Open Source Wins, AGI Is Here, and Scorsese's AI Toolkit with CEOs of Cerebras & Black Forest Labs

(0:00) The AI Buildout: Datacenters Bigger Than Cities (Andrew Feldman) (1:50) Reasoning, Inference, and Breaking Moore's Law (16:28) Open Source, AI Sovereignty, and the Road to AGI (40:54) The Innovation Behind Generative Video (Robin Rombach) (47:31) Martin Scorsese, Robots, and the Future of Hollywood IP Thanks to our partners for making this possible! AppLovin Ads - AppLovin's AI advertising platform reaches over a billion daily active users across mobile games. Full-screen video ads with a 35-second median watch time. Advertisers are profitably spending hundreds of thousands of dollars a day and advertiser access is still in closed beta. The window is open at https://applovin.com/ALLIN Nasdaq - Positioned at the nexus of technology and the capital markets, Nasdaq provides premier platforms and services for global capital markets and beyond with unmatched technology, insights and markets expertise. https://www.nasdaq.com/convergence-economy Follow Andrew: https://x.com/andrewdfeldman Follow Robin: https://x.com/robrombach Follow the besties: https://x.com/chamath https://x.com/Jason https://x.com/DavidSacks https://x.com/friedberg Follow on X: https://x.com/theallinpod Follow on Instagram: https://www.instagram.com/theallinpod Follow on TikTok: https://www.tiktok.com/@allin Follow on LinkedIn: https://www.linkedin.com/company/allinpod Intro Music Credit: https://rb.gy/tppkzl https://x.com/yung_spielburg

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Jason Calacanis?

  We are in the race for super intelligence and Andrew Feldman is back. And obviously CEO and founder of Cerebras, 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.

• 0:22Andrew Feldman?

  That was fun.

• 0:22Jason Calacanis?

  At Liquidity.

• 0:23Andrew Feldman?

  That was really, that was really fun.

• 0:25Jason Calacanis?

  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, you know, the Great Wall of China.

• 0:41Andrew Feldman?

  Right, who knows since?

• 0:42Jason Calacanis?

  The pyramids.

• 0:43Andrew Feldman?

  Right.

• 0:43Jason Calacanis?

  I mean, it feels like the amount of capital, time and intelligent people on the planet dedicating themselves to the build out of something. Um, I can't think of anything in our lifetimes with perhaps, you know, before our lifetimes, the war effort.

• 1:00Andrew Feldman?

  Right.

• 1:01Jason Calacanis?

  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.

• 1:15Jason Calacanis?

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• 1:49Jason Calacanis?

  Maybe you could just enlighten us in 2026, what is Cerebras doing? And what is happening with this build out out in Texas? These are some gigantic, gigantic efforts.

• 2:02Andrew Feldman?

  The, 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.

• 2:16Jason Calacanis?

  Right.

• 2:16Andrew Feldman?

  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 fifty years on earth took.

• 2:27Jason Calacanis?

  Wow.

• 2:27Andrew Feldman?

  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, building out data centers of size, Armenia, everybody's sort of focused.

• 3:00Jason Calacanis?

  Every country, um, and every state, obviously in America, feels they need to participate in this. And the people who are buying the capacity, the OpenAI's Anthropic, SpaceX, I, SpaceX AI, uh, the Googles, they are insatiable right now.

• 3:20Andrew Feldman?

  Yeah.

• 3:22Jason Calacanis?

  And they're building, how many years out, when you talk to them, they were ordering chips from Cerebras before you were finished with the chips. They're putting orders in ahead of time.

• 3:34Andrew Feldman?

  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 twenty-five billion dollar backlog.

• 3:53Jason Calacanis?

  Twenty-five billion dollar backlog.

• 3:56Andrew Feldman?

  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.

• 4:17Jason Calacanis?

  Right.

• 4:17Andrew Feldman?

  How do we keep them from leaving? Right? And, and that, that's extremely unusual.

• 4:22Jason Calacanis?

  It's very unusual and now we have people who are, you know, we have a term for a token maxing.

• 4:29Andrew Feldman?

  Yeah.

• 4:30Jason Calacanis?

  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.

• 4:46Andrew Feldman?

  Yeah.

• 4:47Jason Calacanis?

  But there's also massive experimentation.

• 4:49Andrew Feldman?

  Oh, for sure. You know, I, what, I liken this to when we first started with AWS and it was so good to get around your own IT organization.

• 5:01Jason Calacanis?

  Right.

• 5:02Andrew Feldman?

  That you told every engineer, yeah, go ahead, put on your credit card, sign up.

• 5:05Jason Calacanis?

  Yeah.

• 5:06Andrew Feldman?

  Right? And a lot of it was really useful and some of it was like, God, I wish we didn't do that.

• 5:11Jason Calacanis?

  Yeah.

• 5:11Andrew Feldman?

  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. And, you know, it was the same, I remember when Costco opened up in, in, in the Palo Alto area in nineteen eighty-eight and people used to shop Costco like they shop Safeway. They'd go down every aisle.

• 5:34Jason Calacanis?

  Yes.

• 5:34Andrew Feldman?

  And that's a horrible way to shop Costco because you end up with four things you didn't need and each was twenty-two dollars, right? And as people got more sort of accustomed to it, you go to the back, you get the chicken.

• 5:46Jason Calacanis?

  Yeah.

• 5:46Andrew Feldman?

  Eighteen cupcakes for the kid's birthday party, bang, you were out.

• 5:49Jason Calacanis?

  Strategic.

• 5:50Andrew Feldman?

  Strategic. And it's exactly the same. I think at first people opened up and said, everybody as much tokens as you want. And the, 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.

• 6:19Jason Calacanis?

  And we're really seeing a certain type of person emerge who knows how to deploy this technology. Systems thinking.

• 6:28Andrew Feldman?

  Yes.

• 6:29Jason Calacanis?

  Which developers kind of have innately, CEOs tend to be great strategists and understand systems. Uh, but the, 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.

• 6:55Andrew Feldman?

  Right.

• 6:55Jason Calacanis?

  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 server'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.

• 7:14Andrew Feldman?

  One of, one of my colleagues twenty years ago, a really smart, smart computer scientist said, computers are really dumb. They, they do exactly what you tell them.

• 7:25Jason Calacanis?

  Yeah.

• 7:26Andrew Feldman?

  And at first prompting was like that, right? You modify your prompt a little bit and it changed the answer.

• 7:32Jason Calacanis?

  Dramatically.

• 7:33Andrew Feldman?

  Dramatically. And increasingly, it's understanding what your intent was.

• 7:38Jason Calacanis?

  Right.

• 7:38Andrew Feldman?

  Right? And if you, if you have a chance to, to, to play with Fable or, or five, six from, from OpenAI, increasingly what, you, you don't have to get the prompt just right. You don't have to be a prompt whisperer. Instead, you ask it and it says, well, here are some things and, and by the way, maybe you wanted the chart to, to, 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, and so it's, it's understanding intent and that's a huge leap.

• 8:09Jason Calacanis?

  Which, if we were sitting here two years ago, the idea, we would never have been able to predict in a short twenty-four months that we go from being a great summarizer researcher of web results.

• 8:23Andrew Feldman?

  Right.

• 8:24Jason Calacanis?

  To actually understanding your intent and then providing a solution and abstracting it all from you.

• 8:29Andrew Feldman?

  That's right. That's right.

• 8:30Jason Calacanis?

  Which is a very weird thing. I don't know if you've played with the Hermes agent yet.

• 8:34Andrew Feldman?

  Yeah.

• 8:34Jason Calacanis?

  Have you played with it yet? I mean, I asked it just this morning and I, I was given a secret BitTensor project that has the new, um, ZAI's, uh, model, five-two. And they gave me.

• 8:51Andrew Feldman?

  GLM five-two, yeah.

• 8:52Jason Calacanis?

  GLM five-two. So somebody in that BitTensor, I think you understand BitTensor, you've heard of it, the distributed, um, crypto project. And so they have all this extra capacity. I was, a whisperer told me, 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.

• 9:36Andrew Feldman?

  That's a reasoning model. You were watching a reasoning model work out.

• 9:40Jason Calacanis?

  Yeah.

• 9:41Andrew Feldman?

  Isn't that interesting? I mean, that's amazing.

• 9:44Jason Calacanis?

  And it was collapsed. So as a civilian.

• 9:49Andrew Feldman?

  Right.

• 9:50Jason Calacanis?

  Who doesn't hit the uncollapce moment, and if you were using ChatGPT three point five or you were using four point eight, whatever it was, and you haven't used this new level of reasoning and inference and unlimited compute essentially.

• 10:04Andrew Feldman?

  Right.

• 10:05Jason Calacanis?

  It opened my eyes just this morning of what a world of unlimited tokens might look like.

• 10:11Andrew Feldman?

  Right.

• 10:12Jason Calacanis?

  Because unlimited tokens, I believe means unlimited reasoning.

• 10:15Andrew Feldman?

  It does.

• 10:17Jason Calacanis?

  What does that mean?

• 10:19Andrew Feldman?

  Yeah, it's, uh, I mean, if you run these for 25 or 48 hours, you get amazing things now. And what if by using Cerebras, we were 15 times faster and then you ran it for 24 hours? Right? And you've got weeks or months worth of thinking.

• 10:38Jason Calacanis?

  Yeah.

• 10:38Andrew Feldman?

  And I mean, it is, uh, 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.

• 10:48Jason Calacanis?

  Right.

• 10:49Andrew Feldman?

  Right? And I think when you look back, you say to yourself, holy crap. Those guys

• 10:55Jason Calacanis?

  They knew.

• 10:55Andrew Feldman?

  Saw it.

• 10:56Jason Calacanis?

  Yeah, they were, they could see around the corner.

• 10:58Andrew Feldman?

  That's right.

• 10:58Jason Calacanis?

  And the rest of us were like, what?

• 11:00Andrew Feldman?

  I'm not sure.

• 11:01Jason Calacanis?

  It's when we had Sam on All In, uh, at one point, he, uh, and he said, oh, you know, I'd love to come on at some point. I said, sure, come on. And he, he was talking about it. 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.

• 11:31Andrew Feldman?

  Right, right, right.

• 11:32Jason Calacanis?

  Fill the sentence in, you know, summarize this PDF. Now, Cerebras is at the center of this because this reasoning is inference.

• 11:41Andrew Feldman?

  This reasoning is inference and it's computationally intensive.

• 11:44Jason Calacanis?

  Right.

• 11:44Andrew Feldman?

  Right? And so fast compute makes this sort of work

• 11:49Jason Calacanis?

  Yeah.

• 11:49Andrew Feldman?

  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, that, that this reasoning consumes a huge amount of tokens internally that allows a blisteringly fast machine like ours. And I, I brought one

• 12:08Jason Calacanis?

  Oh, you got, yes.

• 12:09Andrew Feldman?

  I've never far without, you know, when one costs half a billion to make, you, you bring it everywhere with you.

• 12:13Jason Calacanis?

  Uh, we, we were, um, we were tossing this back and forth at Davos. Uh, what's the model number of this one?

• 12:21Andrew Feldman?

  This was in the first eight or 10.

• 12:23Jason Calacanis?

  Got it. So this has a special place.

• 12:25Andrew Feldman?

  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's in his bedroom at night. I, I carry him with me.

• 12:32Jason Calacanis?

  I, I mean, when you have, um, you know, your, your next party at the house, I highly recommend just a little hors d'oeuvres.

• 12:39Andrew Feldman?

  A little hors d'oeuvres.

• 12:40Jason Calacanis?

  I, I think it'd be like a great bit. It would be a great bit if you had some.

• 12:44Andrew Feldman?

  That's right.

• 12:45Jason Calacanis?

  Uh, but what we're looking at here is the ability to do that reasoning at scale. And

• 12:53Jason Calacanis?

  what is Moore's Law for inference and for Cerebras? Do you have something internally you discuss as we're going to double this every X time period?

• 13:06Andrew Feldman?

  So all chips prior to us in the processor world followed Moore's Law.

• 13:12Jason Calacanis?

  Got it.

• 13:13Andrew Feldman?

  And we broke it.

• 13:14Jason Calacanis?

  Doubling every 18 months.

• 13:15Andrew Feldman?

  Doubling about every 18 months.

• 13:17Jason Calacanis?

  Got it.

• 13:17Andrew Feldman?

  Um, and we crushed it with this chip and we've carved out a whole new trajectory. And, uh, my view is in the next 18 months will be way over 2X.

• 13:30Jason Calacanis?

  Interesting.

• 13:31Andrew Feldman?

  And so, uh, I, I think that, uh, early in an architecture, you have room to, to do much better than what was traditionally Moore's Law. Now, if you've got a 20-year-old architecture, like the GPU is much harder.

• 13:44Jason Calacanis?

  Right.

• 13:45Andrew Feldman?

  You, 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, to learn about the, the work that, that is being presented and make optimizations that, that give you huge gains.

• 14:04Jason Calacanis?

  How, how do you run the company? Like, just being the CEO now, in the age of AI, um, you have $25 billion in demand. You have to, you have to deploy at an, just an incredible blistering pace. You have to hire people. You have to create a roadmap. I don't mean to give you a panic attack here.

• 14:25Andrew Feldman?

  You have to keep up with somebody like OpenAI who's moving so unbelievably quickly.

• 14:29Jason Calacanis?

  Yes.

• 14:30Andrew Feldman?

  Right? And their, their competitor, you gotta keep up.

• 14:32Jason Calacanis?

  Right.

• 14:33Andrew Feldman?

  Right? Your hardware, your software, your deployments have to keep up with some of the fastest moving organizations in history.

• 14:40Jason Calacanis?

  They're demanding customers.

• 14:42Andrew Feldman?

  They are not, they, they, they're not pushovers for sure.

• 14:45Jason Calacanis?

  Yeah. And also potentially competitors down the road?

• 14:50Andrew Feldman?

  Look, I, I think there is so much demand right now that, that there is no silicon that will go unused.

• 14:58Jason Calacanis?

  Yeah. But why is an OpenAI releasing jalapeno? Why is Amazon making their own chips? You, 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, we need good pricing. Is it, is it a little bit of a flex that way? Or is that the future that they're going to be in your business?

• 15:22Andrew Feldman?

  No, I, I, I think nobody likes being dependent. And I, I think some of the lessons learned by the, the hyperscalers of the x86 world is they were dependent on Intel. And, uh, some of the lessons learned by, uh, the GPU makers was they were dependent on a small number of hyperscalers.

• 15:45Jason Calacanis?

  Yeah.

• 15:46Andrew Feldman?

  And they wanted more customers. And so they set about to, to help fund these Neo clouds. And so I, I think mostly it's about, uh, an opportunity to control at least an important part of your destiny.

• 15:59Jason Calacanis?

  Got it.

• 15:59Andrew Feldman?

  And I, I think that's a very reasonable thing. I think you don't have to sort of make the fastest chip. You, you just can't be entirely dependent on other people's chips.

• 16:08Jason Calacanis?

  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 OpenClaw and quickly started using Kimi and was like, wait a second. I'm blowing out my Claw tokens, but this Kimi, I can't tell the difference. And then we started smart routing it and suddenly this open source started to figure out reasoning and the gap has suddenly closed this year.

• 16:43Andrew Feldman?

  Well, I, you know, you, you, you don't want to take your, your Ferrari to the grocery store. Right? You, you, you, there, there are times you want to drive your fun car.

• 16:52Jason Calacanis?

  Yeah.

• 16:53Andrew Feldman?

  Right? And there are times you, you want to throw the kids in and, and don't worry if there are Cheerios on the floor.

• 16:58Jason Calacanis?

  Minivan time.

• 16:58Andrew Feldman?

  Right. There's minivan time. And, and, and I think that as the sort of sophistication of the user grows, right? You, you're going to have hard problems and those are going to be frontier model problems. There are going to be OpenAI problems. There are going to be Anthropic problems, maybe Gemini problems. And behind that, there are 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 of workday and getting it in a different cell for?

• 17:25Jason Calacanis?

  Yeah.

• 17:26Andrew Feldman?

  All right. Think about

• 17:27Jason Calacanis?

  The, the cutting and pasting economy is real.

• 17:29Andrew Feldman?

  That's right. And, and this doesn't need, right? Gold medal math.

• 17:34Jason Calacanis?

  No.

• 17:34Andrew Feldman?

  What, what this needs is sort of rock solid open source capabilities.

• 17:39Jason Calacanis?

  Yeah.

• 17:40Andrew Feldman?

  And if you think about what, I mean, well, we've been thinking a lot about it in GNA, but a huge amount of GNA. All right. Is not invention. Right? And you, you may not need sort of the most sophisticated agents for this.

• 17:56Jason Calacanis?

  And another card that's turned over recently is some folks, uh, maybe have concerns with the ambition of the frontier models and maybe sharing their data, lead data leakage and 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.

• 18:28Andrew Feldman?

  You have it on prem domestically and we'd like in an open source version where we have, uh, a little bit more control.

• 18:35Jason Calacanis?

  Yeah.

• 18:36Andrew Feldman?

  And I, I think

• 18:37Jason Calacanis?

  Are you seeing that now?

• 18:38Andrew Feldman?

  We are seeing that for sure. And I, I think OpenAI made a good call releasing OSS 120B some months back. That was a good open source model. Um, 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.

• 18:57Jason Calacanis?

  Nvidia has some.

• 18:58Andrew Feldman?

  Nvidia has seen the same opportunity

• 19:01Jason Calacanis?

  Yes.

• 19:01Andrew Feldman?

  to push open source models. I, I, I think giving them more power might, might be sort of

• 19:07Jason Calacanis?

  Well, I was about to, that was, you, you cut me off at the pass. Like it, my understanding was Jensen was like, hey, we, we don't even want to talk about these open source models we have because our customers

• 19:18Andrew Feldman?

  Right.

• 19:19Jason Calacanis?

  we're now going to be competing with Sam, Dario, Ewan, uh, Sergey. Like, do we want to be in that position?

• 19:27Andrew Feldman?

  Right.

• 19:27Jason Calacanis?

  So, but we do need some more champions here and it's open source. So people can fork it. Um, but that puts you in a more neutral position.

• 19:35Andrew Feldman?

  That's right. We, we, we run today, we run GLM, we run Kimi, we run the Quen set of models and we run OpenAI's models, the closed source ones. We run models for say GalaxoSmithKlein, which they wrote and developed. Um, we run models for, uh, our partner, uh, in the UAE, uh, G42 and MBZ UAI.

• 19:58Jason Calacanis?

  Yeah.

• 19:58Andrew Feldman?

  Um, that are, are, are their models that they designed. So we, we have a, a, a wide variety.

• 20:05Jason Calacanis?

  So sovereignty is a trend.

• 20:06Andrew Feldman?

  Sovereignty is a trend. And I think, uh, the, the government's actions with regard to Fable and, uh, uh, 5.6, um, where they said, oh, whoa, let's think. And then we can act. Um, I, I think sort of particularly here in Europe was a bit of a wake up call.

• 20:27Jason Calacanis?

  And when you saw this going down.

• 20:30Jason Calacanis?

  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, have 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 that if you were to rate Dario's, not communication, because he's a very effervescent communicator. I think is a diplomatic way to say it.

• 21:12Jason Calacanis?

  But to have a scheduled, rolled-out release.

• 21:16Andrew Feldman?

  Right.

• 21:17Jason Calacanis?

  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 it's, there was actually a major threat there?

• 21:25Andrew Feldman?

  So what's interesting is I hadn't seen it before.

• 21:30Andrew Feldman?

  Right? And I, I think, you know, 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, 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.

• 21:53Jason Calacanis?

  Yeah.

• 21:53Andrew Feldman?

  Now, this doesn't seem unreasonable to me.

• 21:55Jason Calacanis?

  Not at all.

• 21:55Andrew Feldman?

  Right? I mean, we, we do this with powerful pharmaceuticals. Right? We, we'd 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 government. So we know our defenses can block this.

• 22:22Jason Calacanis?

  Yeah. Have we checked?

• 22:23Andrew Feldman?

  Have we checked?

• 22:24Jason Calacanis?

  The infrastructure of the country, like?

• 22:26Andrew Feldman?

  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 me an unreasonable thing for the government to ask.

• 22:39Jason Calacanis?

  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?

• 22:53Andrew Feldman?

  I think the polarization hurts a great deal. It hurts clear thinking. Right? It, it hurts clear thinking and, and both sides are going to do some dumb things and some really smart things.

• 23:03Jason Calacanis?

  Right.

• 23:04Andrew Feldman?

  Right? And in fact, what I found is that the people in the government are trying really hard.

• 23:11Jason Calacanis?

  The rank and file.

• 23:12Andrew Feldman?

  The rank and file are trying really hard. And this is moving fast. And I, I think that, that an ability to, to set aside some, some of the polarization and say, how do we do this in a reasonable manner? I mean, we, we want Dario and Sam competing like crazy.

• 23:30Jason Calacanis?

  A hundred percent. It's been awesome to watch.

• 23:32Andrew Feldman?

  It's awesome.

• 23:33Jason Calacanis?

  Yeah.

• 23:33Andrew Feldman?

  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, what you can continue to achieve.

• 23:42Jason Calacanis?

  Right.

• 23:43Andrew Feldman?

  Right? This is a drive.

• 23:44Jason Calacanis?

  Kick Google in the ass, made them get sharper, Amazon started to wake up.

• 23:48Andrew Feldman?

  Everybody got better because of that. We want that. And we certainly don't want to become sort of a region where the first thing we want to do is regulate it.

• 23:55Jason Calacanis?

  Right.

• 23:55Andrew Feldman?

  Right? But as it gets more powerful.

• 23:59Jason Calacanis?

  And the industry really should do a better job of regulating itself, perhaps. And, and it did seem like they were starting that process, but then the communication was lacking, maybe.

• 24:10Andrew Feldman?

  Yeah, it's, you know, I, I think not only are they racing hard, but they're inventing this as they go too.

• 24:18Jason Calacanis?

  Yeah.

• 24:19Andrew Feldman?

  Right? There's not a playbook.

• 24:20Jason Calacanis?

  No.

• 24:21Andrew Feldman?

  Right? They're inventing the, we, we say, oh, just put on guardrails. Well, they have to design the guardrails.

• 24:25Jason Calacanis?

  Sure.

• 24:26Andrew Feldman?

  Right? They, the guardrails have an impact. You know, one of the things that Fast does is it makes the guardrails less painful. And so that, that is, we, we, you know, we discovered that in the last six weeks.

• 24:37Jason Calacanis?

  Yeah.

• 24:37Andrew Feldman?

  Is that the very guardrails can add time and make it feel slower. And so fast chips like ours can really help that. But so they're racing against competition. They're racing against their own sense of greatness.

• 24:52Jason Calacanis?

  Yeah.

• 24:52Andrew Feldman?

  Right? Which is maybe even the biggest driver here. And I think that they're earnest, trying to think about how to do the right thing. And all of those are, are mixed in this bucket. And, and sometimes you're on one side rather than the other.

• 25:10Jason Calacanis?

  And, yeah, and as you're saying, this is a first time, right?

• 25:14Andrew Feldman?

  That's right.

• 25:14Jason Calacanis?

  When we, when 3.5 came out, it wasn't like when we were using ChatGPT 2.5, 3.5, it was taking down networks.

• 25:21Andrew Feldman?

  Right.

• 25:21Jason Calacanis?

  But in talking to Nikesh from Palo Alto Networks, I asked him like, hey, well, how would you grade this? And he said, oh, we put it against our software and we found bugs we were not aware of.

• 25:33Andrew Feldman?

  Yes, it killed them.

• 25:35Jason Calacanis?

  Yeah. He said, we had to stop everything we're doing and do patches for six weeks.

• 25:39Andrew Feldman?

  Right. And, and that's when you know, right? I mean, Nikesh leads, you know, maybe the leading security software firm, right? And when it finds in an hour, right? Tens of critical opens, you're like, whoa, this is a powerful tool. And we need to think. And maybe you, you show it to a, a group first. Right? Maybe you, I, I don't know what the right thing is, but.

• 26:05Jason Calacanis?

  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.

• 26:16Andrew Feldman?

  That's right.

• 26:17Jason Calacanis?

  You know?

• 26:17Andrew Feldman?

  Right.

• 26:17Jason Calacanis?

  And there's like two other betas that you don't even get the chance to opt into as consumers.

• 26:22Andrew Feldman?

  Right.

• 26:22Jason Calacanis?

  Those ones are for security. Those ones are for, you know, making sure you don't lose your data or data that's in the lead.

• 26:28Andrew Feldman?

  That's right. Disappear or leak or.

• 26:30Jason Calacanis?

  Corruption, yeah. Any, any number of these things.

• 26:33Andrew Feldman?

  I think we can also know that, that there will be a massive data leak.

• 26:39Jason Calacanis?

  Of course.

• 26:39Andrew Feldman?

  We, we know this.

• 26:40Jason Calacanis?

  Yeah.

• 26:40Andrew Feldman?

  Right? And it's like Warren Buffett talked about the reinsurance industry, that you know something bad's going to happen. You don't know when.

• 26:48Jason Calacanis?

  Yeah.

• 26:48Andrew Feldman?

  But you got to save up for it, right? You put money away for insurance. But there will be a tornado. There will be a massive earthquake. I mean, we, we know this. And we can do our best to plan, but there'll be a massive breach. And there'll, there'll be, and we, 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 it's going, something's going to happen.

• 27:17Jason Calacanis?

  Something will happen.

• 27:19Andrew Feldman?

  Right.

• 27:19Jason Calacanis?

  Yeah. And yeah, it's typically a black swan, right?

• 27:23Andrew Feldman?

  That's right.

• 27:23Jason Calacanis?

  By definition, it's going to be something we didn't consider or a question we didn't know to ask.

• 27:28Andrew Feldman?

  Right. But, but even knowing that there's some unknown unknowns is a useful place to start.

• 27:33Jason Calacanis?

  Yeah. What are we not asking ourselves?

• 27:35Andrew Feldman?

  That's right.

• 27:36Jason Calacanis?

  With reasoning, the AI is going to be able to tell us, hey, schmuck humans.

• 27:40Andrew Feldman?

  That's right.

• 27:41Jason Calacanis?

  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 example. And then I always say at the end, please check your work.

• 27:56Andrew Feldman?

  Right.

• 27:57Jason Calacanis?

  And then tell me what I haven't considered in terms of my goals. And give me, ask me some questions every time you run the job. And that has changed everything because it's like, I checked my work. By the way, this was incorrect.

• 28:11Andrew Feldman?

  Right.

• 28:12Jason Calacanis?

  And I'm wondering, hey, would you like me to also do this? And some of the tools like perplexity do that automatically. They give you your next three prompts.

• 28:19Andrew Feldman?

  Right.

• 28:20Jason Calacanis?

  But if you give it explicit instructions, my Lord, is it good at that.

• 28:24Andrew Feldman?

  So, you know, over the course of the last 10 years, as I was raising money, I, I thought one of the smarter questions I got at the end of a conversation where someone asked, what was the smartest question you heard that, that wasn't covered by what I asked?

• 28:38Jason Calacanis?

  Incredible.

• 28:39Andrew Feldman?

  Right? Now, now that's somebody who's curious and thinking and humble and trying to sort of use this to get a picture of, of the space. And to the extent that you can ask the AI that, and, and that it can sort of broaden your, your view. You know, maybe what question should I have asked?

• 28:58Jason Calacanis?

  Yeah.

• 28:58Andrew Feldman?

  To be an expert in this, what, what, what would a, a PhD level, a, a question or ask of this? Or a gold medal math? I mean, I, I think those are sort of questions that you, you know, you don't even know how to ask.

• 29:14Jason Calacanis?

  Which, you know, if we start thinking about AGI and superintelligence, you know, they're just definitions, but they're important definitions, I think, to kind of keep in mind because they're waypoints.

• 29:27Andrew Feldman?

  That's right.

• 29:28Jason Calacanis?

  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, we have artificial general intelligence now. It feels like when we're talking about these reasoning moments and, you know, the, the, the ability for it to be as smart as any human, but let's talk about.

• 29:49Andrew Feldman?

  By any definition we had 20 years ago, we've hit it.

• 29:52Jason Calacanis?

  Yes.

• 29:52Andrew Feldman?

  Right? I mean, if you think about it, oh, there was a Turing test, blew it away.

• 29:55Jason Calacanis?

  Yeah.

• 29:56Andrew Feldman?

  I mean, you think about that, that any period of time, sort of 10, 15, 20, 30, 40, 50 years ago, we, we, we, any definition we would have previously put forward.

• 30:06Jason Calacanis?

  Right.

• 30:07Andrew Feldman?

  We've blown past it. And so.

• 30:08Jason Calacanis?

  Which goes back to our previous point of like, do we know the questions to ask?

• 30:11Andrew Feldman?

  That's right.

• 30:12Jason Calacanis?

  20 years ago, science fiction authors, you know, had their say and we answered all their questions.

• 30:19Andrew Feldman?

  Right.

• 30:20Jason Calacanis?

  If they were to look at this today, they'd be like, well, I, I'm out of.

• 30:23Andrew Feldman?

  I'm out of questions.

• 30:23Jason Calacanis?

  I'm out of questions. Sorry.

• 30:25Andrew Feldman?

  That's where sort of the, sort of listening to people who we, who sound sometimes like they're on the fringe. Right? When, when, when Ilya was talking eight or 10 years ago about the need for safety and then, and you're like, what? Dead right.

• 30:43Jason Calacanis?

  Yeah.

• 30:43Andrew Feldman?

  Right? When, when Elon was talking about building rockets and driving the cost to, to, to near zero of, 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.

• 31:00Jason Calacanis?

  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.

• 31:09Andrew Feldman?

  That's right.

• 31:10Jason Calacanis?

  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 trending,

• 31:20Andrew Feldman?

  Right.

• 31:21Jason Calacanis?

  It kept picking up loop, looping, and it kept picking up the maxing stuff. And it created a buzzword for me, loop maxing.

• 31:27Andrew Feldman?

  Right.

• 31:27Jason Calacanis?

  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 superintelligence. And do you have a way, Andrew, that you think about superintelligence and what? It will mean for humanity and how we will define it and how we'll experience it. Yeah.

• 31:52Andrew Feldman?

  I think let's begin on on on loop maxing or sort of recursive learning. I think.

• 32:02Andrew Feldman?

  I think what what what Sam and Ilya and then later Dario and and Dennis saw.

• 32:11Andrew Feldman?

  Six years ago or five years ago was that.

• 32:16Andrew Feldman?

  Powerful recursive gains. Are exponential. Right? You get better, you do it again. And if you continue to get gain, the the the slope of that curve is so steep.

• 32:30Jason Calacanis?

  Yeah.

• 32:31Andrew Feldman?

  And that we're just beginning to see that now. You ask it a question, you learn from the results, you ask it to do it again. It that results get better and more information is added, your answer gets better, you ask it to do again, it covers more material. And the the sort of loops are producing sort of not a little bit better answers, but vastly better answers.

• 32:54Jason Calacanis?

  Yeah.

• 32:55Andrew Feldman?

  And that is enormously powerful because we don't quite know where it ends.

• 33:00Jason Calacanis?

  Right.

• 33:01Andrew Feldman?

  You keep throwing compute at it. I mean, how much better does the answer get? You know, we we run out of tokens or our budget or or but but holy cow, I mean, when does the exponential stop? Or does the answer keep going up and up and up to the right?

• 33:15Jason Calacanis?

  Yeah.

• 33:15Andrew Feldman?

  And that's sort of an enormously interesting intellectual question right now.

• 33:20Jason Calacanis?

  Yeah, like, when do we run out of problems to solve and?

• 33:25Andrew Feldman?

  Well, that's right. And when are the the problems? No longer sort of intellectual problems and they're now people problems.

• 33:34Jason Calacanis?

  Yeah.

• 33:34Andrew Feldman?

  Right? How to organize people to to get done with the AI asked for. Right? I mean, as you know, in running your company, a lot of your problems aren't hard intellectual problems, they're people working together problems.

• 33:47Jason Calacanis?

  Yeah.

• 33:47Andrew Feldman?

  Right?

• 33:48Jason Calacanis?

  And motivation.

• 33:49Andrew Feldman?

  Motivation. You spend a lot of time as a leader spraying WD-40 on your team.

• 33:53Jason Calacanis?

  Right.

• 33:54Andrew Feldman?

  Right? It just so so friction is reduced and.

• 33:59Andrew Feldman?

  How do we learn about those from AI? Right? How do we get behavioral insight? For 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.

• 34:13Jason Calacanis?

  Yeah, we didn't even get to that. This is going to be for another interview, but. When these things jump off the screens.

• 34:20Andrew Feldman?

  Right.

• 34:21Jason Calacanis?

  And they're in the real world. And the recursiveness starts. Not trying to solve math problems and.

• 34:26Andrew Feldman?

  Right.

• 34:27Jason Calacanis?

  You know, humanities, most difficult ones, but. Hey, you know, there's an incredible world out here and here's the Palace of Versailles.

• 34:34Andrew Feldman?

  Right.

• 34:35Jason Calacanis?

  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 100 acres somewhere out in Texas or Nevada. I'll just send 1,000 optimists is out there.

• 34:48Andrew Feldman?

  Right.

• 34:48Jason Calacanis?

  Make me the Palace of Versailles.

• 34:49Andrew Feldman?

  Right.

• 34:50Jason Calacanis?

  Sounds fantastical, but the Palace of Versailles would seem fantastical to people who lived 1,000 years before it.

• 34:56Andrew Feldman?

  And and it was fantastic, I think, to the people who built it. Right? Even to the builders, I I think they were awed. At it as they built it.

• 35:05Jason Calacanis?

  Yeah, they're compounding, they're compounding recursive learning.

• 35:08Andrew Feldman?

  That's right.

• 35:09Jason Calacanis?

  And and generations, we talked about, you had a really such a great insight of in building this place, you had generations of masons.

• 35:16Andrew Feldman?

  Yeah, I I think in in all these large projects. Often there were families. Who were specialists who you and you you apprenticed under 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 stone mason family working on the same structure.

• 35:39Jason Calacanis?

  And passing on the learnings.

• 35:40Andrew Feldman?

  Passing on the learnings.

• 35:42Jason Calacanis?

  New innovations.

• 35:43Andrew Feldman?

  Right.

• 35:44Jason Calacanis?

  Which is what we've modeled. With this new.

• 35:48Andrew Feldman?

  That's right.

• 35:48Jason Calacanis?

  Models and and and what you're building in the infrastructure. It's pretty incredible when you think about it.

• 35:54Andrew Feldman?

  Especially when we're sitting here in.

• 35:56Jason Calacanis?

  But the pace.

• 35:57Andrew Feldman?

  And that's what, I mean, I I think the. The problem with human learning. Is. It often moves it at the pace of a generation. And like.

• 36:11Andrew Feldman?

  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 you you want them happening more like Drosophila, like fruit fly, you want two a day.

• 36:24Jason Calacanis?

  Yeah.

• 36:24Andrew Feldman?

  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 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.

• 36:43Jason Calacanis?

  Darwin would be.

• 36:46Jason Calacanis?

  In awe of this. Pace of evolution.

• 36:50Andrew Feldman?

  That's exactly right.

• 36:51Jason Calacanis?

  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.

• 37:09Andrew Feldman?

  They don't.

• 37:09Jason Calacanis?

  People do.

• 37:10Andrew Feldman?

  That's right.

• 37:12Jason Calacanis?

  That's how Freud.

• 37:13Andrew Feldman?

  He and Thomas Kuhn, that's right.

• 37:15Jason Calacanis?

  Freud and Skinner and Jung, like, it took them dying.

• 37:19Andrew Feldman?

  That's right.

• 37:20Jason Calacanis?

  For the next paradigm, the new generation.

• 37:22Andrew Feldman?

  Question it. And that was 20 years. Sometimes 40 years.

• 37:26Jason Calacanis?

  Right.

• 37:26Andrew Feldman?

  As their students maintained positions of leadership until someone said, maybe we could do it differently? And I I think what you're seeing is this iteration is. Shortening of the. The the intergeneration gap.

• 37:42Andrew Feldman?

  And the learning is so fast.

• 37:44Jason Calacanis?

  It's always so great to talk to you because. One, it's just intellectually.

• 37:52Jason Calacanis?

  So your your approach to it is so intellectually rigorous, but also with so much P doom in the world. I I feel so good that you're such an optimist about this technology and you're building it. With such thoughtfulness and it, 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.

• 38:21Andrew Feldman?

  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.

• 38:34Jason Calacanis?

  Sure.

• 38:34Andrew Feldman?

  There will be. There was dislocation when cars came and and and it was a bad deal to to to be a guy who shooed horses, right, or built carriages.

• 38:42Jason Calacanis?

  Yeah.

• 38:43Andrew Feldman?

  But you got to also against that, you know, make your tea of the cons and the pros.

• 38:48Jason Calacanis?

  Yeah.

• 38:48Andrew Feldman?

  Right? There's a shot that our children, none of them nor their people they love will die of cancer. And that that's one.

• 38:56Jason Calacanis?

  That's but one.

• 38:57Andrew Feldman?

  We can work on with with this technology and we will have great purchase on. And and I think you begin listing those and then it's a more thoughtful discussion.

• 39:06Jason Calacanis?

  Yeah, unlimited energy, unlimited calories, unlimited knowledge, unlimited education, unlimited housing.

• 39:12Andrew Feldman?

  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.

• 39:28Jason Calacanis?

  Yeah.

• 39:29Andrew Feldman?

  They learn better. That's not how we do teaching classes.

• 39:32Jason Calacanis?

  No, factory farming.

• 39:33Andrew Feldman?

  That's right. We we teach to some sort of mid level. Imagine if we built agents that that taught children for their way of learning.

• 39:41Jason Calacanis?

  Right.

• 39:42Andrew Feldman?

  Right? And here's the way we. We've been doing it the same way for 1,000 years and during that entire time, we knew how to do it better and we chose not to.

• 39:49Jason Calacanis?

  Yeah.

• 39:49Andrew Feldman?

  And 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.

• 39:55Jason Calacanis?

  Yeah.

• 39:55Andrew Feldman?

  Writing the good and the bad, I I think it'll come out really well.

• 39:58Jason Calacanis?

  Get out there, Andrew, keep communicating. Your version of the world because some folk 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 abundance.

• 40:15Andrew Feldman?

  I think it'll create abundance, for sure.

• 40:17Jason Calacanis?

  Massive abundance. Andrew.

• 40:18Andrew Feldman?

  Pleasure, always a pleasure to talk to you.

• 40:19Jason Calacanis?

  I'll see you in six months for our checkup.

• 40:21Andrew Feldman?

  That'll be great.

• 40:24Jason Calacanis?

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• 40:52Jason Calacanis?

  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.

• 41:03Speaker 02

  A mountain range, actually.

• 41:04Jason Calacanis?

  A mountain range.

• 41:05Speaker 02

  Yes.

• 41:06Jason Calacanis?

  Where you grew up.

• 41:07Speaker 02

  Where I grew up, yes.

• 41:08Jason Calacanis?

  And you are working on open source image and video models. You worked at Stable Diffusion for a little bit.

• 41:15Speaker 02

  That's correct.

• 41:16Jason Calacanis?

  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 is the goal?

• 41:30Speaker 02

  Hundred percent. One quick addition, we are based in the Black Forest. It's a town called Freiburg and in San Francisco. We have like two offices.

• 41:38Jason Calacanis?

  San Francisco, of course, yeah. You're splitting your time or?

• 41:42Speaker 02

  I'm splitting my time. To a certain degree.

• 41:47Speaker 02

  We yeah, started a company two years ago. Me and my co-founders, as you said, like, we've worked on stable diffusion in the past. Before that, we invented like an algorithm called latent diffusion, which is basically like the fundamental algorithm behind. All of like generative models that are being deployed for image generation, video generation, even like physical AI now.

• 42:08Jason Calacanis?

  Yeah.

• 42:08Speaker 02

  It basically makes use of this principle that you can compress natural data such as images, such as video, such as audio, into a much more like efficient representation. And then train a transformer model on that. And. I mean, this is the stuff where I like. You know, like JPEG, MP3 and all of that works. And we basically translated that into like a neural algorithm. A few years ago, when we were still like PhD students in in Munich, actually. And then build on like on top of that, we built stable diffusion and then on top of that, yeah.

• 42:49Speaker 02

  The generative models that we are developing today, and of course, like the technology has advanced. But we are now tackling, I would say, models that are really made for understanding like 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 it on a robot in the real world.

• 43:27Jason Calacanis?

  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 object.

• 43:51Speaker 02

  I think that's, yeah, I think that's like a really good like way to think about it. It's like, it's like an intuitive way to interact with the world, right? Like, I would say there's like these like complementary forms of intelligence. Ultimately, there's like intuitive intelligence, and then there's like a deep reasoning layer. Now, ultimately, 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 like 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 like we're combining it, it's converging into like a multimodal model. And yeah, we see like, exactly like

• 44:36Speaker 02

  pre-training on videos gives like implicit understanding of the physics of interactions with the real world. And then you can get stuff like action prediction, like robotics out of the same model.

• 44:46Jason Calacanis?

  And with these models and the training, there kind of been a limitation in creating videos and creating images where the criticism of generative AI is it's a bit of a slot machine. I give a prompt, it gives me something back. But how did it come up with that? The training data, but you know, maybe I want a different style, maybe I want a different color, maybe I want a different, you know, aesthetic. Has that, how does that problem get solved? And do you actually understand what's happening

• 45:29Jason Calacanis?

  when the image is being made under the hood? Yeah?

• 45:33Speaker 02

  Yeah, I think like ultimately, it's about like exposing as many like manipulation 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.

• 45:55Jason Calacanis?

  Right.

• 45:55Speaker 02

  Then they've expanded into a text plus image to image systems, which means you could suddenly take an image, like a real image or generated image, and iterate on that based on a text prompt, like edit it, modify it, right? And then this expanded into taking multiple images and a 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 actually even more interesting when like all of these like modalities are actually combined inputs and outputs of the same model.

• 46:29Jason Calacanis?

  So let's talk about video. There's an announcement that you're working with the greatest director of all time, or living director Martin Scorsese. We'll talk about that in a second, yeah?

• 46:39Speaker 02

  Fantastic.

• 46:40Jason Calacanis?

  But in a movie, this promise of being able to make a movie in which the camera angle,

• 46:51Jason Calacanis?

  the sound could be

• 46:55Jason Calacanis?

  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 10 second clips, and then maybe how people struggle making 10 of them, and then they use some post-editing software to put them together. But you immediately understand this is not that. It's not a movie. It's AI slop. It's kluge. It's doesn't pass the uncanny valley.

• 47:39Speaker 02

  Well, I think it's important, and that's at least like the view that we have, is that these AI models, they are a medium, right? They, we don't want to set like 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 his model? Like, he is one of the like greatest filmmakers ever. It was insane sitting in the same room with him multiple times and actually him seeing like exploring our models like as like one of the like core researchers behind it was like just an insane feeling, right? And at the same time, I'm also like a big fan.

• 48:12Jason Calacanis?

  So you sat in a room with Marty Scorsese and showed him your tools?

• 48:16Speaker 02

  Exactly, yeah.

• 48:17Jason Calacanis?

  And what was his reaction? What did he key off of? What was the thing that he found most inspiring or interesting?

• 48:27Speaker 02

  I think it was really this idea of like, he has clearly a vision in his head of like a scene or a scenery where like maybe a new movie will be shot. And he's trying to explore that and kind of like,

• 48:45Speaker 02

  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 ultimately, I think, and that's what he said in the end, is like the like getting like the mental picture of something out of your head and communicating it in a visual way by making like these images or the series of images is something, yeah, that just makes it like easier to communicate and convey like an idea of like what is actually in your head. And I think that's like one of the like very interesting and powerful ways to use this technology. And I think ultimately.

• 49:21Jason Calacanis?

  Is to get the inspiration, to get the vision out of his head onto an image.

• 49:27Speaker 02

  Yeah, I mean, like language ultimately is like a little bit of like a lossy

• 49:32Speaker 02

  communication medium, right?

• 49:33Jason Calacanis?

  Yeah.

• 49:34Speaker 02

  It's also interpreted in different ways, but then visual information is so rich, so rich. 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 think like to your question of making like full movies with,

• 49:55Speaker 02

  I don't know, like a video generation model, for example. I'm not sure if that is like the ultimate goal. Maybe it's like interesting to plug this into like some kind of a genetic workflow and make like a very long video. And I think that's really cool to explore. But I think ultimately, like the real interesting use cases, they come when you have like a human in the loop who iterates and uses it as a medium. And I think this is, this is at least like a perspective that that I take that makes it interesting. And that this is most often when the most interesting outputs arrive or are actually being made.

• 50:27Jason Calacanis?

  The brainstorming production level is so obviously a huge win.

• 50:32Speaker 02

  Yeah, you can paralyze your brainstorming, basically.

• 50:35Jason Calacanis?

  Yeah. And yeah, I like that, paralyze your brainstorming. And they have an analogy for this. They do storyboards. And some of the great directors Ridley Scott of Aliens and Gladiator was known for making his own. I also believe Spielberg was also like 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 obvious place to start. But if we look at startups, startups always want to try to figure out how to do something cheaply. And people used to make a launch video for their startup

• 51:18Jason Calacanis?

  for, you know, a hundred thousand dollars, two hundred fifty thousand dollars. So they take their 10 million dollar 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 or two working with you know, a director to make a launch video. You've probably seen this trend, yeah? And I'm sure people use Flux and some of your models for this. Have you seen this?

• 51:43Speaker 02

  Yeah, of course, yeah.

• 51:44Jason Calacanis?

  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 a fun, engaging, punchy, 30 second, 90 second way, yeah?

• 51:58Speaker 02

  I mean, like, again, like, I think we support this like exploration based on these tools, right? And I think like ultimately, it's great to see like all different kind of like, I don't know, like 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.

• 52:20Jason Calacanis?

  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 Gadot, you know, the woman who played, the actress who played Wonder Woman?

• 52:36Speaker 02

  Oh, right, yeah, of course.

• 52:37Jason Calacanis?

  She, 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 just did a Bitcoin movie. And they did it on a soundstage without green screens. But all the actors just worked in like a soundstage. And then all of the scenery behind them was being done by generative AI. That's a real movie. That's a 30 million dollar 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 backend and the ideation phase, but actually in production yet with your tools?

• 53:19Speaker 02

  Yeah, we see some use cases like that in production. I think like high-end film production is kind of like the one of the like most demanding use cases. And I think I'm glad that it's being explored, but I also really want to.

• 53:33Speaker 02

  Like it's, I think it's important to see that this technology is like on a trajectory and it's improving, it's improving rapidly. I don't know, if I look back at like where we started like a few years ago, when I was doing my PhD in this field, like the only thing that you could do was like images of sixty-four by sixty-four pixels. Now you can do like multi-minute videos, right, at like a high resolution. But it's like, it's not gonna stop there, it's gonna, it's continued to improve and I think like then it's going to unlock like even more of these like high-end use cases, but I think the main thing-

• 54:02Jason Calacanis?

  How soon before we get to that? Yeah.

• 54:04Speaker 02

  Hard to predict, I think, hard to predict and I think ultimately-

• 54:07Jason Calacanis?

  Couple of years.

• 54:09Speaker 02

  Ultimately, I think you still want to have like the tool that enables like this human in the loop kind of-

• 54:14Jason Calacanis?

  Of course, yeah.

• 54:15Speaker 02

  Um, a production workflow, right? But I think, when I look at

• 54:23Speaker 02

  multimodal generative models, um, as a whole, I think what really excites me is you can use the same kind of, um, AI model to make a movie and deploy that as a brain on a robot. Ah, and, and I think this is like, this is so interesting, um, and I don't know, like there's like some thoughts around trying that in the digital world, right, this which would be, for example, computer use remains to be seen if that is actually something that works or not, but I think like the technology is so powerful and so versatile and it's just, just moving into that. You know, all the, all the talk around like world models, world action models, all of that, it's, it's basically all the same.

• 55:06Speaker 02

  And I think that's what's making it so interesting and what I find like most exciting.

• 55:10Jason Calacanis?

  So, do you believe that the technology will be used to analyze, or primarily to analyze real world, like here's a 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 gonna just in some way innately know based on all this massive amounts of training data?

• 55:39Speaker 02

  Um, I think it's a combination of prediction, right, and prediction and is a way of, you can think about it as simulation, as generation. It's, um, predicting actions, which is you have to understand the input, the visual inputs, in order to actually predict a reasonable next action. Um, and it's about perception, it's like you can only do that if you understand, if you perceive the content. You, then you can only, I don't know, like transform it into a new piece of content, or predict an action, or describe what you actually see in that. Um, see, and the, and the combination of all of that is, I think, um, yeah, i- is I think what's, what's driving it, there's not a single one of them, it's a combination of these thoughts.

• 56:23Jason Calacanis?

  And what's the best way to get that training data? Do you need to have people put on glasses, get a first-person perspective? Have them put on gloves, so you have that, you know, fidelity of understanding, hey, this glass is moving, I'm pouring this glass, I'm putting ice into it. You know, and, and here's how that's works, and the splashing and the condensation water, so I can pick it up and not drop it because it's wet on the outside? Or is it gonna 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?

• 57:01Speaker 02

  I mean, ultimately, I think you would wanna go to a place where you could like prompt a robot in context, right, as you can do with like a language model, basically just tell it they go and, I don't know, pick up this glass with the, I don't know, orange juice or whatever it is, yeah, exactly.

• 57:17Speaker 02

  We're not there yet, um, but I think this, this is like one of the goals, and I think like how these models are deployed currently is there's like a lot of like different hardware, different robots that are running in factories, um, that all have like some different kind of action representation that you need to kind of tune the models towards, right? So in practice, um, what you do is you have like all this like visual understanding in the models, um, and then you need only a very little bit of like, a few hours of, um, fine-tuning data to adjust the model on that specific task. And yeah, I think the goal would be to kind of move away from that, uh, towards like as much in context as possible, but it is a little bit of a research problem, uh, I, I think that-

• 58:02Jason Calacanis?

  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 control it and then, hey, this is our IP. They've already made a point of working with ChatGPT and saying, hey, you, you can and cannot use certain characters. In fact, OpenAI had a relationship with them that's, for Sora, that's no longer happening.

• 58:45Jason Calacanis?

  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?

• 58:59Speaker 02

  I think it is, um, look, I think like the most interesting use cases of this, like if you think about like content creation, um, is in generating something, making something that hasn't been there before, right? Like that, that's a fundamental, like, interesting aspect of this technology. And then I think like, yeah, when it comes to IP, what we implement, for example, on like our public-facing tools is you cannot generate certain IP with these models, right? And I think that's something that is a sensible approach, and then, yes, uh, we do work with certain IP, uh, holders to develop models together with them, um, some of them based on our open source models, some of them based on like our more powerful proprietary models. But I think that is like a very like attractive-

• 59:44Jason Calacanis?

  What's the vision there? What, what do you think that will look like for consumers in another couple of years? What would

• 59:51Jason Calacanis?

  potentially happen when you open up Disney Plus?

• 59:53Speaker 02

  I mean, that's a good question, I'm not, I'm, I'm not in Disney, right, so it's up, up to them to decide that, but I think we want to enable them to build all kinds of stuff that they, that they, that they envision. And I think we can support them, we can support like other companies in that space to, I don't know, integrate the technology in the best possible way, I think like one of the very interesting angles of it is that it is like, it's becoming much faster, 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 Plus or elsewhere.

• 1:00:23Jason Calacanis?

  I think the most interesting thing I've seen in this regard is, uh, fan films.

• 1:00:28Speaker 02

  Right.

• 1:00:28Jason Calacanis?

  So there's a category before generative AI, fan fiction, people would write their own Star Wars story. Then there came fan films, where people would dress up as Jedi knights and record their own films. And George 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 even released how to, you know, how-tos on how to make a lightsaber, or, you know, sound files of like how to make the lightsaber sound. Now, people are taking the stories that haven't been told from the Star Wars universe and they're recreating them using AI.

• 1:01:11Jason Calacanis?

  And for the fans, they're becoming quite popular on YouTube. Uh, 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, uh, maybe rent software, or maybe based on the output, and let them be creative with the characters, let them make their own stories. Uh, and you could be in a unique position to empower that.

• 1:01:38Speaker 02

  Well, a hundred percent, I think like if you find like a model that works for like the, um, IP owners, uh, but then also can enable like the super like creative customization use cases, I think that's great, yeah, I mean like, like, I mean, like, yeah, for myself, like I, when I read a book or whatever, like watched a movie, I'd like so many like 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.

• 1:02:02Jason Calacanis?

  Yeah, it's, uh, gonna be incredible. Continued success with it. You have an office in San Francisco, you're hiring people, yeah?

• 1:02:10Speaker 02

  We do, yeah.

• 1:02:10Jason Calacanis?

  You've raised a bunch of money.

• 1:02:11Speaker 02

  We raised a bunch of money, uh, we just crossed a hundred people, we're hiring in Germany and in San Francisco.

• 1:02:17Jason Calacanis?

  Fantastic. Who are you looking for? What's the right type of person, the right type of skill?

• 1:02:21Speaker 02

  Yep, um, on the one hand, we are always looking for researchers who have experience in large-scale model training, um, experience in diffusion model training, flow matching training. We're looking for engineers who wanna be working with the customers to, you know, develop these like customized, um, physical AI solutions, or, for example, with like a IP owner, like develop these models jointly with them. Um, we are looking for engineers who have experience in just like large-scale compute infra, managing that, um, and making sure that the training runs, runs smoothly, that we maximize our MFU and all that. Um, and we are looking for people who have interest in,

• 1:03:08Speaker 02

  you know, like, uh, getting the technology out there in the hands of people.

• 1:03:12Jason Calacanis?

  Uh, the, the, 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 wanna have some additional level of control, but they also need the frontier models or your proprietary ones for some of those refined features, so I think you have a very bright future-

• 1:03:31Speaker 02

  A hundred percent, a hundred percent, exactly, yeah.

• 1:03:33Jason Calacanis?

  All right, continued success, thank you so much for doing the show, a pleasure.