We are in the race for super intelligence and Andrew Feldman is back. And obviously CEO and founder of Cerebris, doing inference chips, pioneered the space, had a successful IPO. We've talked about this a couple of times. We got to see each other in January at Davos, IPO happens. The boys and I got to sit with you recently. That was fun. At Liquidity. That was really fun. Had a great discussion with the boys, but I wanted to deep dive with you about a couple of topics. The first one is the build out of AI. We've never seen a build out like this since the Great Wall of China. Right, who knows since? The pyramids. I mean, it feels like the amount of capital, time, and intelligent people on the planet dedicating themselves to the build out of something. I can't think of anything in our lifetimes, but perhaps before our lifetimes, the war effort. Right. This is a mobilization and a scale that we read about, we hear about, but you're actually doing it. You have customers who are building data centers, and you're a key piece of that. I'm going up. AppLovin started with an $8 domain and no VC funding, and became one of the largest ad platforms in the world. Now that same engine powers AppLovin ads for e-commerce. Your ads run inside mobile games, reaching over a billion people with full-screen, distraction-free attention. The platform finds buyers and optimizes for profit. You set the target, it does the rest. One cookware brand went from $4 million to $16 million, turned profitable, and is on pace for $80 million this year. Visit applovin.com slash all in to launch your first campaign today. I'm going all in Maybe you could just enlighten us in 2026, what is Cerebris doing, and what is happening with this build-out out in Texas? These are some gigantic, gigantic efforts. The size and scope of what is being built, the physical size and scope, usually when we talk about software, or we talk about hardware, we're talking about chips or boxes, and they don't have the same sort of physical enormity. Right. And what we're talking about now are data centers that are in the next several years going to use more power than the previous 50 years on Earth took. Wow. Right. We're talking about individual buildings the size of football fields that have more power coming into them than mid-sized cities. And they're being built, they're being built across the US, they're being built in Canada, they're being built throughout the Nordics, they're being built here in Paris and throughout France and Europe, in the Middle East, in nations that sort of weren't front and center in anybody's mind previously, you know, Kazakhstan, Tajikistan, are building out Georgia, are building out data centers of size, Armenia. Everybody's sort of focused, huge data centers. Every country and every state, obviously, in America feels they need to participate in this. And the people who are buying the capacity, the OpenAIs, Anthropics, SpaceX AI, SpaceX AI, the Googles, they are insatiable right now. Yeah. And they're building, how many years out, when you talk to them, they were ordering chips from Cerebrus before you were finished with the chips, they're putting orders in ahead of time. I mean, the irony is, unlike many sort of exciting times in technology, they're trying to capture yesterday's demand, right? The demand is way outstripping our ability to build data centers and to fill them with hardware, all right? And so, you know, we have a $25 billion backlog. $25 billion backlog. And we are not alone in that. That OpenAI, Anthropic, you go through this list of, of Google wants more data centers, Microsoft wants more data centers, AWS wants more data centers, right? All of these players are not chasing, sort of, if you build it, they will come. They're chasing, the demand is booked. Right. How do we keep them from leaving? Right. And that's extremely unusual. It's very unusual. And now we have people who are, you know, we have a term for a token maxing. Yeah. And there's a great debate. Is this actually creating value? I'm curious where you stand. You know, is it even possible that this much demand could be created if value did not exist? There is clearly massive value happening. Yeah. But there's also massive experimentation. Oh, for sure. You know, I liken this to when we first started with AWS and it was so good to get around your own IT organization. Right. That you told every engineer, yeah, go ahead, put on your credit card, sign up. Yeah. Right. And a lot of it was really useful and some of it was like, God, I wish we didn't do that. Yeah. And so for sure, there's experimentation, but it doesn't mean that the net value isn't enormous. It means some of it is going to go nowhere. Yeah. And, you know, it was the same, I remember when Costco opened up in the Palo Alto area in 1988 and people used to shop Costco like they shop Safeway. They'd go down every aisle. Yes. And that's a horrible way to shop Costco because you end up with four things you didn't need and each was $22, right? And as people got more sort of accustomed to it, you'd go to the back, you'd get the chicken, 18 cupcakes for the kid's birthday party, bang, you were out. Strategic. Strategic. And it's exactly the same. I think at first, people opened up and said, everybody, as much tokens as you want. And in enterprises, there's no open loop. We don't give sort of any resource unconstrained to people. And now we're jumping on and saying, whoa, all right, these guys should have as much as they need. They're enormously productive. Over here, we can use maybe an open source model, maybe a cheaper model. Over here. And now we're sort of running like a business. And we're really seeing a certain type of person emerge who knows how to deploy this technology. Systems thinking. Yes. Which developers kind of have innately. CEOs tend to be great strategists and understand systems. But the intelligence is getting so much better every step along the way that I'm watching individuals, typically startup founders, but also venture capitalists and associates who work at my venture firm. They start playing with the tool and then the tool starts playing with them. They start to go, oh, I haven't clearly defined what my goal is. I don't understand what a system is. I don't under, I've never heard about making a requirements document. And the software's like, do you have a requirements document? What's your goal? The AI starts telling people you're token maxing and you need to get a little more focused here. One of my colleagues 20 years ago, a really smart, smart computer scientist said, computer's really dumb. They do exactly what you tell them. And at first, prompting was like that. Right? You modified your prompt a little bit and it changed the answer. Dramatically. Dramatically. And increasingly, it's understanding what your intent was. Right. Right. And if you have a chance to play with Fable or 5.6 from OpenAI, increasingly, you don't have to get the prompt just right. You don't have to be a prompt whisper. Instead, you ask it and it says, well, here are some things and by the way, maybe you wanted the chart to go two ways. You wanted a line in a bar and it's like, well, that's exactly what I wanted. I didn't ask for it, but that is better. And so it's understanding intent and that's a huge leap. Which, if we were sitting here two years ago, the idea, we would never have been able to predict in a short 24 months that it would go from being a great summarizer researcher of web results to actually understanding your intent and then providing a solution and abstracting it all from you. That's right. Which is a very weird thing. I don't know if you've played with the Hermes agent yet. Yeah. Have you played with it yet? I mean, I asked it just this morning and I was given a secret BitTensor project that has the new ZAI's model, five two and they gave me GLM five two. GLM five two. So somebody in that BitTensor, I think you understand BitTensor, you've heard of it, the distributed crypto project. And so they have all this extra capacity. A whisperer told me there's probably some capacity in China that has free energy. Okay, fine. So they gave me unlimited capacity. So I started having to do some really crazy jobs where I was saying like every hour I want you to tell me what the trends in the world are that nobody else has identified yet and you can do whatever you want to do that but my goal is to be the smartest trend hunter in the world and I watched what it was doing in the background and it started debating itself on where it should find the things. It said, well, we should probably go to Hacker News and Reddit and then it was like, yeah, but there's also social media and trends tend to manifest on Instagram. That's a reasoning model. You were watching a reasoning model work out. Yeah. Isn't that interesting? I mean, that's amazing. And it was collapsed. So as a civilian who doesn't hit the uncollapsed moment and if you were using ChatGPT 3.5 or you were using 4.8 whatever it was and you haven't used this new level of reasoning and inference and unlimited compute essentially, it opened my eyes just this morning of what a world of unlimited tokens might look like because unlimited tokens I believe means unlimited reasoning. It does. What does that mean? Yeah. I mean, if you run these for 25 or 48 hours, you get amazing things now and what if by using Cerebrus we were 15 times faster and then you ran it for 24 hours, right? And you got weeks or months worth of thinking and I mean, it is, it is extraordinary and I think one of the things is people like Ilya and Sam in the early days were saying this was coming. Right. Right? And I think when you look back you say to yourself, holy crap, those guys saw it. Yeah, they could see around the corner. That's right. And the rest of us were like, what? I'm not sure. When we had Sam on All In at one point and he said, you know, I'd love to come on at some point. I said, sure, come on. And he was talking about it and he said, you know, I said, what's next? He said, reasoning. I said, unpack that. What does it mean? He's like, well, understanding what your intent was just as you're saying and then figuring out a strategy and then maybe talking to other agents and other threads about like, is this the right thing to do and vetting each other's work and I'm like, wow, we have come a long way from guess the next word. Fill the sentence in. Summarize this PDF. Now, Cerebrus is at the center of this because this reasoning is inference. This reasoning is inference and it's computationally intensive. Right. Right. And so fast compute makes this sort of work fast and sort of tractable. It doesn't cripple it by taking a huge amount of time to get a good answer. And so it's exactly the fact that this reasoning consumes a huge amount of tokens internally that allows a blisteringly fast machine like ours and I brought one because I'm never far without, you know, when one costs half a billion to make, you bring it everywhere with you. We were tossing this back and forth at Davos. What's the model number of this one? This was in the first eight or ten. Got it. So this has a special place. This has a special place. I mean, my wife says it's like I'm a kid with a dirt bike for his eighth birthday. It was in his bedroom at night and I carry him with me. I mean, when you have, you know, your next party at the house, I highly recommend just a little hors d'oeuvres. A little hors d'oeuvres. I think it would be like a great fit. It would be a great fit if you had some. That's right. But what we're looking at here is the ability to do that reasoning at scale. And what is Moore's law for inference and for cerebris? Do you have something internally you discuss as we're going to double this every X time period? So all chips prior to us in the processor world followed Moore's law. Got it. And we broke it. Doubling every 18 months. Doubling about every 18 months. Got it. And we crushed it with this chip. And we've carved out a whole new trajectory. And my view is in the next 18 months we'll be way over 2X. Interesting. And so I think that early in an architecture you have room to do much better than what was traditionally Moore's law. Now if you've got a 20 year old architecture like the GPU it's much harder. Right. You have to rely on things like smaller geometry. Right. Going to the next fab node. But in a newer architecture you have a huge amount of room still to to learn about the work that is being presented and make optimizations that give you huge gains. How do you run the company just being the CEO now in the age of AI you have $25 billion in demand you have to you have to deploy at just an incredible blistering case you have to hire people you have to create a road map I don't mean to give you a panic attack here you have to keep up with somebody like OpenAI who's moving so unbelievably quickly yes right and they're they're competitive you got to keep up right right your hardware your software your deployments have to keep up with some of the fastest moving organizations in history they're demanding customers they are not they're not pushovers for sure yeah and also potentially competitors down the road look I think there's so much demand right now that there is no silicon that will go unused but why is an OpenAI releasing jalapeno why is Amazon making their own chips you see this reoccurring trend is it a way to let you know to let Jensen and NVIDIA know hey we can do this too so we need good pricing is it a little bit of a flex that way or is that the future that they're going to be in your business no I lessons learned by the hyperscalers of the x86 world is they were dependent on Intel and some of the lessons learned by the GPU makers was they were dependent on a small number of hyperscalers and they wanted more customers and so they set about to help fund these neoclouds and so I think mostly it's about an opportunity to control at least an important part of your destiny got it and I think that's a very reasonable thing I think you don't have to sort of make the fastest chip you just can't be entirely dependent on other people's chips and that dependency has become a hot topic I'm not sure if you caught the episodes over the last two weeks but we've been talking over the last year about open source I've been championing that a lot just because I was early into open claw and quickly started using Kimmy and was like wait a second I'm blowing out my claw tokens but this Kimmy I can't tell the difference and then we started smart routing it and suddenly this open source started to figure out there are times you want to drive your fun car and there are times you want to throw the kids in and don't worry if their cheerios on the floor minivan time and I think that as the sophistication of the user grows you're going to have hard problems and those are going to be front your model problems so you're going to be open AI problems they're going to be anthropic problems maybe Gemini problems and behind that they're going to be a lot of ordinary problems right I mean if you think about a company you know how much time is spent cutting things out we've been thinking a lot about it in GNA but a huge amount of GNA is not invention right and you may not need sort of the most sophisticated agents for this and another card that's turned over recently is some folks maybe have concerns with sovereignty of intelligence and they're saying hey our company is going to choose maybe we're in a regulated industry finance healthcare HIPAA you know FINRA all kinds of different regulations we need to have this on prem and we want to have domestically and we'd like an open source version where we have a little bit more control yeah and I think are you some months back that was good open source model but I think in the US we need more domestic open source models we need to give the world a choice right if they want to run open source right now it's OSS 120B or Chinese models Nvidia has some Nvidia has seen the same opportunity to push open source models I think giving them more power might Jensen was like hey we don't even want to talk about these open source models we have because our customers right we're now going to be competing with Sam Dario Elon Sergey like do we want to be in that position right so but we do need some more champions here and it's open source so people can fork it but that puts you in open AI models the closed source ones we run models for say Glaxo Smith Klein which they wrote and developed we run models for our partner in the UAE G42 and MBZ UAI that are their models that they 5-6 where they said oh whoa let's think and then we can act I think sort of particularly here in Europe was a bit of a wake up call and when you saw this going down there's a layer of partisanship in our country right now it's pretty fervent Dario is pretty explicitly you know not part of this administration they've been very adversarial both sides have been admitted that they're starting to work it out now so it's hard I think for us not being in the room with these parties to understand what's partisanship what's gamesmanship here but do you believe that what they released was truly dangerous for cyber warfare for cyber attacks and but to have a scheduled rolled out release right we'll put aside the government's control of it but do you think that is a wise thing for us to do at this point and do you think there was actually a major threat there so what's interesting is I hadn't seen it before right and I think if we just step back and say is it reasonable I don't know whether this was the right time but at a time that a model is sufficiently creative in its thinking that it poses a meaningful threat for the government to say we'd like you to roll it out in steps yeah this doesn't seem unreasonable to not at all right I mean we do this with powerful pharmaceuticals right we like I mean we're certainly not encouraging seven years of trial and the amount of paperwork and all the garbage that has accrued to the FDA but with a powerful new technology it certainly doesn't seem unreasonable to say hey guys let's at least do some red teaming at the of the country like of the NSA have we checked the infrastructure of right and can you give us two or three weeks to patch any obvious holes that are found this doesn't seem to be an unreasonable thing for the government to ask right we but we in this very polarized time put on top of it well oh my god it's president trump doing it and then you have to think well what if it was president AOC or president anybody in between the two extremes I think the polarization hurts a great deal it hurts clear government are trying really hard the rank and file are trying really hard and this is moving fast and I think that an ability to set aside some of the polarization and say how do we do this in a reasonable manner I mean we want Dario and Sam competing like crazy 100% it's been awesome to watch it's awesome yeah right it's good for the technology it's good for it's good for entrepreneurs to see even with thousands of people this is what you can continue to achieve right right this is a drive everybody got better because of that we want that and we certainly don't want to become sort of a region where but then the communication was lacking maybe you know I think not only are they racing hard but they're inventing this as they go too yeah right there's not a playbook no right they're inventing the we say oh just put on guardrails well they have to design the guardrails sure right the guardrails have an impact you know one of the things that fast does is it makes the guardrails less painful and so that is we we discovered that in the last six weeks yeah is that the very guardrails can add time and make it feel slower and so fast ships like ours right it can really help that but so they're racing against competition they're racing against their own sense of and all of those are mixed in this bucket and and sometimes you're on one side rather than the other and yeah and as you're saying this is a first time right that's right when we when 3.5 came out it wasn't like when we're using chat gpt 2.5 3.5 it was taking down networks right but in talking to Nikesh from Palo Alto networks I asked him like hey well how would you grade this and he said we put it against our software and we found bugs we were not aware of yes and killed them yeah he said we had to stop everything we're doing and do patches for six weeks right and that's when you show it to a group first right maybe you I don't know what the right thing is but I mean red teaming and we've always had just when you were releasing the new version of an operating system you know when you have your iPhone you can say I want to be part of the beta that's right you know right and there's like two other betas that you don't even get the or leak or corruption any number of these things I think we can also know that there will be a massive data leak of course we know this right and it's like Warren Buffett talked about the reinsurance industry that you know something bad is going to happen you don't know when but you got to save up for it right you put money away for reinsurance but there will be a tornado best a plan but there will be a massive breach and there will be we have to steel ourselves in advance and we have to think about it think about the right response at the time and sort of prepare ourselves for a future that is in specific unknown but in general we're pretty sure something is going to happen something will happen right and yeah it's typically a black swan right by definition it's going to be something we didn't consider or a question we didn't know to ask right but even knowing that there's some unknown unknowns is a useful place to start yeah what are we're not asking ourselves that's right with reasoning the AI is going to be able to tell us hey schmuck humans that's right by the way here's what you're not thinking about this is now my closing sentence when I do my prompting is I need you to make me a prompt that will help me do this trend scouting for an example and then I some of the tools like perplexity do that automatically they give your next three prompts right but if you give it explicit instructions my lord is it good at that so you know over the course of the last 10 years as I was raising money I thought one of the smarter questions I picture of the space and to the extent that you can ask the AI that and that it can sort of broaden your view you know maybe what question should I start thinking about AGI and super intelligence you know they're just definitions but they're important definitions I think to kind of keep in mind because they're waypoints that's right and AGI I think I suspect you'll agree with me that we've hit it we just haven't exactly deployed it fully we have artificial general intelligence now it feels like when we're talking about these reasoning moments and you be for it to be as smart as any human but let's talk about any definition we had 20 years ago we've hit it yes right I mean if you think about oh there was a Turing test blew it had had their say and we answered all their questions right if they were to look at this today they'd be like well I'm out of question I'm out of question sorry that's where sort of listening to people who sound sometimes like they're on the fringe right when Ilya was talking 8 or 10 years ago about the need for safety and you're like what dead right yeah right when Elon was talking about building rockets and driving the cost to near zero of a launch vehicle you're like what there it is and now you can see and that's I think that's why it's really fun to be a technologist now right well and with these tools specifically you know we're talking about building all these tools and then the tools are starting to build themselves in this recursive loop that's right we're kind of just starting to see people apply loops in fact loop maxing became when I was doing my trend when I did my trend thing it kept picking up looping and it kept picking up the maxing stuff and it created a buzzword for me loop maxing and then it magically people started talking about loop maxing and I was like wow this is really weird it anticipated that this would other humans would come up with this word but talk a little bit about recursive and then the road to super intelligence and do you have a way Andrew that you think about super intelligence and what it saw six years ago or five years ago was that powerful recursive gains are exponential right you get better you do it again and if you continue to get gain the slope of that curve is so steep and that we're just beginning to see that now you ask a question you learn from the results you ask it to do it again the results get better and more information is added your answer gets better you ask it to do again it covers more material and these sort of loops are producing sort of not a little bit better answers but vastly better answers yeah and that is enormously powerful because we don't quite know where it ends right you but holy cow I mean when does the exponential stop or does the answer keep going up and up and up to the right yeah and that's sort of an enormously interesting intellectual question right now yeah like when do we run out of problems to solve and well that's right and when are the problems no longer sort of intellectual problems and they're now people problems yeah right how to organize people to get done what the AI asked for right I mean as you right and motivation motivation you spend a lot of time as a leader spraying WD-40 on your team right right it just so friction is reduced and how do we learn about those from AI right how do we get behavioral insight from AI and and I think that's some of the things the world models are going to bring us as they begin to watch human behavior yeah we didn't even get to that this is going to be for another interview but when these things jump off the screens right and they're in the real world and the recursiveness starts not trying to solve math problems right you know humanity's most difficult ones but hey you know there's an incredible world out here and here's the palace of Versailles right you're just like now we're like make me a new version of Salesforce and we're like hey you know what I'd like a palace of Versailles I've got a hundred acres somewhere out Texas or Nevada I'll just years before it and it was fantastic I think to the people who built it right even to the builders I think they were awed at it as they built it yeah they're compounding they're compounding recursive learning that's right and generations we talked about you had a really such a great insight of in building this place you had generations of you you apprenticed on your father your uncle and when you had a project that took 50 or 70 or 100 years you might have three or four generations of the same family right the same stonemason family working on the same structure and passing on the learnings new innovations right which is what we've modeled with this new that's right models and what you're building in the infrastructure it's pretty incredible when you think about it especially when we're sitting here and that that that's what I mean I think the problem with human learning is it often moves at the pace of a generation and like elephants and other large mammals we don't have generations but every 15 or 20 years and if you want to move really quickly across generations you want them happening more like drosophila like fruit fly you want two a day yeah right then you see that in genetics that's why we study them in genetics because learning encoded in the DNA you can study over thousands of generations and I think that what we're getting is that equivalent in AI we're getting sort of learning so quickly over the equivalent of thousands of generations yeah Darwin would be in awe of this pace of evolution and that's exactly right you think about it as there was I remember when I was getting my psychology degree and they were teaching us about paradigms and I was like trying to understand how the paradigms shifted and the professor said to me Jason which you have to understand is paradigms don't die they don't people do that's right and that's how Freud he and Thomas Kuhn that's right Freud and Skinner and Young like it took them question it and that was 20 years sometimes 40 years as their students maintained positions of leadership until someone said maybe we could do it differently and I think what you're seeing is this iteration is a shortening of the intergeneration gap and the learning your approach to it is so intellectually rigorous but also with so much p-doom in the world I feel so good that you're such an optimist about this technology and you're building it with such thoughtfulness and I think for people who are hearing these horror stories about AI and job loss and everything they need to understand there are people like yourself who are building this in an incredibly thoughtful way and this is going to be a net benefit for humanity that just is unimaginable yeah we have a shot with this technology so not our children nor anyone they know dies of cancer all right I mean say it like that there will be some dislocation in the economy sure there will be there was dislocation when cars came and and it was a bad deal to be a guy who shooed horses right or build carriages yeah but you gotta also against that you know make your tea of the cons and the pros yeah right there's a shot that our children of them nor their people they love will die of cancer and that's one that's thing that we can work on with this technology and we will have great purchase on and I think you begin listing those and then it's a more thoughtful discussion yeah unlimited energy unlimited calories unlimited knowledge unlimited education unlimited housing and how we do it we imagine imagine sort of we know how to teach children and we don't do it right Aristotle was a tutor to Alexander the Great Socrates was his tutor we know that if you give a child a tutor and the tutor modifies the teaching for the child they learn better that's not how we do teaching classes no factory farming that's right we teach to some sort of mid-level imagine if we built agents that taught children for their way of learning right right and here's we've been doing it the same way for a thousand years and during that entire time we knew how to do it better and we chose not to and here's a way we can do it put that on the pro side and so as long as we're sort of thoughtfully and fairly writing the good and the bad I think it'll come out you gotta get out there Andrew keep communicating your version of the world because some people see around the corner and they get a little nervous and okay fair enough but I think the ledger as you describe it is heavily weighted towards massive abundance Andrew pleasure always I'll see you in six months for our checkup that'll be great I'm going all in industries capital and intelligence are converging into a single interconnected system and the infrastructure behind it needs to evolve just as quickly Nasdaq was built for this moment powering more than 135 marketplaces and regulators globally and connecting capital to companies shaping the future as the innovation economy accelerates connectivity becomes the critical ascent Nasdaq is the leading technology possible and scalable learn more at nasdaq.com I'm doing all in Robin Rombach is the co-founder and CEO of Black Forest Labs you are based in Germany in Black Forest which is a city in Germany it's a mountain range actually a mountain range yes where you grew up where I grew up yes and you are working on open source image and video models you worked at stable diffusion for a little bit that's correct cut your teeth on that and you're known for the open source model flux and maybe also for some closed source models tell us about the business of Black Forest Labs what is the business and what we started we started a company me and my co-founders as you said we've worked on stable diffusion in the past before that we invented an algorithm called latent diffusion which is basically the fundamental algorithm behind all of generative models that are being deployed for image generation video generation even like physical AI now it basically makes use of this principle that you can compress natural data such as images such as video such as audio into much more efficient representation and then train a transformer model on that and I mean this is the stuff why JPEG MP3 and all that works and we basically translated that into a neural algorithm a few years ago when we built on top of that we built stable diffusion and then on top of that yeah the generative models that we are developing today and of course the technology has advanced but we are now tackling models that are really made for understanding the whole world around us multimodal visual models pre-trained on images videos audio data at the same time and we are now like entering a new paradigm which is combining that with something that's called action prediction such that you can actually use the same model to make images to make videos to make audio and to predict actions which means you can ultimately deploy deploy deploy on a robot in the real world wow so from the image to the video the audio and then eventually the real world with robotics and a real world model because if you can make the image you and you can train the model that means by default you understand the world in order to make a video of the world you have to understand the world yeah and the objects in you need for like a kind of like complete form you need both and you need them to interact and I think like we've been approaching it more from the intuitive side images is like a very natural way to approach this whole field because it's not as computationally intensive as let's say video right but now yeah I think we're combining it of interactions with the real world and then you can get stuff like action prediction like robotics out of the same model and with these models and the training there kind but you know maybe I want a different style maybe I want a different color maybe I want a different you know aesthetic how does that problem get solved and do you actually understand what's happening when the image is being made under the hood yeah yeah I layers as possible to like I don't know like a user or developer that builds on top of this model right and I think like we've seen that in the past with like in the past image models they basically started from simple text to image systems right then they've expanded into text plus image to image systems which means you could suddenly take an image like a real image right and then this expanded into taking multiple images and the text prompt and combining them in a semantic way and producing new content and the same principle now applies to video and I think now it becomes even more interesting when all of these modalities are actually combined inputs and outputs of the same but in a movie this promise of being able to make a movie in which the camera angle the sound could be something that a Martin Scorsese would be proud to release to his fans how close are we and maybe tell us a little bit about this partnership the technology being able to make an actual movie like Goodfellas or a scene from Goodfellas versus where it is today where you can make interesting five or ten second clips and then maybe people struggle making ten of them and then they use some post editing software to put them together but you I think it's important and that's at least the view that we have is that these AI models are a medium right we don't want to set any way of how they are supposed to be used we don't want to tell anyone especially not someone like Martin Scorsese how is he supposed to use and at the same time I am also a big fan so you sat in a room with Marty Scorsese and showed him your tools exactly yeah and what was his reaction what did he key off of what was the thing that he found most inspiring or interesting I think it was really this idea of like yes clearly a vision in will be shot and he is trying to explore that and kind of like we basically looked at the scenery of like a village in Eastern Europe somewhere and he was describing it we saw some outputs we iterated on the outputs and it just makes it easier to communicate and convey an idea of what is actually in your head and I think that's like one of the very interesting and powerful ways to use this technology and I think ultimately is to get the inspiration to get the vision out like an image or video there's so much signal in it and it's just like another way of communicating and I think that's like one of the beautiful things that this technology ultimately enables and I like the real interesting use cases they come when you have like a human in the loop who iterates and uses it story boards and some of the great directors Ridley Scott of Aliens and Gladiator was known for making his own I also believe Spielberg was also liked to sketch Raiders of the Lost Ark and some of these George Lucas was known for collaborating with many amazing artists even making miniatures and making storyboards for the Star Wars franchise he had those people on full time helping him with that so that's the start-ups always want to try to figure out how to do something cheaply and people used to make a launch video for their startup for $100,000 $250,000 so they take their $10 million venture raise and spend $250,000 on a launch video I've seen with a lot of the startups I'm investing in now they'll just spend a week use flux and some of your models for this have you seen this yeah of course yeah yeah what's your take on that because that feels like the early stage of storytelling you're trying to communicate a product or service in launch videos products being built on top of like the same kind of like base model or the same technology and I think that's what's making it so interesting and also so powerful yeah and what else are people using the technology for I understand there's a Bitcoin movie coming out instead of using a green screen in this Bitcoin movie I was talking to Gal Godot you know the woman who played the actress who played Wonder Woman Gal Godot I was talking to her at an event and she was telling me it was the breakthrough prize Yuri Milner's event and she was telling me she all of the scenery behind them was being done by generative AI that's a real movie that's a $30 million budget movie she said it would have cost $150 million if they had to build sets and the film would have never been greenlit are you starting to see people use that in production not just in the back end and the ideation phase but actually in production yet with your tools yeah we see some use cases like that in production I think like high-end film production is kind of like one of the most demanding use cases and I think I'm glad that it's being 64x64 pixels now you can do like multi minute videos right at like a high resolution but it's like it's not going to stop there it's going to continue to improve and I workflow right but I think when I look at multimodal generative models as a whole I think what really excites me is you can use the same kind of AI model to make a movie and deploy that as a brain on a robot and I example computer use remains to be seen if that is actually something that works or not but I think the technology is so powerful and so versatile and it's just moving into that and all the talk around world models world action models all of that it's basically all the same and I think that's what's making it so video of somebody you know making a sandwich now we have the robot study it and make the sandwich or do you think there'll be a lot of synthetic data made that then the robots will just study the synthetic or they're going to just in some way innately know based on all this massive amounts of training data I think generation it's predicting actions which is you have to understand the input the visual inputs in order to actually predict a reasonable next action and it's about perception it's like you can only do that if you understand if you perceive the content then you think what's what's driving it there's not a single one of them it's a combination of these tasks and what's the best way to get that training data do you need to have people put on glasses get the outside or is it going to be just hey take the corpus of youtube videos and the robots know exactly what to do because they'll find a thousand videos of people pouring drinks I mean ultimately I but I think this is one of the goals and I think how these models are deployed currently is there's a lot of different hardware robots that are running in factories that all have some different kind of action representation that you need to tune the models towards so in practice what you do is you have all this visual understanding in the to adjust the model on that specific task and I think the goal would be to kind of move away from that to what's like as much in context as possible but it is a little bit of a research problem I think that open source has kind of having a moment right now we've been discussing it on the podcast a whole bunch recently and people are also talking about sovereignty you have companies that own incredible IP libraries I mentioned Star Wars before Disney owns an incredible library what should your advice what would your advice be to a company like Disney should they take your open source software train their own models or work with you to train their own models to that no longer happening but they officially licensed on the output some characters so how do you think about those major IP holders what's your advice to them are you in discussions with them we know about the Martin Scorsese or Tor deal but how do you think about content libraries I think it is look I think like the interesting aspect of this technology and then I think when it comes to IP what we implement for example on our public facing tools is you cannot generate certain IP with these models right and I attractive value proposition what do you think that will look like for consumers in another couple of years what would potentially happen when you open up Disney plus I mean that's a good question I'm not I'm not in Disney right so it's up to them to decide that but I it's becoming more interactive I can envision like a whole bunch of like very interesting interactive content creation tools that you could host on Disney plus or elsewhere I think the Lucas said as long as you're not doing it commercially you're not selling it I give you permission to go make Jedi movies and they popular on YouTube Star Wars Stories Untold is I think the biggest one it's getting millions of views per video already and I think that's really the future is letting the customer base pay a licensing fee or pay a fee maybe rent software or maybe based on the output and let them be creative with the characters let them make their own stories and you could be in a unique position to but then also can enable like the super creative customization I think that's great yeah I mean like I mean like for myself like I when I read a book or whatever like watch the movie I like so many ideas how it could be done differently or this could have happened right this is like it's so nice that you can actually enable people to visualize these ideas yeah it's going to be incredible continued success with it you have an office in San Francisco you're hiring people yeah we do yeah we raised a bunch of money we raised a bunch of money we just crossed 100 engineers who want to be working with the customers to you know develop these like customized physical AI solutions or for example with like a IP owner like develop these models jointly with them we are looking for engineers who have experience in just like large scale compute infra managing that and making sure that the looking for people who have interest in you know like getting the technology out there in the hands of people the forward deployment of this there's just so many great ideas and so many great partners for you I think you're going to with the open source specifically you know it seems like the corporates really want to have some additional level of pleasure