Open models can drive demand for a full stack

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 37:27

   a lot of great investors. I think the last known valuation was around $25 billion. And that brings me to my question about the business. How do you make money on open models? I think we were talking about the Chinese labs are starting to, but the margin is all at the frontier closed right now, it seems. And you guys have also been striking some pretty big compute deals. There's the $1 billion with Nebius, the over $6 billion with SpaceX. So it implies you see the revenue ramp and you have an understanding of what you're going to do, and I'd love to hear how you all will approach this.

2. 37:59

   Yeah, I'll share some thoughts and obviously would love to hear Yanis's as well. At a high level, when we think about open source, open models, or anything else that is open source, open source technology is a demand driver for something. And an example is Kubernetes, demand driver for CPU clouds, effectively. And open models are really, like, when we think about where the market is going, is that the largest buyers of open intelligence, well, they're definitely going to be, well, the scaled-up startups that became enterprises, large enterprises, public sector, sovereign entities, and allied countries. And the thing is, if you just give them an open model, they don't know what to do with it. Because it turns out that when you're buying a token from, let's say, a closed model lab, they've packaged up and abstracted a whole

3. 38:57

   host of technology that went into that, which is the cluster management software, the serving software, the inference software, kind of application layer, the whole system around it. So what we see open models as is that it's a demand driver for that. So when you go to an enterprise and you're their trusted open intelligence provider, well, you need to serve inference into them. You need to help them set up agents. And you set up that infrastructure for them and serve that for them. And so the business is really, when you sell a token, or ClosedLab sells a token, they're not just selling a model. They're selling everything else around it. And that's what you go and do. You go and help enterprise to set up in a package turnkey way all the other stuff around that they need to make the open models useful. And we see open models as trust and

4. 39:47

   demand drivers for that full stack. Do you worry at all about the scalability of that model, or in terms of how high touch that is to do that forward deployed work? I think it's not just the forward deployed work. I mean, this is part of it, but if you actually take a step back and you just kind of think of what the future will look like, I think that people, especially big enterprises, allied nations, everyone realizes that AEI is an extremely important technology that cannot be outsourced to a third party. It needs to just be controlled and owned, which means also the data, it means the infrastructure, it means the deployment of it. And owning your intelligence is actually something that everyone realizes that's extremely important. You just see that there is a lot of movement in terms of capital, in terms of investment, in terms of

5. 40:38

   capex in this direction. And this is something that we also observe. So what we're becoming and what we're building is the company that's the intelligence providers for anyone who wants to own their own fate and own their own intelligence. And that includes the software, includes the inference, it includes the applications that are actually used internally. And I think this is going to be a significant part of the market because the way that I always think about it is that AEI, in a way, is like a form of labor. I feel like this is why it's priced the way it's priced.

6. 41:13

   And if you think of who are the main consumers of labor in the market, it's actually like big enterprises. It's government. It's like big institutions. It's exactly the institutions that want to consume their own intelligence. And this is why serving these particular customers is, I think, not like in the medium term even. It's actually like the biggest chunk of the AI economy. Yeah, I think there's maybe somewhat of a misconception that this kind of business is somehow, you know, like a forward deployed, you know, engineer services only kind of business. Really, it's like what you're doing is you're unlocking inference, right? And so just like closed labs are unlocking inference, they're, you know, the open labs, right, in China have APIs are unlocking inference.

7. 42:01

   So you're serving inference, right? And the estimates are that there'll be tens of gigawatts of open model inference served within the next few years, which is a lot, right? And the way you think about everything else is how do you kind of remove frictions to unlocking more inference, right? And what you do is you help enterprises and you help sovereigns and public sector agencies serve their own inference and unlock it at scale.