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

Key Takeaway

When implementing AI in your workflow, add this closing instruction to every prompt: 'Please check your work, tell me what I haven't considered in terms of my goals, and ask me questions.' This simple addition transforms AI from a tool that follows instructions into a thinking partner that identifies blind spots and suggests better approaches you didn't know to consider.

Episode Overview

Andrew Feldman, CEO of Cerebras (the AI inference chip company), discusses the unprecedented scale of AI infrastructure buildout, the shift from training to inference computing, and the race toward superintelligence. The conversation explores how reasoning models are changing human-AI interaction, the importance of AI sovereignty, and practical approaches to maximizing AI's capabilities through better prompting and systems thinking.

Full Transcript

Transcript of Open Source Wins, AGI Is Here, and Scorsese’s AI Toolkit with CEOs of Cerebras & Black Forest Labs from All-In Podcast. Auto-generated from episode audio; may contain minor errors.

We are in the race for superintelligence and Andrew Feldman is back and and and obviously CEO and founder of Cerebras doing inference chips pioneered the space had a successful IPO. We've talked about this a couple times. We got to see each other in January at Davos IPO happens. happens. happens. The boys and I got to sit with you recently. recently. recently. That was fun. at liquidity. at liquidity. at liquidity. That was really 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. AI. AI. We've never seen a build out like this since you know 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. of something. of something. Um I can't think of anything in our lifetimes but perhaps you know before our lifetimes the war effort. Right. Right. Right. This is a mobilization at a scale that we read about, we hear about but you're actually doing it.

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Visit applovin.com/allin applovin.com/allin applovin.com/allin to launch your first campaign today. Maybe you could just enlighten us in 2026 what is Cerebras doing and what is happening with this build out in Texas, these are some gigantic gigantic efforts. gigantic efforts. gigantic efforts. The the size and scope of what is being built, physical size and scope. Usually when we talk about software or we talk about hardware, we're talking about chips and boxes and and they don't have the same sort of physical enormity. Right. Right. Right. 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.

took. took. Wow. Wow. Wow. Right. We're talking about individual buildings the size of football fields that have more power coming into them than midsize 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 and the Middle East in nations that sort of [snorts] weren't front and center in anybody's mind previously, you know, Kazakhstan, Tajikistan or building out Georgia building out data centers of size, Armenia.

Armenia. Armenia. Everybody's sort of focused huge data centers. centers. centers. um and every state obviously in America feels they need to participate in this. And the people who are buying the capacity, the OpenAIs, Anthropic, SpaceX I SpaceX AI uh the Googles, they are insatiable right now. Yeah. Yeah. Yeah. And [clears throat] 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. The irony is unlike many sort of exciting times in technology, th- 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. [snorts] [snorts] [snorts] And we we are not alone in that. Open AI, Anthropic, you go through this list of 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.

Right. Right. How do we keep them from leaving? Right. Right. Right. And and that 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. token maxing. token maxing. Yeah. Yeah. 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. value happening. value happening. Yeah. Yeah.

Yeah. But there's also massive experimentation. experimentation. experimentation. Oh, for sure. You know, I You know, what I I I liken this to when we first started with AWS and it was so good to get around your own IT organization. Right. Right. Right. That you told every engineer, yeah, go ahead, put it on your credit card, sign up. up. up. Yeah. Yeah. 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. Yeah. Yeah.

And so, for sure, there's experimentation. experimentation. experimentation. But But But it doesn't mean that that the net value isn't enormous. It 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 in the Palo Alto area in 1988 and people used to shop Costco like they shop Safeway. They'd go down every aisle. Yes. Yes. Yes. That's a horrible way to shop Costco cuz 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 go to the back, you get the chicken. chicken. chicken. Yeah. Yeah. Yeah. 18 cupcakes for the kids' birthday party. party. 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 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. And and And and And and we're really seeing a certain type of person emerge who knows how to deploy this technology. Systems thinking. Systems thinking. Systems thinking. Yeah. Yeah. Yeah. Which developers kind of have innately. CEOs tend to be great strategists and understand systems. understand systems. understand systems. Uh but this 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 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?" goal?" goal?" The AI starts telling people you're token maxing and you need to get a little more focused here. One of one of my colleagues 20 years ago, a really smart smart computer scientist, said, "Computer's really dumb. dumb. dumb. They they do exactly what you tell them." them." them." Yeah.

Yeah. Yeah. And at first prompting was like that. Right? You modified your prompt a little bit and it changed the answer. Dramatically. Dramatically. Dramatically. Dramatically. And increasingly, it's understanding what your intent was. Right. Right. Right. Right. And if you if you have a chance to to to play with Fable or or 5/6 from from Open AI, increasingly what 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's some things and and by the way, maybe you wanted the chart to to to go two ways.

You wanted it aligned 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. 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 Right. Right. Right. to actually understanding your intent and then providing a solution and abstracting it all from you.

That's right. That's right. Which is a very weird thing. I I don't know if you've played with the Hermes agent yet. agent yet. agent yet. Have you played with it yet? I mean, I asked it just this morning, uh and I I was given a secret BitTensor project that has the new um ZAI's uh model 5/2. And they gave me GLM 5/2, yeah. GLM 5/2. So, somebody in that BitTensor I don't think you understand BitTensor, you've heard of it, the distributed um um 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. Okay, fine. Okay, fine. So, they gave me unlimited capacity, so I started having to do some really crazy jobs jobs 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. Yeah. Yeah. Isn't that interesting? I mean, that's amazing. amazing. amazing. And and And and And and it was collapsed. So, as a civilian Right. Right. Right. 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 Right.

Right. Right. reasoning and inference and unlimited compute, essentially. compute, essentially. compute, essentially. Right. Right. Right. It opened my eyes just this morning of what a world of unlimited tokens might look like. Right. Right. Right. Cuz unlimited tokens, I believe, means unlimited reasoning. unlimited reasoning. unlimited reasoning. It does. It does. It does. What does that mean? Yeah. It's uh I mean, if you run these for 25 or 48 hours, you get amazing things now. And what what if by using Cerebras we were 15 times faster, and then you ran it for 24 hours?

Right. Right. Right. And you got weeks or [clears throat] months worth of thinking. Yeah. Yeah. Yeah. And I mean, it it is uh it is extraordinary. And I I think one of the things is people like Ilya and Sam in the early days were saying this was coming. coming. coming. Right. Right. Right. Right. And I think when you look back, you say to yourself, "Holy crap. Those guys guys guys They knew. They knew. They knew. saw it. saw it. saw it. Yeah, they were They could see around the corner.

the corner. the corner. That's right. And the rest of us were like, "What? I'm not sure." 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 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." Right.

Right. Right. Fill the sentence in, you know, summarize this PDF. Now, Cerebras is at the center of this because this reasoning is inference. This reasoning is inference and it's computationally intensive. computationally intensive. computationally intensive. Right. Right. Right. Right. And so, fast compute makes this sort of work fast and sort of tractable. It doesn't 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 a blisteringly fast machine like ours and I I brought one Oh, you Oh, you Oh, you I I'm never far without, you know, when one costs half a billion to make, you you bring it everywhere with you.

And we we were we were tossing this back and forth at Davos. Uh what's the model number of this one? This was in the first eight or 10. Got it. Got it. 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 a dirt bike for his eighth birthday. He's in his bedroom at night. I I carry him with me. with me. with me. I I mean, when you have you know, your your next party at the house, I highly recommend just a little hors d'oeuvres something.

I think it'd be like a great fit. It would be a great fit if you had some That's right. That's right. That's right. Um but what we're looking at here is the ability to do that reasoning at scale and and and what is Moore's law for inference and for Cerebras? Do you have something internally you discuss as ev- we're going to double this every x time period? time period? time period? So, So, So, all chips prior to us in the processor world followed Moore's law. Got it.

Got it. Got it. And we broke it Doubling every 18 months. Doubling about every 18 months. Got it. Got it. Got it. Um and we crushed it with this chip and we've carved out a whole new trajectory. trajectory. trajectory. And uh my view is in the next 18 months will be way over 2x. Interesting. Interesting. Interesting. And so And so And so I 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. Right. Right. You have to rely on things like smaller geometry. geometry. geometry. Mhm. Mhm. Mhm. 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 that is being presented and make optimizations that that give you huge gains. How do you run the company? Like just being the CEO now in the age of AI um [snorts] um [snorts] um [snorts] 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 road map. I don't mean to give you a panic attack here. You have to keep up with somebody like Open AI who's moving so unbelievably quickly. quickly. quickly. Yes. Yes. Yes. Right? And they're they're competing. You got to keep up. Right. Right. Right. Right? Your hardware, your software, your deployments have to keep up with some of the fastest moving organizations in history. in history. in history. They're demanding customers. They are not They're not pushovers for sure.

Yeah. And Yeah. And Yeah. And also potentially competitors down the road. road. road. Look, I think there's so much demand right now right now right now that that that there is no silicon that will go unused. Right. Right. Right. But why isn't Open AI releasing jalapeno? Why is Amazon making their own chips? You see this recurring 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 is it a little bit of a flex that way or is that the future that they're going to be in your business.

your business. your business. No, I I I think nobody likes being dependent. dependent. dependent. And I I think some of the lessons learned by the the hyperscalers of the x86 world is they were dependent on Intel. Intel. Intel. Mhm. Mhm. Mhm. And uh some of the lessons learned by uh the GPU makers was they were dependent on a small number of hyperscalers. Yeah. Yeah. Yeah. And they wanted more customers. And so they set about to to help fund these neo clouds. clouds. clouds. Mhm. Mhm. Mhm.

And so I I think mostly it's about uh an opportunity to control at least an important part of your destiny. Got it. Got it. Got it. 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. people's chips. people's chips. And that dependency has become a hot topic. Um not sure if you caught the episodes over the last 2 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 reasoning and the gap gap gap as as as Well, Well, Well, suddenly closed this year. Well, I 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. Yeah. Yeah. Yeah. Right? And there times you you want to throw the kids in and and don't worry if there's Cheerios on the floor. Minivan time. Minivan time. Minivan time. Right. There's minivan time. And I 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. They're going to be open AI problems.

They're going to be Anthropic problems. They're going to be 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 of work day and getting it in a different cell for Yeah. Yeah. Yeah. Right? Think about The cutting and pasting economy is real. Yeah. Yeah. Yeah. That's right. And And this doesn't need, right, gold medal math. No. No. No. What what this needs is sort of rock-solid open-source capabilities.

Yeah. Yeah. Yeah. And if you think about what I mean, well, we've been thinking a lot about it in G&A, but a huge amount of G&A, all right, is not invention. No. No. No. Right? And you you may not need sort of the most sophisticated agents for this. for this. for this. And And And another another another card that's turned over recently is some folks some folks some folks maybe have concerns with the ambition of the frontier models and maybe sharing their data data leakage and sovereignty of intelligence and they're saying, "Hey, saying, "Hey, saying, "Hey, our company is going to choose maybe we're in a regulated industry, finance, health care, HIPAA, you know, FINRA, all kinds of different regulations.

We need to have this on prem prem prem Yeah, but on prem domestically and we'd like you know, an open-source version where we have a little bit more control. Yeah. Yeah. Yeah. And I I think Are you seeing that now? seeing that for sure and I I think OpenAI made a good call releasing OSS 120B 120B 120B some months back. That was a good open-source model. open-source model. 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. models. models. Nvidia has some. Nvidia has seen the same opportunity to push open-source models. I I I think giving them more power might might be sort of Well, I was about to That was you 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 Right. Right. Right. we're now going to be competing with Sam, Dario, Elon, uh Sergey.

Like, do we want to be in that position? that position? that position? Right. Right. Right. 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. That's right. We we we run today we run GLM, we run Kimmy, we run the Quincy set of models, and we run OpenAI's models, the closed-source ones. We run models for, say, GlaxoSmithKline, which they wrote and developed. Um we run models for our partner in the UAE, G42 and MBZUAI, MBZUAI, MBZUAI, um that are are are their models that they designed.

So, we have a a wide variety. So, sovereignty is a trend. Sovereignty is a trend, and I think uh the the the the government's actions with regard to Fable Fable Fable and and and uh 56, uh 56, uh 56, um where they said, "Ho, whoa. Let's think. think. think. And then we can act." Um I 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 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. gamesmanship here. gamesmanship here. But do you believe that what they released released 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.

effervescent communicator. effervescent communicator. Um, I think it's a diplomatic way to say it. it. it. Um, Um, Um, but but 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 is a wise thing for us to do at this point? And do you think it's there was actually a major threat there? So, what's interesting is I hadn't seen it before. it before. it before. Mhm. Mhm. Mhm. Right. And I I think, you know, if we just step back and say, "Is it reasonable?

I don't know whe- whether this was the right time, but at a time Mhm. Mhm. Mhm. th- that that a model is sufficiently uh, creative in its thinking that it poses a meaningful threat for the government to say, 'We like you to roll it out in steps.'" Yeah. Yeah. Yeah. Now, this doesn't seem unreasonable to me. me. me. Not at all. Right? I mean, we we do this with powerful pharmaceuticals. powerful pharmaceuticals. powerful pharmaceuticals. Right? We We We like I mean, we're certainly not encouraging 7 years of trial and the amount of paperwork and all the garbage that has accrued to the FDA.

FDA. FDA. But, with a powerful new technology, it certainly doesn't seem unreasonable to say, "Hey guys, um, let's at least do some red teaming at the government so we know our defenses can block this." Yeah, have we checked Have we checked the infrastructure of the country like NSA? Have we checked the infrastructure of Right? [clears throat] And can you give us 2 or 3 weeks to patch any obvious holes that are found? Right. This doesn't seem to me an unreasonable thing for the government to ask.

to ask. to ask. Right. We but we in this very polarized time time time Yeah. Yeah. Yeah. 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 OAC AOC or President anybody in between the the two extremes. I think the polarization hurts a great deal. It hurts clear thinking. It does. It does. It does. It It It hurts clear thinking, and and and both sides are going to do some dumb things and some really smart things.

Right. Right. Right. Right. And And in fact, what I found is that the people in the government are are trying really hard. Uh The rank and file. and file are trying really hard. And this is moving fast. And I I think that that that that our ability to to set aside some some of the the polarization and say, "How do we do this in a reasonable manner?" I mean, we we want Dario and Sam competing like crazy. 100% It's been awesome to watch. Yeah. Yeah. Yeah.

Right. It's good for the technology. It's good for entrepreneurs to see even with thousands of people that this is what what you can continue to achieve. Right. Right. Right. Right. This is a drive. in the ass, make them get sharper, Amazon do a better job at that. 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 is regulated. Right. Right. Right. Right. But as it gets more powerful, 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. lacking, maybe. lacking, maybe. uh uh uh You know, I 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 that we we say, "Oh, just put on guardrails." Well, they have to design the guardrails. Sure. Sure. Sure. Right. They the guardrails have an impact. Um you know, one of the things that Fast does is it makes the guardrails less painful.

And so that that is we we we discovered that in the last 6 weeks. Yeah. Yeah. Yeah. It is that the very guardrails can add time and make it feel slower, and so Fast chips like ours it can really help that. But so they're Right. Right. Right. against competition. They're racing against their own sense of greatness. Yeah. Yeah. Yeah. Right. Which is maybe even the biggest driver here. driver here. 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 are mixed in this bucket. And, and sometimes you're on one side rather than the other. other. other. Yeah, and as you're saying, this is a first time, right? That's right. That's right. That's right. When we with 3.5 came out, it wasn't like when we were using ChatGPT 2.5, 3.5, it was taking down networks. Right. Right. Right. But, in talking to Nikesh from Palo Alto Networks, I asked him like, "Hey, like, "Hey, like, "Hey, how would you grade this?" And he said, "We put it against our software, and we found bugs we were not aware of." Yes, it killed them.

Yeah, he said, "We had to stop everything we're doing and do patches for 6 weeks." Right. And and that's when you know, right? I mean, Nikesh leads a 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." Yeah, I mean, And we need to think. And and maybe you you show it to a group first, right? May- maybe you I don't know what the right thing is, but red teaming and uh we've always had just when you were releasing the new version of version of version of um an operating system, you know, when you have your iPhone, you can say, "I want to be part of the beta." That's right.

That's right. That's right. You know. You know. You know. Right. And there's like two other betas that you don't even get the chance to opt into as consumers. Right. Right. Right. Those ones are for security. Those ones are for, you know, making sure you don't lose your data or data that's deleted. That's right. Disappear or leak or corruption. corruption. corruption. Right. Right. Right. Any any number of uh these things. I think we can also know that that there will be a massive data leak. leak. leak.

Of course. Of course. Of course. We we we know this, right? And it's like uh Warren Buffett talked about the reinsurance industry that you know something bad's going to happen. You don't know when, Yeah. Yeah. Yeah. but you got to save up for it. Right. You you money away for for insurance. But But But there will be a tornado. There will be a massive earthquake. I mean, we we know this. this. this. And we can do our best to plan. But there'll be a massive breach.

And they'll they'll be and we have to steel ourselves in advance. And we have to think about it and think about the right response at the time. And so to prepare ourselves for a future that is in specific unknown, but in general we're pretty sure it's going to something's going to happen. Something Something Something [snorts] [snorts] [snorts] will happen. Uh and yeah, it's typically a black swan, right? I mean, by definition it's going to be something we didn't consider or a question we didn't know to ask.

Right. But but even knowing that there's some unknown unknowns is a useful place to start. to start. to start. Yeah. What are we not asking ourselves? That's right. That's right. That's right. With reasoning, the AI is going to be able to tell us, "Hey, schmuck humans. That's right. That's right. 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 always say at the end, um "Please uh check your work.

work. work. Right. Right. Right. Uh and Uh and Uh and uh 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. Right. Right. Right. And I'm wondering, "Hey, would you like me to also do this?" And some of the tools like Perplexity do that automatically to give you your next three prompts. three prompts. three prompts.

Right. Right. 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 I thought one of the smarter questions I got at the end of a conversation where someone asked, "What what was the smartest question you heard that that wasn't covered by what I asked?" asked?" asked?" Incredible. Incredible. Incredible. Right. Now now that's somebody who's curious and thinking and humble and and trying to sort of use this to get a picture picture picture of of of of the space.

And to the extent that you can ask the AI that and that it can sort of broaden your your view. You know, maybe what questions should I have asked? asked? asked? Yeah. Yeah. Yeah. To be an expert in this. What what what would a PhD level uh questioner ask? questioner ask? questioner ask? Right. Right. Right. Or a gold medal math. I mean, I I think those are sort of questions that you know you don't even know how to ask. Which You know, if we start thinking about AGI and superintelligence and superintelligence and superintelligence um you know, they're just definitions, but but but they're important definitions I think to kind of keep in mind because they're waypoints.

waypoints. waypoints. That's right. And AGI I think I think I think I suspect you'll agree with me that we've hit it. We just haven't exactly deployed it deployed it deployed it fully. We 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 human, but human, but Let's talk about By any definition we had 20 years ago we've hit it. Yes. Yes. Yes. Right.

I mean, if you think about it, all those Turing tests blew it away. 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 Right. Right. Right. we've blown past it. And so Which goes back to our previous point of like do we know the questions to ask? That's right. That's right. That's right. 20 years ago science fiction authors you know, you know, you know, had their say and we answered all their questions.

questions. questions. If they were to look at this today, they'd be like, well, I'm out of questions. Sorry. That's where sort of the sort of listening to people who we who sound sometimes like they're on the fringe. fringe. fringe. Yeah. Yeah. Yeah. 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. Dead right. Dead right. Yeah. Yeah. Yeah. Right, when when when Elon was talking about building rockets and driving the cost to to to near zero of of of a launch vehicle, you're like, what?

And there it is. And and now you can see it. And that's And that's And that's I think that's why it's really fun to be a technologist now. [snorts] [snorts] [snorts] Well, and with these tools specifically, you know, we're talking about building all of these tools, and then the tools are starting to build themselves in this recursive loop. recursive loop. recursive loop. That's right. That's right. That's right. And we're And we're And we're kind of just starting to see people apply loops. Uh in fact, loop maxing became when I was doing my trend when I did my trending, it kept picking up loop looping, and it kept picking up the maxing stuff, and it created a buzzword for me, loop maxing.

Right. Right. Right. 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 [snorts] [snorts] [snorts] it will mean for humanity, and how we will define it, and how we'll experience it. Yeah. it. Yeah. it. Yeah. I I I think let's begin on on on loop maxing, or sort of recursive learning.

I I I think um I think what what what Sam Ennillia, and then later Dario, and and and Dennis saw Dennis saw Dennis saw um um um 6 years ago, or 5 years ago, was that um powerful recursive games are exponential. are exponential. are exponential. Mhm. Mhm. Mhm. Right? You get better, you do it again, and if you continue to get gain, the the the the slope of that curve is so steep. so steep. so steep. Yeah. Yeah. Yeah. And that um we're just beginning to see that now.

that now. that now. You ask it a question, you learn from the results, you ask it to do it again. Uh-huh. It 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. answers. answers. Yeah. Yeah. Yeah. And that is a enormously powerful because we don't quite know where it ends. Right. Right. Right. 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 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? Yeah. Yeah. Yeah. And that's sort of an enormously interesting intellectual question right now. now. now. Uh-huh. Uh-huh. Uh-huh. Yeah, like when do we run out of problems to solve and Well, that's right. And and when are are the the problems no longer sort of intellectual problems and that you're now people problems.

Yeah. Yeah. Yeah. Right. How to organize people to to get done with the AI asked for. Right. I mean, as you know in running your company, your company, your company, a lot of your problems aren't hard intellectual problems. They're people working together problems. Yeah. Yeah. Yeah. Right. Right. Right. And you motivation Motivation. You spend a lot of time as a leader spraying WD-40 on your team. Right. Right. Right. Right. It just so so so friction is reduced and reduced and reduced and um um um how do we learn about those from AI?

Right. How do we get behavioral insights? From 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 but but um when these things jump off the screens screens screens Right. Right. Right. and they're in the real world and the recursiveness starts recursiveness starts recursiveness starts not trying to solve math problems and Right. Right. Right. humanities' 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 100 acres somewhere out in right Texas or Nevada. I'll just send a thousand optimists out there. Make me the Palace of Versailles. Right. Right. Right. Sounds fantastical, but the Palace of Versailles would seem fantastical to people who lived a thousand years before it. it. it. And it was fantastical, I think, to the people who built it. Right, even to the builders.

I I think they were awed Yeah. Yeah. Yeah. at it as they built it. Yeah, they're compounding they're compounding recursive learning That's right. That's right. 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 Masons. Yeah. I I think in in in all these large projects, projects, projects, um, often there were families uh, who were specialists who and uh, you you apprenticed under your father, your uncle. And when you had a project that took the 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.

And passing on the learning. on the learnings. New innovations. New innovations. New innovations. Right. Right. Right. Which is what we've modeled with this new That's right. That's right. That's right. models models models and what you're building in the infrastructure. infrastructure. infrastructure. It's pretty incredible when you think about it. about it. about it. when we're sitting here in But the Versailles, the And that's what I mean, I I think the the problem with human learning human learning human learning is um, is um, is um, it often moves at at the pace of a generation.

generation. generation. Huh. Huh. Huh. And like And like And like uh, 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 food fly. You want two a day. a day. a day. Yeah. Yeah. Yeah. Right. Then and you see that in genetics. that's why we study them in genetics cuz learning encoded in the DNA, you can study over thousands of generations. generations. 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.

generations. generations. would be would be would be in awe of this uh pace of evolution. That's exactly right. You think about it as as as There was I remember when I was getting my psychology degree and they were teaching us about paradigms and I was like I trying to understand how the paradigms shifted and uh the professor said to me, uh Jason Jason Jason uh what you have to understand is paradigms don't die. They don't. They don't. They don't. People do. People do. People do. That's right.

That's right. That's right. That's how Freud [snorts] Freud and Skinner and Young, like it took them dying That's right. It took the new generation to question it and that was 20 years. Yeah, sometimes 40 years. Right. As their students maintained positions of leadership until someone said, maybe we can do it differently. And I I think what you're seeing is this iteration is a shortening of the the the intergenerational gap and the learning is so fast. It's uh always so great to talk to you because uh one, it's just intellectually uh uh uh so your your approach to it is so intellectually rigorous, but also um 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 everything 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 [snorts] [snorts] [snorts] that that that just is unimaginable.

Yeah. We have a shot with this technology so no not our children nor anyone they know dies of cancer. Right? I mean, say it like that you there will be some dislocation in the economy. There will be. There was dislocation when cars came and and and it was a bad deal to to to be a guy who shoed horses, right? Or built carriages. Yeah. Yeah. Yeah. But you got to also against that, you know, make your tea of the cons and the pros. pros. pros. Yeah.

Yeah. Yeah. 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. That's funny. That's funny. That's funny. that we can work on with this technology and we will have great purchase on. And [snorts] and I think you begin listing those and then it's a more thoughtful discussion. discussion. discussion. Yeah, unlimited energy, unlimited calories, unlimited knowledge, unlimited education, unlimited housing. And how we do it. We imagine imagine sort of sort of 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. better. better. Adapt. That's not how we do it teaching classes. classes. classes. No, factory farming. That's right. We we teach to some sort of middle level. Imagine if we beat built agents that that taught children for their way of learning. Right. Right. Right. Right? And here's the way we 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.

not to. not to. Yeah. Yeah. Yeah. And and here's the 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 I think it'll come out okay. get out there Andrew and 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.

I think it'll create abundance for sure. abundance. abundance. abundance. Andrew, pleasure always. I'll see you in 6 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 asset. Nasdaq is the leading technology platform that makes it possible and scalable.

Learn more at nasdaq.com. I'm going 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. in Germany. in Germany. a mountain range actually. A mountain range. Yes. Yes. Yes. Where you grew up. Well, I grew up this. And you are working on open-source image and video models. You worked at stable diffusion for a little bit. little bit. little bit. That's correct. That's correct. 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 is the goal? 100% 100% 100% one quick addition. We are based in the Black Forest. It's a town called Freiburg and in San Francisco. Oh, and in San Francisco of course, yeah. yeah. yeah. Um we Um we Um we You're splitting your time or I'm splitting my time to a certain degree. degree. degree. We We We yeah, started a company 2 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. Yeah. Yeah. Yeah. 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 like efficient representation representation representation and then train a transformer model on that. that. that. And um And um And um I mean this is the stuff where like you know, like JPEG, MP3 and all of that works.

And we basically translated that into like a neural algorithm neural algorithm 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 and then and then on top of that yeah, uh the generative models that we are developing today. And of course like the technology has advanced. Um but we are now tackling I would say models that are really made for understanding like the whole world around us.

Multi-modal 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 prediction prediction such that you can actually use the same model to make images, to make videos, to make audio, make audio, make audio, and to predict actions which means you can ultimately deploy it 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? yeah? yeah? [snorts] [snorts] [snorts] And the objects in it. I think that's yeah, I think that's like a a really good like way to think about it. It's like it's like an intuitive way to to to interact with the world, right? Like I 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? [snorts] [snorts] [snorts] But now, yeah, I think like we're combining it. It's converging into like a a a a more multimodal model, and yeah, we see like exactly like pre-training on videos gives like implicit understanding of the physics of interactions with the real world. real world.

real world. And then you can get stuff like action prediction like robotics out of the same model. model. model. And And And with these models and the training, they're kind of been a limitation in creating videos, in 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. something back. 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 different different you know, aesthetic. Yep. Yep. Yep. It has that How does that problem get solved? solved? solved? And do you actually understand what's happening happening happening when the image is being made under the hood? Yeah. hood? Yeah. hood? Yeah. Yeah, I 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.

Right. Then they have expanded into text-plus-image to image systems, which means you could suddenly uh take an image, like a real image or a generated image, uh and iterate on that based on a text prompt, like edit it, modify it, right? And then this expanded into taking multiple images multiple images multiple images and a text prompt and combining them in a 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.

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? second, yeah? second, yeah? Fantastic. Fantastic. Fantastic. Uh but Uh but Uh but in a movie, uh this promise of being able to make a movie in which the camera angle uh the sound uh could be something that a Martin Scorsese would be proud be proud be proud to release to his fans. How close are we, and maybe tell us a little bit about this partnership?

this partnership? this partnership? The technology being able to make an actual movie like Goodfellas, or a scene from Goodfellas, uh versus where it is today, where you can make interesting five or 10-second clips, and then maybe uh 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 kludgy. It's doesn't pass the uncanny valley. valley. valley. 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 he how is he supposed to use his model. Like he's 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.

Um So you sat in a room with Marty Scorsese and showed him your tools. Exactly, yeah. Exactly, yeah. Exactly, yeah. And what was his reaction? What what did he key off of? What was the thing that he found most inspiring or interesting? Um I think it was really this idea of like he has clearly um a a vision in his head of like a scene or a scenery where like maybe a a new movie um will be shot. And he's trying to explore that and kind of like um we we 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 the outputs the outputs um 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 um or the series of images um is something that it just makes it like easier to communicate and convey convey like an idea of like what is actually in your head and I think that's like one of the like the like the like very interesting and powerful ways to use this technology.

And I think ultimately ultimately ultimately is to get the inspiration to get the vision out of his head onto an image. Yeah, I mean like language ultimately is like a little bit of like a lossy um communication medium, right? Yeah. Yeah. Yeah. Um it's also interpreted in different ways, but then visual information is so rich so rich uh like an an image or a video there's so much signal in it and it's just like another way of communicating. communicating. 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 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 ultimately ultimately like the real interesting use cases, they come when you have like a human in the loop who interacts and uses it as a medium and I think this is this is at least like the perspective that that I take um that makes it interesting and that this is most often when the most interesting outputs um arrive or are actually being made.

production level is so obviously a huge win for Gen-AI. parallelize your brainstorming basically. basically. basically. Yeah. And yeah, I like that. Parallelize your brainstorming. And 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, um 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, startups, startups uh always want to try to figure out how to do something cheaply. And people used to make a launch video for their startup for, you know, $100,000, $250,000. So, they take their $10 million million million venture raise and spend $250,000 on a launch video. I've seen it with [snorts] a lot of the startups I'm investing in now. They'll just spend a week or two working with [snorts] um um um you know, uh a director to make uh a launch video.

You've probably seen this trend, yeah? And I'm sure people use Flux and some of your models for this. Uh Uh Uh have you seen this? Yeah, of course, yeah. Yeah, what 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? I mean, like, again, like, I think we support these like exploration based on these tools, right? And um I think like ultimately it's great to see like all different kind of like I don't know, like, launch videos, uh products being built on top of like the same kind of like base model or the same technology.

technology. technology. Um and I think that's what's making it so interesting and also so powerful. Yeah. Yeah. 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, movie, movie, um I was talking to Gal Gadot, you know, the woman who played the actress who played Wonder Woman. Yeah, Gal Gadot. Gal, of course. She I was talking to her in an event and she was telling me um it was the Breakthrough Prize uh Yuri Milner's event and she was telling me she just did a Bitcoin movie and they did it on a sound stage without green screens.

green screens. green screens. But all the actors just worked in like a sound stage and then 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 animation phase, but actually in production yet with your tools? Yeah.

Um 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 I think I'm glad that it's being explored, but I also really want to um 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 64 by 64 pixels.

Now you can do like multi-minute videos, right? And like a high resolution. But it's like it's not going to stop there. It's going to it's going to continue 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 like like like the main thing we get to that? Yeah. How to predict I think how to predict and I think ultimately of years. of years. of years. Ultimately I think you still want to have like the tool that enables like this human in the loop kind of Of course, yeah.

Um Um Um production 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 um AI model to make a movie and deploy that as a brain on a robot. Uh 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 that 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. so versatile. so versatile. And it's just just moving into that. You know the all the talk around like world models, world action models, all of that it's basically all the same. And I think that's what's making it so interesting and what I find like most exciting. exciting. exciting. So, So, So, do you believe that the technology will be used to analyze or primarily to analyze real world? Like here is a video of somebody of somebody of somebody you know, making a sandwich.

Now we have the robot study it and make the sandwich or do you think they'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. Um I think it's a combination of prediction, right? And pre- 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 can 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 scene. And the And the combination of all of that is I think um yeah, is I 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 a first-person perspective, have them put on gloves so you have that Yeah. Yeah. Yeah. 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 going to be just, "Hey, take the corpus of YouTube videos and the robots know exactly what to do because they'll find a a thousand videos of people pouring drinks." I mean, ultimately I think you would want to 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, "Hey, go and I don't know, pick up this glass with the I don't know, orange juice or whatever it is." Yeah, exactly. We're not there yet. Um but I think this this is like one of the goals and I think like think like think like how these models are deployed currently is there's a 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 Um Um and then you need only a very little bit of like a few hours of um fine-tuning data to data to data to adjust the model on that specific task. And you I think the goal would be to kind of move away from that towards like as much as much as much in context as possible. But it is a little bit of a research problem. Uh I I think that that has to has kind of having a moment right now.

We've been discussing it on the podcast a whole bunch recently. And And 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. incredible library. 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." our IP." 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 sure that's 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 Martin Scorsese Martin Scorsese or tour deal. But how do you think about content libraries? content libraries? content libraries? 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 generating something generating something making something that hasn't been there before, right?

So that that's a fundamental like fundamental like 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 is is you cannot generate certain IP with these models, right? And I think that's something that is a a sensible approach, and then yes, we do work with certain IP holders to holders to holders to develop models together with them. 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 value proposition.

do you think that will look like for consumers in another couple of years? What would What would What would potentially happen when you open up Disney Plus? Disney Plus? Disney Plus? 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. I think the most interesting thing I've seen in this regard is fan films. Right. Right. Right. So, there is 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 had them, you know, how-tos on how to make a lightsaber or, you know, sound files of like how to make a lightsaber sound. lightsaber sound. 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, and for the fans, they're becoming quite popular on YouTube. Star Wars Stories Untold is I think the biggest one.

It's getting millions of views per video already. already. 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 empower that. Well, 100% I think like if you find like a model that works for like the IP owners, owners, owners, but then also can enable like the super like creative customization use cases, I think that's great.

Yeah, and I mean like like like I mean like for myself like I when I read a book or whatever like watch the movie I had like so many like ideas how it could be done differently or this could have happened, right? And this is like so nice that you can actually enable people to visualize these ideas. Yeah, it's uh going to be incredible. Continue success with it. You have an office in San Francisco. You're hiring people, yeah? people, yeah? people, yeah? We do, yeah. We just a bunch of money.

We raised a bunch of money. Uh we just crossed 100 people. We're hiring in Germany and in San Francisco. Fantastic. Who are you looking for? What's the right type of person, the right type of skill? Yeah. Um Yeah. Um Yeah. Um on the one hand, we are always looking for for 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 want to be working with the customers to, you know, develop these like customized um physically eye solutions or for example with like a IP owner like develop these models jointly with them.

Um we're 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 of that. Um and we're looking for people who have interest in you know, like uh getting the technology out there out there out there Yeah. In the hands of people. I 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 want to 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 ahead of you. and executive. and executive. and executive. All right, continued success. Thank you so much for doing the show. My pleasure. I'm going all in.