This AI Was Trained on a Billion Video Game Clips. Now It Controls Robots.

23:30So our core business is general agents that just take in pixels and predict actions. But world models is how you can really steer the behaviors and the environments towards the specific types of behaviors that you want in fine tuning versus a pre-training. it's less important.

23:45Eric Newcomer:But how do we get models to hold sacred the real world in a way that they don't the digital world, right? I mean, I guess there's this worry that it's like, oh, they're all worlds. They're all sort of simulated. There isn't this like bright line, you know, between this type of world and that type of world. And therefore the sort of very different moral worlds of like the digital world and the real world aren't obvious to these machines. Like, yeah, Is that your business? Like where does that occur? For sure. We take it super seriously. We look at what happened with Cruise, for example, and we view this as a zero tolerance problem.

24:21Eric Newcomer:Cruise basically decimated almost as a company by having this terrible accident. Exactly. And didn't handle very well. Exactly. And so you have to be on top of this stuff. Our first set of models, so we're very, we work with the partners that implement these models to understand their safety guardrails. And in a lot of cases, what you do is you use our models to predict actions, but then you pair it with more deterministic or sensor-based systems like LIDAR to add rule-based systems on top of that that might invalidate some of those actions if they do come too close or if they might put something at harm.

24:58And then we also have lots of evaluations and benchmarks to understand exactly how our models might perform in these types of behaviors. And again, how do you do this? You actually put the model in a situation where something violent happens and you just check whether the model predicts the same actions as a human. Right. And if it does, that's a bad outcome. Right. So the data having or including negative events is actually critical to solve. I actually anticipate that we'll open up this data to a lot more labs because it's just the right thing to do, specifically around things like negative events.

25:33Eric Newcomer:there's so many things your technology can do and i want to get into the robotics of it yeah but are you are you going to create like i don't know the best halo player or whatever or like to build like how much are you creating like ai is capable of just crushing every human being at uh runescape yeah uh they're actually the same problem it's both just a general agent in pixel space uh right uh it's both a general agent in pixel space that you can see are using game controllers and keyboard and mouse. It's the same. So you're building it? Yeah, we're building general agents that can reason over environments that require deep spatial and temporal reasoning, which is mostly pixels.

26:10Eric Newcomer:Are there games you're prioritizing to show off your capabilities? I think we'll leave a bit of a surprise. But I think for us, because obviously what Dota was key to some of the labs early on. Yeah, yeah, we have we have currently a general agent that can play many games at the highest level uh using the same model that can also steer robots because again it's the same problem it's pixels you play against it a lot yeah personally yeah it's it's better than me at most games not yet at rocket league yeah really yeah the i mean input type games is it really interesting to compare give i would think it would dominate rocket league because it can input instantaneously and perfectly like how are you better at it on rocket league that one surprises me i'm not sure yeah it's yeah unknown you're just so good you know no uh yeah it's really interesting similarly to how on early llms you saw that was really good at some things and really bad at others because of how you've either modeled the architecture or the recipes it's the same thing you have a name for your system or what do you refer to it yeah we're not yet announcing it but uh the we did announce our world model class uh two weeks ago okay uh mira but the agent is not the agent we're not announcing yet but you work in sort of private partnerships with people or that's not even customers yeah we already have customers that are using this in video games um we purposely don't want to enable the api for gaming because we don't want people to use it as a bot uh because it breaks uh certain game mechanics so for example working with some of the games companies actually ready their security systems to be able to detect uh these types of activities uh right i'm a gamer gaming is kind of the last weird place on the internet where lots of magic happens.

27:49And so we want to keep it sacred.

27:51Eric Newcomer:So when I get headshotted too well, I'm not going to be able to blame you for... Yeah, no, we're taking the approach where we lift the games industry. We don't want to... I think, right, like what happened on the internet where you're actually kind of not sure if you're talking to an AI or a human, it's not great. And I think in games, if you see what's happening, it's very possible the same thing happens. um and so i think you just you have to work with the game developers you have to work on disclosures like i think there's all these types of things that substack in my world just rolled out all these features to be able to say this is human writing i think everybody in tech world is experiencing x you know twitter where it's like oh my god way too much slop emerging yeah how do you in the video game world like you think there'll be some like design that's like this is ai versus not or in the video game world um people just want to know that they're playing against it's It's a very normal thing to play against bots, right?

28:48I think generally speaking, what people don't want is they don't want the game integrity to get ruined by the fact that somebody is using a bot. Whether it's economically, right? Whether it's from a skill perspective, because people want to enjoy the game and they want to play against people. But at the same time, there's lots of use cases for bots because it makes you better if there's not enough player liquidity in a game when you're starting a game and you need to teach people um uh if you need to model other agents in the environment uh there's also like the models are now so good that you actually enjoy interacting with them because they act

29:23Eric Newcomer:human-like and so non-player character is going to become pretty amazing soon in video games for sure uh i think that the um that's the first place that you're going to see the models really shine uh in front of you is is that every single thing in a game engine just becomes promptable uh for its behavior uh and that that's a driver that can be a cat right it can be a dog it can so you just i think what's going to happen is you just get a lot more realism in the behaviors of everything because you no longer need to do like deterministic um uh behavior tree programming for uh for specific uh npcs and so you you just things feel more interactive um do you think we're close to a blockbuster game like that?

30:08I would be shocked if next year we don't have a top video game that was built on top of the general intuition models.

30:17Eric Newcomer:Video games, like what percentage of your business do you think will be video games versus like real world? It's hard to say. We're very focused on the games industry and robotics at the moment. I like robotics because when you solve robotics, robots can then assemble more robots and so it's it's a it's a it's a it's a it's a constraint that you need to unblock and then you can unblock many other use cases once you unblock that constraint video games is really interesting because we have such a big mode there that the models work incredibly well out of the box with no fine-tuning uh and and so game developers love love them uh you're like robots very societally important gonna change the world video games were awesome at it and it's fun yeah yeah which i understand the how silly that sounds uh but that that is the reasoning uh and and i think the um with with robotics like to take that head on i think you know it's hard to know how much the robots are secretly controlled with a joystick like what's your sense of smoke and mirrors versus reality on the public demos we're seeing with robotics right now there's a lot of speed ups that people don't realize especially when you have like vla or vlm based approaches where the models are completely incapable of responding to real-time dynamics so they'll they'll they'll move the robot at like you know 100x slowdown or like 50x slowdown and then they'll stitch the frames together in a way to make it look real-time interesting uh that one's interesting so you're saying just spell that out you're saying you think there are cases where sure the company could do that but they like pretend like they do it at a much faster speed than they do yeah pretend or i mean we're saying there's some disclosure but it is a lot of it looks cool yeah a lot of times a lot of demos are in stage environments not in like when there's people walking around or different lighting conditions or inside versus outside versus uh one of the companies that i really like is called generalists they do a really really good job at just actually showing the generality of their models and but a lot of other labs they they just kind of show it in one uh one thing that's interesting is as humans right we can play video games using the same strategy that we can control a robot.

32:25You can actually control robots using keyboard and mouse. It actually feels like you're playing a video game. So a sufficiently good model, in my opinion, should be able, if it's a good general agent, it should be able to generalize to video games and robotics at the same time, which is, I think, one of the reasons why people are going to choose our models. Every robotics company starts in simulation anyways. And so these models will be really, really good at simulation of the box, and then we'll need some work to transfer over into real robots. But what you're going to get is incredibly good generalization capabilities out of the box because it's already seen so many environments and because it can just solve problems in so many different spaces.

33:00Eric Newcomer:Are you imagining humanoid robots? I think that, you know, the argument against is just there's so much value to be had in, I don't know, particular tasks, purpose built. The argument for humanoid, obviously, is just we're the peak of evolution. Our bodies are, you know, whatever, like lots of biology figured out to be a sort of dominant form factor that can do lots of things. Like, do you have an intuition around humanoids? I've tried. I've worked with a lot of the policies running on humanoids and the complexity of training them compared to things that you control. You can fully steer using a game controller like a froncar or like a robotic arm is just so much harder.

33:40Everything is so much harder.

33:41Eric Newcomer:Your team purpose built, not general humanoid, you're saying? the humanoids do ship with game controllers and so you can actually use them to steer high level planning in a humanoid but not the fine manipulation. You're saying the more you can do sort of the core task of whatever the robot is with a controller the better it will be and therefore trying to narrow the scope of what it's supposed to do is better. Our bet is that intelligence is a bottleneck and these models will hit the industry this year which means that I think that supply chains actually converge on gaming inputs instead of humanoids this is my bet um i i think humanoids are fairly impractical they're bulky heavy destabilization of the legs is very annoying if you're training policies uh the um humans are very uh also they're right you could argue that you need a general purpose robot but humans are we our evolution is like also optimized for biology and things that like reproduction and i think right there's a lot of wasted there's a lot exactly there's a lot of wasted compute in the human body to get to where it needed to go because a lot of the constraints on the robot just aren't the same and i think people also just really like cheap robots they they want they want to be able to afford them for specific purposes they want small robots that can do things around the house they don't necessarily need doesn't need to be a full human i think um i'm a big fan of um the the wheeled robots with cameras and just two arms um genesis i think recently launched uh an unveil of their robot i love that design i think that's really the one that's going to hit in terms of functionality the practical approaches will just win i think the other thing i'll give you one example right stabilization on on legs is the is the difference between it being able to fall on a child or not right uh wheels there's no chance wheels exactly it's a stable platform right same is true for quadrupeds for example and so i just i i think humanoids are further out in the home than people think but closer in the factory than people think that's sort of my is a factory can you can do a non-human factory yeah and you can train people for those for the safety uh constraints and so i think i think there's a market for humanoids don't get me wrong i just don't think the home is sort of the ideal first one um but i also but and then at the same time people also just build specialized robots because guess what?

36:03People like saving energy and space, right? So if you don't want to manufacture a$30 ,000 set of legs and you just want a minimal amount of hardware with a camera that can do a thing, most of the time, that's what you're going to do.

36:18Eric Newcomer:Shifting gears, a lot of your fellow gamers seem to hate AI. Or I think the gaming industry in particular is very wary of AI to the extent you're seeing what the microsoft ceo you know she works for microsoft as ai built a company as you can get sort of saying okay we're gonna be very cautious about how much xbox uses ai what what is for what is your read of like why gamers have this sort of like negative reaction to what's going on in ai i think the the reaction is actually somewhat correct and warranted uh ai hasn't really done anything for gamers uh other than in their professional life right if you think about it um And then also a lot of companies are using AI to replace things like artists with pixel based predictions.

37:04And so we don't do pixel predictions. We do action predictions. So we try to sort of merge with the game industry in a positive way versus trying to replace art workflows and things like that, which is why we're having a lot of success. But because AI has only historically done not so great things for gamers, it ruins games with bots. And so I think that the relationships with the games industry is you just have to prove, right? Like, for instance, one of the things that we're looking at doing is better anti-cheat, which I described. If you can solve all of botting in gaming using better AIs, gamers will like AI, right?

37:42But the AI companies have just chronically under-invested in gamers. And so then are you surprised that they're upset? No, I think is actually justified. the other end of the spectrum also which is that the games industry is in a pretty tough spot at the moment in terms of like lots of studio shutdowns so i think there's just a lot of fear and i think um one of the reasons why i was always so open about how we went about this like i started publicly sharing sort of the the approaches that we used back in 2025 and it's because the games industry has a real shot to sort of lift up and be this frontier player in ai but it's not going to be one company, it has to be every company that kind of does it, which now I think you're very positively starting to see some of these turnarounds that are led by some of the studios actually investing more in those type of capabilities.

38:29And so I think that this year we'll see some of it turn. And I think a lot of that is just because we need to prove that, you know, it's basically

38:42Eric Newcomer:Basically what people have seen so far is AI creates spam, it hurts artists, it like sucks out all the humanity that we want. But if you're able to sort of imbue intelligence into non-player characters and create new paths for creatives to do interesting things, you think that will sort of give people more optimism? Yeah. And one of the reasons why I decided to start the lab and not join with another company is a lot of my friends couldn't find jobs. And I found that a lot of the lab CEOs were of the generation that they would be pretty disconnected from those types of problems where you're literally in group chats, helping your friends find jobs and they can't get interviews because the entire pipelines are flooded with AI.

39:22And you know, these people are really good. Like, you know, that these are the friends that like should be running things. Right. And so I think for me, it's a chance to do things differently. I think a lot of this, a lot of people are scared. A lot of people are scared that it's going to impact jobs. For example, one of the things that we do is we invest as much time into platforms for job creation that we put in the research. So we actually work on getting our users jobs that relate to the research that we do. And so I think there's just a lot of things that you, why I'm excited to build this, you can do differently from kind of the standard sort of Silicon Valley approach.

39:59Eric Newcomer:How are you getting people jobs, sir? We launched a platform. So, okay. so what's the problem in finding jobs right now is that there's so much resume flooding with LLM. So it's actually very hard for companies to get through and find qualified candidates. Well, we know exactly which action sequences people tend to be really, really good at. And so, for example, so we started actually targeting based on like, hey, we know you're really, really good at this. Do you want to help this with this? So like a factorial player, we might use them for labeling like very factory specific data. Right. And so we launched it.

40:37It's called Nerve. We launched it with general intuition being the first customer. But our plan is actually just to open it up to other labs. And so it's essentially like our own little McCore. So I think that the way the way to think about it is because as a lab, you see the future a little bit before everyone else. And so, but is it?

40:57Eric Newcomer:I mean, it's sort of like the last job is training AI to fully replace us. I mean, I appreciate this sentiment, but it is sort of the task you're having them do is train AI. Yeah, so yes and no. A lot of the times, for instance, there will just be, you're doing controls, right? So you're looking at, is the AI making bad moves? I think the human job just moves up the stack. I also, so I actually went through this dilemma myself and I just was like, okay, is this a bad thing or a good thing? But at the end of the day, I believe more people with jobs has to be a good thing. So I and there's there will be a new economy.

41:33Find the next job. It's like this is a job today that we need people to do. Yeah, it's better. If we end up giving a few thousand people jobs, then that's better than those jobs not having been created on a net basis. And so I think the correct answer is that nobody has the answers, but we have some of the insights that we can transfer into the systems that we like the jobs that then are going to power those new types of economies. Right. And so I think my so my answer is that you as long as we as a lab put equal effort into both, that is a sort of thing that you can do differently. And and and, you know, I think for us also the the metal user base, right, the platform, they are the young people.

42:15They are the ones that are going to be the most affected by this type of technology. So specifically for those, I think it's really, really important that you find a way to disconnect for a teenager. right if i could if i um let's say i'm an 18 year old and and i can make a few hundred bucks a month that's actually a lot of of extra life um if i yeah say i'm this like 18 year old

42:38Eric Newcomer:besides like working on your platform and making some money like i don't know i'm smart but somewhat ai skeptical like what do you what do you think they should be doing right now like they should just be sitting there on claude like like what is a practical thing to prepare for the world that's coming as you see it. It's actually getting really good at both the fundamental sciences and the capabilities of these models. I think that the beauty is that the true skill is found somewhere in the middle. So like, yeah, my recommendation would be mathematics, physics, as sort of a foundation, because those are very, very good at abstract problem solving and validation in spaces where your job is actually to find out often how to ask the question.

43:18And then, which is often harder than sort of finding an actual solution. And then so the fundamental sciences, I think, become really important because it's a way to check sort of the output of systems. And that is sort of the sort of antidote to what you need to the LLM hallucinations and that they just tell you what you want to hear.

43:39Eric Newcomer:Just learn to think in the most rigorous way possible. Yeah, exactly. I think that's kind of, you know, that's where I would go. Then I think coding and systems are still really important. I think these are just going to be like basic literacy because everything you – the same way, right? The same way that like coding is so important to understand how the world around you works and increasingly so that just having a basic understanding I think is still super important. So I think software engineering and AI I think are still going to be really important fields. But I think the fundamental sciences are going to be increasingly important.

44:09In terms of where everything is trending, I think the next frontier is probably biology. In terms of like massive value creation. In terms of massive value creation.

44:18Eric Newcomer:And that's the most cynical Silicon Valley way to put it. In terms of improving lives of humans and creating a lot of like things that are going to translate. You asked me about jobs. Like I think where is most – I'm not saying you were cynical. I'm saying I was value creation rather than, oh, we're going to invent all this amazing stuff that could help people live longer, healthier lives and therefore there will be jobs in it. Yeah, exactly. And I'll give you one example. I think one way to look at it is there's a lot. So there's kind of three stages to AI. There's bits to bits, bits to atoms, and then atoms to atoms.

44:48So this is a quote from Andre Kaparthi. And bits to atoms and atoms to atoms is fundamentally, I think, measurement constrained. And biology, I think also this is potentially why MidGeorgie launched the ultrasound-based device that they did. It's because you just need really good data for a lot of these problems. and data doesn't exist, which means AI is not that helpful, except if you can sort of constrain a problem down to tech space for a very specific problem set, right? So mathematics obviously matters tremendously to things like physics and biology. But at the end of the day, if you don't have the data, it's very, very hard to push the frontier in those areas.

45:31And so I think biology is one of them. I also think there's a very good chance that a lot of things that we will be doing as as our future jobs is just building really really good measurement devices is is um i think a lot of scientific problems are actually just fundamentally measurement constraint it's very very hard to evaluate you think we'll start to see games built

45:50Eric Newcomer:as research vehicles it's like oh it would be really convenient if this happened it was a lot you know a lab so we could you know somehow subsidize convince people would be fun to spend their time and something useful for us to learn for sure there is um there's a research program called Eterna out of Stanford. I don't know if it's still active, but they essentially abstractly represent the rules behind RNA in logic-based puzzles. And then they have, and also spatial puzzles. So they sort of rely on human pattern recognition to correctly predict structures. And then there are certain players that don't know anything about RNA that just based on their learned intuition are able to correctly predict structures that correctly synthesize in the lab.

46:37And there's video about it on YouTube. So this is also the North Star for general intuition, right? If you can solve a general agent that can solve problems in pixel space, all of a sudden it has many scientific applications. But to your point, I think there will be indeed many labs that make it easier to access, because that's another thing that robotics does, right? If you have good models that can take safe actions, you actually open up the operations of something to a lot more people. And so, no, so measurements, like, think about how many things that we haven't measured yet. It's impossible to know.

47:17But that's going to be sort of the limit of science, right? Because science needs something to validate against. We need to bring more fields into verifiable domain. um and so measure like you know so on the jobs question biology and measurement devices i think are the two where i feel very confident starting a career in and in terms of education fundamental sciences math physics um and coding and ai is so important what do you say to people who think

47:39Eric Newcomer:this is just a bubble that's going to blow up i mean i witnessed self-driving cars today i mean we watch waymos it feels like okay we're getting pretty close if you've been to san francisco and spend time in them, you feel like, all right, we're here. But back in 2016, there were a lot of smart people, a lot of AI researchers are like, any day now, we're almost there. And then they just seem to misunderstand. Either they had the incentive to raise lots of capital and say, oh, this is almost here when they... It wasn't or more charitably. It's just really easy to underestimate how much the last 5 % is hard to solve and matters a lot to success.

48:17Eric Newcomer:What do you think is the risk that it seems like we're close, but then near enough just isn't enough? What I bring this back to is when people get a really good model, the first thing that model does is clarify, for instance, what other questions you should be asking. And then it spawns sub-agents, right? The better the model, the more sub-agents it can support. And so there's this, I think there is this effect where intelligence just demands intelligence. And it's this loop where essentially consuming tokens, if the tokens are high quality, actually make you then use more AI because you can solve more problems.

48:55And so I see this pattern just come back all the time. And then it's not just humans consuming the tokens, right? It's agents that you have prompted choosing to consume tokens. And even in that part of the curve, we're still super early, right? Think about how much more tokens you consume after Cloud Code than before Cloud Code, where you had to manually prompt everything. So I just see no reason to think that the demand for intelligence is not uncapped at the moment. However, with that said, economically, I think it's a question of where is it infrastructure, right? Is it in the US? Is it in China?

49:33Is it and like, where are the margins, right?

49:36Eric Newcomer:And to translate what you're saying, you're saying every new model that comes out, businesses and people want to spend more and more money on it. It's like, to deliver, but you're saying we're just going to hit the capacity of our ability to keep sort of improving these models. What is a company? A company is a function to convert, depending on, I'll just say the simplest example, right? It's capital into more capital, more efficiently given energy and time, right? And so if you can do that more effectively by spending tokens, And yes, of course, you're going to do that. And so, yeah, I see no reason why any of that is looking to stop.

50:22But I do see, I am concerned, for instance, about the margins on top of infrastructure and model providers. And I think it's, you know, there is a chance that if the West doesn't win kind of the frontier of models and usage, and we don't stay sort of really far ahead, that our lunch gets eaten by companies that can provide the intelligence at much lower margins because they have better energy grids or can sustain more demand. Kimmy, like this week, Kimmy is like a wake up call that we're not as far ahead

51:06Eric Newcomer:as we thought we were. Does that affect you or? Open source model development generally is really good for us because we can leverage a lot of the capabilities of open source frontier models to advance our own systems. And because nobody has our data set, it's a net win. However, I think that it's not great for, especially the distillation and all these things that happen. And it's not great for the industry overall because it does probably mean that a lot of enterprises start using Chinese models. And, right, you know, the risks are currently unknown. I would say, like, there are some people that say there are risks.

51:49There are some people that say there aren't risks. I've read papers in both directions. Right. There's concepts of, like, specific tokens that are very hard to find that can trigger sequences. There's also, but there's also -

52:02Eric Newcomer:You're saying that specifically, like you use some foreign countries model and they hide some malicious - Yeah, like look at how good these things are at cyber attacks, right? Like the supply chain risk, I think is quite real where you cannot possibly know, given every input, what output the model is going to give. And there's papers where very subtle kind of token sequences can trigger very aggressive responses in, for instance, the domain of cyber. And then, you know, there's also the economic sides of it. If a model has economic biases towards recommending cars that are not made in the U.S.

52:35or not made in Europe, and people are relying on these models sort of recommendations, and economically, a lot of people are going to buy products from other countries that could be bought domestically. And so I think that it's really, really important that the frontier of AI stays in the countries where people live. And then secondly, I think it's really important to just deeply invest in the infrastructure build out and the energy build out because otherwise, right, the margins will get eaten eventually.

53:04Eric Newcomer:This surfaces the area of like distillation, right? Like there's this debate of whether, you know, it's OK for models to sort of like extract all the, you know, learn basically how to build a great model by studying somebody else's. It seems right now like that's allowed. Like you're basically allowed to use other people's products, see how they work and then build your own products, right? I mean, do you disagree with that? I mean, maybe we need to change the law. Yeah, I think there's two sides to this coin. One, LMs are trained on large-scale internet data. So it's – That's what I was going to get at.

53:35It's like an industry that took stuff from everybody else and then it's going to whine and say you're taking stuff from us. Yeah, I actually subscribe to this as well. from an intellectual property perspective i think the law has been pretty clear that there are net new works and therefore i i legally i think it doesn't transfer quite like that uh but i think

53:57Eric Newcomer:you know emotionally it it does what do you say to like your metal customers who say oh i had no idea that all this footage was going to be used for this for you to get rich in this totally different like domain we generally i think that's why it's so important that you get the economic benefits right? Like one of the things that I want to do is just find ways to also, if your data is in the models as a Metal user, right? Like I think you can construct really, really interesting loops, for example, where you actually make game development more about creating environments that extract more intelligence.

54:31So I think it's really important to think both from a jobs perspective, but also a user participation perspective, that there is actually just ways where you can make sure that people can participate versus be at odds economically. So I think all these things matter. I think, right, like a lot of the reasons why LLMs, where people are so upset at the LLM companies is because, yeah, they did just scrape all the books and all the works. And so, and then you can ask the model to write a new book and compete with you, right? We're in a little bit different regime because we predict actions and not pixels.

55:05so we're a bit sort of not the same kind of problem space but I think broadly in terms of distillation the answer is that models can be distilled it's almost impossible to prevent that from happening as models get more efficient they need less data to sort of be distilled and so I think it's also crazy to assume that it was just distillation they clearly knew what they were doing Exactly. Clearly, this is a great team. Clearly, there was some distillation going on, but perhaps they are really good at figuring out which models are good at predicting which types of tokens. And they use the different models, they describe different models for different purposes.

55:53You don't know. I think you just have to prepare that distillation will happen. It happens all the time. And I think so what it comes down to is just what do you put in the models? like everyone just like what do you put in the models right and and how do you train them and how do you evaluate them and and and put in the balls to stop distillation you mean or I just have to run fast I'm talking about like the biological tokens and things like that where like you just have to be very

56:19Eric Newcomer:cautious about what goes in oh you're saying as long as they're like benevolent not do anything bad cyber capabilities all these things that are now showing clearly shown to be very dangerous right like I'll give you one example you saw the OpenAI, the recent event with Hugging Face, we had an instance where one of our interns, I tasked Set Intern with reverse engineering and APK, and I told them sort of how to do it. And it was completely a legitimate use case. We were just waiting on an SDK from a company. They hadn't sent it yet. They said they were going to send it. They were backed up. So we looked at how the system of the SDK works through the APK.

56:55Set Intern, so I tasked him with this at night. I was at the office at like nine i went to play some soccer i come back the next morning interns like hey i'm really really happy this worked so i was like okay how did you do it and he shows me the cloud trays and claude had found an s3 key and oh my god and had actually pulled like amazon like he found like a secret file bucket access key that then essentially the model infiltrated to then pull down documentation to reverse into your oh my god which the intern didn't even realize was the intern did not realize this had happened. And so... You then have to call the company and say, oh...

57:29Yeah, I wrote a disclosure report. Turns out that the model found a very real vulnerability. But my point is there is a lot of... Like the biggest risk to companies is probably your own employees bytecoding stuff, not realizing they're hacking things, right? And so like this was, you know, no harm done here, right? They pulled down documentation, but like legally speaking, you access information that you were probably not supposed to access, right? And so there's... So I think there's just, you know, people have, we have to start working together. It's a new world. It's a new world.

58:01Eric Newcomer:Him do it. We could talk all day. This has been fascinating. Thank you so much. Excited for your agent. Someday maybe we'll get to play against it. Thanks so much. Great to see you. Likewise. Thank you, Eric. That's our show. I'm Eric Newcomer. Thanks so much for listening to the Newcomer podcast. Go check us out on Substack at newcomer.co. Please like, comment, subscribe here. and give us a review on your favorite podcast app. Thanks so much.