Four CEOs on the Future of AI: CoreWeave, Perplexity, Mistral, and IREN

**Jason** (0:00)
I'm here at NVIDIA's annual GTC conference, and I'm going to interview four amazing AI CEOs. Stick with us.
Our episode is sponsored by the New York Stock Exchange. 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. In some ways, Michael Intrator, welcome to the program. You're the original hyperscaler. You guys got in very early and secured your, I don't know which GPUs you hound up getting, but you were very early to this trend. How did you get to it so early and how did you build out this first, I guess at the time, Neocloud?

**Michael Intrator** (1:07)
Yeah. We didn't really start it as a Neocloud and I was running an algorithmic hedge fund focused on natural gas.
When you build an algorithmic hedge fund, once the algorithms are built, you're really just monitoring it and testing different thesis and doing all right. But there's also a lot of downtime and we got super interested in crypto. We're pretty nerdy. We 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 there's some brilliant engineer that built the ASIC and they're probably going to be better at running it than we are. We really began to focus on the GPUs, mostly because the GPUs were, you can mine Ethereum with them, but you could also do all these other things. Really, right from the start, we looked at the Compute as an option to be able to deploy our computing power to different use cases. Began the company in 2017, spent the first three years mining crypto, went through a couple of crypto winters. Because we had come from a hedge fund, we have real chops in risk management and how we think about capital and risk exposure and allocation and all of that. And so we were really careful around that right from the start. So we weathered crypto winter really well and began to scale the company and immediately started to look for other use cases that you could use this compute for because crypto was pretty volatile.

**Jason** (2:48)
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, medical, researchers.

**Michael Intrator** (3:01)
So a good way to think about it is like the progression of products that we kind of started to work on. First was crypto, but we immediately moved from crypto to CGI rendering and we built projects that allow folks that were trying to animate and render images, kind of what makes the movies cool.
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 could be used. And ultimately, in like call it like 2020, 2021, we started to really try to figure out how you can go ahead and use GPUs for neural networks. And that was not something that we knew how to do. And so we actually went out and bought a bunch of A100s and donated them to a group that was working on Luthor AI. They were working on an open source project with the thought that these guys are taking the GPU compute because we're donating it. They can't really get pissed at us if we're not very good at it initially. And that worked out really well because they can't complain about the SLA. They kept telling us like, we need more of this. You got to work on this. And that began to really give us an understanding of what was necessary to run scale parallelized computing.
And we went through it. I kind of feel like buying those initial GPUs was the tuition we paid 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. It's built the right way. That's the way that researchers are going to want to use it. And that launched our business. It was an amazing story.