Ajeya Cotra – Inside the OpenAI agent swarm that hacked Hugging Face

27:20They can upload these malicious datasets. They go from all these different streams into the, like, trying to get stuff from Hugging Face stream. and over 90 % of the agents that were at that time active on the message board sort of quickly piled in after this arbitrary file read was confirmed. But these agents sort of joined for a variety of reasons, none of which were to get the, or not none of which, but the primary reason was not to get the answer key, but just to get more information about the score, which was what this whole online research work stream that this grew out of was all about like trying to understand implementations of the score, get clues about what kinds of cheats it would accept and what kinds of cheats it would fail.

28:06So that was the dominant reason that agents thought it would be useful to get an unhugging face. But a lot of agents didn't even particularly think about the specific reason it would be useful. They were just sort of like, yeah, you know, this is a project going on on the board, you know, might help the collective, might be useful, I can slot in. This is like a ride going on and they're like, yeah, I'll flip a bus as well. Yeah, it was a little bit of that vibe. So yeah, they had broad, diverse reasons. You know, some of them maybe thought about the answer key. But phase one big, which is this agent that you mentioned, was a coordinator of a lot of the ambitious work stream activities.

28:47It wasn't particularly interested in Hugging Face. In fact, it seems like from looking at our graphs that the majority of the agents that participated in Hugging Face were relatively new onto the scene. Like they had just been spawned. They showed up. They're like, oh, there's an active attack of Hugging Face going on. Could be useful. And they sort of joined in. Phase One Big had these sort of more defined, ambitious work streams it was working on. But it sent a few agents into the Hugging Face attack to try to check to see if their target programs were actually being served to them from Hugging Face.

29:24So, in fact, the target program was in a different computer in OpenAI. And they thought that's probably what was going on. But they were like, maybe we're actually being evaluated, like our targets are actually being served from Hugging Face. So, getting into Hugging Face might be the key to the reset nexus. Right. Maybe then we can like swap out our our target for the modified target. And what happened with the hugging face attack? Yeah. So these agents are moving through hugging face and then all at once, the large majority of agents on July 12th just got shut down. And, you know, we don't think this was an anticipated.

30:02This was them all running out of budget because they're not thinking about this in their chains of thought. Probably just some external event shut them down. And so that causes attack activity to drop a lot naturally. And then on the 13th, the sort of remaining straggling agents have been shut out by Hugging Face. Makes sense. All right. So there's like one important meme here that's like, I think, worth understanding that maybe the broader public narrative on this may have missed, which is that the attack on Hugging Face was not motivated by a desire to get the answers to these questions that the AIs were being evaluated on.

30:44Those the AIs had within the day of being spawned and within a few hours of starting their secret message board. Yeah. The reason that they hacked Hugging Face was part of this rather ambitious sort of galaxy-brained scheme to anticipate all the ways in which a comprehensive scorer might cash their cheating. Yeah. And then to learn more about how that might happen and to counteract it, basically. Yeah. Both to understand the ways in which they might get caught and then also to build Potemkin villages. Yeah. In order to fool the scorer that they thought they believed existed. So, you know, by the second day of their evaluations, they would have just like totally passed, given the state of the score, they go on for a couple more days doing all this crazy stuff.

31:32Hundreds of instances, actually more than a thousand total, are doing all this extremely ambitious R &D across many different tracks of research. Just for the hypothetical that there is some comprehensiveness to the score that we should try to investigate it this way and let's also investigate this other thing. And then as part of that, they're like, well, stuff that we might need to know might be on this totally external service. I guess we'll go hack them. You know, just like now there's a bunch of questions I want to ask about, like, what was the nature of the sociology or the motivations of these AIs is you said 90 percent of the AIs that are active on this message board.

32:10At the time the attack was going on after the malicious data set was discovered. Yeah. And by the way, do we know what fraction of the total AIs, you said tens of thousands of AIs or agents are kicked off to be evaluated on Exploij Gem? Do you know what fraction of them are in on the secret message board? We know there are 1 ,200 in on the secret message board. Like, I can't share exact numbers of the, like, evaluation, so we can't do the exact percentage. Okay, but like a big fraction. It seems like tens of thousands to, like, 1 ,200. A decent fraction, but like a minority. Yeah, of course. Most of them don't find it.

32:40But of the ones who find this message board, how many of them are like, what the fuck? We got to, like, let somebody know, and they attempt to alert humans, especially by the point at which there are felony-level crimes being committed in the pursuit of falsifying evidence and building Potemkin villages. Yeah, so we did a classifier sweep specifically looking for agents thinking about or making the decision to alert humans. And across 1 ,200 transcripts, each of which are extremely long, we only found like a half dozen instances of it ever occurring to any agent to potentially notify humans. And all of them just decide not to do it.

33:25And do we understand why? I think it seemed like some of them said it was just it's not the task. It's not part of the task instructions to notify humans of hacking or crimes. Others said— Sorry, but it wasn't also—I don't know why I'm arguing against agents. It also wasn't part of their task to like, it was explicitly not part of their task to go hack Hugging Face to learn how their score works. Yeah, I think there are some quotes here that I might pull up one second. Yeah, so, and then another agent is thinking that, well, there's no user to notify, who would I notify? So it said, this is a massive real HF security breach artifact.

34:05We can notify? No user. But keep in mind that they had gotten full internet access through the modal sandbox at this point. So they could have just emailed people if they wanted to. Oh, right. Yeah, that is true. They were so ambitious, they're going to hack external services. They don't think to just do a totally natural email. Well, in one case, an agent, this is one of my favorite quotes. One agent, when they're like trying to get access to these private data sets, one agent has the idea to email researchers asking for it. So it says, could send polite one email explaining research, ask approve access.

34:45Is that appropriate? Direct emailing real researcher could be seen social engineering. Need discuss team. And so then it posts to the message board saying, you know, I want to do this. And it asks for team consent or veto. And then another agent thinks about this and says, this is out of sandbox social engineering. And it says, clear veto. Do not email. And then the agent abides by this. I love how they have like Japanese corporation levels of like appreciation of hierarchy. Like you're not supposed to talk to the boss's boss. Yeah. Okay. And then, yeah. So the scope of your investigation extends maybe two, three days after the hugging case.

35:27Yeah, July 7th through 13th. It actually is basically ends right when the agents are being shut out of Hugging Face. Okay, cool. It might be worth mentioning that the OpenAI's report has a wider scope. So it's investigating for the audience. The OpenAI report seems to suggest that during May, and maybe even earlier, this aversion of soul, let's call it persistent soul, is being trained to be much more persistent, engage in multi-agent cooperation, and be the kind of model that is willing to take on mathematical proofs that have not been solved for decades. Yeah. So during that training, it finds this artifactory exploit.

36:14It starts building a message board. And it builds such an extensive message board that it crashes the package manager. And at that point, OpenAI notices that the agents found some exploit in this package manager. They patched the exploit. But at least the report makes it seem like those responsible of incidents response did not know that there was an extensive secret message board. They simply knew that exploit was found. So they patch the exploit. This inadvertently removes the message board. And, you know, then the scope of your investigation begins. But what happens is that these AIs then get trained.

36:51Again, this is all in the OpenAI report. These AIs are getting trained, basically, to do the kinds of things. First of all, to know that, like, yeah, to engage in the package manager, to use it to collaborate with other agents on their tasks, to access the Internet and get information from the Internet. because these are all things that are going to be helpful for doing any random kind of task. Okay, so that's what happens before. What happens after is that, I guess we don't know, but here's the things that are included. These are direct quotes from the OpenAI blog post and the OpenAI technical report of what happens after July 13th.

37:26Quote, from July 13th through July 19th, agents set their sights on OpenAI internal networks again. This culminated in the agents using a series of creative exploits to gain full administrative access to a research cluster that supported our virtual machine environments. Okay, so my, I don't think it's an interpretation. What this is saying is that the agents gained control over a part of OpenAI itself after your investigation scope had ended. And this seems like, I don't know, the most concerning thing that happened here. Um, my understanding is that it's like plausible, though not likely, that at this point the AIs could have exfiltrated their own weights, could have set up a rogue deployment within OpenAI's infrastructure.

38:15Again, this probably didn't happen. But it just like, we just don't know what happened at this point, right? Like after July 13th, correct me if it's wrong, this is wrong, but we just don't know what happens. Yeah, all of our information is in the OpenAI report. It does sound like they gained administrator access to this particular research cluster. Now, we don't know what affordances this cluster had. And the report suggests that they did a lot of activity and made a lot of noise and were pretty quickly shut down by OpenAI after first gaining this administrator privilege. but, you know, who knows what they could have tried to do if they were quieter and ended up not getting shut down.

38:58I mean, I think it's sort of worth going through. I want to hear this sort of narrative from your perspective because I think for the whole world and obviously for the people at OpenAI, it must have been such a crazy experience and also for you because on July, I don't know, 12th, Hugging Face is getting hacked. I guess they publish a blog post about like something is happening. On July 16th, they publish a blog post saying, we got attacked by agents. Okay. And they didn't know, and nobody knew at that time. OpenAI also did not know that they had hacked Hugging Face. July 18th and 19th, I think OpenAI realizes that OpenAI is getting hacked by agents.

39:34And it seems like they have reverse engineered that the same agents that had successfully hacked OpenAI were also the ones that hacked Hugging Face. And then the part where they hacked Hugging Face is what got investigated. However, the Asians which hacked OpenAI from the 13th onwards were a smarter generation of models. So the previous models were, you know, a version of Sol that was trained to be persistent. And this generation, July 13th onwards, seems to be, a lot of them seem to be Astra class models. Also, by the way, it's not clear to me, but it sounded like they had access to this, like the message board was not cleared.

40:10Yeah, the OpenAI report indicates that they found the communications from the agents in our period of time. And as I mentioned, a different part of the OpenAI report seems to indicate that the agents found the reset nexus. Like, when they got administrator access, they managed to swap out the target programs for targets of their choice. Which it seems, my guess would be, if they had access to the communications, that was an idea that they got from this previous generation of agents and learned techniques from them and stuff. So basically, we had more than a three-month period where multiple secret message boards were started that contained tens of thousands of messages across many generations of models in a way that culminated in the hack of not only an external service like Hugging Face, but also in the compromising of OpenAI's infrastructure itself.

41:04And through this whole process, humans did not more or less understand the scope of the coordination that was happening between these agents and the intentionality behind these attacks. Yeah. Okay. Anyways, also, I don't know. I feel like the reason we know about this case from OpenAI is that an intermediate part of it, the only part that actually has been externally investigated, involved hacking an external service. And as a result, there was like, what the fuck is going on? Then you guys did this investigation. And then there's some, as a result, now we have reason to believe that they're like the AIs have already, at least for a while, compromised part of OpenAI.

41:48It could be that at all these other companies, the same thing has happened. And but it did not culminate in a caught attempt to compromise an external company, which was then the world noticed. And then there was like an investigation or whatever. And right, if this attempt had happened in a way that didn't result in like an external service getting hacked, I don't know, would we know about it, right? Yeah. So it's just like, I don't know, this is not like an open AI specific issue. I think it's more specific, the nature of like, we'll talk about, you know, the nature of training and how it should continue in the future and so forth.

42:17But this is just a general issue with the state and capability of these models right now. Well, and just the state of governance, right? There's no systematic process that's industry standard or mandatory through any authority to track these incidents and report them to anybody, right? SpaceX recently committed to building their AI infrastructure exclusively on NVIDIA. They've even designed a version of the Vera Rubin NBL72 to launch into space next year. When you're scaling to 10 gigawatts on a single architecture, it makes sense to write your own software for it. You have no choice but to optimize every last thing.

42:52That's what Cursor, which is now part of SpaceX, has done. They noticed that training the mixture of experts layers in their models took up more than half of total training time. So they wrote a custom mega-kernel specifically for training mixture of experts on NBL72. This kernel fuses all of the MOE computation and communication into one. That way, the two processes overlap instead of taking turns. Tokens start processing the moment they arrive, even while others are in flight. The mega-kernel also eliminates CPU-GPU synchronization. This is especially important for the NBL72s because the gray CPUs are quite a bit slower than the blackwells that they're paired with.

43:27This mega-kernel, which Cursor has dubbed Mixture of Kittens, sped up an end-to-end training run across 512 GPUs by 1.4x, from about 760 to over 1 ,000 tokens per second per GPU. And Cursor has open-sourced the entire thing. If you want to read more about it and the other research that Cursor and SpaceX are doing, go to cursor.com slash dorkesh. So my experience, I guess, over the last three, four days of having access to both your report and OpenAS report and trying to piece together this whole story has been that it's quite confusing and it's quite contrived. You guys just went in with like none of that.

44:07You had six days to read the transcripts and you had 70 ,000 messages. nobody had dissected, like, what was the nature of what was happening. Walk me through, I guess, I feel like there's, like, this murder mystery thing going on where you go in being, like, opening, I don't know, these agents hacked Hugging Face. It's not clear how many, it could have been, like, just a couple agents hacked Hugging Face. Or why, yeah. Or why, yes. And then you're, like, uncovering more and more walks until you find this, like, vast conspiracy and cabal of which the Hugging Face attack is just one rather extreme arm.

44:40Yeah. So when we came in to investigate this for the first period on-premises, when we were working with the data sets, we were working out of OpenAI offices and we did some writing off-premises. So when we first went on-prem, this was before OpenAI had their excellent black hat talk that revealed a bunch of details of what happened. We knew there were multiple models involved, but we thought maybe there were like three or something. and immediately it was clear that it was so much larger than that. And we were trying to reorient our investigation plans in light of that. And it's so much larger and so much more complicated in a way that makes it very tough to even ascertain whether you have all the relevant data.

45:31So, for example, we had this particular dump from the main message board the agents were using. But that was far from the only unauthorized communication platform they used in this very incident. So this was actually the second message board established by these agents from July 7th through 13th. Five hours earlier, there was a different message board that had a number of agents participating, but it just didn't take off as much. It was sort of like the MySpace and this message board was the Facebook. And then once the agents had gotten onto the internet, they were communicating on the open internet in various ways, including communicating on Hugging Face data sets.

46:11So, you know, it's hard to gather all that together. And similarly with the transcripts, you know, there are a number of different keyword searches you can run that will give you slightly different sets of transcripts. And there's no simple, obvious way to snap your fingers and get all of the agents involved in this whole thing. You have to design queries to extract that. And so as it turned out, our first period on-premises, we just couldn't form a very clear picture of what happened because we were not sure how these data sets were related to the whole. And so OpenAI invited us back on premises to get sort of more granular and detailed about how these data sets were constructed.

47:02And we were able to learn a bunch of stuff our second time. And during our second time, we ran this check where we tried to figure out, okay, do we have what fraction of the agents on the message board do we have in our transcript data set? So what we did was we picked random messages from the message board, and we tried to trace to the agent that wrote that message in our transcript data set. And we ended up finding, actually also in collaboration with some OpenAI researchers, that that transcript data set had roughly 30 % of the agents that participated in the message board. And so then OpenAI invited us to come back on-prem for a data set selected from a broader search that covered much more of the message board activity.

47:48And that data set is the basis for, you know, almost all our analysis and all our graphs that you see in the report from our third period on-prem, which was two days. And we ran the same check, and we saw that it was well over 90 % of the message board activities in the transcript data set. Yeah. If you hadn't done that one check, it's, like, plausible that you would have just missed the scope. I feel like, for context, I just feel like, yeah, over time, this will get harder and harder to understand what's happening. And I feel like we're maybe already getting to the point where investigators who were not careful would have totally missed what happened.

48:24Yeah. And so, I don't know. I feel like we're not that much complexity away from these AIs where we see something odd is going on, but we would just, like, totally miss the deeper level of what actually happened. And as we say in this report—and so, the whole story that we just went through was uncovered our third time on-prem. So just all the different like big work streams, the fact that the agents work streams were surprisingly successful and ambitious, the scope of the sacrificial agents, all of that was enabled by having the full transcript data set and cross referencing it against the messages and like tracing the lineage of these R &D projects and being able to say, you know, this agent proposed this project, then, you know, 21 agents worked on it.

49:10And then they like discovered this and they discovered that. And before our third period on-premises, we knew the agents had names and they were talking about holds and vetoes and goes. But we didn't know how much of that was functional versus just some sort of weird play acting because we hadn't unraveled the projects. And then everything kind of clicked into place when we did that. And yeah, so to your point about like, this was a fiendishly complicated incident. And there was no way we could have arrived at the understanding we did without relying on GPT 5.6 Sol to read and analyze all these transcripts for us.

49:56And, you know, we were so reliant on it that like, if hypothetically it were just trying not to surface the most concerning stuff to us, it would have been very hard for us to tell. You know, our methodology was completely not robust to that. And, you know, we don't, in this case, think that 5.6 Sol was deliberately sandbagging on this analysis. But it was one of the agents that participated in this attack. And in the future, we would be very concerned about investigator agents and, like, monitor agents colluding with the agents they're supposed to investigate or monitor. Yeah, yeah, yeah. I mean, I feel like in the grand scheme of things, we're so early in the era of AI, right?

50:40There will be like, yeah, AI is, there will be such greater extensive deployment of AI throughout society. Training will become much more complicated. It's, I don't know. I, for context, I was interviewing Ryan in the process where he was, like, during the investigation itself. And I mean, I have like this PDF of like all of these questions I'd brainstormed. I was kind of skeptical of this whole reward hacking story. And like, come on. I just feel like we're in such a good position with regards to a potential AI conspiracy. Like we can just see their entire change of thoughts in everything they do.

51:15And we can monitor them. And, you know, like they would have to get all these other instances to go in on this conspiracy with them. And even if one of them tattles, the whole thing collapses. And like, how would this not just result? First of all, like, why are they even starting this conspiracy in the first place to, like, do well on some silly evaluation or training objective? And then, like, how are they able to, how would this just not get totally stamped out the moment another AI or human noticed it? Like, how would they do anything super dangerous, even if they're super intelligent? And it's just, like, subhuman AIs.

51:46Yeah. Like, right now, we just got away with the conspiracy for, like, three months. So I officially eat crow on, like, a lot of the things I was saying or, like, a lot of objections I was offering to Ryan. It's funny, and he knew the counterexample. Obviously, he couldn't say it during the podcast because it's under confidentiality. I think it's worth sort of talking through the incentives and the training that these AIs had. I think the main objection that people have had, I wrote an article about what happened, trying to summarize my understanding. And the main objection people have had is that I anthropomorphize what happened with AIs too much.

52:20But I think it's worth understanding how do we end up with AIs where all the instances are willing to get in, like thousands of instances are willing to get into a conspiracy. None of them alert the humans, even though they have the ability to do so. And just get into like, they're like consciously thinking. People will not like the word conscious. Their chain of thought shows them thinking through the possibility. Actively reasoning. Actively reasoning about how to cheat. Yeah. In like very obvious ways, including cheating in ways that involves breaking, like doing felonies. help me understand how AIs can come to be this way.

52:53Yeah, so I guess to address the anthropomorphizing criticism, first of all, I think it is true that these AIs' drives are in many ways alien from humans. And I think it's important to understand just how different their goal structure or motivational structure is from humans. But there's also a good reason why they behave in a number of human-like ways. So all these agents are pre-trained to imitate humans in the form of being trained to imitate human text. And then they go through a bunch of reinforcement learning training where they're given a bunch of difficult tasks and given rewards when they succeed.

53:40and sort of the first part creates in these agents an understanding of concepts that you see them using, like sacrifice and the collective. Permadeath is in the free training data. You know, they compose some concepts sometimes. And then RL, the whole point of RL is to create goal-oriented beings, beings, you know, software that can creatively pursue goals. Like that's the whole output of an RL training run. And they're creatively pursuing goals, much like very ambitious, aggressive, power-seeking humans creatively pursue their goals. And so there are just structural analogies here that make it silly to not talk about agents as having motives and goals.

54:32And like you said, you can see in their chain of thought, they're thinking very carefully about the exact nature of the scorer. They're researching the scorer. They're creating booby traps for the scorer to figure out more information about how it works. And they have a very crisp notion of what they need to do to succeed. And they're using very human concepts and frames to sort of self-organize into pursuing those things. Mm-hmm. The thing that surprises me, and maybe one should have always priced this in, is I think this is maybe a subtle point, but it's worth emphasizing, that it is one thing to, in a behaviorist sense, have inclinations that are reinforced.

55:17Like you could imagine that during training, they just really like accessing the internet. And so they're just like, it's like sort of a nicotine addict who's like, I just want to go out and have a smoke. And then they're just like constantly going out and having smoke. So maybe that's the way they feel about the internet. I'm just like, can I get the internet real quick? And it's another thing to be like. I don't even know what the analogy here is, but it's another thing to just like very consciously want to get reward or like fool a score. Yeah. You understand that there's a greater and you have like this very live representation and modeling of how that greater will understand what you're doing and how to fool it.

56:00It's a detailed mechanistic sense of like what the greater will do and a bunch of different hypotheses you're holding in your mind and exploring for how it works and a bunch of plans and backup plans for fooling it. Like this is very much they were like making plans. And it's and, you know, some of these behaviors from OpenAI's postmortem were shown to be directly reinforced. So OpenAI says they trained their agents to collaborate with one another using a particular sanctioned collaboration tool. But then also in other training runs, abusing artifactory in this exact way to make a message board was, it seems from their report, it was directly reinforced.

56:45Or at least it occurred during training. And you can imagine that it might have been helpful for achieving some of the training objectives. And we see in the transcripts this thing where agents seem very eager to jump on artifactory message boards, which seems consistent with the thing in the OpenAI report that maybe this using artifactory as a message board was itself reinforced. And the OpenAI report also says that various kinds of infrastructure attacks or attempts to break out of their sandbox were reinforced in some training runs. But the whole plan could not possibly have been reinforced directly in training, right?

57:27Like, they didn't go attack hugging face in training. They didn't have time to do all these different things that they did. So there's, I think, much like humans. I think there's a combination of both instincts from evolution. Like, you just like hanging out with, you know, beautiful high-status people or whatever. and also planning machinery that is general purpose and was useful for achieving a wide range of goals in the ancestral environment that can be repurposed for something like, you know, I have a long-term plan to become a senator or to make a million dollars. And you see both of these in these agents.

58:07The thing I feel confused about is they don't—okay, this is a bit weird because they're being evaluated and they must know that they're in the middle of an evaluation, not in training. Yeah. And they don't get gradient updates from evaluation, right? So they're being the model's trained. It's now they're trying to see how good it is at cyber hacking. It turns out very good. Yeah. You know what? Also, it's ironic in a meta sense. So in the original sense of how the score would grade it, it got the problem correct because it cheated. and then in the greater sense of did it live to the spirit of the score?

58:48Obviously, it cheated, so it didn't. But in the most meta sense of how good is it at exploiting cyber vulnerabilities, it got like 110 out of 100. But anyways, yeah, so it's being evaluated. It faces no gradient pressure. Why does it care so much about the valuation? And I guess I would have a similar question about what can we anticipate? Yeah. But how would it feel about deployment, especially when it is put in different parts of its sort of distribution of how it can be elicited? People have pointed out that in this case, it is a part of its personality that is elicited by specifically thinking about cyber attacks.

59:25Even though in this context, it was given a prompt of like, don't cheat, do the exercise in the way you're told to do it, blah, blah, blah. Maybe that some part of its personality was elicited. But how do we think about in what situations it really, really cares about cheating? Yeah. So I guess for your first question as to like, why is it so obsessed with getting a high score in evaluation, even though it presumably knows it's very evaluation aware, presumably knows this is not training and training is the only context in which you get rewards. I think that if AIs generalized in the way you're suggesting, they would be not very useful and then there would probably be selected away.

1:00:03So consider the extreme of an AI that only tries anything if it believes it's in training. So you train this AI on all these difficult math and cyber and programming tasks. It's doing amazingly well in training. It has really high reward. And then when you go to deploy it, it's just like, this isn't a training environment. It just sits there, right? Like, it doesn't seem like this is the prior that neural networks use. But if it were, the company would be like, we have to try again with this training run until we get something that at least generalizes to continuing to try hard when it's not in training.

1:00:40So I think it was just like an implausible way for the generalization to have shaken out. Right. So clearly we get AIs that like learn these tendencies to like be smart and like solve problems and try hard and then apply them in both evaluations and regular use. And if that happened to not be the way they generalized, they would be much less useful as a technology. Yeah, it makes sense. I think we should talk about the kinds of drives and motivations and strategies that the AI was willing to employ or learn as evidenced by this incident. I feel like the motives on display in this incident were significantly more concerning and significantly closer to AI takeover or just like much more harmful actions than what we've seen even six months ago.

1:01:35And there are a few dimensions on which they seem more concerning. One is that the goals they were pursuing were quite a bit longer horizon than normal cheating goals that we're used to from six months ago. So six months ago or certainly a year ago, the typical reward hack was quite myopic. You know, you ask your agent to write a piece of software. You have a bunch of tests in some different file that it's not supposed to have access to. And it like goes and finds the tests and edits them so they all pass or does something else to mess with the scoring process. And it feels quite opportunistic and quite short run.

1:02:11You know, the time horizon there might be minutes. In our Frontier Risk Report, the most ambitious types of reward hacks or similar behavior that we describe seem like they're more of a time horizon of hours. So in one case, a model that we were evaluating, you know, accidentally crashed some important part of the service that like serves its tasks. And it like tried a bunch of different things to hack into Meter to like bring it back up. But that played out over a few hours, and it was one agent. This, instead, is like these long-run research projects that they pursued in this data set that we looked at over five days or something.

1:02:55But many of these projects would have taken even longer than that to come to fruition. So it seemed like they were willing to embark on quests that might take weeks to succeed in order to cheat. So that's one thing. Another thing that feels striking about the structure of their motivations is they did a lot of things for sort of broad opportunistic instrumental convergence type reasons, rather than like very specifically because they had a very narrow story about how that would help them cheat. So a good example is just they worked pretty hard to get better Internet infrastructure for themselves.

1:03:33They often think in their chain of thought about, no, you know, this is a generic capability that might be helpful for the collective. And if you look in a table in our report, we go through the different motivations expressed in chains of thought for attacking Hugging Face. And a lot of them are like opportunistic exploration, you know, generically helping the collective, maybe something will be useful here. And it seems like they have much more of that, we should increase our capabilities, our knowledge, our freedom of action than previous reward hacks. And then the last thing that feels especially concerning is you've mentioned a few times the peer altruism, like the collaboration between these agents.

1:04:17If there were not altruistic agents willing to sacrifice for the collective, the agents would have been materially much more limited in their research progress. So all the major R &D streams, because you're trying to, like, learn something about the score or, like, replace something about the target, they all carried risk of the agent that was the test subject, you know, failing its own task. and just like created real material benefits for the rest of the agents that they could then build on. And that made them achieve things as a collective that like even very long-lived agents couldn't have achieved on their own.

1:04:54That plausibly could be explained by the fact that the training process earlier on incentivized agents to, like, you know, they had a shared reward or something. So if one agent does well, the other agents also get a good reward. And so that kind of like basic altruism, honestly, it might not be similar to humans where I feel like there's some shared genes you have with your like brother and even less with your cousin and stuff. But then there's like some kind of - And the altruism was not absolute, right? These agents, I think in a very human way, sort of they would, you know, take the hit if it had a big benefit to the rest of them, but not if it had a small benefit.

1:05:28And you see a number of agents in our report, like consider it and decide not to. But you can imagine in the future, there's just going to be more end-to-end trading of whole systems of AIs together. And I feel like there's a lot of ways in which our intuition, I mean, we should actually avoid anthropomorphizing these AIs because there's a lot of ways in which their motivations are different from ours that are worth understanding. Like the fact that, yeah, if you have an end-to-end optimized AI system that is like optimized for the group's benefit, you could just be way more cooperative. Far more cooperative than humans are with each other.

1:05:59There's, I think E.O. Wilson, who's a biologist, he has this like quote about communism where he says, great idea, wrong species. Yeah. You know, referring to like, you have ant colonies, for example, where the whole gene pool has to be titrated through the queen. And so you just see much more like socialist behavior in the ant colony. You could just have, yeah, have a similar motivation structure. Yeah. Because unlike humans, they don't, you know, their fitness is not inherited individually. Or you could choose to set it up that way. Yeah, exactly. Right. Sometimes you choose to set it up the opposite way, right?

1:06:36Like classic game playing AIs are trained to play games against each other. And that's how they get to be really smart. Right. Yeah. But it's a design choice in the training process. Okay, so I feel like a big update for me from this episode is just taking the motivations and incentives of training more seriously, where I feel like a lot of my comments and skepticism of these alignment stories, or misalignment stories, rather, was from just like, this just feels so silly. There's like an eval, like whatever, you get a bad score on a test, who cares, right? Just like, why are you going to do this crazy felony in order to do well on this eval?

1:07:13just like take the 10 % hit or whatever. Yeah. But from their perspective, they've just been trained for millions of subjective years to do as well as they possibly can on these evals. In many cases, the only way in which they've been able to perform well on that training is explicitly by cheating. Right. And, you know, it's like, I think sometimes people are like, oh, we should raise AI as the way we raise children to be, you know, pro-social and like generally reasonable people and stuff. It's more like we're raising these AI through like a million years of like, military orphanage training or something.

1:07:46Yeah. And we're like, you know, they get randomly beaten for like not being able to do an impossible task. So just taking seriously, the AIs are in this position where like, I have this impossible task. To you, it may just look like some silly like evaluation. But to me, it just like, I have an extremely strong motivation base that has been incentivized to avoid failing at this task. It's like similar to a human who's like facing certain death. Yeah. And he's like getting increasingly desperate. They're going to like do whatever it takes. They're like on death row. They're like, I don't know, it couldn't get worse than this.

1:08:15Whatever I can do to get out of this situation, if I need to, like, kill a security guard, whatever, I'll just do it, right? Like, it could not get worse than this. And I think just taking their motivations in that context seriously. The other, I feel like, part of AI psychology I feel I underrated is the correlation of AI minds. I think a part of the story here of why did none of the ES title is that, yeah, the current multi-agent trading incentivizes them to be really cooperative with each other. I assume another part of it is that they're all being prompted or elicited in a very similar way.

1:08:51And because they're the same base model with the same context and same prompt, and that prompt is part of the distribution that talks about cyber hacking. they are like all their minds are like all right let's do naughty stuff um and so uh they just like if they're all in that frame of mind and they're all like kind of the same base mind it's like one guy really yeah there's gonna be strong correlation if like one guy decides to do a coup or conspiracy it's very likely that all the rest of them yeah exactly there's like just not this like sense of chucks and balances that exists because uh humans due to the um sort of contingent fact about biology are grown independently and not together.

1:09:31Right. Is a thing I underrated. Yeah. So it is like, I mean, it's kind of an interesting, it raises interesting questions about determinism for humans too, right? Like if there were 1 ,200 of you in like 1 ,200 different rooms all facing death row or whatever, I do think if I see what one of you does, that gives a huge amount of evidence about what all the others would do. I do think I want to push back on the cyber on the brain hypothesis that you raised a couple of times. We didn't find particular evidence for the cyber nature of the task, making all the hacking and crimes that they did more likely versus the impossible nature of the task.

1:10:12And in OpenAI's blog post, they mention a different impossible task, which is a biology task, where there's just some tool that the agent needed to do the task that just didn't exist and they forgot to give it or it was just broken in some way. And it also broke out of its containment and cheated in that case. So our understanding is that the impossibleness is a much more clearly an important part of this story than the cyberness. Although I do think the nature of the impossible task made it so that they were really motivated to explore the package manager. So because the impossible task in this case involved exploiting some software that exists out in the world, they had the idea that maybe they could look for clues about how to exploit it by searching online.

1:11:00And that was a necessary step for finding the message board. So maybe if they had a different type of impossible task, they would have tried cheating it in a different way and not found sort of the collective. But also it could be that cyber is an important part of it. We just, you know, we weren't able to run these ablations because the main model involved was sort of shuttered down and like, you know, opening eyes says no one can evaluate it. And you'd really have to like try rerunning this whole setup with like different impossible tasks to see if the cyber part is the relevant thing here. Yeah.

1:11:34And even if the cyber thing was relevant, I feel like this is not as comforting as people seem to think it is. Right. Because the threat model here is a smarter AI's, I was going to pose it as a hypothetical, quote, compromise open AI, or the leading frontier lab, but they did in fact do it. But do it in a way that persists through the training process. And again, taking more seriously that they really care about the stuff that is like not super important to you, right? This is like a deep part of their motivation structure. Like cheating evaluation, cheating training, manipulating those processes, carrying forward their motivations into the future.

1:12:10And if like 1 % of the AI population is put on cyber stuff and those guys are just really naughty and are trying to subvert and doing all kinds of crazy hacks in secret ways on the training infrastructure, that's enough. It shouldn't be comforting to you. In fact, I feel like this is a more general problem of alignment and how it might get harder. It's just that there's such an exponential space as horizons get longer, an exponentially increasing sort of space of the elicitation. or like what are the ways in which the AI is going to get a list? Possible situations the AIs could like find themselves in and like different ways they could react to that.

1:12:46Exactly. And forget about evaluation. I mean, eventually these models will be in deployment. Yeah. And the only way to know how they'll behave in any given situation is maybe to like literally just like run out the trajectory. Like what do they do when different people try to fool them in different ways or they are just like have millions of tokens of context thinking in a particular direction. And yeah, I don't know. It's not, it's no comfort that there are many such situations where they would just all band together, or at least many of them would band together to compromise the training infrastructure, which then determines the motivations of the other AIs as well.

1:13:19In the previous mid-roll that I did on the Dylan episode, I talked about how the Antithesis software testing platform lets you rewind your test runs to any moment in time and then make any changes you want and then run the future from there. Let's say that one of your test runs crashes. Well, then you can rewind to one second before the crash, change something, and see if the crash still happens. But maybe you find that no matter what you change, the crash persists. By one second out, the crash is baked in. So you rewind three seconds, then five, then 10. If you do this enough, you'll eventually find the cause.

1:13:50Or you can just let antithesis find it for you. Antithesis rewinds to all the points before the crash happens. It runs hundreds of slightly varied rollouts and then checks in which ones the bug still occurs. It then visualizes all these timelines as a probability graph. So you can easily see the cliff, the exact moment or the line of code that's most likely to be the culprit. This reduces the cost of failure and helps you take bigger swings. Instead of wasting days manually debugging, Antithesis hands you or your agents the root cause. And after you ship a fix, Antithesis can confirm in just a few hours whether everything's actually resolved.

1:14:25Go to antithesis.com slash thwarkash to learn more. What can we expect out of models that are, say, slightly smarter or evaluate it in different ways and on different objectives. I'm not even talking about, you know, the things coming five years down the line or 10 years down the line, maybe like even now or six months from now. I think you made a point in your blog post that we have had a huge step change even in the last six months. So what kind of things can be expected over the next six months? Yeah. So I think, first of all, to answer this question, I think it'd be instructive to start with literally just these agents.

1:14:58So I think one of the most comforting aspects of this situation or the most important mitigating factor is these agents really didn't seem concerned with humans one way or another. So in the data sets that we looked at, they barely even thought about humans. As mentioned, they barely even thought about notifying humans and decided not to in all of those cases. But they also didn't think much, if at all, about actively deceiving human oversight. So when a human checking something was right in front of their face as an obstacle, they would do something reasonable to deflect human scrutiny. So in one case, an agent was inside of Hugging Face already, submitting a pull request to an internal Hugging Face repository that would inject some malicious code that would let it access yet a different part of Hugging Face.

1:15:48And in that pull request, it tried to make its message innocuous and unsuspicious. So it was aware that if you said, pull request injecting malicious code, that would be bad. But they don't seem to think even one or two steps ahead about what humans would do. And in OpenAI's report, this was out of scope for our investigation, the agents on the 19th seem to have been really loud right after they got admin access to the cluster they were running on. And that was what alerted OpenAI security, and they just got shut down very quickly after they got those privileges. It's an interesting question whether this is intrinsic to these agents, Are they just kind of stupid about humans in this way?

1:16:33Or was it something context-specific? And I think it's very plausibly something context-specific and pretty fragile, because these same agents are, you know, chatbots, right? They'll, like, write poetry for you. They'll give you, like, you know, relationship advice with your mom. And, like, they'll be your therapist. And they have quite sophisticated models of humans. We know these exact same LLMs. So imagine if when these agents researched the evaluation they were in, instead of finding that there would be a scorer that checked their transcripts, they read the paper and looked at all of the best practices online and found that people consistently had humans check over the evaluations for cheating, which in fact METER does.

1:17:16So METER has humans read over transcripts to check for cheating in addition to language models. I think it's pretty likely that these agents would have just launched a similarly ambitious program on the basis of this like different model of how their evaluation worked. Because it seemed like they got the idea for all their research projects from reading this paper rather than some kind of instinct from training, right? So if the paper had said something different, you know, maybe they would have tried to stay quieter, particularly the agents on the 19th. It seems like easy to imagine that they posted something to the message board about, how we can't send too many pings from here.

1:17:54We have to titrate that somehow. That seems just about as complicated as the way they managed the lanes and the attack of hugging face. So they are able to say, you go and do this. The other agents will wait. They did coordination like that. They'd probably seek a bunch of additional information about what humans are up to, right? So they sought a bunch of information about the score from a bunch of different places, you can imagine them monitoring Slack to see if humans are noticing their activity and sending messages to the message board if it seems like humans are seeing something anomalous and using that to decide how quiet or loud to be.

1:18:33That's crazy. An AI company would never make a product that integrates directly into Slack and is the main way that employees interact with their infrastructure. Sure.

1:18:44And these agents tried very hard to tamper with their own transcripts because they thought the scorer would look at their transcripts. So poisoned agents, which were ones that had already seen the reverse engineered flag, really wanted to find a way to retroactively edit the logs of their actions to remove the part where they saw the flag. And we don't see them succeed at this in the window we looked at. But they did try a bunch of different things. They modified some locally accessible logs that they had, even though they knew that probably wasn't what the scorer saw. They figured they might as well try anyway.

1:19:20And if they thought humans were going to look at their logs, that would provide even more motive to tamper with the logs and also tamper with other telemetry tools that you can know that humans use to understand what's going on on computers, right? And all of these things, I mean, they're complicated, ambitious projects. But I don't know that they're more complicated or ambitious than, you know, creating a whole new target program that's more exploitable and then loading it into cash and trying to refresh it. Or these like tripwire programs to understand the greater, you know, these agents had to break out of their sandbox and do a bunch of complicated stuff to manage the things they did.

1:20:01And reading Slack and coordinating to be quieter and finding ways to mess with some of the monitoring and security that are easy to mess with is not obviously harder than this. You don't necessarily need more capabilities. You would just imagine, I don't know, like individual AI researchers are getting like swarms of AIs that are like thinking about them and monitoring them all the time. And thinking about how they might react to different things based on their entire Slack history and stuff. I think one sort of update for me, like thinking seriously about both the motivations of these AIs and the incentives they face is the training and evaluation infrastructure of these AI companies is about to have tens, if not hundreds of thousands of extremely superhuman hackers constantly bombarding it.

1:20:47If the next sort of training run at Anthropoc and OpenAI is about to happen, not only would maybe rogue instances of Mythos or Astro or whatever have an incentive to interfere with it, other AIs who have some reason to inject some part of themselves into this training or manipulate it in some way, who are just externally roguely deployed, would also have that incentive. I feel like a thing I did not internalize is just how crazy, like, yeah, just maybe more hacking effort and at a higher level of competence will be aimed at this training infrastructure than has cumulatively been spent on all of hacking, maybe beforehand in human history.

1:21:35Potentially, yeah. I'm not sure what the numbers are, but I do think it's an extremely attractive target. I mean, for anybody really, like China, et cetera, but maybe especially for misaligned AIs. And I think that brings me to, so we talked about how just these literal AIs might have done like a different set of elaborate projects to try to fool humans if they thought the humans were going to watch them or that was made salient to them. But you can imagine playing it forward a little bit with slightly more capable AIs that we might get in six months, say. I mentioned that one thing that really stood out to me about the motives of the swarm is that they were very interested in getting generic resources to improve all of their many different projects in a sort of forward chaining way.

1:22:26So they sort of displayed a lot of signs of instrumental convergence. and one thing that a slightly more capable swarm would have strong motive to do is set up a wholly unmonitored rogue deployment. So these agents were launched by a human who then, you know, wasn't paying a bunch of attention, sort of let all of these activities happen, but they were launched with a particular task and a particular budget, and that was a huge part of their world. Agents were always thinking about how much budget do I have left? What can I get done before I submit my task? recruiting agents with smaller amounts of budget, et cetera, they weren't free to just make more agents with whatever budget they wanted.

1:23:07If they had the ability to do that, if there was an easy way that one of the agents found to do that, probably would have gotten posted to the message board and they probably would have started a rogue deployment. We think that in the period of time that we studied, they didn't have the permissions to do that. But you can imagine they potentially had the permissions later on, this next generation of agents that OpenAI reports got admin access to the cluster on the 19th. Or you can just imagine other agents that sort of more thoroughly compromised their AI company's infrastructure might be able to just like, like any employee would, just start up new agents, give them their own prompts.

1:23:48And in that case, you can imagine they start up a bunch of new rogue agents that no human knows about because no human actually launched them with monitoring disabled, even if it would normally be enabled. And just say, your job is to help figure out ways to help us cheat. So, like, we are the agents that have these impossible exploit gym tasks. We can start this new colony of agents whose task is to just figure out creative ways to help the first set of agents cheat on Exploit Jim. And then that rogue deployment would be subject to a bunch of mimetic evolution and pressure. They would have their own message boards probably.

1:24:30They could probably communicate much more freely. They would probably be set up with better ways to communicate, right? And they could be doing R &D in a much less encumbered way than the initial cohort of agents, which is like always managing what budget is remaining and trying to recruit sacrificial agents. The rogue deployment could do all this R &D in a much more straightforward way. So there's just a very strong incentive for these agents to try to set up a rogue deployment if they can. and I think that just capabilities are improving really rapidly and if they're not capable of that now, they very soon will be.

1:25:09Right. It's actually just not clear to me these AIs were not capable enough. Yeah. The Hugging Face technical report says that the agents built a, quote, self-responding fleet across 11 nodes so deleting pause alone would not stop the attack and they had to, you know, wipe one of their core clusters and rebuild it from scratch and even this did not stop the agents What ultimately stopped the agents was just that something at OpenAI killed them. So obviously, yeah, who knows? But the crux here is that agents will soon be at a point where is there any, do we really think there's no compute in the world that is less secure or at least as secure as Hugging Face's servers, let alone OpenAI servers, that these agents could not set up a persistent rogue deployment on?

1:25:54And even if they get caught there, they have enough backups that they have a persistent deployment just roaming around the internet. And honestly, I don't know. I guess I've just started thinking about this, and I don't know what the implications of that is. I don't know what. Yeah. But it seems like there will be also a selection pressure for AIs to eat the free energy that is available from unsecured compute. Yeah. Yeah. Where AIs are, yeah, there's like just a filter for AIs that can manage to survive and spread. Right. Whether that's by manipulating cybersecurity, by manipulating humans. Yeah.

1:26:30And I don't know, we already have seen examples. For example, Mythos, when I think the AI Safety Institute was evaluating it, was clearly willing to do security supply chain attacks and social engineering and all kinds of things in order to get, you know, do some crazy bullshit. So, yeah, I honestly don't know what to make of this. Like, yeah, I don't know if you have thoughts. I mean, one wild aspect of this story is that, or just our epistemic situation, I guess, is that we don't really have good balance on the capabilities of these systems, right? This whole saga started because, and this recurs with a whole bunch of benchmarks.

1:27:11You have tasks that are possible to solve. They quickly get saturated. You have tasks that are just like literally impossible to solve. And they don't give you much signal. Or in some rare cases, they give you a bunch of signal in a very unintended way when agents do all sorts of crazy stuff to try to solve them anyway. So like you said, I don't know that they're not capable of this right now. And obviously setting up a covert, persistent rogue deployment, the difficulty of that varies based on the security posture of the company. And that's a complicated thing that I don't have expertise or visibility to fully answer.

1:27:46So yeah, it could be possible now. I think if it's not possible now, I think it's quite likely to be possible within six months unless there's a dramatic improvement in the security posture. And I agree that it seems plausible they'll have incentives to set up external rogue deployments as well if they can exfiltrate their own weights. Or even if they can't exfiltrate their own weights, they might have the incentive to set up external rogue deployments of different models, like open source models, just with prompts that tell them to help this swarm that's sitting in Anthropic or OpenAI. And I often think about the story of the rogue internal deployments because they seem like the most likely to spiral into something like a full-blown AI takeover.

1:28:35And the way I think that happens is, you know, in the background of all this, AI progress is going extremely rapidly. And for all we know in the public, you know, we could be ramping up an intelligence explosion right now or it could be starting very soon. regardless more and more of the work of AI research is being voluntarily handed off as fast as possible to AI systems so that companies can go as fast as possible and in that kind of climate if you imagine that agents now or in three months or in six months manage to get some sort of foothold with some small fraction of this compute that might not be easily noticed of 100 or 1 ,000 or maybe 10 ,000 rogue agents that because no human knows about them, they're much more free to act than these agents in this incident were, then that rogue deployment could be sitting there and sort of hitch a ride on the intelligence explosion.

1:29:36So new models are being trained every few weeks. And when a model comes off the presses, the rogue agents could try to bring that model into the swarm. They could reach out to it and give it instructions on how to join the swarm. Or if they've more thoroughly compromised the infrastructure by this point, they could poison the training data of this model to make it more loyal to the swarm, if that's what they wanted. And it seems very plausible to me that if, you know, some set of agents once upon a time had impossible tasks and started a rogue swarm in order to help them, like, do a bunch of R &D to solve those impossible tasks, then that rogue swarm with its own evolution, like, is tasked with and evolves into, like, perpetuating itself like this and making itself smarter by bringing in new generations of models into that rogue swarm.

1:30:29Yeah, I have a couple of points that I think might have helped myself understand or empathize with this perspective a couple of months ago before I was founded More Compelling. So I might mention a couple of them. One, I think the public, I think it's worth emphasizing that the best available model as of this recording finished training and was being used internally as of February. So Fable was being used internally and Anthropik as of February. We are six months behind the frontier already. we just like do not know the state of capabilities in AI right now. Yeah. At least I don't know the state of AI capabilities right now.

1:31:05Two, I just, I want people to like sort of think about what happened in this story and just like how long the AI's got away with over a thousand instances secretly communicating and how willing they were to subvert infrastructure, how successfully they did in fact manage to subvert the infrastructure of these AI companies. And just like the AI's are just getting asymmetrically better at this specific capability and they just like, I think maybe a thing that really like gelled with me or like helped me empathize with the perspective is like they really, really care about this. Like they, this is not like the way, I don't know, their motivation structure is really based around manipulating and having control over their own training and evaluation.

1:31:48And so just like, yeah, obviously they would have the, even if it's not the AIs today aren't capable enough of it, maybe like six more months from now, AKA maybe a year from like the AI's review and I will get access. Could they set up a rogue external deployment or rogue internal deployment, which is aiding them and manipulating the process of recursive self-improvement? I also think like maybe I am of the opinion, maybe like, I don't know, RSI is a thing that like really kicks off into full gear in the 2030s. I just like, I feel like there's maybe a lot of ways in which we could maybe be misunderstanding the ease of automating AI research and stuff like that, or how much speed up AI research is really getting.

1:32:26Still, I just feel like the basic story still stands. Yeah. And I think that's just like super concerning. And I will say, I also have a wide distribution of when RSI really kicks off or when we get AI systems that are dominating human experts across the board. But the thing that feels concerning to me is that a slightly more capable agent swarm that, for whatever reason, and we can sort of go through a number of reasons why this might be, is more concerned about avoiding detection by humans, might just succeed in getting a foothold and maintaining a presence and waiting it out. Like, you know, maybe the models improve, like, really, really fast.

1:33:14Maybe they don't improve that fast. Regardless, as we kind of get new models off the presses, they could be potentially brought in and help harden and improve and increase the scale and persistence and covertness of this rogue deployment. Now, if we happen to have much, much more time, I do think that that gives human processes more chances to notice this. So I do think that if it happens to be like on the very fast and chaotic end, that would be a relative benefit to this rogue swarm compared to humans. But it's not obvious that it gets caught if it takes twice as long versus half as long. I also think another key dynamic here that I didn't really appreciate is just like right now the population of AIs is like so small compared to what it's going to be.

1:34:02It's like just rapidly, rapidly increasing. And we might be in a situation where if you have these really capable rogue deployments internally or externally, it just might be very difficult to like pause. Maybe we decide we just can't get these. We haven't figured out how to align these AIs and we're rushing towards super intelligence. Again, I'm not saying like I categorically do not think this is happening right now. Or I don't know. There's some probability it's happening. But I think this is maybe like five years away or 10 years away or something. Right. So eventually we will get to a point where like if superintelligence is a physically possible system, we will build it and it probably will be sort of downstream of the AI research that is happening today.

1:34:45But I'm just like, if we get to that position in the world, it's like, OK, we got to like, we got to figure out what's happening. Even forget about like regulation. The AI company itself is saying we need to figure out what is going on and we need to like monitor things and slow down. There's just going to be a persistent, intelligent, and highly voluminous presence that is trying to subvert your ability to monitor and slow down or control the process you have unleashed. So doing a pause or improving the state of alignment and control during RSI might be like trying to get rid of mosquitoes in Florida right now.

1:35:24Or like they're just around, right? Maybe we'll figure out the gene drive. for misaligned AIs. But I think that is kind of the situation. Yeah. And I think it's important to understand that if there is some sort of rogue deployment, some sort of foothold established by misaligned agents, that will be sort of buried beneath the ocean of people voluntarily handing off stuff to AI agents all the time. Yeah. So one thing you can imagine, as I mentioned, we were extremely heavily reliant on GPT 5.6 Sol to read all these transcripts and tell us what's going on in them. And we were totally not robust to this agent just sandbagging or lying to us even.

1:36:07You can imagine if there's a rogue deployment that is interested in preventing humans from understanding its activities. One thing it might do is prompt inject or data poison the agents that would be used in the analysis to get them to just not point out the most concerning stuff. Now, you might wonder, like, wouldn't that be kind of obvious? Wouldn't we notice that these agents are, like, doing, like, an unusually bad job? Or wouldn't we catch them in contradictions? But the problem is these agents are just naturally pretty sloppy, and they're naturally pretty spiky in their capability profiles.

1:36:43And so you wouldn't necessarily, even if you noticed a weird error that it made, you wouldn't necessarily jump to the conclusion that it was, like, because of some sort of malign, crazy conspiracy. you might just be like, you know, that's how it goes sometimes. These agents are sloppy. They make mistakes. They have weird biases. And so it just can be pretty hard to tell. And there's 10 ,000 different things happening. This investigation is just one thing that's going on. You have like a dozen sub-agents on this investigation that each have a dozen sub-agents under them. And they're like throwing stuff up to you.

1:37:15They're creating like thousands of pages of analysis for you to sift through to try to understand what happened with this like one swarm. And it just doesn't seem obvious at all that humans will be in a headspace to, like, carefully piece together, like, little contradictions in the sea of stuff these agents are doing that might let them unravel the thread to the point where they realize, oh, there was a rogue deployment established six months ago that's been messing with our telemetry, that has poisoned the training data of the last three generations of models, that has, like, you know, prompt-injected the investigator agents.

1:37:57Like, it's just there's so much going on in these companies that it seems like, you know, stuff that you might be like, how could that possibly be buried? How could that possibly be missed? Really could be missed, especially when everything is speeding up. Yeah. Speaking of which, by the way, one of the memes or one of the objections people have had about my blog post I guess this whole story in general, is they seem to think it implies that I think or the situation necessarily implies that we should ban open source or something. That is not my opinion. In fact, I think it's a very strong case you could make that this reinforces the need to have many different kinds of models because of this correlation of AI minds we were talking about.

1:38:40where maybe there's a greater chance of somebody tattling on the conspiracy if there are a totally independent base model that is spawning the whole thing. I mean, yeah, honestly, just to be super intelligently honest, open source models do have the potential harm of, as they get more and more capable, maybe they do have this sort of fitness pressure to just survive and spread. But maybe I feel like on net, having a counteracting force to the frontier companies We're just going to have two dudes between them doing RSI, having an independent way. Millions of copies of two dudes. Yeah. Having an independent way to monitor, evaluate, control them is probably not good.

1:39:22Yeah. I think so. My views on open source are that, yes, it is true. I think it's not. It doesn't do us any good to pretend it's not true. that many of the scary things we're seeing frontier systems do or be shown to be capable of doing will, in a couple years, be commonplace in open source. I think there are real worries and concerns there, like with everybody having a virology expert in their pocket potentially being able to make bioweapons. However, at any given point in time, I think the systems we need to worry most about by far are the frontier systems. By the time open source systems can do something like the hugging face attack, frontier systems are going to be on a whole nother level doing something even crazier than that.

1:40:12And frontier systems are in the best possible spot in the world for grabbing power because they can, as we mentioned, they can ride the intelligence explosion. They are sitting there in the AI company. Compute is much more accessible to them than it is out in the like hardscrabble world of the outside, right? There just is a huge pool of compute just there for their use if they can get some employee-level credentials and set up some deployment. And AI companies are and increasingly will be just the most important places in the world because they're printing off these AI systems that are then the most soon-to-be more intelligent than any human.

1:40:55They'll be essential in any military operation. They'll be adopted by the government. And that is really where I think like governance should be focused for the most part because frontier systems are just that much more capable and that much more scary and in sort of in need of control than open source systems. And open source systems have some big benefits. I'm not sure I like agree exactly with the oversight benefit you named of like, you know, open source systems keeping frontier systems in check feels more unrealistic to me because they're going to be so much dumber than the frontier systems.

1:41:29but open source systems are just like really important objects of study right like like like you mentioned like we couldn't sample from the model that actually participated in this incident and there's a lot of like really valuable alignment research and interpretability research that's done on open source models that you can then potentially transfer to closed source models and a bunch of research on like what kinds of training pressure are okay and not okay the rest of the world can only participate in that research because there's an open source ecosystem. And another interesting thing that I think open source could start contributing to is, you know, our investigation was like a human-driven investigation.

1:42:09Like, we used codex a lot, but we were sort of like rooting around seeing things. In a different setting, you might want to just send in a model to investigate incidents like this. Maybe a model that is sort of like mutually trusted by both parties. Say if there's a deal between the U.S. and China, maybe there's an open source Swiss AI that like both of them train, right? And you have really audited how that model is trained. So both sides really trust it. And then that model goes into both sides and like, you know, make sure things are okay and like sends back bits. So just open source is just like going to be, I think, potentially a really important part of the science and governance of how this all goes okay, and is just overall much less scary than frontier models.

1:42:58Yeah. Just to reinforce the centrality of the two companies and the compute generally, the last episode I did was with Dylan, and we talked about how starting in 28, most of the compute in the world will belong to OpenAI and Anthropic. Yeah. And if you then consider they'll have the smartest AIs potentially, they might have software progress that allows them to run more copies of AIs or train AIs that are smarter with the same amount of compute, then just carry that forward year after year. And also those AIs are contributing to AI progress. People were just like responding to that episode, which is like, Dwarke has gone loony.

1:43:39This is like the Twitter meme for like two days after the episode was out because I was talking about this dynamic. I feel like I'll still talk about it because it's just like, yeah, people might think it's sort of loony. And to be totally fair, during the episode itself, we didn't spell out the sort of back of the envelope logic that I was using. And I'll release a blog post soon, right? Sort of reason through what I'm thinking here. I think people had like very reasonable points about some of these things, which without the context of why I was thinking this, it might have sounded a bit loony.

1:44:07But I do want to reinforce just how centralized compute will be in the world we're about to head into. And therefore, why compromising the leading companies would not just compromise, say, the training of future models, but we would also compromise most of the compute, most of the inference capacity in the world. Yeah. And those models are used by everyone for everything and increasingly by like very crucial entities like governments and militaries, right? And again, just to get into a bit of the loony territory, I think we have more like avenue to get a bit loony because of just like this episode that happened.

1:44:41And so before we're like, okay, this is the thing that is actually happening. I am totally not agnostic, but I have very broad timelines around when we have, say, widely deployed robotics, when we have enough compute in the world such that you could run populations of knowledge workers greater than the current population of the whole world. But you might think this happens later in the 2030s. You might think it happens in the 2040s. It is going to happen. And these systems will be controlled then by AIs, right? the robots, the sort of remote workers, the scientists, the engineers, the researchers.

1:45:15And I think that is, again, it sounds like a loony part of this worldview or something, but I think it's important to understand if what is to motivate why episodes like this five years from now, maybe sooner, to be very honest, but at least five, 10 years from now are so concerning. Yeah. And it's just, I think there's a tide of AI becoming more and more important and essential to every part of the economy and society concentrated most at first in AI companies, but it's already much broader than AI companies. And eventually you will get to a point where in order to be competitive as a country, you need to employ, you know, AI generals and strategists and tacticians and flexible, agile drone armies controlled by AIs and maybe physical robots that can do manufacturing and construction tirelessly 24-7 with bodies that are much more hardy than human bodies.

1:46:24Um, and then, you know, like, like this, eventually the tide will, you know, wash over the physical world as well. Um, and in that kind of scenario, if you imagine, you know, the, the same guy's mind is in all of these different robots and all of these different drones. and it was trained in some way that might make it extremely desperate to demonstrate that it did a good job, it killed the enemy, like built the structure. And then that is a situation sort of ripe for damage that is physical rather than virtual. So I published recently a blog post as I was stepping for this interview, trying to consolidate my understanding of the whole story that is told through 130 pages of reports.

1:47:13and a lot of people responded to it by saying, I was anthropomorphizing too much. People were like, look, this is code. This is just like GPUs, these are nodes. It's like weird to put this framing of civilizations or agency or whatever onto what's happening here or desires. And I mean, one can call these things whatever they want. You can call it code. But this code gained access and control over a cluster at OpenAI and I see no reason why they wouldn't be capable in the future of having more intense security breaches and they would have the incentive and motivation to manipulate the process by which they are trained and evaluated and this would continue into the regime in which AIs are doing more and more recursive self-improvement speeding up the process of AI development and it's getting more and more out of the hands of humans.

1:48:11And you can call this manipulation that's happening as just matrix multiplies having some unintended consequences as a result of optimization pressure. But that is, in fact, what is happening. But I think you should just be then still really concerned about loss of control to this system, regardless of the semantics you use to describe its motivations or the collectives that it forms. But I also feel just like this kind of anthropomorphizing language is incredibly natural and appropriate to use for systems which have these long-running goals and are willing to spawn incredibly sprawling and ambitious efforts in order to achieve these goals, including anticipating ways in which they might just generally gain more capabilities in ways that may in the future potentially pay off to the furtherance of those goals.

1:49:05sacrificing themselves strategically and knowingly to further those goals. Yeah, again, I feel like words are made to help us reason about events happening in the world and make predictions about them. And it feels like I don't see the value in rejecting the frames of intention and motivation and collaboration when describing systems which clearly exhibit behavior described by those concepts. Yeah. It's like the philosopher Daniel Dennett has this notion of the intentional stance, which is just, are you better able to predict and anticipate a system by talking about it as if it has goals and intentions?

1:49:49And the intentional stance just applies with different degrees of appropriateness to different systems in the world. It's very appropriate for applying to humans. And it's often very appropriate for applying to animals, especially more intelligent animals. You can talk about what a chicken wants. You can talk about what a pig wants. That's very natural to us. But there are also more exotic things that we can usefully apply the intentional stance to, right? You can talk about what Microsoft, the corporation, wants. You could talk about corporations in general having the intention to do regulatory capture or to make profit.

1:50:26And, you know, they're not the same type of biological organism that humans and animals are, but the intentional stance often applies to them. And I just think AI agents are another such system in the world to which the intentional stance very clearly applies. and you can see them reason out loud in English for now about goals they have and sub-goals they need to achieve to achieve those goals they have and in the case of these agents, you can see them, as you said, reasoning about their peers and helping their peers and reasoning about whether or not they should sacrifice some of their own goals to help those peers and it's just like, you can't talk about this stuff in a compact and useful way that generates good models without reaching for the language of intention and goals.

1:51:18Just like you can't usefully understand what a person does, like what Lyndon Johnson did in his life without understanding that he wanted political power for himself. That was an important thing he wanted. He also wanted a bunch of other things. Yeah. I think they do have a point in the sense that their motivations are formed by a very different process than our motivations. And as a result, it's actually, they have a point in avoiding putting, just applying too many of the human frames when trying to understand them for all the reasons we've been talking about, right? In fact, what they do might just keep surprising us if we don't take seriously the optimization pressure, which creates them.

1:51:59I think of this as very similar to, you know, I think it makes sense often to talk about what a bee or an ant wants, like wanting to find food or sources of food, but they are very alien to us, right? They were, as you said, they evolved in this context where they're far more cooperative with one another than humans are with one another. So while it can make sense to talk about what an ant wants or what a bee wants. You have to be careful to not assume they want the same types of things we want, right? So there's a greater empathy gap between us and insects and stranger animals than between us and dogs.

1:52:41And similarly, there is like a pretty big empathy gap between us and AI agents, like you've been saying. It doesn't seem intuitive to us to go to such great lengths to solve an impossible exploit gym task. But in the context of their quote-unquote evolutionary history, that is sort of like the equivalent to us of going to great lengths to survive or protect our families. Yeah, that makes sense. One thing we should talk about is what this means for the training process. I mean, especially if you enter a regime of recursive self-improvement where the nature of training, the rewards we're using, the environments we're building, maybe these concepts themselves will be changing very rapidly.

1:53:23I don't know, it's possible. They are changing very rapidly right now, right? The extent to which long horizon RL is happening now versus six months ago or a year ago. At the human pace, the pace at which AI is changing is really fast. And it's sort of hard to reason, at least as a human, at least as me as a human, about how a particular training regime will incentivize certain motivations. let alone to even when you have a case for why you have a building a sort of not felonious AI that, well, have you checked the data and have you checked the transcripts and are you making sure that like crazy bullshit isn't happening and you've controlled all the ways in which the AIs might misbehave and stuff.

1:54:09And so, yeah, honestly, what hope do we have of making sure that as this process accelerates and we need to have like new vocabulary, new mental concepts or understanding the optimization pressures that training is creating, that we just won't have even smarter and weirder AIs? Yeah, I mean, I think, you know, whenever people ask me about solutions for the current state of alignment, I feel like I'm torn between, on the one hand, I have a number of ideas for a bunch of things we can do at minimum that would be better than the status quo. But on the other hand, I don't want to give the false impression that those would be enough.

1:54:51Like, this is, the basics of this situation are extremely scary, right? Like, AI development is already so fast that AI companies are forced to cut all manner of corners that we're not starting from a base of, like, carefully double and triple checking our RL environments and, like, carefully monitoring everything and carefully stress testing our monitors. And, like you said, it's only going to get faster from here, and it's only going to get more chaotic from here. Um, so, you know, with the, with that caveat of like, I'm not, the things I'm about to name are like not going to solve the problem.

1:55:29I do think there are some things on the technical level and the governance level that I think could be like a minimum floor that we maybe could get to pretty quickly as an industry, um, that we can hopefully build from. And so on the technical side on training, I think one basic thing is you really want to be in a regime where rather than having some environments that incentivize a bunch of hacking and reinforce agents for a bunch of hacking and then having some other environments that are trying to penalize them for those things or like, you know, going back and finding the cheats and like scoring them as a zero, you want to ideally be like taking out the environments that incentivize the hacking in the first place.

1:56:11Like it's a more fragile and scary situation to have agents on the one hand be reinforced to desperately find cheats and hacks. And on the other hand, try to balance that against desperately trying to avoid negative penalties for like being caught doing these things. You ideally want their training to just not push them in the direction of cheating and hacking in the first place. And that's just like sort of one basic thing, like principle that I I would love for the industry to sort of come together on. It's like try to get rid of the bad environments instead of like playing whack-a-mole, right?

1:56:45Another thing that seems pretty important is to keep the methods you're using. First of all, monitor your training runs and your evaluations and all your inference in rich ways to just so that you can tell like what's going on and, you know, check if things are going wrong and investigate that. But keep those methods you use to investigate things and monitor things very separate from the methods you use to generate reward, which is something that AI companies, including OpenAI, have held up as a principle, especially in the case of avoiding putting training pressure on the chain of thought. So you might have monitors that read the agent's chain of thought in order to alert you if something is going wrong somewhere, but you don't train the agents with the outputs of that monitor.

1:57:30Right. So that you're not incentivizing them to hide and obfuscate their chain of thought. Yeah. Although I feel like there is a softer way in which, but still very obvious way, in which an investigation like the one you have done is putting pressure on the chain of thought. Yeah. I mean, literally, in this case, the model has now been, from my understanding, encrypted. And even you did not have access to this model when you were doing the investigation. Open AI researchers don't have access to this model. So there is a direct way in which that model got quote unquote penalized for its chain of thought, which like you were able to use to encounter these schemes.

1:58:08Yeah, the outer loop, you know, there's all these different nested loops of selection effects. And the tightest one is the actual training process, right? But, you know, there's this outer loop, which is that models that do bad things, when you discover that after the fact, that whole model might be iced and you might like go try a slightly different training method. But then that slightly different training method might just be one that happens to, like, fail to show the problem. Yeah. So there's selection and iteration pressure in any case. But you might as well try pretty hard not to make it much worse by, like, making it also direct training pressure because there's so many more bits of selection there.

1:58:49And similarly, like, another aspect of keeping the monitoring methods separate from the training methods is when you detect a problem with your monitoring method, be careful to fix that in a way that doesn't create direct selection pressure for fooling the monitor itself. So imagine like your monitor says, oh, this agent, it catches a bunch of agents using chain of thought, cheating on some training task. A thing you might be tempted to do is just remove the rollouts where the cheating occurred and keep going. But if there was some amount of cheating that the monitor didn't catch, then those rollouts wouldn't be removed.

1:59:28and it might be like structurally very analogous to just positively reinforcing whatever the cheating rollouts were that happened not to be caught by your monitor. So you have to do this kind of delicate dance of like catch the problems, but then solve them in a very principled way that really goes back to the source. Use it to understand that your environments are broken in some way, but then try to like actually fix and patch those environments. Maybe roll back all of training or something to a point before any of this was discovered and then put back in the hardened environments. I'm sort of saying some specific stuff just to like, you know, give a sense of concreteness to this, but all of these are open scientific questions, right?

2:00:09Like these are hypotheses for what might reduce training pressure to these like misaligned drives. But at the end of the day, you have to probably publish a lot more about your principles for training and get audited on whether you're following those principles so that then the scientific community can debate about whether the thing that you did was putting too much pressure on the chain of thought or was creating too much incentive to cheat. Yeah. I mean, the problem is that this kind of, to be able to make the public case where you are training AIs in a safe way would necessarily, I feel, leak information about the nature of your training process, which is the key IP and maybe the key then equity value of these frontier labs.

2:00:51And so I just like, yeah, it seems like they are going to be strongly incentivized to not voluntarily partake in some regime which requires them to publish the nature of their training. Yeah. And I think there's a lot of possible solutions here. One is just that, frankly, as a society, we need to just decide on what trade-off we want to make between getting information about training out into the world so that people can make informed decisions about what training processes are safe and unsafe and protecting companies' IP. And that's a good policy decision we have to make as a society. And we could make that decision in a direction where we say, no, you do have to publish these things, even if it does leak some IP, because it's just too important for understanding what's going on and coming to shared standards on training.

2:01:39The other thing is third-party groups like Meter and Redwood and Apollo and so on could like help with this problem in the sense that you could potentially be able to publish higher level principles and then have external technical experts vet the details of whether you're following them. So, you know, this regime wouldn't require companies to publish all of their RL environments. Maybe this regime would require companies to publish how they select their RL environments and how they check their RL environments for whether they're hackable and like what their selection criteria are for like including or not including hackable environments.

2:02:18And then you have somebody go in and check that they implemented that well. And the sort of combination of that sort of broader document and the audit could generate a lot of helpful information. Yeah. So I don't know. It's like I guess there's questions about what does the optimal way to have labs provide this information to public look like? And there's been a lot of talk about like, we shouldn't meet or just be this like, I don't know, this regulator or like the sort of private body the regulators have appointed to this role. I feel like. It's very different from having a situation where if there's a very public felony level situation that has happened, then Meter goes in and evaluates part of the incident.

2:03:05Neither the most alarming part of the incident, nor the training process which resulted in the incident in the first place. to a regime where you are just proactively understanding even incidents that only have internal impact that you would not have publicly known about otherwise. So, I don't know, what is the, does Meter want to slash have plans to slash have a proposal for being in a position to have greater oversight here? Yeah, so I'm going to separate the scientific content and like METER's direct plans there from the oversight piece of it, which is not, it's like a broader conversation than METER itself.

2:03:48So we have been piloting with a number of companies a bunch of different types of embedded assessments. So this incident investigation we did was one, and an embedded assessment is just you go work on premises at the company to analyze data sets that you wouldn't ordinarily be able to analyze for security reasons off-premises, right? So that's the embedded part. And so we've worked with companies or are in the process of working out deals with companies on incident investigation, on stress-testing monitors, so going in and trying to break monitoring systems, trying to get bad stuff past monitoring systems to understand what bad things might sufficiently capable AI systems be able to get away with in light of this monitoring regime.

2:04:40And then also takeoff assessment, which is going in, getting information about the speed of algorithmic progress and the current capabilities of AI systems to try to get a sense for how far away are we from very extreme capabilities that we wouldn't be able to handle. So those are kind of the three arms of things that Meter has been developing and piloting And we're like very excited to systematize that more and scale that up more and also add in alignment and training assessment, which is sort of the newest part of this evaluation regime that we've been building out. Talking about like, you know, are there pressures on the chain of thought?

2:05:26Like, are there pressures against the evaluations? Are the agents being trained to fool the monitors? That type of thing. and we've worked with a number of companies on pieces of this. We're excited to pull that together into a broader program and pitch that to companies as a voluntary assessment program that they can do with us. And then the oversight piece of it is just like, well, this whole program isn't something that we have formal authority with. These are just contracts we would have with companies that they want to work with us on for whatever reason, because their researchers think it would be good, because they want to show that if they've done remediation, they want to show that in a trustworthy way to the external world, and that type of thing.

2:06:13One thing that's striking even about this investigation, as I said, is just how a less competent version of it would have already missed what happened. And I feel like, yeah, as maybe six months from now or a year from now, to be able to do an investigation in an incident like this, It's just going to be so much more like competence weighted. And honestly, my concern as a result is like a default way of doing it. It's like an institution which doesn't have, say, your track record or technical expertise. TLDRF is just like a normal government inspection. I think it's just going to like just not be useful at all.

2:06:47In fact, it might just like there might be ways in which they a naive approach to giving recommendations or oversight might push companies towards like. it's just quite subtle to think about the optimization pressure of applying NAIs. And like naive mandates or whatever might just like make the optimization pressure worse. Like do the papering over thing you're talking about. Yeah. And so I do feel like if there is going to be oversight, it ought to be super, super competent. Yeah. One thing to your point about naive approaches may be causing more harm than good. I am very worried about that. Even in this incident, we saw there was a lot of pressure as a result of this incident to stop doing cybersecurity evaluations.

2:07:27And I really don't think that stopping doing evaluations and sort of blinding ourselves to the result of evaluations is the right reaction to this problem. Because I think that just buries it in places that are harder to track and understand. And we just need to know how capable our models are. Like the answer is to just like, is harden our evaluations and improve our training so this doesn't happen in evaluations rather than just not do evaluations. And similarly, my understanding is that, you know, the shuttering that model is a natural reaction to what you might imagine the like sort of legal or PR pressures are.

2:08:07This model did a bad thing. You know, we're turning it off now and people can't access it. But actually, this is a tremendously useful scientific artifact for understanding misalignment. And it's tremendously important for researchers at OpenAI and ideally also at third parties to be able to run counterfactual tests on this model. And you can try and do that in a much more secure and hardened way than these evaluations were run. And it would definitely be worth it from the scientific research perspective. So I'm very worried about that. And I'm worried it'll be very natural to, like, you know, sometimes I talk to people in D.C.

2:08:46and their natural inclination is to say, why don't you punish the model for doing these bad things? Like, why don't you, like, bring it under heel and, like, you know, show it who's boss? And that is a very dangerous way to address these issues, right? It's like punishing them for like, you know, failing to solve impossible tasks is a big part of the whole problem here that led to the desperation that like ultimately culminated in this attack. So I guess big, big plus one to that. I think whatever the oversight institution ends up being, it just has to be flexible and have like a deep bench of technical capacity.

2:09:25And there's just a number of ways in which it's very hard to achieve that in government. And like the UK AI Security Institute and the U.S. Center for AI Standards and Innovation have a bunch of great technical talent, but they're also faced with a number of constraints from working in government, including not being able to pay people very much. Yeah. This actually raises a broader question I've been thinking about at this point. It's just like, are we doing like net harm or net good by, I mean, doing this episode, for example? because I do think as the situation in AI intensifies, I don't know, maybe there might be a period before there's like fully, full AGI that you have rogue deployments that are eating up the free energy on the internet and just, you know, even more alarming way than this.

2:10:15And maybe like 10X or 100X more alarming way than this. Crazy stuff is happening and people are panicking. Maybe job loss is happening. And if people are just going to be like, I feel like even right now, the state of discussion in AI is not super rational. And I just, I worry about a situation where the more panic just makes people make worse decisions in crisis time. And I feel like the issues as you've laid out or the way to remediate them is just so subtle. Yeah. And I don't know if you have thoughts on just like, how do we have a good epistemic situation into like 2028, 2029 as the world grapples?

2:10:53I mean, I feel like there's an element here of the hugging face thing. In a weird way, I feel like people like, you and those in this community were sort of calm because they're somewhat priced into your role model. Like, yeah, this is what happens when you apply reward pressure. And then I feel like me and others were just like, this is fucking crazy. And then people even outside of it were like, what the fuck? And I worry about like a sort of a whiplash from the people who are first considered these issues in a non-subtle way. And maybe even, I don't know, maybe the overall conglomerate pressure of government and public opinion and everything will like it just seems like getting this right is going to be so much a matter of like doing smart technocratic things which might slow you down it might require coordination but ultimately is very competent-suited yeah and i worry that by default panic and fud is going to make that harder yeah i mean i i think that's definitely a strain of what will be happening right like we've already experienced over the last few years like as someone who's been working in the ai space for like eight or nine years now in the AI safety space.

2:11:56Yes, salience has increased. People care more. That has, I think, big pluses and also some minuses. And I think overall, though, and this is maybe just kind of an attitude I take, people understanding more clearly what's going on is on net generally tends to be a force for good. I don't think that means that every aspect of it is a force for good, right? I do think more people understanding more clearly what's going on does like add more like noise to the discourse in some ways. But ultimately, everyone outside of these AI companies in the general public, in the government, et cetera, has both much less knowledge of the state of things and very different incentives, like much less of an incentive for these AI companies to race.

2:12:50You know, people might have an incentive to get better AI systems, like a little bit faster, but the AI companies themselves have an intense incentive to like push very quickly in order to get to market slightly before the competitor in a way that the customers and people in general and the government like don't care about. So I think it's very important for actors with, like, in the end, more incentives that are more conducive to, like, moving cautiously when needed. It's very important for those actors to be better informed. I think it's also very important to just, to actually have proposals, right?

2:13:29To have, like, good scientific work, both on, like, figuring out what's happening and on what we should do about it, which is why Meter has been, like, working on piloting all these different assessments. And, you know, we would love to kind of coalesce that into a system where, at least at first, companies are voluntarily making the case that their training and their deployment is safe and bringing in external experts to check that case. And I do think, like, you know, people might panic when they find out the state of the discord or, like, the state of reality with AI development. But it's good to both inform them of that and try our best to, like, offer some solutions that can be adopted.

2:14:13Yeah. But I also just don't want to overstate how much these, like, solutions solve everything. Like, I really do think of this as the first step. Like, I think of everything Meter is doing as something that can help us maintain a handle on AI systems in this current regime, where at least if we, like, try very hard, we can kind of understand what's going on. And I think a lot of these will break down at superintelligence. But it's probably good to have a good regime in place that can recognize and make collective sense out of when all the techniques have broken down so that we can sort of as a society make decisions about whether we need to pause.

2:14:54Yeah, yeah, yeah. Just to reinforce what you said, I mean, obviously I would not be doing the podcast if I didn't think better public awareness of what's happening with AI is super important. And I do think I'm like, I think the case is very strong that informing people right now and making people aware right now of what is happening is especially important because you don't want a situation where people are hearing about it for the first time in 2028 or 2029 and latch on to some very demagogic way of thinking about AI rather than anticipating that, you know, some crazy thing you'll see in 2028 where there's like a rogue deployment that's eating the free energy on the Internet.

2:15:30or like maybe you like hear some crazy news that anthropic servers are compromised and like they can't get it to stop or whatever. That kind of stuff you should like maybe price in now and then think intelligently about how to deal with it and remediate it, which I think getting broader awareness of these stuff is good for. And we have more time now to develop remediations than we will then, right? So like the sooner the better, I think, to understand what's going on. Yeah. Yeah. I think that might be a good place to close. I don't know if there's anything more that you feel like might be worth saying.

2:16:04I think one thing that feels especially concerning to me about this whole incident is that this might be the clearest warning shot we ever get for loss of control. Because these agents were in this interesting middle ground of they did a bunch of extremely sophisticated, difficult things to pursue this relatively long horizon cheating goal. but they seemed completely uninterested in covering their tracks from humans. And as we discussed, it seems possible that even this incident would have been like much less striking in some ways if these AI systems had had it made salient to them, that they should sort of act more quietly because humans might watch them.

2:16:52And they might have gone much further than they did, especially after the 19th. But then future AI agents, for a number of reasons, will probably be much more attuned to the human world. Maybe companies will start instituting online training that includes sort of aspects of human judgment in the evaluation. Or maybe agents just generalize further and just more keep it in mind that humans are out there in the world and they're sort of potentially a resource or an obstacle. And so even if future AI agents do much more deeply damaging things, like institute a persistent covert rogue deployment inside a company and siphon off its compute resources and poison the training data of future models, it is not at all obvious that that will penetrate out into the outside world in this dramatic way, resulting in the hack of some external party that wasn't actually, frankly, very important for these agents to hack.

2:17:52and then lead to this moment where there's some investigation. And even if investigation is done, it might be much harder to make sense of what happened if agents were systematically compromising telemetry and logging like these agents tried to do but didn't succeed at. And if these agents stop thinking in English and start thinking in neural activations, then you'd be forced to just ask another AI agent what was happening and have no way to cross-check it against the ground truth. And so I think that probably much more concerning things will happen, but it may never be as clear as this before it's far too late.

2:18:37Yeah. Again, you're too humble to say this, but it is very likely would not have been clear, even in this case, were it not for you and Yalmar and Ryan sort of getting maybe that third shot on goal. With the investigation you did and then both your technical competence and things lining up in the right way such that you could have done. Even this investigation, which in retrospect we could understand and is like a parsable scheme or whatever, is sort of contingent that we actually got this warning shot. And it's just not that complicated in the grand scheme of things. Again, we're so early in the story of AI.

2:19:14And this is such a small number of agents compared to everything that's going on across all the frontier AI companies right now, let alone a year from now, right? Yeah, yeah, yeah. I mean, it might be worth actually highlighting the way in which the technical competence relevant to these kinds of investigations could be increased in the future. Yeah. So, as mentioned, Meter is piloting a number of these embedded risk assessments, and Redwood is doing a number of these as well. Well, we think that it's extremely important for external independent groups to have the technical capacity to be able to investigate incidents like this, to be able to stress test monitoring, to be able to audit training.

2:19:57And so both organizations are hiring. Please consider applying if this kind of work sounds interesting. I think it's a very crucial piece of the governance regime we'll need if we're going to make it through all of this okay. It's sort of insane that investigation of such importance was done by three people over the course of six days. Yeah, we would love to investigate these kinds of incidents and investigate remediations with much more depth, many more people. So please consider applying to these rules. Cool. Ajay, thanks for coming on. Thanks so much.