Four CEOs on the Future of AI: CoreWeave, Perplexity, Mistral, and IREN
Episode publishedPodcast: All-In with Chamath, Jason, Sa...All-In with Chamath, Jason, Sacks & Friedberg
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I'm here at NVIDIA's annual GTC conference, and I'm gonna interview four amazing AI CEOs. Stick with us. I'm going all of you. Our are you looking to change the world and raise capital? Do it at the NYSE. The NYSE is a modern marketplace and a massive platform built for scale and long term impact. So if you're building for the future, the NYSE is where it happens. One of the great companies of the AI era is, of course, CoreWeave. They're building massive infrastructure for these hyperscalers. And in some ways, Michael in Trader, welcome to the program. You're the original hyperscaler. You guys
Yeah. So we didn't really start it as a Neo Cloud. I was running an algorithmic hedge fund focused on natural gas. And when when you build an algorithmic hedge fund, once the algorithms are built, you're really just monitoring it and testing different theses and doing all that. But there's also a lot of downtime. And we got super interested in crypto. And, you know, we're pretty nerdy. We kind of dig under the hood, and we started to get interested in the security layer. We looked at Bitcoin and the mining for Bitcoin, and we didn't like it. We just thought that, like, there's some brilliant e
Yeah. And crypto was a question mark at that time. Absolutely. Yeah. I mean, Bitcoin was speculative, and there were many other speculative projects. The only other people using this type of hardware, quants Yeah. Medical researchers.
So a good way to think about it is, like, the progression of products that we kind of started to work on. You know, first was crypto, but we immediately moved from crypto to CGI rendering, and we built projects that would allow folks that were trying to animate and render images, you know, kinda what makes the movies cool. Right? Yeah. And and we started to work on that, and then we moved to batch computing and started to look at medical research and different ways of using the compute to be able to drive science. And we just kind of kept moving up the stack in terms of complexity on how GPUs
They can't complain about the SLA.
They they kept telling us like, we need more of this. You gotta work on this. And that began to really give us an understanding of what was necessary to run scale parallelized computing. And, you know, that that we went through it. I I I kinda feel like buying those initial GPUs was the tuition we paid Yeah. To learn how to run this business. And then one of the interesting things is all of those guys went back to their day jobs because they were all volunteers working on this. They were like minded scientists. And when they got to their day jobs, they were all like, I want that infrastructure
And So you went from crypto to these researchers into academia and deep research. What's the next card to turn over in the poker game?
Yeah. So so what became very clear to us very, very early on was that the scaling laws were going to drive And remember, this is really back in the 2020, 2021, before ChatGPT moment occurred. And we began to understand that computing decommoditizes at scale. Anybody can run a GPU, but can you run a cluster that's large enough to train a model that can change the world? And that's a different question. And so we really began to think about, like, how do you go about scaling up your delivery of this computing to clients, larger and larger clients? And that was the next card to turn, is to think
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