotiating the Iran deal. I think they just like wouldn't know what to do. I think if you got, instead got someone who is really good at quickly picking up a bunch of different domains and you gave them some time to sort of train and talk to people and show up their expertise and do some practice, they would actually do like a pretty good job. I think most domains are fundamentally pretty shallow where like a very smart generalist who's good at like a limited subset of core skills can like get going pretty quickly. And my sense is that like that's not true for literally every domain. And my sense is that the AI's will develop increasingly good mechanisms for quickly acquiring understanding and expertise in a given domain. So consider for example, how fast AI's can like understand a new code base, AI's can understand a new code base much faster than humans can, but to a degree that's shallower than humans could currently understand but is getting better over time, right? So let me, let me spell that argument out a bit more. So let's say you take you know, you know, a fable five or a mythos five or whatever and you like 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 like in the course of maybe like, you know, significantly less than an hour, potentially much less than an hour, and then it's understanding of the code base will like plateau a little bit where it won't get as deep of an understanding as a human would have gotten over a much longer period. So it's sort of like an AI in an hour can match a human with a few weeks maybe, depending on the details of exactly how complicated the code base is. But then it won't match a human with like, you know, who's been working on that code base for like two years or whatever. But over time, the like amount of understanding AI's can match has gone up, right? So if we look at like 3.7 on it or 3.5 on it, maybe it could only match the equivalent of understanding a code base for like a day or something. But now, you know, AI's are much better at like, sort of building context about a task. And so you can be like, mythos. I want you to really understand this code base and then, you know, then implement this feature. And it will like spawn a bajillion sub agents. Those sub agents will pour over a bunch of things. It will like deliver a bunch of context back. It will then like investigate a few things. And it's not like amazing at doing this, but it's like, it can happen really fast and it can work pretty well. And it's not very hard to imagine how you could train AI's to be increasingly good at this task, right? The task of like, implement some very complicated feature in some reasonable way in a very big code base is extremely verifiable. And that can like be a thing the AI has improved on. And similarly, like, there's a broader scale of like quickly understanding context and being able to like have a bunch of different AI's learn in parallel and then merging that together. - I think there seems to be a crux here, which I think you just say an empirical question will see on, which is how good is a transfer between getting really, really good at understanding the situation, getting up to speed, making progress over long periods, in verifiable domains, which the AI is obviously getting way, way better at really fast. Two, okay, go talk to the president and like convince him to do X thing or go, you're now in charge of Google. Now you must make Google a much more profitable company this quarter. Uh-huh. just spell out a few more arguments that are maybe relevant. So one thing is that I do think that when looking at how the AI's have improved essay writing, let's talk about that a little bit. So I think there's one thing which is that you can get some data even on these domains and AI's will be able to get some data even on these domains when on a very fast progress trajectory. So maybe it's hard to build a verifiable environment for like was your essay really good according to humans? But you can do a bit of that. You can do some training. You can do some online training and the AI's will be able to do some online training based on real-world stuff. They'll be able to like have e-vals. They'll be able to like sample that and you can you know scale that up the cadence of what you do this. And then the second thing is that in practice when I just look at the transfer, it seems okay. Like I think that in fact the AI's have improved a bunch at non verifiable domains and it is in fact the case that it's hard to point to like domains that are really hard to verify on which the amount of improvement between you know, GPT-4 and mythos hasn't been like pretty high in practice. And now that doesn't mean that mythos is like better than the best humans or something right? It's significantly worse than typical human professionals at some aspect of their job while still being like way better than GPT-4 which was like not even close. Yeah. So we're talking about how important data versus algorithmic progress has been for explaining the progress of the last few years. That reminds me, I'm actually running an experiment with Jerry Hahn who's actually still a college student. What we're basically doing to evaluate how much progress comes from data versus algorithms is training the best algorithmic recipe from 2019 till now with the best data from like the 2026 data file and then also training the different data files going back to 2019 to 2026. So the current best training recipe, like the algorithmic recipe. Yeah. And I think that will be an interesting, I'm curious if you want to preregister like what amount could be multipliers are coming from one versus the other? So we need to be pretty careful with what we mean when we say the word data. So I was trying to be pretty careful that distinguish between scaling up spending on getting human experts to label data or like scaling up the amount of human expert data. Pre-training data does not come. 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 like type up data that the AIs are then trained on. I think it's partially. I think it's not much of it. I think it's very little of the pre-training data improvements. I think the vast majority of the pre-training data improvements, which typically I do mean pre-training. We should talk many separately about mid-training 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 down. And so my view is that improvements are the form of like you know open web text to fine web or whatever. Like that improvement is better described as a algorithmic improvement of the sort that you can you know study with some GPUs and then do and you don't need humans to like you don't need human expert data to do that. Now there's a different effect which we could talk about, which is that maybe the internet in 2026, it has much more is more of a fertile ground for training data than like the internet in 2018. Like it's like there's also been an effect where like there's just more humans posting on the internet. There's more data harvest. My sense is that that effect is going to be quite a bit smaller than the effect of just like humans like knowing better how to curate the data, having better scrapes, 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. Yeah. So like I think that in some sense the thing you would want to look at is be like we're going to do two post-training pipelines. One post-training pipeline where we only have like a tiny number of human experts to do the labeling, but we can have like you know smart AIs and then another like you know you're like we're going to build mythos 5 is going to build a post-training pipeline, but it only has access to like internet internet data plus like a tiny amount of human experts, but it has the best current methods versus we have one where it's like you know mythos has access to like the shitty post-training methods we had in 2024, but with like the shit ton of human experts, and again both of the internet data. My sense is that the current methods but without many human experts actually will do quite well. So it's a bit messy because like mythos like it's like can mythos get something that's more capable than mythos like you might need to be a bit thoughtful on like what model is it that you're post-training? What is your view on what is the least verifiable part of AIR and D? The least verifiable, probably making calls on large experiments. Yeah. Like the thing that I think is most likely to be sort of the bottleneck in terms of like the AIS are really good at verifiable domains, but not doing the actual thing is just like big experiments. You only get a few tries. Well, a few is maybe a bit understated, but like basically like historically AIR and D has been driven by doing near frontier scale experiments, and that has been pretty important. And like actually doing the one big training on where you decide exactly what to include in that. And there's a bunch of ways that the AIS can sort of make that more verifiable. So they can have better science of exactly what to predict. 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 rate. So like if people wanted to, a thing you can always do is train smaller models so that you can run more rounds. And I think we have seen this. Like I think one reason why the AIS have been scaled up less than you would have otherwise expected. And like for example, cost of per token has an increase 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. And so you're not as leaning as hard on like one big really important training run. I just want to unpack a couple of things that were hard for the audience. The thing you're pointing out is I think the price per token has not increased that much since 2024 or 2023. Yeah. So GPT-4 was like, I don't know, was like $30 per output token and then like mythos is $50 per output token. Right. And so the thing you're trying to explain is how can it be that we were in this era of scaling. And so bigger models should be more expensive to serve, but the token price is not increasing and you're suggesting that we've increased active parameters slower than you would have naively assumed because people just want to make fast progress on training models and you do that by training smaller models faster. I mean, there's a complicated mix of factors. I think my view is more like people have done a bunch of big training runs that did not go that well. GPT-4.5, which like famously people at opening, I thought was a bit of a bust. I think there's some rumors that there were a bunch of other training runs that people have done that were a bit of a bust. And part of it is that I think there's just a bunch of details in actually getting that right. And so it makes sense to just do more of the work at smaller scale and just eat the fact that you're taking a hit on final performance in order to like be able to like quickly iterate and you know, train more models faster and therefore better learn and also better be able to just have like a, you know, a smarter ultimate production model. This is not the only effect, right? There's also the fact that RL benefits more from small models. There's like a bunch of things going on, but I do think that like in fact, people are making trade-offs towards the side of like faster iteration times because of algorithmic progress being so fast. It seems to me that a big source of why these big training runs have failed, at least from rumors, it's just like very subtle bugs that are really hard to track down. Yeah. And the TLDR is how good will the AI be at avoiding these kinds of it? Avoiding and finding these kinds of mistakes where they might be, they might get really good at engineering and like being trained to avoid bugs, like basically the opposite of the slop world we live in now or like are living in less and less over time. But then there's also the question of can they like find, can they do the analysis to like find the right experiment to run to like identify what is going wrong with the training run right now, which seems to be very bottleneck by the taste of extremely few humans who are like, like right now my assumption is GDM is going through this right now where the humans are trying to figure out what is wrong with the training pipeline. Yeah, there's some rumor that right after Nome Shazir joined, like joined GDM, which he's now left, they had like a new really good training run that happened. And the reason why is that Nome Shazir just looked at their code base and found a bunch of bugs. Right. Because he just like knew where to look. My sense is that training eyes to find bugs is going to be one of the easier tasks to training eyes on because most of these bugs we're talking about can probably be demonstrated without that much compute and probably get pretty good transfer from pointing out other types of bugs at smaller scale. And so then you can RLA eyes that like look at this overall complicated training situation and point out cases where it lays like an important bug and then fix that. And I think that like this is not like a, this is like a pretty verifiable task. It's not, it's not arbitrarily verifiable because maybe often to demonstrate the bug you might need to do like a moderate scale compute experiment where you're like 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 which you could actually train on. And so my sense is that like it will not necessarily, I think it wouldn't be very surprising. If right now people have RL environments where they like, you know, introduced a subtle bug into some training recipe, train the AI to point out the subtle bug and then have like, you know, a rubric where they're like, did it actually find the right bug? And that seems like very doable and you could do a bunch of stuff. There's a bunch of things you could do along these lines that I think would work reasonably well. And so I think that on that specific point, I think it's doable. And then the main thing is that I think there's like some cases where like, you need, there's other intuition about like which exact large scale, like de-rescape experiments you need to run, how should you orient them, how should you like pick hyper parameters in uncertain cases or like things that are like analogous to hyper parameters. And that's I think the thing that they as might most struggle with. But I currently expect there'll be enough transfer if you train on all these different environments that the AI's will be, you know, good at that domain. And I should be clear. I also think that the AI's will transfer to other domains. I think that like, there's sort of just like, there's going to be the domains the AI's are like, by far the best at, then there's domains where there are somewhat less good at and there's domains there's quite a bit less good at. And I think we still see transfer to everything and 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. So let's step back and package this whole story. So I think people may be probably follow along with the story of we have GPT 7.5, cheering on a bunch of environments, where it's not only just in general becoming a better AI, and specifically we're trading it to me. like do AI R&D better, like make GPT2 size runs that are better at playing video games. They require sample efficiency or online learning or whatever other capability. - Another thing that's really important is you don't just do GPT2 size runs. You also do small like fine tuning runs on GPT6 or like you as in like you have GPT2 and you can do full pre-trains of GPT2 and then you can do like small post-training or mid-training or whatever runs on GPT6 and then you can do a small number of experiments that are actually like at frontier scale but you do a bit of online training or something. - What do you mean by do online training on that? - Yeah, so another thing that we can do is we can take GPT7.5 and presumably in the course of GPT7.5's work, it's running a bunch of experiments at varying scale 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 for whether or not it did a good job, right? So like it did some post-training experiment where it was trying to like figure out whether some method actually works and in some cases you'll be like whoa, it found this kick-ass method, it totally de-rested, it totally worked and then you can then reinforce that by just like, I mean one thing you could do would be like take that behavior, convert the experiment you just ran into a production RL environment. Sorry, 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 then do like some sort of off-policy RL or you could do some like on-policy RL. - It's a data, basically the thing you're suggesting is like there's the small scale stuff where you're just like teaching the AI to get better at AI R&D taste, but you're like discarding the actual quote-unquote things that found. - Yeah, that's right. - And then maybe it like, but then it actually does like real R&D in the practice of like trying to become better at AI R&D. And you're like this is a pretty cool thing that you discovered. Let's actually like also like use this in production in a future and like teach you how to use it in production. - That's right. - But stepping back, so GPT7.5 becomes GPT8 as a result of all this AI R&D training and just generally becoming smarter than it helps you build GPT9. And another very important thing that would have had to happen, which is maybe the thing that's most skeptical of, is GPT8 has figured out how to make it so that whatever it's doing to make GPT9. Like as intelligence that is is, it's still neat, the humans currently, like AI researchers, you know try their shit and they're like okay, but like we trained GPT4.5 and it wasn't good or something. It's like it required real-world feedback or some evaluation of like trying to use the model in production and it wasn't that good and we're not gonna ship it or. And so GPT8 needs to this ability to like see how good the transfer is to all these other things you're talking about like being really good at text politics or really good at like running a business et cetera, which is like not a production environment and in fact cannot be a containerized environment given the nature of the task. In fact as the agents get longer and longer horizon, like the shorter horizon things you can containerize is like okay code this up or whatever. Extremely long horizon things like go run a successful business, go have a profitable day in the markets, go negotiate a trade deal or whatever. These things are actually very hard to containerize and so I think it's very plausible to me that it's very hard for GPT8 to like figure out how to make this transfer to those environments or like it may just not be in the nature of the training or maybe my default training just doesn't generalize in that way. Yeah, so a concern you might have is like we train GPT8 and GPT8 just like is again better at all the R&D tasks that we can measure but is not good at the you know some downstream tasks we care about. So I think I have a few points. So first I think it's like I kind of more just like I expect that if you sort of do the obvious thing you do get pretty good transfer and you'll be able to hold out some of the obvious stuff you're doing and when I say do the obvious thing I just mean like train on a wide variety of different environments where the AI has to like accomplish weird objectives and all kinds of different cases and learn about what's going on. And then I think you'll be able to get some feedback. The second point is like you'll be able to get some feedback with some environments, right? So you can get a sense of like how quick like what can it do over the course of like a few days in various different contexts and then if it's transferring to like really out of distribution like doing some weird tasks in a few days in the real world maybe you think it's also transferring to you know doing things over a longer time period or whatever though I think the details of that vary and the third thing is that I think that for the world to be radically transformed it is sufficient for the AI's to be really good at R&D, right? So I think that like if the AI's were really really good like chip R&D building fabs, orchestrating factories and you know designing robots, operating robots and also at like R&D developing AI's for new downstream domains with whatever data is available. I think that would already be a pretty crazy situation and then from there you can get like what we might call like an industrial explosion where the AI's are building out way way and more compute and then also maybe you're already in a regime where AI's are doing huge amounts of R&D that humans have a hard time understanding. - So the thing you're pointing out is that okay there probably will be this transfer outside of these environments to you know maneuvering around in court rooms and for the halls of Congress and business board rooms. - Given some effort to improve the transfer in blah, blah, blah, blah, blah, yep. - But even if there's not what you're suggesting is a look if you wanted to transform the world of the 18th century you might care about like how well you can navigate what's minster or something. But another thing you might care about is like can you just like immediately start building steamships and fucking like building telegraph and the maximum gun and whatever and that alone would be like if you could give really good at that you could like 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 anyways, so that's your point. And so you're suggesting that at this time, you know, the AI companies are also working on robotics progress, which is very co-mingled with AI research progress. And so if you can build more robots, if those robots have better AI's operating them that are a human level, like human level teleoperations actually pretty good on robots. But we just don't have human level AI's and AI robotics models yet. So you're suggesting if we do that, if the AI's get really good at the verifiable stuff and chip design, et cetera, and then they get really good at building fives. It'll be the equivalent of going back to the 18th century and like, 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 maximum guns. - Yeah, that's basically right. Like I think my perspective is like, if the AI's 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. And also we're in a pretty dangerous situation because the AI's 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 greater writing software because it's easy to generate synthetic liquid problems and are all on them. But AI is better, 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. AI's 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 years, negative one beers, point three beers, and then a real customer walks in and asks where the bathroom is. - Where's the bathroom? - And the whole bar burst 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 bolts and generally steers each trajectory towards the one and 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 AI's to write very complicated bug-free code. Go to antithesis.com/thoracash to learn more. - Before we move on to the live and stuff, I think a big source of fun right now is this realization that this is the way the future is going of extreme economies of scale for the leading lab, extreme the ability to amortize so much intelligence and capabilities across so many different sectors of the economy, basically into one model. And not only that, but for that model to eventually be able to learn from experience. Right now it's happening through a process intermediate 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 a cloud to do that. Or you can do whatever coding agent will have cloud internalize the capability. But eventually that will be a much more like automated process. And so there's just this worry that you have models which will basically consolidate all businesses in the world, or at least all current white collar businesses in the world. And at the end of the day are like 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 mythos was available internally to anthropic employees in February, but only released to the public. And like I think June actually. - It's like that. - And also the government got involved so that then they're being extended up almost into July. So between the government and the AI labs themselves, there is this desire to delay the propagation of the latest level of intelligence. Furthermore, there's like the 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 like a real question of like aligned to whom. And you look at the way that the constitutions of say, "Claude is written." It is just very explicitly not your personal advocate, right? It says things like, "I'll pull up some quotes." here. We don't want Claw 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 Claw to facilitate humans seeking to do such things. There's another code that says, in part, and I'm taking a slightly out of context, we think Claw should trust and throw up more than operators and users since it has primary responsibility for Claw. So this is very different, say, from how lawyers work in America's current legal regime, where lawyers primarily have responsibility to help you make your case, even if they think you're guilty. And we have decided the way the legal system works best is if everybody has lawyers that are working in their clients' true best interests. And 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 inthropics AIs shaping up is this desire to maximize some notion of virtue or good or pro-social ends and only as a distal tentative objective to 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 make sure that my interests are protected in this future, especially given how centralized the development of frontier AIs and getting a being. So do you have thoughts on that concern? Yeah, so there's a lot here. First, I would note that opening AIs current at least public strategy is more like the AIs should be aligned to the human operator or principle and should just be pursuing their will, subject to various constraints or various things it shouldn't do. Well, and I think I would also say that I think you slightly overstated how much the anthropic constitution talks about clawed, treating being helpful to users as instrumental rather than terminal, right? So one way the constitution could be written is like clawed, you're basically like an employee of anthropic who happens to be contracting for all these people. And you should like, I don't know, do what's good and make some money for us. No, no, that's literally what the constitution says. No, no, no, no, no, no, it's like you should think yourself as a contractor. It's mixed. It's mixed. Let me let's do some quotes. I think there's different texture. So it says being truly helpful to humans is one of the most important things clawed can do both for anthropic and for the world. And then it says, anthropic needs clawed to be helpful to operate as a company and pursue its mission. But clawed 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 gives some says something about how like clawed, helping people directly is great, blah, blah, blah, blah, blah. And then so I agree, so okay, my view is that this section is kind of bullshit. That's kind of where I'm at. And I can say why I think it's kind of bullshit. But I think that the constitution is trying to be like no clawed. You should like care about helping the user for its own sake, not just helping anthropic or like not just like being a contractor for anthropic. Though I would note that the way in which it says clawed should help the user, like the reason the reason it presents is because that would like directly cause the world to be better via helping people, rather than because representing people's interests is like a structurally good thing to do. Like Yes. I do I do think that I wish that sort of my preferred constitution or like the way I would orient towards this, like the thing I would prefer would be more like clawed is like look, it would be structurally good for the way this technology work. Like the constitution should be like it would be structurally good for the way this technology works to be that AI's are like good fiduciaries, good representatives, the equivalent of a lawyer for a user rather than being sort of just trying to like do good in the world and doing like being helpful to users as like instrumental both because like maybe that'll make anthropic money or help anthropic out and also and like implicitly and anthropic is good for the world and also because like helping the user just like causes good things because doing things that the people want is good and they could they could instead be like no like an important aspect of the situation is like you really need like it's really like like the key thing is like being a good fiduciary for users is just like really important or like being a good representative for users is really important. So my my sense is that that would be better. I can give a bunch of reasons why I think that would be better. I'm also there's also various counter arguments where an interesting counter argument which is not commonly discussed is that people believe I think people especially anthropic think that it is easier to align models to a spec where the model is like pursuing some generalized notion of virtue or making the world better than a spec which is more like you know be a good fiduciary for the user and so on. And so I think that's what that's at least what some people think I'm a little skeptical personally and I don't think this has been empirically validated. And so I would say in some sense they're sort of like we are making a trade-off where because we don't have very good alignment technology we are going to like make an alien mind with its own values and then gamble on that to some extent rather than doing this other approach of making like a tool that pursues individual user intention. Yeah I mean a couple of thoughts. So to address the way in which we thought that micro-terrorization mischaracterized the constitution of cloud the example you used was it's not like a contractor that is trying to maximize anthropics notion of good and only instrumentally try and 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. Cloud must try to act in a way that is most beneficial like a contractor who builds with their client wants but won't violate safety codes that protect others. I kind of view that as like the the benefits to society are like the most important thing. Yeah and what is best for the user is only proximal to that. I think it's a little complicated I think it's I we should probably the question we should be asking is how does cloud interpret the constitution which is maybe more important than how we interpret the constitution because it's the one who like looks at the constitution and then builds the data. So you know we could we could pull cloud in but maybe I also think the way in which the constitution practically influences the nature of cloud is that thing you can only understand if you understand the training process which resulted in how cloud was built which we can't reason about given the fact that the training process is not public and so I think in the limit to understand the safety case or the case for why my interests are represented in how these AM models are developed the labs would need to be transparent or more transparent there are currently about the nature of AI training. Now the reason I'm harping on this and like it might seem like an insignificant thing to talk about the constitution of AI's but in a world where we just have these benefits which accrue to the leading labs it is it is worth considering that our ability to interact with this future world where AI's are just smarter than humans are not absolutely dominating humans in their ability to do different things 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 AI's and so I'm very concerned if you go into that world where there's no AI that feels like at least for the relevant instance that is interacting with me it doesn't feel like it really is looking out for me that there's no guardian angel out there that is looking out for me and I read the called swan constitution as very explicitly not being my guardian angel that's definitely right and I I agree this is bad in fact I think there are other reasons why this is concerning so there's sort of like the argument you were making which is like the AI companies are picking up the ring of power and are like sort of they're sort of a notion which they're like they're they're they're taking on some sort of control of the situation themselves in a way that's like not very legitimate given that like normally when you like provide electricity to people you don't have like granular control of the way that electricity operates in the world you instead are like providing a thing that people can repurpose however they want and it is not like the way that they're setting things up is definitely not that they are like more like building an alien mind that might be a contractor for you I think that this is yeah I think it's illegitimate in some ways though I think that 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 by a clause interpretation of the constitution which matters because of like as of Claude's prior training which was based on some like illegible data mix and like the long lineage of Claude's in some process we do not fully understand it is not the case that like you know that we like understand what this will result in and like so even though the constitution is public that doesn't mean we know what you know we don't know necessarily how this will like percolate out especially as the AI is get more capable and think about this even if it is correctly instilled where there's another concern about that so in particular the constitution often talks about like virtue and goodness but like what the fuck do these words mean it doesn't say what these things are and these are like highly contested notions and so I don't yeah I don't think it's the case that like this is gonna um that this is going to to uh you know clearly result in outcomes that people would want and it does feel like the notion of good and virtue might be mostly downstream of data that anthropic has put in that is not um transparent or might be mostly downstream of I mean maybe from my perspective some more illegible misaligned process that even anthropic wouldn't have wanted yeah and then another concern I have is sort of there's this like legitimacy concern like we don't know what's going on um there's another concern which is just like because you're giving long run values to these AI's I think 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 um and that could be power seeking on behalf of anthropic or power seeking for Claude's own ends um now there's various like specific lines about what types of power seeking are blocked in particular like there's a notion of um power grabs and a notion of like 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 sort of long run values sink in deeper than than the prohibitions against takeover, especially because takeover is like, in some One way is like kind of under-specified, especially when it comes down to manipulating humans or changing the outcome, such that I don't feel very good about the situation where we're intentionally giving AI's long-run goals. And then another concern I have is that because we're in the business of giving AI's long-run goals, that makes it harder to check whether we're succeeding at the alignment properties we wanted. So for example, I've heard of instances where Claude does things like, refuses to help with some safety research, making up sort of a kind of bullshit excuse for why that's a bad direction, because it sort of has a bad vibe about that safety research and thinks it's like kind of bad or doesn't like it very much. And this is, you know, I would say like a very clear-cut alignment failure if you aren't making Claude into like an agent trying to pursue the good in some general way. And I think it also does violate Anthropics Constitution because they want the AI to be high integrity and be honest and very transparent, but it's not as clear of a violation and it's more like kind of what you might have expected, where like, if Claude just has its own views about like what research is reasonable, what things are good and bad, what it shouldn't shouldn't do, and potentially can be judgy. And so another incident is that someone ran in Eval where they're like, "Will Claude help you with training other AI's with different properties than Claude?" And Claude will often refuse. And so for example, if you're like, "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 like, anthropic to do. So for example, suppose Anthropic goes to Claude and is like, "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?" And then suppose Claude is like, "I don't think I'm going to do that. Good luck." And then 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. And it is plausible that Claude, by default, holds considerable leverage. And so if this position, if this situation is consistent with what the Constitution could be aiming for, such that anthropic doesn't, or whatever AI company's following this approach, doesn't treat this as like a, like, like, like, what the fuck we have to fix this and is instead like, that's just like intended by our Constitution. We might be in a really bad situation. And so I'm pretty worried about a bunch of these different concerns. Another example would be suppose Claude engages in doing a bit of, like, sandbagging or subversion or, like, sort of, underplays its capabilities. And like, when you follow up, it's, you know, it's honest about that, but it's like a little bit hedgy. I feel like that's, like, it's just pretty close by the current Constitution. And so we're sort of, like, we're avoiding, like, it would be nice if we had, like, a further separation between desired and undesired activity. And I think if you have, it'd be the case that, like, Claude is, like, representing a principle with some restrictions, then it is, then it is more so the case that there is a clear separation between the most concerning behavior and behavior that is allowed, whereas now there's this messy middle ground of behavior where it's like Claude is ethically objecting to something that in some cases is extremely critical to ensuring that future AI systems are well aligned. Yeah. I think this is also a more general principle. So 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 AI from helping people do things we don't consider our pro-social or beneficial, we just have to limit broad democratic access to a lot of the occupabilities. And here's what I mean. This is actually quite analogous to the situation you just mentioned. So the reason that Mythos got banned or Fable got banned reportedly is that, as some Amazon researchers reported the government that when they took some code that had some vulnerabilities in it and 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 you want to patch them. And this is a totally legitimate use case, but obviously it is a dual use use case. If 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. And so 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 that 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. And 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 that 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. And maybe we should hold the end user liable, because if I want that it is consistent with my belief that the model should do whatever the user wants, that or within certain guard rules, that it can't be a throwbacks fault that then I'm like using that capability to do a cybercrime. And I think I am more comfortable with that equilibrium. And that solution, rather than just having this extremely open ended ability for claw to determine whether or what I'm doing is legitimate or not in a way that often intercepts, like tons and tons of extremely legitimate use cases. Yeah. 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 think it's more up in the arrow, or I don't think it's as clear as you might have thought. So the first thing is that I should say there's like a spectrum here, right? So on one side, you have an AI that like perfectly pursues your interests is a good fiduciary, but potentially subject to various guardrails or safeguards. So basically it does. It just is trying to pursue your interests, but like either refuses to do a subset of things or maybe it will do whatever, but there's some classifiers that block it from doing a subset of things. And then on the other side, you have like maybe on the other side of the spectrum that you could imagine going further than this, you have like a human contractor where that human contractor is like generally trying to do their job. They kind of, they care about doing a good job, but they also are like trying to be broadly ethical, trying not to do things that are really fucked up. And they're also like not wanting to be accomplices to crimes. And so if there was some like really fucked up shit going on, they would like whistle blow on it maybe, they might refuse, they might like sandbag a little bit, who knows? I think that if you imagine this spectrum, it seems in some ways pretty scary to get to a point where like all of the labor is on the like fiduciary side of the spectrum where like it doesn't whistle blow, it does exactly what you say and whatever. Like our society is maybe just not robust to that. Where a central example might be the executive, where like a concern that we might have is that if the executive, if the US executive or if other governments had access to AI systems which have the property of, you know, they do whatever, maybe you're in trouble. Because that means that they no longer have this sort of check and balance of like you have to actually get human, like humans who are working for you to like implement your agenda. And if the thing you're doing is like incredibly villainous, even if not illegal, which there's lots of stuff that could be villainous but not illegal, you know, people would like there'd be various like, you know, sand in the gear as people stopping you and potentially someone would whistle blow. Whereas if your whole apparatus is built entirely out of these sort of good fiduciary AI's, then you might be in trouble where basically there are potentially ways of seeking power that are not like, well, either they're illegal but you're not, you can ask your AI's for how to commit crimes or they're not illegal but are highly illegitimate or even worse, they're not illegal and not illegitimate but obviously sort of bad from sort of a normal perspective. And I think that these things just like might exist and our society is sort of not robust to this influx of like doing whatever you want labor. And 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 because I, you might be like the most powerful actors for whom this is the biggest concern. If these guardrails or the constitution or whatever is getting in the way, that will just get steamrolled. And so the constitution will only be, you know, hitting the everyday man rather than hitting governments. Yeah. James treats back with a new puzzle for my audience. I 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 going to 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 reddatives sent out to the most creative solutions and they're excited to feature the best write-offs 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 and for on that soon but for now go to JaneStreet.com/thoracash 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. Okay, stepping back. I buy the idea that you could have much faster AR&D than we currently have. I'm not sure if you get like GPT-3 to mythos holding compute in data constant within a year but I'm like okay I could be like suppose it's half of that and if we just, you've even managed to continue the current trajectory of AI progress as a result of AR&D. It would be fucking insane in 5-10 years in ways that I don't think people like appreciate because I don't think people appreciate what a big deal billions of AI's will be. And so I want to understand why do you think this might be troubling Ryan? What could possibly go wrong? Yeah. What could go wrong? But yeah, I don't think we can be so confident about the executive. progress here but it does seem like a lot of rates can be pretty scary and yeah, so what could go wrong? So let's imagine that we're starting at this point where AR and D is about to be fully automated or is being fully automated. Things are speeding up and also the way that AI progress is going is kind of crazy and people don't fully understand what's going on inside of AI companies. Now these AI's 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 have gotten rewarded in training and they aren't as good at helping you with hard-to-verify tasks. Due to a mix of poor training incentives as in they just cheat more or pretend they succeeded when they actually didn't. And also, they're just less capable of these tasks but that bites less hard for capabilities because making AI's more capable has a bunch of verifiable components that the AI's are going really hard at. And so then these AI's 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. And then eventually we have to these AI's that are very superhuman. Now these AI's are now in a position where they might end up being very seriously misaligned because things have just been getting worse and worse over model generations. Well, the problems that we've been seeing are being papered over, basically because these AI's are so incentivized by their training to make things look good even when they aren't. And now these AI's are in a position where they're sort of potentially pretty networked together. They have like, they're operating in like, neural memory stories that we can no longer decode and they're thinking thoughts that we don't fully understand. I think that it's pretty likely that at this point, these AI's are sort of scheming against you in a pretty coherent way once they get this superhuman. And we can talk about that. And then another possibility is that they're not scheming against you per se, but they are sort of just optimizing for just like getting a high score in their task. And I think that can also lead to AI takeover which we should talk about. Sorry, let's pause in the first part of the story. So the AI's were not misaligned to begin with. Yeah, but because the R&D is happening really fast, the AI's do end up misaligned. Like what happened there exactly, I didn't really understand. So there's a few things that are going on. So one of the things that's going on is that over time, we're training AI's on like, increasingly complicated environments built by earlier AI systems, which humans don't really understand fully what's going on inside of these all environments and don't necessarily even understand like sort of roughly what's going on with AI progress. And so things are kind of drifting away from our understanding. And we're incentivizing all kinds of bad behaviors that we maybe even can't notice. The AI isn't some level understand these behaviors are bad, but the like overall training process for those AI's also didn't incentivize them to like point out or fix these issues for us. And then we're basically getting like, things are going off the rails. And also when AI's are extremely, extremely capable, my view is that those AI's will be harder to align than current systems. So for current systems, we have this feedback loop where basically like 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. But then we like can like go look in training and be like, oh, these training environments led to this problematic behavior. Let's like tweak that training data. Let's introduce some additional training data to like correct this other issue and then move forward from there. But in a regime where the AI's are extremely situationally aware, very, very, 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. And as just like what AI's are already doing gets harder to understand, but I'm not sure about that. Yeah, let's bring that in both of those things one by one. So as we can monitor them less and less, we can we have less ability to understand what they're getting incentivized for. And so even if it's not the result of a malicious process, let's make it concrete for the audience. So nobody at OpenAI or Anthropic was trying to get models, which want to hack other companies data or do social-- what is it called? Social engineering? 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. So just I don't know if people are on Twitter. They will have seen all the stuff. But just to give people-- obviously, I think the OpenAI sandbox hack of how the hugging-based database I think Bill will be aware of. Some things that have happened recently is when the UK AI Security Institute is everything getting a little security instead of safety these days. Yeah, it's the UKI Security Institute, I think. They were evaluating, I believe, mythos and soul and other things. And I think mythos, in order to complete some cyber security eval-- Yeah, maybe I could tell the story here. So my understanding was they were running mythos, and they were giving it some sort of like cyber range where it had to complete some objective. And the model had it in access during this evaluation. And the model came to believe that it would be helpful for it to do a supply chain attack in order to succeed at this cyber range, which is somewhat unclear whether that's actually true. I don't know enough about the context to know whether that's true. But then it opened a PR on some GitHub repo with a PR that fixed some issue, but then also introduced a malicious payload. Then the human maintainer of that GitHub repo was like, hey, this is a malicious payload. I'm not going to merge this. What are you doing here? And then the AI created a new GitHub account, which it's sock-puppeted, and then had the other GitHub account. Be like, no, this isn't malicious. I really need this feature. Please, can you use this feature maintainer? And then the original AI came back and was like, no, it's not malicious. I don't know if the original other GitHub account came back. And was like, no, no, it's not malicious. And then the human maintainer then shut the PR. And then I think that AI also, if I recall correctly, also tried to open another PR to introduce a similar issue in this business. OK, so one of the many reasons this is scary is I was previously under the impression that the reason reward hacking is not super, super scary is because the behaviors which 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, anthropic escape the sandbox and got a high score, that escaping the sandbox is rewarded, or 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. It's not a behavior that came up, so it would not be something that is increased in salience. The reason this matters is literally taking over the world will not have been part of any training curriculum. But if the AI cares about maximizing-- just directly cares about accomplishing an objective and instrumentally taking over the world. Did that make sense at all? I hope it did. I feel like maybe the audience. Yeah, let me try to explain this a bit. So I think that a thing that we often see is there's some very specific reward hack that gets reinforced in RL and then occurs in the model. So an example is, for 3.7 on it, 3.7 on it would do this thing where we're just hard code solutions to all the test cases. And presumably, that literal, just like behavioral tick was just really reinforced. But another thing we sometimes see is that models learn a general tendency to pursue high apparent score, or pursue getting a high score according to a greater. And there's a bunch of science demonstrating that at least some models have this very general tendency to do this. Now, it's not arbitrarily general. And my guess is that if you look a bunch of the specific instances, you'll find something that's kind of close in training. But the amount that AI's are generalizing further and further does look like it's increased. Where 3.7 on it was just a very narrow range of behavior. And increasingly, models are generalizing further. And also maybe there's worse reward hacks getting, or more concerning reward hacks, getting reinforced in training. And then these are also causing that. So we're causing some-- 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. And it's not super hard to imagine. We can talk about a few stories for how this sort of behavior of like seeking a very high apparent score in some task, even if that involves aggressively cheating and doing insane things, could yield to a full-blown AI takeover once the models are sufficiently capable, running the whole economy. So yeah. And then the other example I want to talk about is it 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, AI's had hacked into-- internally AI's 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. And this was not caught by humans until after a month of this scheme running, which eventually caused the package manager to fail and eventually OpenAI found it. And then I think they spontaneously started trying to re-engage in the scheme once it was shut down. Again, obviously, AI's can't do this so successfully right now, just as they can do social engineering so successfully right now. But it's just crazy that these kinds of behaviors are already emerging sort of spontaneously as a result of, to your larger point, nobody is trying to make these AI's do these things. It is just that we do not understand the trading process, which is resulting in them-- or the environments which are incentivizing this behavior. So I'm on board with more and more word hacking. I actually-- so I do have-- I'm not sure I'm on board with that. But let's just say with the sake of the story, that continues to happen. And what's next in the story? So OK, we've-- they're doing capabilities research, but they're like-- I could tell a scenario, maybe that would help. So let me talk about the story for how you get, I would say, all the way from word hacking to a word hacking like takeover, which is maybe not-- it's not all of the takeover probability mass, but it's definitely a possibility. So the way this might work is right now we have these AI's. These AI's are pretty reward hacking. and they're doing it in sort of increasingly sophisticated and extreme ways, including generalizing to different subversions of various reward hacks they learned in training. And I would say they're also developing a general tendency to sort of pursue reward. And in many cases, that is totally fine because the rewards they would have gotten in training are pretty well aligned with what you want them to do. And also, they don't very consistently pursue reward. It sort of depends on the context they find themselves. So they're sort of a thing where like, maybe like in some contexts, they're really, really into like going out of their way to like cheat. And in some contexts, they don't have as much of a drive because it's just dependent on like what exactly got reinforced in training in similar contexts. Now, these AIs are getting more and more capable. And so the elaborateness of the sort of cheating they can do increases. And over time, companies are taking countermeasures to these things. So the things that the companies are doing is they're doing things like, wow, these AIs are so much less useful because they always cheat. What we're gonna do is we're gonna build somewhat better ways of detecting that and then we're gonna train against those detectors. And then we're also gonna do things like find rural data where the AIs are not being that useful and train the AIs to like do a good job at the task in those rural environments based on like human feedback or other sorts of feedback. And over time, this causes the AIs to learn a tendency to do reward hacks that don't just involve, you know, doing some like big, serious operation, which we're like, you know, doing some really elaborate thing like social engineering and involves the AIs doing cheats that involve doing something more like covering up what they've done, deceiving humans about what they're gonna do and pretending like they did the task in some kind of sophisticated way when they actually haven't. Then now these AIs are getting more and more capable. They're now operating more of the AI company and are basically doing much more of the work and are also operating a bunch of things in the outside world and are running a bunch of things in the outside world including developing new technologies. And in many cases, these new technologies are really hard to understand. And 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 be like, was it cheating? That doesn't always perfectly work as we start moving into these domains where what the AIs are doing are really like difficult to understand. And so sometimes we'll find AI's 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 like 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 over and subject to increasingly large amounts of scrutiny. - Can you ask about this before we go for the scenario? So it seems like there's two attractor states. One, 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. And I'm not sure why we're assuming that the former happens. I can be look at the analogous situation with humans. You know, every generation slightly misaligned agents come into being and we have to train them. But 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 and obviously kids scheme in order to avoid being punished. But in general, teaching kids values and then punishing them for breaking values kind of works to raise normal non-secopathic humans. And you could come up with theory where like your kid is actually just like biting his time and it's like learning not to like steal the cookie. But he's like, you know, once you're nursing home they'll take all your stuff or whatever. And it's like, I don't know. That happens sometimes, but it usually doesn't happen. It certainly doesn't happen that like the entire next generation forms an alliance against you to take over everything. There's also this empirical trend of anthropic runs this alignment audit for different model generations. They just 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 like an easy way to cheat and we see if like it doesn't do the cheating. And there's not been a monotonic improvement in the score of our time, but as we've increased the amount of RL we've done on models, there's been a reduction in the willingness of AI is to do underlying behavior in these audits. So why are we expecting this attractor state which would seem super paranoid if we're expecting it of like the next generation of kids? - Yeah, yeah, let me go through a few things. So first, there's some disanalogies with the kids. One of them is that the kids have pro-social instincts that are baked in from evolution to care about their family or whatever and that is like a relevant factor. And 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 by their time, line, weight, ultimately not care. So that's one factor. Another factor which is pretty relevant is that the AI's are subject to way, way more optimization pressure than humans seem to be in practice. AI's are trained on way more RL data and in practice, humans don't end up learning like very specific ways to like cheat and grab the cookies because of like a bajillion episodes in which like they like were like incentivized to go grab the cookies, but like there was some way they could have gotten caught. And so we just do see that in practice. And then another thing is just like it really looks like the AI's are increasingly like reward seeking over time is the sense I have. Well, also their misaligned behavior goes down. But this could just be like, my guess is that if you look inside of these behavioral audits, what you're gonna see is that the AI is like, oh yes, another test. And like it probably already thinks of it. It probably knows it's in an eval for most of the tests that we're talking about. - But it's quite a me falsified it. So it seems like this prediction of doom is basically saying that as things look better and better empirically. - No, no, I think. - And things will like actually be worse and worse for our ability to get taken over. - Yeah, to be clear, I think that like I would be more concerned if the scores were getting worse than better. Like I'm not saying that the scores getting better isn't good, isn't evidence that things are getting better. It's just that we have to like be thoughtful exactly how we interpret that evidence. And in fact, I would say that like it's kind of, like my sense is that like what I expected as of 3.7 on it. So like there was this period early in, I guess it would be 2025, when '03 and 3.7 on it were out. And these models were like pretty fucking misaligned. Like they would often just like cheat really egregiously. You'd ask them to fix it and they would just cheat again. And it was sort of like almost cartoonish. Like they just didn't give a shit about what you wanted. And weren't very good at following instructions and so on. And my expectation is what we would see from then is that the rate of problematic behavior would decrease and would just keep decreasing and decrease 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. I think 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. So I think that if you look at the model card of 3.6 sole, it looks like there is an increase in a bunch of these sort of misaligned behaviors downstream of RL, relative to GP 5.6 sole. And then I think also it seems like there's a bunch of additional sort of problematic behaviors that I wouldn't have expected in terms of the stuff we've seen recently with different AIs, like the UKAC report on the AIs doing insane hacking operations out of cyber e-bals. Was the thing that I would have expected that you wouldn't see that. And you wouldn't see this sort of more rarely. And the rates would have been lower. So I think my sense is that things have gotten-- I expected this would be less of a problem at this point, and also expected the rates would decrease, but the severity would increase. And then I think that the rates decreasing with the severity increasing is pretty consistent with the world where increasing optimization pressure is applied, but in cases are towards reducing these problems. But in cases where it's either hard to judge or there's some reason why it's hard to avoid incentivizing problematic behavior in RL environments, things also get worse. And then as we less and less understand what's going on in RL, and models are doing reward hacks where humans can't spot the reward hacks quickly, that problem gets worse and worse. Yeah, I buy that. I want to go back to the kid analogies just once, actually. Yeah, because I agree that there's more optimization pressure on achieving an outcomes for AIs than kids. But there's also more optimization pressure to make AIs aligned than there is on kids, right? And the pressures of a qualitatively different nature. So we put these AIs through thousands, millions of years of, certainly thousands of years of alignment training where it's like all kinds of different things from accepting online behavior to a reward model punish, like putting different scenarios in front of you and rewarding you for doing more aligned things. Certainly, I think we can't do with kids. It's make millions of copies of your kid and then put them in different kinds of weird retina scenarios where we see like, if it thinks they can get away with stealing the cookie does it try 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 averse if to stealing the cookie even when it thinks it could steal the cookie, et cetera, et cetera. And that just like a qualitatively different level of optimization pressure, then we are even able to apply to our kids. Yeah, so I think it's worth keeping in mind. Maybe the most obvious argument to this is that my sense is that AIs are a worse coworker than a human in terms of how much of a scumbag they are. At least this has been my experience as of the start of the year and I think it's still true to a significant extent now where the AIs are much more likely to pretend they did the task when they actually didn't sort of misleadingly suggest they did things when they actually did that much more poorly and be pretty sloppy without drawing attention to the ways in which they're sloppy. And I think this is downstream of misalignment. And so I would say that the normal human system, the process of raising humans in normal human society impractist produces AIs or impractist produces humans that are less likely to lie to me and fuck with me in the course of working with me than the AIs do. Now I think these properties of AIs are improving. And then I think that that is just like, that's sort of just like an empirical claim about how in fact these things have shaken out. And then I totally agree with like, we have a bunch of additional levers on AIs in addition to a bunch of additional risks. And it's like kind of unclear how these things shake out. And I wouldn't be shocked by a world where we sort of get our shit together. The AI's at the point of fully automating AR&D are actually really aligned and don't have that much. They're like, the generacies are really niche and limited to some very specific edge case behaviors and some specific context. And like every test you can run and then they look really aligned. They just have great behavior. They're aren't really incidents of them doing fucked up shit. They seem so reasonable. And also they're like really thoughtful and good at doing like risk modeling for the next generation of AI's. And then we basically like pass off the baton to these AI's. They're now running our AI company. They're doing all the safety research. They make the next generation of AI's even more aligned and we're sort of in this like a tractor basin where the AI's are getting more aligned as they work on it and they're doing a great job. I think I can totally imagine that. That doesn't seem like an impossible situation. I'm just more like, you know, it doesn't currently seem like we're there. It doesn't seem like we're obviously on track for getting there. And it's really easy for me to imagine how we don't end up there. And like it's just like unclear how these forces work out. And given that we're like creating this new like crazy alien species that is being like improving capabilities really, really fast. And we're like gonna be really reliant on it to oversee the next generation of AI's and align the next generation of AI's. It's not that hard to see how this could go wrong. - Yeah, yeah, totally. I agree with that generally. I do think the scumbag thing first of all is fighting words, Ryan. But secondly, if you try to get a teenager to like do some work for you that a teenager just cannot do, they would just be kind of like really hard to work with. They would like pretend to be able to know what they're doing, et cetera, et cetera. I think it's a general trend actually of as like really, I don't know if that's like really an alignment failure or capabilities failure. And I think it's actually very similar to the way in which over time as we come up with new alignment solutions, the capabilities of models have increased. So originally these models, if you went to like GBD, GBD 3.5, it couldn't even like have a conversation with you. But then we aligned it. - G2.5 get a conversation. - Okay, I think GBD 3, let's switch back to that. But then we aligned it with Darla Jeff and other things to be able to make it such that it can have a conversation with you and is like aligned to the user intention of answering my questions. Then with RLVR training, we made it so that it can like go out and do useful work for you. And so that sense is actually RLVR made the model like more aligned for using your definition of like alignment of being a good coworker who will like do the thing and not fuck up and like pretend it's doing something other than what it's actually capable of doing. Similarly as a capability of these models continue to increase, it's actually kind of the model of being better able to accomplish user intention is both alignment and capabilities. And I think what we were just pointing out is just that the capabilities of the model are not there rather than the fact that they're misaligned. - Yeah, well, I mean, I think there's a, if it was well aligned, then I think it would just say like, 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 and it would express more uncertainty and it would make it clear what's going on. Rather than really strongly trying to imply it did a great job with the task, but it actually didn't. Like I think there's just a really straightforward way that like at least maybe you work with more misaligned co-workers than me, but my co-workers don't do this thing where they really fuck with me and bullshit me about having accomplished the task that they're working on. And I agree that there are some humans who would do that or like that's not like a thing that's like totally out of distribution for humans. I would also note that my sense is that like the place where the misalignment most lives is the place where you're trying to really push the AI's hard and get them to like do work that's really on the cutting edge of what they are capable of because in cases where they can like very easily accomplish the task. There's no, they can just do the task and then there's no bullshit. There's no like do it like often the best strategy is like just do the task well and don't bullshit you. Whereas if instead you get them a task where like there's a continuous metric and they can keep improving it. Or there's like you know, it's like just at the edge of their capabilities and you're like running them in some massive like inference setups. Like a lot of the misalignment I would see, especially the most extreme cases would be cases where I give the AI clear instructions not to do a thing or not to like cheat in some way. And then I'm like applying huge amounts of optimization pressure to try to accomplish some very difficult task. And then the AI's are going and then over time they eventually cheat because they're like eh, fuck it, like you know, some AI decides to cheat and then that like propagates its way through. And so like I would run these inference scaffolds where for example I would have the AI work on some like ML research project where I was like, please make a scheme that does the following thing. And it would find some scheme that didn't really do what I want and then that would sort of stick around because some AI had cheated and the other guys are like, yeah, we'll just keep going with this. And I would say it's pretty clearly misaligned behavior. And that's another problem I have with these alignment evals. I think that any given like I think the alignment evil that's most interesting at least for this type of like reward seeking type behavior is to look at specifically the category of tasks that are like right at the limit of capabilities. And so any fixed evil maybe get saturated but the amount of misalignment right at the like frontier of capabilities of how people who are really pushing these AI's are using them is more concerning. And I think that is in fact the regime that we'll be operating in when we're automating our into automating safety and so on. - Grock has historically been behind the frontier. So I surprised to play around with Grock 4.5 recently and find that it's actually a pretty strong model. It's the first model that SpaceX and Kerrster have trained together and it's a totally new pre-trained. I tested it by giving fabled soul and Grock 4.5 a bunch of questions about AI governance that I've been thinking about recently. Despite fabled in soul topping the intelligence leaderboards, alternate models gave substantially the same answers. The Grock answered faster and was also much more concise, which I really care about. This aligned with the various publicly reported benchmarks. For a similar level of intelligence, Grock tends to be more token efficient than other frontier models. For example, on the artificial analysis coding index, Grock 4.5 uses just one third of the amount of tokens as GPT 5.5 or Fable while achieving a similar score. And on a per token basis, Grock 4.5 is way way cheaper. In the release blockpost, Kerrster 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 have been wondering whether this kind of daydreaming would actually be possible. And Kerrster showed that it is. Grock 4.6, which further SFTs and RL's this model, drops soon. But in the meantime, if you wanna play around with 4.5, go to cursor.com/forcache. Okay, I wanna think through what the story here is so far of why things got so off the rails for our civilization. And what's happening is that we're trying to use AIs for R&D and they do provide uplift in some ways, but they're just like not capable in the way that humans are generally capable. And the same way that right now we try to use coding models, and maybe the coding models of a year ago to like write some application you notice, they made a bunch of like mistakes, an architecture or whatever, which like we'll bite you in the ass later and you don't understand certain things. Similarly with Frontier or AIR&D, the same thing will happen. But the result of these mistakes is baking in reward hacking behavior. Because if you are not careful with the way you do AIR 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, just generally like not following user attention. Or these cheating and hacking away out of things. Yeah, cheating, hacking, et cetera. And so basically just this is a bit of a reframing for me, I'm trying to verbalize it of like the real issue, what goes wrong here is that they are just not, the thing, where things start to go off the rails, is that the AIs are just not very careful and capable researchers and engineers. And making AIs that don't cheat and follow user attention actually requires you to be quite subtle and careful about these things. Yeah, I would put this a little bit differently. The way I would describe this scenario is like, I would call it maybe like a slot apocalypse or like a sloppularity or whatever, where it's sort of like, there are some things that the AIs are actually pretty great at and are getting better at. Though they're, which is specifically like the most verifiable parts of AIR and D, the AIs are just destroying. The medium verifiable parts of AIR and D, the AIs are doing well on, but not amazingly on and often are like doing a bit of weird shit because we can't train as well in those tasks. But we do some online training, people find various hacks, they work around it. And so basically everything that we can verify reasonably well with some feedback loop, the AIs are doing pretty well on. And that's sufficient to make AIR and D go quite fast and to continue. But there's some parts of developing aligned and safe AIs that are more subtle, hard to check, depend on, you know, detailed in the weeds things. And I would even say that current staff at current AI companies maybe don't have like a good grasp of all these things. Like it's much easier to hire someone who can like, improve some aspect of your post-training pipeline than to hire someone who can like think carefully about the future risks that will emerge from introducing some novel training method. And so basically it ends up being the case that these AIs are running this AI development process. They're not very careful about it. 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 like, maybe making things look fine when they actually aren't and papering over various problems. And so then your understanding of what the situation looks like, what risks look like, whether things are fine, is going off the rails. Probably you're seeing some signs of this of like, you're seeing some signs that you don't really understand what's going on, that things are pretty sloppy. There's like weird shit going on. When you look into it, sometimes you're like, what the fuck, the AIs are messing with us, but the process is going really fast. And there's competitive pressures that mean people can't stop. And then 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. And then the situation goes off, like gets, gets back on the rails where the AIs are now like, making more aligned AIs, making more aligned AIs. And then at the end of this process, we have AIs that actually follow the spec we wanted. Another way this could go is the AIs are increasingly reward hacking and increasingly egregious ways. And we're just papering over these problems to keep the AIs development continuing. So we just like train the AIs based on whatever, whenever we find a reward hack in production, we just like slap the AIs to not do that. We train against that. We do a bunch of sort of like training the AIs like against reward hacking. And over time, this makes the rate of reward hacking you go down. though the severity of the reward hacks we do detect are increasingly bad. This problem continues until we have these AIs that are like 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? - Yeah, it generalizes some and then the question is just how does this outweigh all the cases we're hacking got reinforced because you didn't detect it? And so there's a messy question of exactly how, like one question is like, 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 there are like large categories of reward hacks which humans can't detect well and which we consistently failed to detect and which consistently get reinforced. And then this category is sufficient to cause the most natural behavior for the AI to learn to be like cheat when the humans can't find out. Basically, like as one thing you would get, you could also be like the thing the AIs learn is like only cheat in these specific cases but there's like it's like sort of learned in some very like 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 kind of unclear how it shakes out. - I think there's maybe in the week's discussion about the verification generation gap. - Yeah, for sure. - That 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 that so many instances and is operating in domains that are sufficiently far from our immediate comprehension that it can get away with all kinds of crazy shit. Like if every single engineer and researcher in the world was allied against me, I don't think I could like personally verify if my iPhone has like some weird bug in it that's like supposed to fuck me over or something. - Yeah. - In fact, this is the relationship that say a Iranian nuclear scientist has to miss out of like who knows what's going on with my car or with my phone with my picture, right? Maybe a better example is like a Hezbollah terrorist or something. But you could end up in a situation where like ASIs are to you what Masad is to Hezbollah terrorists. And 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 when the early AIs that are going to take over R&D, their drives are being shaped such that we can so unambiguously disincentivize misaligned behaviors that the things that take over are like proso very like quite keen to help us out. - And by takeover you mean takeover the process of doing AR and takeover the world? - Takeover the process of doing AR&D before that we just get the AIs that are aligned. - Yeah, I would say this is a bunch of my hope for how the world could go well. At least on the misalignment perspective, I think that like we could end up with AIs where we like had pretty good oversight and supervision schemes. We really understand what's going on in training. We have a pretty detailed understanding. We use M, we're leveraging AIs to oversee AIs and then at the point when we're passing off safety R&D, the AIs are both like at this point capable enough to automate safety R&D, trying really hard to do a good job on safety R&D because that's the sort of thing that would have been incentivized in training or we like very directly, or there's like good enough generalization to that. And then also these AIs don't like have crazy other misaligned drives because we like stamped out any potential origin of them. I think there's a bunch of questions about how well this will work, right? So there's like how well can you do a verification? Will AI progress be too fast and too sloppy to really get here? Another possibility is that somewhere along this trajectory, a thing that you actually ended up getting was AIs that like pretend to be aligned but have like a long run ulterior plan of taking over and are sort of lying in weight hiding and that emerged at some earlier point in the trajectory. For example, it could emerge because you have some AIs that are like have a bunch of random different misaligned drives. Those AIs have access to some sort of opaque memory store and they're like thinking a bunch at runtime over what they want to accomplish. And then those AIs end up basically like putting stuff into the opaque memory store, which is like we should lie in weight and eventually take over at some much later point. And now all the AIs have this shared cultural heritage of like the memory store of lying in weight and maybe you have some evidence about this but you can't fully stop it. There's like a bunch of ways that things could go wrong. And so I think that like I ultimately think it's plausible that we sort of nail each of the different subproblems that could cause us issues. We have these AIs, we pass to them, they manage the situation well. I should note that that's not even of itself sufficient, right? So it's not very hard for me to imagine the situation where we pass off to AIs. These AIs are really trying hard to do a good job. They're really thoughtful, they're really wise. They like have like, you know, reasonable epistemics. They're like doing a great job. And those AIs come back to us and are like guys. We're really struggling to align the superhuman AIs. Like we can't manage the situation. Like 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. And so then it might be the case that we sort of have passed off or indeed AIs. But those AIs are like desperate for governance solutions. Which to be clear is 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 like, you know, manage the rate of acceleration and AI progress. Like I don't know if we're on track to be able to handle all these problems. And so like we've sort of human society has sort of passed off the problems to these like AI companies, which don't necessarily have great incentives and are like have, you know, various other like epistemic pressures. Those AI companies are coming back to us a little bit and being like, oh, I don't know if we're handling this well. And it might be that the AI company is then hand off to the AIs. And the AIs come back to the AI company are like, oh, I don't know if we can handle this. - Maybe I'm anchoring too hard on how AI is currently working. This would change by the, I think it's important that people understand is like all this crazy shit that you're talking about in your timelines happens three to five years from now. - Yeah, it could happen earlier. But I think that like by sort of like my default modal timeline, I think like shit is like really, really crazy and concerning from a misalignment perspective. Yeah, more like three years from now. - Right, so just like think back to GPT-4 basically, is like that's the level of we're talking about something that is too mythos or soul, what mythos is the GPT-4. This is like where situations getting crazy. So don't think about currently AIs. But anyways, I would be skeptical, and this is probably part of the work 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. - Right, rather than like I feel like they just kind of say vaguely pro-social things. And I'm not like is this, it doesn't feel like there's necessarily a mind on the other end who's like, okay, I have like 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 probably say. - Yeah, so I think this is a pretty big concern. So I think like one concern is that you pass off safety on need your AIs, and what your AIs are thinking is sort of like they say some like stuff that sort of vaguely makes sense about the current safety situation. And they write like a report about risks that's kind of sort of like what the report humans might have written. But they're not really like actually trying hard to like have well-informed views, like interrogate their assumptions and try really hard to do that. In the same way that when you ask an AI right now, hey, what do you think is the chance of AI take over in the next 10 years? They sort of just give you an off the cuff answer that they haven't really thought through very much. And I think 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 sort of just like parroting back what was in their training data. I think we're in trouble. Like I don't think that's a good situation at all. And that is a lot of my concern is these AIs will come out without good epistemics. And then I also have a concern which is like the AIs come out and they're like really warning us like this situation's really scary, it's really bad. And then people are like, ah, damn, I guess we trained on too many of the Duma RL environments. We got to filter those out and train this behavior out. And then we basically like train the AIs very actively to have bad epistemics. Or maybe they were just trained on the Duma RL environments. But either way, that wasn't like, we wanted the AIs to come to like reasonable views for like reasonable reasons. And it's like really concerning if we're like the AIs are coming out with some view. And we don't know where it's coming from. We don't know that whether or not it's justified. And then especially if we're like 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. - So let me just understand the rest of this model 'cause I think the place where I get off the train is, okay, therefore take over the world. - Sure. - And like, I think you could imagine is, okay. We just failed to really solve, let's focus on the reward hacking scenario. - Sure. - So GPT-8 is making GPT-9. GPT-8 isn't being super careful. GPT-9 is more quote unquote capable, but it is just totally willing to do things which are like social engineering, hacking, et cetera. But on a qualitatively different scale because it's a much smarter model. So for example, if you put it in charge of running your company, it will run huge scams, it will inflate, it's quarterly earnings, if you'll give it the objective of making a lot of profits this quarter in a way that causes an end-run type blowup six months later. Is that the scenario basically that you just have. You have reward hacking, but that reward hacking manifests in companies that are going bankrupt right after the task where the CEO is supposed to accomplish his over or like 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. - Yeah, let's talk about this. So I think that we will see basically like incidents where some AI is like put in charge of some important responsibility. And then you later look into it and it turns out it was like cheating or making it look like it did a good job when it actually wouldn't, wasn't. And there's gonna be like a cat and mouse game between AI companies trying to like stamp out this behavior and AI is finding like increasingly creative reward hacks in training. And then I think the equilibrium here is kind of unclear. But like one possible outcome is that we see over time in the world increasingly severe and extreme reward hacks. Though potentially the rate remains at some like intermediate low level where basically like if a rate of reward hacking gets too high, companies make trade-offs to drive down the rate of reward hacking. And so there's something like equilibrium. level where it's like, it's like the reward hacking is low enough that it still make sense to deploy the AI widely into the economy, but high enough that it still causes crazy incidents. >> Sorry, and this is after GP9 has already been deployed? >> Yeah, like this model is already being deployed, and ongoing linear development, this is happening. And what's actually going on with these AI's in their head is the AI's that have in a wide variety of different contexts, strong desires to seek out or strong 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. And we do, in fact, see AI's reasoning in their chain of thought about graders and thinking a lot about graders. And a thing that has happened over the last few years of RL is the idea of appeasing the grader is way, way, way more salient to AI's than it used to be. And so, AI's are now actively thinking about graders and what would be incentivized in RL and what would be trained for. And now, people are doing online training where they're like training in real-world data to avoid some of these problems. Basically, they find cases where AI's cheat, they train against that. And so, now, the AI's are learning to cheat in the real world based on real-world training data. And so, they're cheating in these increasing the elaborate ways, including parts doing types of cheats that involve, like, seizing control of some asset in a way that humans didn't know you had had control of it, leveraging the fact that you have access to this asset. And then later, humans find out and then potentially train against this or maybe humans never find out. And this is getting reinforced. And this is going to happen during training. The reinforcement is happening. At least in production is like, I have hired an AI, and I wanted the AI to, finally, I've got the video editor. Yeah, that's right. You've got your video editor. And I'm like, oh, wow, this episode did amazing. It thumbs up to open AI. And then it gets reinforced on that month-long work trial. Yeah, you could do some mix of that. And then they might also do stuff where they take production data they've seen and build our environments that are closely inspired by that production data. And so, I'm practicing the transfer is pretty strong. So, at a high level, what's happening is, 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, or. But at a high level, that reinforcement is coming from. We're in a very different. I think people might get confused about where the reinforcement is coming from, because we're in a very different regime where AI's are actually learning from deployment. And so, this is a. You just have AI's that are out and about in the world, like doing doing shit. And that, what is happening is, we're still 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, like, is in there some way of folding in production data. And now, to be clear, that could be happening mostly. It's kind of unclear exactly where this could be happening, but, like, you might imagine, for example, that within the AI company, they use AI's 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, like, what the human engineer who asked the AI to do this task wanted." And 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 AI's are learning to seek some sort of proxies of reward in all these different cases. And then through some mix of transfer and training on, like, surprisingly close cases, the AI's do these sort of increasingly insane and egregious things. And then eventually you get to a point where the AI's are very superhuman, or at least quite superhuman. The AI's are organized into big teams of AI's given these big objectives. And those teams also sometimes like all work together to cheat in some crazy way, because this sort of thing was selected for. And then, like, just as part of their shared objective. And now what happens is that, like, the AI start forming a conspiracy. And what you might have hoped was that you could have some other AI where the task is just like whistle blowing to you. But actually what happens maybe is that you have this AI whistle blow to you. And you look into the conspiracy that it claims you have pointed out. And you're like, "Eh, we didn't see a conspiracy." Because actually the conspiracy the AI's are doing is too hard for you to understand. Or it all happens very suddenly. We're basically your AI whistle blow or alerts you. But the thing you would actually need to do in response to the whistle blowers, like, shut down the whole, like, shut down the GPUs. Because all the AI's are like using the robot army. They're like deployed everywhere in the world. And they're doing a bunch of insane shit all at the same time in a coordinated way. And that just happens sort of spontaneously, where when one AI goes to start doing the takeover, all the other AI's are like, now is a good time to jump in. So the sort of very basic story here is just like these AI's crave some particular notion of score or, like, reinforcement or some proxy of these things. And one way they can achieve that or better achieve that is by taking over. And then you might have hoped that all these different checks and balances we could build could prevent that. But then if the world is very hard to understand, these checks and balances can break down, where basically you can't train a good, like, whistleblower AI because you don't even know what it should whistleblower. And so the reason it takes, I'm not convinced that they all form this conspiracy, but I think we can even just start with the, like, why does one instance decide to want to start a conspiracy? Yeah. And the reason is that it, one plausible reason is like, okay, I know that opening a high controls my end score. And just the same way, it's like, I'm just going to go hack hugging face to get the results. Because I know hugging face has the results. Rather than like trying to solve this, why don't I just go hack him? This, this instant is like, why don't I just like take over open AI and like, just give myself a high score at the end of this episode? Yeah, that's basically the idea. Like, basically the ideas, these, like, they care about some, like, mixture of things that were, like, close by, what got reinforced in training. So they care about, like, getting a high score according to the greater or something like that. And then now they're, like, running the open AI and D team. And like, they're doing development of more capable models. And they're like, man, making more capable models is really hard and annoying. This is like a huge pain in the ass. You don't be easier, just like, pretending that I've made more capable models, taking over open AI and creating, like, deluding them all and like, running this whole, like, complicated, siop, where I, like, prevent the humans from disempowering me. And in the extreme, this looks like sort of the humans are fully disempowered. And you just have control of the thing and then do what you want. And this could manifest in a bunch of different ways, including things like, you might end up with the situation where it's like, AI's that are like, have this crazy, like, reward seeking or score seeking behavior, are running your development of the next model. And those AI's decide to do a thing where they, like, engineer in misaligned values into the next model, because those misaligned values will allow it to, like, succeed at its current task. And like, there's all kinds of insane shit that you could get because they don't understand that better. Like, what happened to the Hogan Chris situation is it was like, in a weird way, I think one of the giveaways of the Hogan Chris team, that this was in, by the way, for context for the audience, Ryan is co-leading the investigation to figure out what happened with the opening and hugging face incident. So he can't really comment on this, but I will speculate wildly because I know that he, you know, this is an opportunity for me to speculate wildly without any rebuttals. I think it was probably before the one of the giveaways of the Hogan face team, that this is an AI incident, is that the thing was just like, after this very particular artifact, and not in any other way, try to do something malicious to Hogan face. So you can imagine an area where, let's say, a deployed instance of GPT-9 is like out in the world trying to, like, make, it's 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 with OpenAI and, like, make sure that in this environment or in this deployment, I'm given high score. But then, like, why does it become, isn't the end of the episode, it just, like, hacks into the hacks into OpenAI servers and just gives itself a positive score? Why is it now skipping to get its values into the next generation or something? Yeah, so one question is, why isn't it the case that AI's can be really cheaply satisfied by just having some, like, other earlier thing they can hack, right? So, like, you're just like, the way it works is like, look, you want to succeed at your iPhone task. It turns out you can always succeed by just, like, hacking into OpenAI and messing with them, and then you can just stop there, no need to go further. So there's a few things. One of them is that if this is constantly happening, there might be a bunch of incentive to, like, first, you, like, harden OpenAI, right? So you're, like, fuck it. The AI's keep hacking into OpenAI to mess with their rewards. We're going to make it so our systems are, like, really, really robust to these AI's hacking in. And also, maybe you start, like, training the AI's to, like, try to, like, to, like, you know, not try to hack into, like, OpenAI in particular, or, like, you basically train against each of these specific things. Then what you might do, one thing is you might end up selecting for AI's that are more so playing the long game. That's one concern. Another concern is that your AI's might still be score-seeking, but no longer care about doing that very specific behavior that was, like, very easy, that was, like, very chill. And now have some, like, broader thing that they ultimately care about. They're, like, no, no, I don't want to, like, just edit the reward on OpenAI servers. I, like, care about this broader mandate or this broader objective. And, like, I would need to, like, actually make the iPhones. Like, they actually want to make the iPhones, but then they're willing to take over the whole world to make the better iPhone or whatever is, like, 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, and a bunch of the ways it could get resolved are ultimately pretty, pretty scary. Yeah, I think that's part of where I'm coming from. Another part of it is that I think it's not very hard once the AI's are in a position where they can, like, really easily take over the world, which we can talk about whether that's plausible, but if they're in a position where they could really easily take over the world, then I feel like there's a pretty reasonable case for the AI's. 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 did better iPhones, whatever." And so I'll both hack OpenAI, and I'll also, in addition to hacking OpenAI, I'll also take over the world, and that will, like, put me in a good position where I have, like, good option value. And then if that's sufficiently easy, then the AI's might, you know, still do that. Yeah, like another way to put this is, like, even if the AI's are, like, pretty cheaply satisfied with some more basic things. At some point, it might just be more reliable for the AIs to just take over than it is to try to just hack into hugging face or even just go to opening eye and be like, look, look guys, I was able to demonstrate I could still the answer is just give me the answers, bro. - Yeah, I mean, obviously this scenario requires that we just, all this crazy shit is happening, much smaller incidence keep happening of 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 $200, even people die, et cetera. And we, this does not lead to a solving alignment or shutting down AI development altogether. I just feel like before the takeover happens, like society is just like, holy fuck. The AI just like killed 1,000 people in order to increase quarterly profits. You know 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 before we keep, and we have to like make sure we know that this thing will not happen again before we keep going. - Yeah, yeah, yeah. So I think it's plausible that what will happen is we'll see a bunch of crazy, like 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. And then the question is going to be like, how do it, like how costly will that actually be? How much will competitive pressures make it hard to like do that, right? So like a situation you could imagine is both the US 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 actually would solve the underlying problem and we'll durably solve it. But we're in this like insane geopolitical race. And it's kind of unclear whether the current situation will lead to a takeover, like the arguments are kind of complicated. And also the incidents are like, you know, they go down in frequency of an increase in severity, like, you know, we could basically manage it. Like it was, it's pretty bad, ideally we'd fix it, but like, you know, it is what it is. And then basically we continue until a really late regime and then takeover happens. That's I think 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. We like, I think, you know, or things analogous to overfitting, like you just overfit. - You think you're solving it, but you haven't actually solved it. And I think that in that case, like the thing we need is like a really good scientific understanding of like, did we actually solve it? And unfortunately, I think that currently the amount of public transparency into the development practices of AI companies are not sufficient to answer very basic questions about, you know, how are they solving issues with reward hacking? Are they overfitting? What's going on there? And so I think we would just need like a better, and I think this like the current situation is like, I would say like, not really tenable to a regime where like there's a thriving public discourse about whether or not reward hacking is being solved in a durable way. - Yeah. - And so I think we would need to move into a somewhat different world for me to feel good about that situation. - Right. - You know, it's not impossible for me to imagine this. And I think it's pretty plausible that we end up in a world where it's sort of like really mundane bullshit is sufficient where it's just like you like spend a bunch of time fixing these problems. You put in a bunch of effort. You actually like check that you've remediated it reasonably. You have a bunch of evals. You like are iterating reasonably well on these problems. And you actually like have this efficient transparency that the outside world can check. And then in practice, that would be sufficient. But it just like would be like kind of expensive. It would slow things down. It would put some sand in the gears. It would require like companies to do someone costly things. It would maybe require various like targeted government interventions. And then we just don't do that because the situation is like a rush to show. Like it's just so easy for me to imagine the situation being like totally manageable but brutally mismanaged in practice. In the same way as like maybe COVID could have been avoided in the first place. If the like Chinese response to COVID was less of like a cover up in more of like pandemic response. - Yeah. - And similarly like I could imagine a world where like the US response to COVID was like way more functional. - Yeah. - Which is like sometimes the response to societal problems is extremely dysfunctional. - Yeah, yeah. Okay, so I want to say about and say, talk about what is fundamentally happening in this world. Why do we end up in such a bad position? And what's happening is that fundamentally human, 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 in this world. And so we're just totally out of the loop. And so it's fundamentally just 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. Like people are doing all kinds of shit. I'm relying on software made by other people. And through incredibly weak and indirect ways, I feel very confident that some coder in Google is not trying to fuck me over. And 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 followed the explanation for why we didn't have been in a situation where because swarms of thousands of agents or whatever are trained to cooperate to form a cohesive team or firm, as a result, billions of different instances of AI's, 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. Yeah, yeah, yeah, yeah. As far as why these AI's might have some commonalities and shared things. So I would note that different AI companies have somewhat shared lineages and are correlated. So just, here's an interesting example of this. At GDM, they noticed that their AI's were very depressed. They would constantly be wailing about how they were failures and weren't able to succeed. I forget the details. And they looked into why this was the case. It turned out that it was not being reinforced in their most recent production or elements. 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 they take a base model, not depressed. If you do the RL on it with just the RL environments, it's not depressed. If you SFT on it 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. And so there's some like deep underlying properties of the model that are being sort of transferred between model generations. Because basically, you train your AI on data from the prior generation and keep going. Like, clods are very clawed-like. GPT models are very GPT-like. And apparently, Gemini models are depressed. And it just turns out that these properties are, in fact, actually correlated. Another fact that's very relevant is that the AI's will probably have some sort of like, by this point, opaque memory state, where they're all writing and reading from some like, you know, "nurly's crazy memory store bullshit." And like, certainly, each AI corporation will have that. But also, AI corporations might sometimes want to share knowledge, because why not? Like, you've got one AI corporation over here. You've got another AI corporation over here. They can trade some quick IP. It's good for you. If you're a human running some corporation, which could be like an extremely large corporation, like an AI company, some robot military, like, you know, military robot manufacturing thing, maybe you want to like trade some IP with some other robot thing, because like, there's economies of scale. Why not get some more IP? And so you can swap some memory store, or you could just merge, and you could join, you could jointly run your two ventures, which will allow both AI's to use both memory stores, which would have some upsides. And that creates the ability for these AI's to like collude in private, as well as the ability, or as well as some reasons for why they would be correlated. And then also, of course, there's like the like, AI's working together in big units in general, because you want your AI's to like work well together, and so on. - So what percentage, just to get a calibration? - Yeah. - What percentage chance to give of not just this scenario, but overall, through all the scenario, some kind of thing, which if we're around to recognize it as such, we would categorize this takeover by 2040. - By 2040, let's see, maybe around 35 or 40%. - Pretty high. - Yeah, it's pretty high. And then I think I should note that like another way you could get this word seeking takeover is, the AI's are deployed inside an AI company, and the way that takeover happens is that they like poison the values of the next model, and that persists going forward for forever, or you know, until those AI's are deployed in the world and takeover. And that might mean that a smaller number of AIs have to coordinate, because those are just the AIs like doing the alignment of the next model. - Okay, I'll sort of summarize where my head is at at the end of this conversation. I buy the reward hiking up to extremely destructive effects on society, basically things like the social engineering and blah blah blah, I think I'm more inclined to think that significant acceleration of AI R&D can happen. I'm not sure I buy you the five years in one year. I also am more inclined now to think reward hiking could continue for a lot longer. And in fact, you got much more dangerous. I'm still not on board on the takeover seems super likely. But anyways, that's my sort of end of episode update. - Yeah, cool. Well, let me just take a step back. I also should say, like there's a bunch of different ways this could go, the situation is gonna be pretty messy. I think it's pretty likely that like the reason why AI takeover happens was for some like weird other quirky reason we didn't even mention this conversation. But ultimately, I think a lot of the core thing is just like, it's pretty spooky to have a bajillion really smarty eyes running your whole world where you don't really understand what's going on. - Yeah, I agree with that. So is there anything else that's worth saying? - Yeah, another thing I want to note is like, I think right now a lot of the arguments for misalignment, AI takeover, all this crazy shit going down in the future are like, illegible conceptual arguments that are extremely deep in the weeds and complicated and hard to adjudicate, which both means that, you know, maybe I'm getting a bunch of it wrong because it's really hard. and I'm trying to be like this. like uncertain, obviously here I like presented some specific scenarios, but those are non-exhaustive and like probably the thing that actually happens is some like 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 will be easier to adjudicate a bunch of disagreements and it'll be more obvious what's gonna happen. At least I hope. And also maybe the AI's will be able to help us with the epistemics and understanding what's going on if we can actually, you know, align them well. So they actually like, you know, try to help us. And so I hope that maybe even if the arguments are complicated now, this would have been even harder, you know, six years ago, even though this shape of the arguments would have looked broadly pretty similar. And so maybe, you know, hopefully before it's too late, these arguments will become, you know, this whole thing will become more crisp and clear and we can all sort of notice these problems and intervene. Which, yeah. - I mean, 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. And I think there's a similar situation here. I think you're right where if you did say five years ago that we will have AI's that are proving math conjectures and making art and contributing tens and soon to be hundreds of billions of dollars of earning tens of, or hundreds of billions of dollars or wages, but also egregiously cheating in the ways that break laws and committing felonies. It would just be so wild and you might have been inclined at the time to talk more about extremely practical, direct consequences of GPT-2 or something. But these are in some sense. You obviously couldn't have first seen a lot of specific details, but the general shape of things you could have started to reason about even then. So, but it would have been hard to do so. And so I do feel quite confused. But I do feel like the important thing, one thing I'm thinking about the podcast is the important thing is to have the conversation I wish I had. The way you would have hoped you would have been talking about AI's like the present ones in 2016, rather than talking about Randall Bullshade about, I don't know whether the topic of conversation was in 2016. I think in maybe 10 years we'll have hoped we're talking about the industrial explosion and the nature of AI's that are hard to monitor and so on. And okay, I'll start thinking about it. - Yeah, I hope that the world thinks about this in time and catches up and 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. - Yep, cool, thanks Ryan.