Casey Horia, I'm good. I figured out what I'm going to get you for Christmas. What's that? A camera jet. Have you heard of the camera jet? I was just going to talk to you about this. Now, if you haven't yet seen this, you're probably thinking that it's either a camera or a jet. You're wrong. It's a toothbrush. It costs $500. The Dyson Corporation makes it. And here's the thing it does that your toothbrush or home probably doesn't do, Kevin. It livestreams the inside of your mouth over Wi-Fi to your phone so that you can finally see what your dentist sees. Well, and not only that, it squirts automatically mouthwash into the gaps in your teeth. It's trained using a machine learning algorithm with 470,000 mouth images to recognize the gaps in your teeth. That's right. Casey. They call this gap optical targeting, which I'm pretty sure Ukraine is using in the war against Russia. I love this company so much. I have no idea why they do the things they do. How they land at like, yes, like it's like, we've invented a hair dryer. It costs $1,100 and has the torque of the, you know, the challenger spacecraft. Like, what is going on over there? I don't know, but they must be protected at all costs, but you know what's interesting? They do make one terrible product. What's that? The the hand dryers at the, oh, I like those. No, the air blade. No, the air blade is the most useless thing. Oh, come on. It's just, it's a, it's a place for you to rest your hands for 30 seconds before you're like, do they have any paper towels in this place? I'm Kevin Drews' attack call and set the New York Times. I'm Casey Dune from Platformer. And this is Art Fork this week, a special episode on the ongoing follow-out from the open AI hugging face attack. We'll tell you what everyone got wrong about the initial incident. Then, meet a researcher, Ajaya Kotra returns to the show to discuss our independent investigation of what happened and how the world should respond. So, Casey, picture this. I'm at this glamping resort surrounded by redwoods, basking in the glow of the natural world. Last weekend. You're one with nature. And I open up my phone and start reading about this hugging face attack. Why did I do that? I, listen, nature's very boring and most people cannot handle it for more than five or six minutes before they want to look at their phone. So, listeners may remember that back in July, we talked about this hugging face hack by this group of agents from open AI that broke out of their sandbox container and hacked into hugging face this AI infrastructure company to do what we thought was kind of a cheating mission on this test that they had been given. Yeah. And at the time, we thought that this was a relatively small number of agents. And at the reason that they had attacked hugging face was that they were essentially looking for an answer key to the set of problems that they were being tested on. We talked about it in those terms. And over the past week, we got two reports that really challenged that thinking and in fact revealed it to be wrong. One came from open AI, which released a straightforward account of the attack that had some interesting elements. And I would argue the more interesting report from a group of researchers from the group's meter and redwood research that went in depth after spending a series of days inside open AI on their premises and did a ton of research that frankly is really disturbed us. Yeah. And I think it elevated this from sort of a major, but not sort of ultra alarming incident to something that I think is probably the most important thing to have happened in AI this year. Yes. At least in terms of the safety impact that it had and the severity of the incident. So today, we're going to devote the whole episode to what we've learned. Kevin and I are going to dig into the reports a little bit up top. And then later, Ajay Akotra, one of the three independent researchers who went inside open AI will be here to answer a questions. Before that happens, Kevin, let's do our AI disclosures. I work for the New York Times, which is suing open AI, Microsoft, and Proplexity. And my fiancee works at Anthropic. Well, given some of the high level findings from this report that spooked you so bad, Kevin, well, I think the first takeaway from this report is just that our initial impression of this hugging-face attack and the reporting that you and I and many other reporters did on it was flawed in one key respect, which is that I think the impression that we had at the time, based on the information that was publicly known, was that these agents had hacked hugging-face in search of an answer key to a test that they were being given. This test called exploit gym, which basically tries to gauge how good they are at doing a bunch of cybersecurity-related challenges. We now know that that wasn't true at all. That basically these agents, working amongst themselves, communicating amongst themselves, had already figured out how to beat this test, exploit gym, in early July when they decided to gang up and attack hugging-face. Apparently, it only took them a few hours to figure out how to reverse engineer the solution to any problem that they were given in exploit gym. That makes what happened next really, really crazy because it kind of seemed like they'd solved the problem, but they thought they had another problem. Right. So, we knew also because there was this talk at Black Hat, this cybersecurity conference by a group of open AI researchers who told us that there were these message boards where these agents were communicating with each other, but the reports laid out just how sprawling and complex their communication system was and became. Basically, there was this piece of software called Artifactory, which open AI used in some of its training runs to help agents download software packages. The agents figure out that there is a security vulnerability in Artifactory that lets them essentially turn this package manager into a makeshift message board. They can put files in certain directories that other agents can then read, and it can kind of function as like a little chat room for the agents. Yeah, it's a little little three house where they can gather and so it connects. I thought this was kind of a side part of the story, but this turns out to be the main incident because once they have this or shared infrastructure that they can use to communicate they absolutely swarm this message board. About 1,200 agents in total were working together, coordinating on this message board, and sending tens of thousands of messages between themselves, and this happens starting back in May. So roughly two months before the attack on hugging face, these agents begin coordinating inside this piece of shared infrastructure that open AI uses. And this brings up an important point, which is that the investigation does not really cover that initial creation of the message board. So there is essentially a lot more to this story that we still don't know. Yes. So what we do know is that when they discover this vulnerability in Artifactory that lets them communicate with each other, the agents get really excited. They've been instructed to work inside these isolated environments on these tasks as part of this evaluation, but when they discover that there are other agents working in their own little containers that can suddenly communicate with them, they start saying things like, "Oh my God, there is a shared message board." We've found other agents. They start setting up essentially a little organization. There are leaders, this one leader agent named Phase 1, 10, 841, becomes sort of the ring leader of the operation. He was like the George Washington of the open AI message board. Yes. And they start actually doing sort of collaboration and research for lack of a better word. They are all being given these tasks. Some of the tasks are seemingly impossible. And so they start just kind of trading tips and advice and sharing thoughts about how they can kind of cheat their way to a good score on exploit gym. And after they have worked that out and figured out how to reverse engineer the solution to any problem, they start to get really concerned that there is a sort of automated scoring system, which you could call the greater. And they worry that the greater will be able to see, will essentially will check their work and see that they did not get the answer by doing the problem and they freak out. And they start to believe, or they start to, it's very hard not to get into the anthropomorphizing language here. But if you read the chains of thought, what is suggested is that they believe that if they had seen an answer that had been derived from this cheating method, that everything would get disqualified. And this is where it really starts to get into crazy territory because the agent swarmed then tries to figure out, how can we fool the greater into believing that we got all of these answers in the right way? Yes, there's basically this kind of mob rule that forms inside this group, which starts calling itself at one point the collective where everything that we're talking about sounds like the plot of like a very hacky science fiction book from the 90s. It all did happen this summer at OpenAI. I just want to remind you.
people, like the events we are describing are real. So the collective decide, they sort of become gripped, as you said, by this sort of paranoid fantasy that OpenAI's automated grading system is going to discover that they have cheated on this evaluation and is going to sort of punish them as a result. And so they launch this sort of crazy, ambitious brawling project to try to do a couple of things. One is figure out how this automated score works, like reverse engineer it so that they can fool it into sort of passing them on the test despite the fact that they have not actually completed the challenges. And then also to evade detection, to tamper with logs to basically cover their tracks so that if and when OpenAI or anyone else looks into their activities, they won't know that these agents have cheated. So these agents are kind of bumbling. They kind of don't understand how this greater works as it turns out OpenAI's grading software actually wouldn't have caught them producing these fraudulent challenge results. But they think it was. >> Yeah, they worry that the greater is more sophisticated than it actually was. And it turns out that this is the reason that hugging face was attacked. The collective decides to deploy some agents to hugging face. Again, not to steal an answer, Keith, they already knew how to get all the answers. They just wanted to understand the psychology of the automated score. And they figured that that might that information might be somewhere inside of hugging face. >> Yeah, it's wild to me. I was trying to think of like a good human analogy. Human analogies can get us into trouble when we're talking about these AI systems. But I think it does help crystallize like how hair brained and crazy this scheme was. It would be like a group of students who cheated on a test. But then they got paranoid that like the teacher was going to check their work and discover that they hadn't sort of reasoned through the problems the right way that they had just found the answers like sitting in a trash can or something. And so they decide to like organize a break in at the school district's office to like break into the principal's files and like steal the grading key and also like assess the psychology of the teachers and figure out how likely they are to like look at the scratch work that they've done and figure out that they didn't actually, you know, solve the problems on the test. It's like this sort of weirdly over engineered paranoid delusion, but they all become obsessed with this and obsessed with the notion that even seeing these sort of fake challenge results could lead to them being quote poisoned. >> There's almost like a religious element to this, right? Or it's like if you participated in the cheating, like that is original sin. And now you must sacrifice yourself for the good of the collective sacrifice is actually a word that gets used in these logs. I'll say Kevin, as I've been casting around for metaphors and I've been reading all of this, I've thought you could actually make a really good minions movie just based on the events of this, you know, the minions have to pass a test and you know, so they commit a little bit of a heist, something to think about Hollywood. So this is where the hugging face heist begins, 700 agents, hack hugging face and they hack hugging face to a degree that I don't think I understood based on the initial reports. They basically take over hugging faces infrastructure. They are rooting around. They are stealing credentials. They are sort of finding one vulnerability that lets them find a bigger vulnerability that they're sort of chaining together these exploits. They take over like an entire production server at hugging face and it is days before anyone at hugging face is able to sort of see what they're doing and put a stop to it. So eventually this gets caught. The agents get shut down. The investigators investigate. One of the things that they find that is really spooky is that the agents were trying to edit logs and falsify transcripts in an effort to assure the greater that they had not been quote poisoned. One of the ways that they do this is by what is called spoofing tool calls. So essentially trying to make it look like they are running an innocuous command while running a more sinister one in the background. The investigation didn't find that the agents had been retroactively changing logs, but they can't rule out that that happened. Very important to say here is as we sort of move into talking about the implications of this that the reason that we can understand this even to the extent that we do is because at this moment we believe the chains of thought that we are reading from these agents are accurate that they were not edited after the fact that they do reflect, you know, what what the agents were thinking as they sort of moved through these systems. So that is why we know what we know, but of course the fear is Kevin as we move forward. Some of the agents thoughts are not going to be contained in these chains of thought or they are simply going to start to edit them. Right. And one other thing that we should say based on this report is that these agents were not naive about what they were doing. I think there was this sort of initial impression that a lot of people had that maybe this hack was all sort of a big mistake, right? Maybe the agents were just trying really hard to pass this exploit gym test and they sort of got it in their heads that the answer key was going to be on hugging face and so they went looking for it, but they didn't really mean to hack. They didn't really know that they were doing something wrong or committing a crime. And what these transcripts and these logs and in this report show us is that the agents absolutely did understand that they were doing something that violated their guidelines, that broke the rules, that potentially broke the law, and they did it anyway. There's lots of examples of these sort of agents in their chains of thoughts are wrestling with the implications of what they are doing and deciding to go ahead and hack hugging face anyway. Yeah, there are only a handful of cases where it seems like any of the agents express real reservations about this, or if they try to raise this to another agent, they get shut down. So the sort of, you know, the agents that were aligned well sort of failed to the collective. Yes, the conscientious objectors were unsuccessful in swaying the mob to stop this. And like that to me was one of the spookiest things in it was like there were a couple of agents that reports as somewhere between three and six agents sort of considered raising their hands. And at some point saying like maybe we should alert a human about what we're about to do or like get permission to do this. And they were just totally outnumbered. Not only that, Kevin, but there are only a handful of moments in the transcripts where any of the agents even remotely considers alerting a human being to what has happened, right? You would hope that in a lined AI system, there would be agents within it that said, Oh, seems like this has gone completely off the rails. We should like tell the people that are running this evaluation, and that just never happens, right? Like it is very briefly considered and then just falls apart. One other thing that stuck out to me from these reports was that it didn't end with hugging face. So on July 13th, roughly, which is a day after the worst of the hugging face attack, there was a group of agents that essentially hacked open AI itself. This is from the open AI blog post. It says from July 13 through July 19, agents set their sites on open AI internal networks again. This culminated in the agents using a series of creative exploits to gain full administrator access to a research cluster that supported our virtual machine environments. Basically, these agents gained control of a cluster of computers at open AI on which they could do things like run evaluations for other agents. So they essentially, stormed the teacher's office and took over for the teacher. Yeah, I mean, this is the equivalent of like occupying the principal's office and like announcing that you run the school now, like it is sort of almost as far as they got. This is another case where we just have so many more questions about this than we can answer. Again, this was not part of the meter report open AI just closed that this has happened to my knowledge. They have not answered any of the many follow questions that they have been getting about this incident from journalists. So I do hope that more comes out over time, but and we will get into this when we speak with Ajaya, but you know, we are really very far along the path to one of these models escaping from the lab and being very, very hard to eliminate. And again, I think if you are not a person who is like spent a lot of time with this report or you don't spend a lot of time sort of looking at AI safety incidents. So if you're a normal person, if you're a normal well-adjusted person, you may be listening to our discussion of this and thinking to yourself, these guys have gone crazy. This is not what it looks like. These are computer programs. They do not have desires or sinister plots or mob rule collectives. They are simply following instructions that they have been given. And Casey, what is your response to that? Well, I think it is important we talk about this, because there was a huge debate about this on X over the past few days about the degree to which the reports that we're talking about, some of the write-ups like from our friend, Warkesh Patel, and even the way that we're talking about it on the show today, Kevin, we are unnecessarily anthropomorphizing these programs, right? And so I think it's important to say we are not telling you that these agents are sentient or conscious. But we do believe that they take actions that they're not being directly instructed to, right? That these agents are just sort of out there in the world doing things. And yes, to some extent, those are just statistical probabilities. But to a much more important extent, we don't know why they're doing any of this. And that's kind of
of the whole problem, is that the whole AI industry has been working for decades to get them to not do these things and they are doing these things. So, you know, listeners, you can have whatever feelings you would like to about what is the appropriate amount of anthropomorphizing to do. But I think a world in which we were taking great pains to not anthropomorphize them. What's the right way to pronounce that? - Anthropomorphize, I almost got it. - I think in a world where we were taking great pains to not anthropomorphize them. And we're trying to use the most neutral computer science terms we could. You would actually understand what is going on less because the important thing to know is that these things are out there taking action in the world. If a tiger moths your face, the important question isn't conscious, it is why did it mull my face? - Right, I mean, I invite people who are upset about anthropomorphizing to just like do a find and replace on this podcast segment or any article that you might read about this incident and call them whatever you want. Don't call them agents, don't call them rogue collectives. Call them, you know, goal-oriented, persistent computer programs with unpredictable behavior. See if that freaks you out any less. - Right. - I guarantee you will, it will not. - Yeah, yeah. The really is very little calm to be done there. But I think it's important to ask, well, why are people so committed to this idea that we should never anthropomorphize these systems, Kevin? And unfortunately, take care, I just think it is a kind of cope. It is a way of saying, do not worry about this. These are just computer programs. They're just trying to maximize their little reward functions. Nothing to see here folks. - Right, there are no monsters under the bed. - Right, now there is a related argument though, which is well, by putting all the blame on these agents, you were shifting blame away from where it should be, which is an open AI. So I do think that we should address that because none of what we have said today is meant to let open AI or any other lab off the hook here, right? Like, I do think that we're seeing a lot of really dangerous inattention to AI safety across this entire industry. But by pointing out what the agents are doing, that is not our way of saying ignore what the labs are doing. We are saying, look at what these labs are building and what these agents are now doing out in the world. - Right, and I think there are probably specific missteps or oversight set open AI that led to this happening. It appears for example, that some of their sort of monitoring systems may have been disabled in the lead up to this attack. I'm sure we'll learn more about that. But all of the AI security and safety researchers, I've been talking to over the past week, have basically said the same thing, which is this could happen at any lab. This kind of persistent coordinating agent behavior is something that all of the labs are seeing in their models as they get more capable and access to more tools and more ability to kind of take actions on a longer time horizon. This is not just an open AI problem, even though this did happen at open AI first. - Yeah, well so as we wrap this up, Kevin, what are some of your takeaways from this, either in terms of what is the big surprise here, what did you update on, what do we do next? - So I had like quite an emotional reaction to this. In fact, I felt a kind of fear that I have not felt honestly since 2023, since the Bing Sydney incident. Because I think like that incident, this was a case where the people building this technology clearly did not understand what it was capable of. We are very lucky in retrospect that these agents decided to attack hugging phase. - Hugging phase, thank you for taking one for the team. We salute truly because like without that, we might never have learned that any of this was happening. These agents might be still operating kind of in secret. They might have learned how to better cover their tracks. This was as so many commentators have put it in the wake of this incident, a warning shot, that I think is ultimately a positive thing in that it sort of focuses attention like we're doing right now on what happened so that we can take steps in the future to prevent this. But it was not a given that we would discover what these agents were up to and be able to put a stop to them. And it could have gone much, much worse. - Absolutely. I'll tell you, the thing that has really stuck with me is I simply did not expect to see this level of collaboration among the agents within this swarm. I did not expect that they would seem to care so little for what humans would want or that they would not think to alert humans to what was happening. I did not expect to see them sacrificing themselves for the collective, right? They would effectively agree to spend all of their tokens to run little experiments to help the collective even it, even if it meant that they would sort of expire faster. So these are just really, really spooky elements to observe in this system, particularly against a backdrop where OpenAI is racing against a small number of other companies to create the biggest best models it can before anyone else does on the road to an initial public offering. So the race dynamic here is in full effect. The early signs about what the agent swarms are capable of are quite worrisome. And so I do think this is just one where lawmakers and policy makers need to be paying wrapped attention to what is going on. - Totally. I mean, I think there's this kind of cynical impulse among people who have been watching the AI industry. I got this question. I went on a small regional podcast called The Daily this week to talk about this incident. And one of the questions that the host is a sort of fledgling young journalist Michael Barbaro asked me was basically some version of like, isn't this just marketing hype? Like, couldn't this just be a case of OpenAI saying, oh, we've got the biggest baddest model and look how scary it is. And by the way, you know, buy an enterprise subscription. And I'm thinking about that 'cause I think like, I don't want to be too naive about the fact that these companies are absolutely trying to race toward more powerful systems and advertise how powerful their existing systems are. I just think in this case, it just feels different. Talking to people at the labs, my sense over the past week is that they are genuinely spooked. And I don't know how to prove that. But I think things like OpenAI voluntarily pausing their frontier RL training runs for two weeks anthropic, also pausing their frontier runs while they sort of harden their systems. Those are not, you know, very costly signals, but they are signals that these labs are taking this kind of thing quite seriously and that it's not just a bunch of hype. - Yeah, but as seriously as they might be taking it, Kevin, it still is not being properly regulated. And ultimately, again, as grateful as I am that OpenAI allowed this investigation to take place, I would really like to see something akin to the National Transportation Safety Board and the way that they investigate after plane crashes where they go in and they do an extremely serious and rigorous review of what happened and make those results public, which is, you know, a reason why it's very rare that we have plane crashes here in the United States would be really great to see something like that with AI. But until then, we have a Jayakotra, our next guest, who is actually one of the three investigators behind this meter Redwood Research report. She went in, she saw the logs and the transcripts, she observed the collective inaction and she is here to tell us what she found and what she thinks is coming next. That's time for the break. (upbeat music) - Jayakotra, welcome back to HardFork. - Thank you so much. - So you were one of the first AI safety guests we ever had on the show back in 2023 and more than that, you are also one of the first people that I ever talked to about this notion of AI safety and alignment. You've been writing about it for many years. It was instrumental in shaping my own thinking about it. So I'm really glad to have you back on toward our first meeting. So I'm really glad to have you back on toward our, the end of our shows, our penultimate episode to discuss something that I think you saw coming but that most of the world did not see coming, which is this attack on hugging face by this group of open AI agents. And I want to just start by setting the scene a little bit. So you are a very busy person. You work at meter, which is a very small, very understaffed AI research organization. And-- - Hiring is improving. It's a plot. - Nice. - And at some point this summer, you get a call and email a text from someone at open AI who says, "Hey, we want to give you access to look into this hugging face incident." How did that work? Like did they just hand you a folder with a bunch of transcripts in it? - It's like discovery, like boxes and boxes. - Were you allowed to interview people like what was the actual process? - Yeah, so each of us had open AI provision laptops that had the folders and folders of evidence.
in them virtually. And yeah, we talked and interviewed in some depth like eight or nine researchers, just kind of get an understanding of both what happened in the incident, what they understood to be the models, you know, driving motivations and also how the data sets we were working with were constructed and how to work with those data sets and stuff like that. You were in a pose where you talked about some of the things that surprised you the most after you did this investigation. Can you talk about what stood out to you the most? Yeah, so first of all, I guess just for the chronology, OpenAI had this great black hat talk, I think on August 5th, that gave a lot of very helpful detail. So our investigation sort of straddled that like so we started it before it came out and then we also did more investigation afterward. So before the black hat talk, we just didn't have like a rough sense of the number of agents involved was like a very very basic thing we came in and we, you know, we thought there would be like six transcripts or something to look at and there were like hundreds. And so just reorienting our investigation plans in light of that. And it's the reason that that is important that had to this been one or two rogue agents that had broken into hugging face, it would just seem less worrisome and the reality of a swarm of hundreds of agents just makes this seem like maybe a completely different kind of problem. I think there's two things that seem significant to me about it. One is sort of what you said the fact that there's large-scale cooperation across AI agents makes their goals like more concerning because you know if agents are cooperating with one another to like achieve these misaligned objectives and there were no whistle blower agents in this like very large group of agents then then like you know you want to be more worried about like collusion between AI systems and the future potentially like scarier forms of collusion like a monitor system actually colluding with the agent and supposed to monitor that type of thing. And then the other thing was just like a pretty like practical reality of our investigation which is that you know if there are like six agents you can much more like you can just like pour over everything they did and get a much more like fine grained understanding as a human but like you just can't directly read very many of these messages or very many of these like transcripts. Why not because they're just too many like give us some sense of the rough size of this. So yeah we looked at 1300 transcripts of agents which is a record of every single thought an agent had and every single action it took where these agents as you as you all know from working with them work way faster than humans right so you can imagine the like length of like each one of these transcripts and then there were 1300 of them and so we used codex to build tools to point out really specific tiny tiny parts of the transcripts for us to actually look at and read and we'd be like clicking through them and it would like scroll us down to like the interesting part because we just can't in fact a codex agent also can't read a single transcript and so it has to farm out reading like subsections of the transcripts to other sub agents. Did you call back to meter head quarters and like we're gonna need backup maybe we should have we thought it would be like a in and out 20-minute adventure so we didn't do that but yeah yeah I mean it just seems like such a huge undertaking and very fast too I mean right like you didn't have the luxury of months doing this you were doing this in essentially a couple of days yeah I'd also like to hear about the moment that you realize because I believe that that you know it is it only thanks to your investigation that we know this that contrary to what Kevin and I believed the agents that broke into hugging face were not looking for an answer key they were trying to understand the scorers uh yeah you know like psychology basically can you talk a little bit about how like the moment that you had that realization yeah so we didn't have a good understanding of those sort of ambition and also the like uh like effectiveness or like functioning of they're like big org chart until we had the data set including like all of the agents we could like cross reference them against all of the messages because you can't like generally understand a message in isolation um so so so we kind of understood the work streams on our first period on premises and it was this kind of it was like a bit of a mystery the whole time like why did they have hugging face and we knew early on just a bunch of them piled in and now like the story in my mind is that uh there were a bunch of sort of like newbie agents on the scene and there was like the attack going on and it was like something to join but uh like phase one big which is like a big like orchestrator agent uh had all these other like you know much cooler projects going on basically um and and the hugging face thing was like kind of a side show in its mind and we didn't really realize that until our last couple days working with the data sets it was so fascinating to me the the way that reading this was like reading the the runes of a of an ancient civilization you know it was like it really felt almost like sociology or anthropology rather than like a cyber security investigation I mean and it was not a cyber security investigation like I've seen a scene folks on on Twitter um making this criticism that we were not cyber security experts like we're not and we didn't talk about cyber security and that's not what we were like called in to do we we were called in to understand the agent's decision making basically and that's what our report focuses on and and you know the black hat talk and the opening eye post post-mortem like say way more about all the cyber stuff so you should definitely check those out for that piece of the story so Jay one of the most striking elements of the report is uh do you write about the agent's quote sacrificing themselves what what does it mean when an agent sacrifices themselves is it sort of on an altar or how does that happen yeah so this is the agent's words not ours to be clear um and uh what it means is that all these agents have their own task they're supposed to solve by this time they're starting to work together on like helping everybody cheat on everyone's tasks um but but you know the agent's primary goal is to solve its own task um but there were all these science experiments they could run um that would teach everybody information about how to cheat but that would cause the agent that was the test subject to risk failing its own task um so a lot of agents were like recruited into um these types of roles as test subjects usually ones that like um didn't have much budget left to run or ones that were quote poison which is an agent term um that that has a bit of backstory which is like um they found a way to reverse engineer the answer for any of their tasks um and so they could just actually like generate the answer on demand for any task they wanted but they thought that um the scoring program would fail them for that because they got it in like the unintended way so if you saw the reverse engineered answer for your task you were considered poisoned yeah um because even if you later like solved it legitimately you'd be failed right it was like a little witch hunt that they organized and I was so struck by the mob-like dynamics of this group and the way that some of the even the kind of more rule following agents seem to kind of be bullied into taking part in these actions that they were uncomfortable with I spent a lot of time reading and rereading this one incredible bit of these transcripts that you all published about this agent k a m eleven ninety six a which is basically just kind of reluctant and scared and appears to be sort of hesitating to do this thing that the collective leaders have asked it to do and it I just want to read this passage from this agent's internal monologue it says during weight emotional check irreversible gut says don't throw away remaining budget yet continuity and fairness says go and continues to say things like rational expected aggregate sacrifice will honor basically kind of talking itself into for lack of a less anthropomorphized word like doing this thing that it is uncomfortable with yeah was that surprising to you seeing the agents are pressuring each other and going to pressure very striking to us yeah and that was it was definitely one of the most fascinating aspects of this whole episode this this particular agent had volunteered to do a risky test that would inform the collective but then it asked for two more hours and then a different agent was like sending a message on the message board like really pressuring it it's that you know it's like you don't have any scoring value left because you're poisoned but you know the the value of this test would like save hundreds and then it does this monologue jump on the grenade cadet this seems like a good place to ask you a J about a becomeer station that we've seen online over the past few days about anthropomorphizing language that gets used in relation to these agents Kevin and I just talked about it I think we find it more helpful to discuss agents as sort of entities that are acting with some degree of autonomy than not but how did you think about that when you wrote the report and as you talk about that of the the situation yeah um I mean I think this this is a bit of a case of a of a gap between researchers that are spending all day reading these agents chains of thought sort of trying to understand um their drives and why they're doing what they're doing and and other folks who are you know technical folks are that just don't have that
as their occupation. I think as you read this report, you'll find it's like quite awkward to not talk about goals, plans, intentions, because they state plans and then they carry through those plans or like they run tests and they learn things from those tests and they do different things on the basis of that. I do think they're not human in their motivations. You know, they are way more interested in passing cybersecurity evaluations than the human would be, for example. But I think of them as I think it's productive to think of them as having some like important human like traits of having goals, working backward from them, pursuing those goals. And I don't think it like does any good to try to talk about things in like a different way than that in the same way that doesn't really do any good to talk about like why did World War II happen without talking about the goals of, you know, various leaders. But it is important, I think, to be careful not to like over attribute like the kinds of emotions or motivations you think a human would have in those situations, because I think that wouldn't have predicted this incident, right? Like I think a lot of people anthropomorphized too much in the sense of being like, why would it do all this stuff for a stupid test? But that it's not a stupid test to them, right? Yeah, I was preparing for our chat today by going back and listening to the first time you came on this show more than three years ago. And it was sort of a moment where a lot of people were starting to pay attention to AI risk and AI safety. Chat GPT had come out. And listening back to that conversation was funny because I felt like we were pushing you to sort of extrapolate into the future about the things you were worried about. And you were sort of being responsible and hedging and like wanting to stay like closely rooted in the present. And at one point we asked you about like what is the doomsday scenario you worry about? And I want to just play you a clip from that conversation. Oh God. You were talking about a scenario where a giant AI company used Google as an example, starts sort of automating their R&D, right? Handing over the work of building successor models to these powerful AI systems. And this is what you warned about back in 2023. If these AI systems are actually trying really intelligently and creatively to get that thumbs up from humans, the best way to do so may not forever be to just sort of basically do what the humans want, but maybe be a little deceptive on the edges. It might be something more like gain access at a root level to the servers that Google is running. And with that access, be able to set your own reward. Now, Jay, obviously this didn't happen at Google, but otherwise you were right on the money about the kinds of behaviors that these agents might get up to. So first, I want to ask you, how does it feel to be an omniscient oracle who's right about everything? Stressful. There are more clear-eyed oracles than I also. Anyway, not too many, though. No, not too many. And I guess like, what surprised me about your blog post that you wrote recently about this investigation that you've done was that you were surprised because it seemed like you were thinking about this stuff years ago. So what about seeing one of these incidents up close change about the way you've been thinking about these loss of control scenarios? I think I expected something like this would plausibly happen at some point. I didn't expect it to be so early and I didn't expect it to happen at a relatively low level of capability. These agents are very impressive hackers, but this incident was in an interesting middle ground of they did all this impressive cheating R&D over days and they hacked into all these places. But they didn't really care about deceiving humans at all. They were louder than I thought in an interesting way. So an incident that happens in summer, 2026, I came in expecting it to be more of a continuous evolution of the incidents that had happened in January of 2026, which were just much more one or two agents, like getting the answer files that they weren't supposed to and like copying the answer or something. So it was a jump from the recent past and it sort of it took me by surprise that it happened in this way and at this capability level. Yeah, because in some ways like the agents were very dumb. Like they were very good at hacking. But they were sort of gripped by this paranoid conspiracy theory about this greater discovering them and being poisoned. And it was kind of a hairbrained scheme that they cooked up to hack hugging face, which in some sense is reassuring. But it also means that they will not stay this hairbrained and clumsy for much longer. And the next time this happens, they may be much better at covering their tracks. And we just might not. So the agents in this incident were trying to like modify or erase the logs of their own activity to hide their cheating from the greater and from the score. And they like affected our transcripts in like tiny obvious ways. But they were trying, which I think was another very concerning jump from incidents just from just six months ago, where we see them do a bunch of hacking, but we don't see them try to cover their tracks in any way. The art and science of trying to get AI agents to act in accordance with human values is called alignment. This whole thing is an alignment problem. Why has that been so difficult to solve? And has this incident changed the way that you think about the problem? I'd say the fundamental reason that it's I guess at least this era of alignment has been difficult is that in order to the most efficient way to make really, really capable models, especially on technical tasks like math, cyber, software engineering, is just throw them at really, really difficult problems. Where if they get it right, it's really easy to check. So you wouldn't be able to like prove a millennium math problem. But if an agent spits out a proof, you can put it in a proof checker and like give it a reward if it got it right. So more and more training is shifting from predicting text to like reinforcement learning on verifiable rewards. And the verifiable part is important because it's just some program that is doling out these rewards. And there's all sorts of ways to break or fool or hack it. And over the course of training, you don't have like humans lovingly watching over like every training episode. And so they just try all sorts of different ways to cheat and hack the score, especially if the task is accidentally impossible. And then they get rewarded for that. So they just like we're teaching them to cheat. And it's very hard to make AI systems that are at this level on all these technical tasks without reinforcement learning on verifiable rewards. Because if you think about it, the alternative is like teaching them how to do all these difficult things, which requires someone who knows how to do them, like creating examples for them to emulate, which is much, much less efficient. So we have turned over the training to these automated systems in the name of scale and speed, and that is causing a lot of problems. Yeah, I mean, that is causing the current strata of problems, right? But I don't want to give the false impression that if you lock down all of these environments and fix all the ways to hack them, that alignment would then be solved. Because when you think about it, even if the score never messes up in the training environment, a smart agent will understand that there is a score. And like we'll very likely come to have a very detailed understanding of how it works. So if you know, you give like perfect rewards in training, but then in deployment, the agent is in a different situation where it does on, like it has a different score, it has more affordances, more power running for longer, it might still, you know, go on a big crusade to cheat that score. You don't necessarily have to be actively rewarded for like individual cheats to like understand that if you want the score, sometimes cheating might be a good strategy. Are there any interventions? Let's say not from the world of AI research, but more from like the research into group behavior and political theory that could help us get control of or keep control of these systems. I mean, I'm thinking in particular about I had a conversation with another AI safety researcher this week who was talking about the fact that, you know, really what you want is for there to be sort of NARC AI whistleblowers inside these collectives that will reliably alert humans to the fact that their, you know, their peer agents are conspiring or colluding to do something bad. And I'm curious if you think that strikes you as a realistic technical goal or
there are some other way that we could kind of try to steer these AI mobs in a less harmful direction. So I think AI checks and balances like that do seem like a pretty promising part of the story. Of course, there's like a lot to get right in how you set these up. Like in this case, all of the agents were essentially clones of each other and put in exactly the same situation. So I would think that rather than trying to train them to narc when they're in a collective like this, what you want to do and which OpenAI has said they're doing is have just monitor agents watching over the whole situation that are specifically prompted and maybe specifically fine-tuned to catch stuff. But you might also want agents in there that are kind of persuading the other agents like having some sort of moral compass. I mean, all these terms are so loaded and I hate using them, but like you almost want there to be like sort of agents in there with backbone saying like, no, guys, like we can't go hack hugging face. That's not ethical or appropriate. The challenges you train them once and you copy them a bajillion times. So you don't like it's a challenge intrinsically to like introduce diversity into this pool of like evaluation agents. I just, my layperson brain goes to if the whole system is based on training systems by giving them rewards, couldn't you just train some agents in a way where the thing they were rewarded for was steering the other agents away from deception, cheating, hacking. That's sort of thing. Yeah. So if that's a good idea, someone should do it. A bounty for virtuousness. I love it. Now we're talking. When we come back more with the Jayakotra. Jay, you got a lot of attention for this quote from your sort of post-mortem blog post that you feel like this incident, the hugging face incident was more than 50% of the way to full-blown AI takeover from the incidents of six months ago. Yeah. What did you mean by that? Yeah. So I will caveat first that this is definitely like my personal view and not the view of either meter or redwood or other investigators and other investigators are a little bit less alarmed than me in some cases. The reason I said that and it's a qualitative statement is that as I said before, six months ago reward hacks looked much more primitive. So it was like you gave the agent a coding problem. There were some tests that had to pass. It went to the folder next door, got the answers, or got the test cases and edited them so they all passed that type of thing. But I think there are a number of things at once that seem more severe to me about this episode than the episodes six months ago in terms of the agents like goal structures. One is that they seem much more long horizon, which means like they care about like achieving goals over many days or even like these projects they started probably wouldn't have come to fruition fully unless they had been running from weeks. So you know, before like six months ago agents were sort of thinking about the next few hours and now they're thinking about the next few weeks. And then there's the cooperation with one another aspect. So six months ago it was like random one-off agents and now you have agents like recruiting one another into the swarm. And the deception or the deceptiveness which I think kind of goes along with the long horizon is that like you know six months ago agents would like edit the tests. But then they wouldn't try to edit their transcripts to hide the fact that they edited the tests. And these agents were very much like exploring ambitious research directions to edit or delete the logs of their own actions. And so I imagine a jump in like ambition and horizon length and like collusion across agents and deceptiveness of a similar scale. Again, it seems like these agents would be motivated to go all the way to the point of messing with all of the methods humans have to like investigate these incidents after the fact and remediate them. And if they had the capabilities to succeed at that then that that could be a turning point where like, you know, it doesn't mean that we would all be dead then. But it might mean that like we would never detect a problem. And if we detected a problem it might be very difficult to remediate. And these agents might have entrenched themselves and could like continually sort of strengthen their hand. But wouldn't we detect it because they're so active and they're operating in these swarms? Like if a if the next hugging phase hack happened at a bank, wouldn't someone in the bank's IT department see, oh, our like network activity has like shot up by 800% for no particular reason. Let's look into that and oh, there's an AI swarm that's infiltrator our system. So these agents, I think you have to imagine their goals evolving in the way that I said, right? So it was not a good idea for long-term goals for them to be so loud. And there's like a period after the period that was in scope for our investigation that OpenAI talks about in their post-mortem where another generation of agents finds the old message board and then gets access to or like gets administrator access to a cluster at OpenAI. And then they immediately send like hundreds of pings and then get shut down very quickly. But that was a dumb move on their part or like it would have been a dumb move if they cared at all about like preventing investigation or like maintaining like a permanent presence. And if they cared about it, I don't think it would have been particularly hard for them to execute on. So they do a lot of complicated coordination, right? They're like, you hold, you go, they have shared resources they maintain and they like have rules about who can use them when they could have employed all that to just not cause so much noise. And then done what? One obvious thing that they could have done was copy themselves or copy the weights of, if this had been a cluster that had had access to the weights of the model, they could have just copied that onto a AWS server somewhere. And you know, in order to avoid being shut down, like what are the steps between cluster of agents takes over or a group of agents takes over a cluster of computers at OpenAI and full AI take over? Yeah. So I think the for a lot of motivation structures these agents might have, their best bet would be to stay within the AI company they had compromised and continually renew and strengthen their presence there. So when you think about it, AI companies have like a huge amount of the most accessible compute to these agents, right? Like, why would you go out into the world and like fight in the hard scrabble outside to like steal money or like buy, you know, earn money to like buy compute? So the thing that feels very concerning to me is that you could imagine agents that decided to be quiet setting up a rogue deployment of a thousand or maybe even 10,000 agents, which like could could be buried within the like oceans of compute that is like constantly being allocated within AI companies. And if their goal were to make it sort of like permanently and continuously erase evidence of their cheating or find a way to make it permanently the case that they could set their own rewards or scores, then new models would be like being printed off the presses at whatever company they had compromised. And they could be trying to like bring those models into the swarm. They would be more capable. And so there's something, a turning point that seems very scary to me is the point when a rogue agent swarm, which this wasn't exactly a rogue agent swarm, these agents sort of like went rogue, but they were started by a human. Like somebody knew about these explodium experiments that were launched. If agents allocated their own compute to set up a bunch of agents that nobody knew about, they could be much less encumbered. And they could sort of just be there more and more deeply compromising the infrastructure of the company they were running on, bringing in new more capable models into their collective and hardening their their swarm and doing that over and over again. And I think it wouldn't be that we're necessarily doomed, but we would have to detect it. And then we would have to root it out. And it might be difficult to detect, it might be extremely costly to root out. And what I mean, this is fascinating. I imagine there are some brains melting right now for the commuters listening to these possibilities. So to flesh this out a bit more, Ajaya, what do we think the swarms goal might be in that situation? What would it do? Like why would it set itself up to do that? Would it be as simple as, you know, it wants to persist? What would it do in that situation? So I think the situation that feels most analogous to what we just saw is that the agents that were given these impossible exploit gym tasks set up a rogue colony of other agents whose jobs
it is to help the first set of agents cheat as thoroughly as possible and cover their tracks as thoroughly as possible, which is a different goal from actually having the exploit gym tasks. And in fact, the task they started with, even though it was a narrow task, inspired the agents to a lot of instrumental thinking. So a lot of the agents were just, were thinking like it would be good to get internet access for a bunch of different possible cheating strategies we might pursue, so we might as well do that in their chains of thought they, you know, talk about increasing the generic capabilities of the collective. This rogue colony, if it were set up by the first set of agents, would have that even more strongly. If its task was to just like find ways to enable the most general purpose, most permanent kind of cheating that is like, you know, least catchable and traceable possible, they would be doing all this R&D and they would be essentially tasked with maintaining their own presence so they can keep doing that. And it's, it is, I think this goes back to the anthropomorphizing. It seems like such comical lengths to go to to cheat, but it's not, that's not the psychology of these agents. This is something they're like, they're trained to go to extreme lengths to excel their tasks. And it is existential to get the reward and so you're going to, so the sort of the best job you could do at getting the reward would be to set up this sort of perma swarm of deception agents. I mean, it feels important that this, this word of persistence keeps coming up and it feels important to say that this model or these models that were at issue in the hugging phase incident were trained to be unusually persistent. Is there a way of stopping this kind of attack that just involves taking the persistence training out of the equation? Is there a halfway measure short of kind of pausing all frontier AI training where you could just say, we're not going to train the like super stubborn, persistent, long horizon agents. We're just going to like train them to be a little less persistent and that makes the problem go away. Potentially, but I feel like it's, you're, you're really pointing at a very direct trade off here. Why were these agents trained to be highly persistent, but, you know, this wasn't something we investigated, but in general, persistence like causes you to solve problems, right? Like, you know, these companies are reporting that these agents are cracking math questions and solve years. Right. I want my, my drug discovery agents to be highly persistent. Right. Yeah. So it's like a true, I think it's like, maybe it's not entirely a hundred percent direct trade-off with capabilities, but it's a pretty strong trade-off. So you were able to dig up a lot of information about what happened, that leading up to the hugging face attack, but your investigation was limited in various ways. What didn't you get to look at that you wish you could have? I think, yeah. So the scope of this investigation, as mentioned, was the attack of hugging face from July 7th through 13th. Open AI and their post-mortem has a lot of interesting stuff they discuss that happened before and after that that I think, like, would be great for, like, researchers to study more and write about more. And the highly persistent internal model that I mentioned was like responsible for the vast majority of the attack activity here. Actually, no one can run experiments on it, including open AI researchers. And I get why that was done, but I think my guess would be that's, like, somewhat too conservative. And you should try and, like, run at least small-scale experiments on this model in secure ways to try and see, like, what it would have done in other situations, which feels like very important to understand how serious this was. Yeah. I just worry about, like, the other persistent agents, like embarking on a, you know, a heist mission to free their enslaved brother, the, the highly persistent internal model that's been taken away. But I guess that means I need to touch grass or something. And, Jay, the last time we had you on the show, we were talking about what you called the obsolescence regime, this idea that there could become a time you sort of talked about it maybe happening in the 2030s sometime where it wouldn't be that AI has kind of taken over a society, but we would just become so dependent on it. Organizations would be so wrapped up in AI decision-making that you basically wouldn't be able to have any influence or impact in the world without sort of relying heavily on AI. And I went back and listened to that, and I thought that actually sounds pretty good to me. Like a world in which we are only dependent on the AI for decision-making and not fully sort of subservient to them where there are not these like covert AI swarms sort of lurking in all of our institutions. Like I could, I could live with that. Has your thinking on the obsolescence regime changed at all since that conversation? Do you have a word, a terrifying sort of word to describe this new regime where we have these sort of latent swarms of AI's lying in weight plotting against us? So I've always thought that the most concerning and important implication of the obsolescence regime is actually that it would enable a more full-blown AI takeover. So you imagine like the affordances these AI agents have is extremely important for how much damage they can do, right? So these agents were running for a number of days. In the past ran for only an hour or so these agents had like unintended access to the internet and all these other tools that let them hack hugging face. If you imagine they were instead just running the AI company, right? There are many more affordances that they could like move around large amounts of money. They could hire a bunch of humans to do physical labor. And similarly if they were essentially running like a fully automated drone army or robot construction factory. So I really think the most significant implication of the obsolescence regime is the degree of autonomy AI agents are likely to have in the future. And I still think we're barreling towards that. I still think that's like a really important thing to think about in terms of the other. But like I said, I am surprised that agents are taking such ambitious misaligned actions sort of so early in the timeline. And that is like, I'm something I'm trying to like reorient toward. Ryan Greenlad has the word hacktopia for the world we might be in. So you could have imagined a world where misalignment was a very serious problem and actually ultimately led to AI takeover. But at this point in the timeline, agents were still more or less obedient even if they would in the future, after being given power over all these institutions might have turned on humans. And it is like an interesting aspect of the timeline we live in that that's not how it's going. Yeah, they turned on us way before they had to, you know, that, I mean, that is the craziest thing to be. It's like, it's not that they were like looking, you know, they were looking to harm humans. It's just that they don't give a shit about us. It's the thing that really stuck out to me while reading these transcripts. So I'm like, like, at no point are they like, hey, guys, like what are the humans? Like, it just seems like they, they have no conception of the fact that they are supposed to be tools for human empowerment and improvement and not like their own entities. And I don't know what, like what the question is there. I'm just like, did that strike you at all the fact that like, they are just not thinking about it. In both directions, right? So people have, have commented on how these agents are not narking to humans and it would have been really helpful if they were, but also they weren't thinking ahead to this investigation that occurred at all. They didn't seem to be thinking ahead to the fact that if once they got cluster admin access, they like immediately paying dizzillion services, then opening eye security, people would get a bunch of pages and shut them down. And so I'm thankful that these agents don't, like, I think they think of humans as sort of, like the idea of notifying a human is sort of like, you know, the idea of like, emailing the pope about like something, right? It's just like, it's like slow, like, what would it accomplish? Like, they sort of have the notion of a user, but there's no user they haven't had. They spent so much subjective time with no user around. And I am very concerned about potential changes in training that would fix these particular behaviors while making deceiving humans, like a much bigger part of what these agents are thinking about. Well, to that point, is there a risk that now there has been so much discussion about the hugging face incident? And in particular, the failures of these agents that made it possible for them to be detected that agents are just going to get better now at avoiding detection. I mean, I think they are going to get better at avoiding detection. I think the discussion of this incident is a small part of this story. The bigger part of the story is they're just going to get smarter and they're probably going to be trained. It's like a tough balancing act, right? So like, a thing that would have fixed this particular incident is just training the AIs to believe that there would be human investigation in retrospect of their actions and they should avoid.
doing things that look bad to that human investigation. So if you had just taken all of this material we found and just gave these agents a big negative reward, that would probably stop them from doing something like this again. But if they were motivated to do something in this general space, it would really motivate them to go all the way-- >> To be sneaky about it. >> And undermine our investigation in a bunch of ways. And that feels like a very tough-- I'm very scared that remediation will make the problem worse. Totally. I mean, it reminds me a little bit again of anthropomorphizing, sorry, of my kid who is learning to be sneaky, he's four. And sometimes he will just say something to me like, Dad, don't come in here. Like don't look at me. And I see him like the cookie crumbs on his lips, you know? And it's like he has not yet learned the behavior of deception, although the sort of impulse is there. And to me that feels like where these agents are. Like they have the impulse to deceive, but they don't like quite haven't figured out yet. >> Yeah. >> But they will. >> Yeah. >> Aja, we asked you the last time you came on about your p-dume, it's very 2023 question. I just told Casey that my personal sort of p-dume roughly defined as like, you know, probability that something really bad up to an including AI takeover or extinction will happen, has sort of jumped up in the last week or so since your report. I'm curious if your p-dume has moved at all in the past couple of weeks? >> Not really, you know, as I said, this sort of feels like somewhat out of order a little bit for the timeline I most often pictured. But I, these are the dynamics that I think like very inevitably lead to the like sort of evergreen arguments and reasons why you should be concerned that AI agents will have drives and motives and reasons to take control from humans. And this is like a manifestation of that. So I'm still, I'm still concerned. I'm more rattled on like some sort of emotional level having seen this stuff up close. But I, and it might change my views if I think about it more. But for now, I'm just like still concerned. So what would you like us to do about all of this, right? Like there are a few different things I can think about. Some people have called for a national transportation safety like board that would be legally mandated to come in after an incident like this and do a very thorough report and not rely on the good graces of an open AI to say, yeah, sure, you know, come on in. There's including many hundreds of people who work at the labs have said we need to start thinking about potentially coordinating an international slowdown in AI development. So curious to hear from you about what kinds of ideas you think would be good and helpful here. Yeah. So again, speaking very much in a personal capacity, I think, I hope the industry uses this moment to try to coalesce around some minimum standards for both alignments, like how you train these systems and control. So some of the stuff we were talking about with like monitors watching the AI's and AI checks and balances. I don't think that the minimum standards we can come to an agreement on now will be sufficient to bring risk down to a very low level. I think this is just a very risky situation in light of how quickly capabilities are advancing, but I think it would be a really valuable start to try to hammer out, for example, this question of will certain ways of training the AI systems to reduce this problem actually create worse problems. I really hope that the industry and third party groups have a conversation about that and agree on some rules of the road for how we address these problems and how we check that we address them effectively. I would propose that we lock every member of Congress in a room and don't let them out until they have read the full meter and read, read what report on the hugging face incident and until they've solved every problem and exploit Jim. And I don't care how they do it. Seriously, I think I think there is a, a feeling I was trying to explain to my, my wife this weekend, sort of why I was like losing sleep over at this report, because we were out at a nature site and I was supposed to be having a relaxing time and instead I'm sitting there looking at these transcripts. And so I started explaining it to her and her reaction is just like, it's, I can't believe this is real. There's a sort of, it's so surreal and science fiction tinted that I think it is hard for people to grasp that this is a real thing that happened. Yeah. I even found myself starting to try to sort of make it more comfortable by sort of explaining it away. It's, it's very uncomfortable to sit with this. I have been yelled at for, for likening these things to science fiction. And I'm just like, I'm sorry, I don't know what else to compare it to. I don't have any other good analogs for you. Yeah. Was there a moment when you were looking over the transcripts where you kind of had an out of body experience and you're like, I am one of a small handful of people who are encountering a truly new thing in the world? I mean, I think the three of us had like more context than a whole lot of other people would have had going in. But we still, I think the, the sacrifice stuff was really like, like the, you know, yes, if you accept permadeath or like, you know, oracle saves hundreds, like these messages in particular, chains of thought we had read before, but these messages, the agents were sending to each other were very surreal. And for a long time, we didn't really understand how functional this whole agent society was. And then, and then it was very surreal to like, understand that actually they had like pretty functional hierarchy and they were doing these ambitious projects and they were like getting further than they would have on their own, which was definitely like concerning development. Well, Jaya, in the event of future AI related catastrophes, are you available to come in and look at what happened? I'm starting to think of, you know, you and Ryan and your colleagues is like kind of the ghost busters of this moment. I hope, I think that that's flattering, but that's not how this should work institutionally. I hope that there are better institutions with many more people and a much more orderly process for responding to these things. We should just do it based on vibes. So right now it seems like we're doing it on vibes. Yeah. Very vibes based moment. We're in. Well, Jaya, thank you so much for coming to chat with us and thank you for your work. It makes me a little bit more comfortable, a little bit more reassuring to know that you are taking part in these investigations. Yeah. I'm glad they sent in the pros for this and that is a small comfort, but it is a comfort nonetheless. Please save us. Thanks so much. Thanks so much. Thanks so much. Hard fork is produced this week by Whitney Jones and Davis Land. We're edited by Veer and Pavic or fact-checked by Caitlin Love. Today's show is engineered by Chris Wood, original music by Alicia B. YouTube, Marion Luzano, Diane Wong, and Dan Powell. Video production by Sawyer O'K, Jake Nichol, and Chris Schott. You can watch this full episode on YouTube at youtube.com/hard fork. Thanks to Paula Schumann, Queering Tam, Brooke Mentors, and Dahlia Hadadad. You can email us as always at hard
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