All-In with Chamath, Jason, Sacks & Friedberg
Four CEOs on the Future of AI: CoreWeave, Perplexity, Mistral, and IREN
23 Mar 2026
Chapter 1: What insights does Michael Intrator share about the early days of CoreWeave?
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. And in some ways, Michael, Intrader, 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 wound up getting.
You were very early to this trend. How did you... get to it so early and how did you build out this, you know, first, I guess at the time, Neocloud?
Yeah, so we didn't really start it as a Neocloud and I was running an algorithmic hedge fund focused on natural gas and 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 engineer that built the ASIC and they're probably gonna be better at running it than we are. So 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. And really, so right from the start, we looked at the compute as an option to be able to deploy our computing power to different use cases. And so, you know, began the company in 2017, you know, spent the first kind of 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.
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.
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Chapter 2: How did CoreWeave transition from crypto mining to AI computing?
What's the next card to turn over in the poker game?
Yeah, 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, you know, 2020, 2021, before ChatGPT moment occurred. And we began to understand that, like, computing... decommoditizes at scale, right?
Like when, you know, 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 about it from a, okay, you know, there's a component of this that is going to lean into our ability to access the capital to be able to deliver our solution to the broadest possible audience, to the most sophisticated consumers of this compute.
And that was really the next card is thinking about it as a business rather than as a engineering project to be able to deliver the infrastructure and the software and really everything between, you know, when you're thinking about what we do, we kind of live... above the NVIDIA GPUs, but below the models.
And everything in there, all the software, the integration of software and operations and observability and all the things that you need to be able to build a cloud that's purpose-built for this one specific use case, right? So we don't do everything. We really focus on one use case, which allows us to- You wanna do web servers, it's different, you got AWS. You know what, they do a great job.
It's a great solution. It was a brilliant solution to solve a problem. We just looked at it and said, there's a new problem. And let's go about looking at this problem and try and come up with a solution to deliver compute that solves that problem.
And when did the language model start dialing and calling you for capacity?
Yeah, so our first, well, our first language model was really a Luther. But our first like large commercial was inflection. And so, you know, we work with Mustafa and inflection, and then we really diversified from there into the hyperscalers, into, you know,
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Chapter 3: What challenges did CoreWeave face during the crypto winters?
That's still got great life left in it. Absolutely.
And so, look, we find these amazing use cases, new companies that have come into existence or existing companies that have integrated new models into their workflow that are able to use the amperes. And so they keep buying any GPUs that we have available. And once again, the concept that a GPU is no longer relevant or commercially viable after 16 more, 18 months or two years.
Yeah, that's farcical.
It just doesn't make any sense. It's obviously farcical. I think sometimes people get caught up in Moore's law or in just how fast our industry is growing and that there's so much at stake that big companies are demanding the most recent products That doesn't mean that the lifespan has gotten shorter. It means the opportunity and the surface area of the opportunity has gotten much larger.
Yeah. One of the things is like, you know, the industry has gotten so much attention for the unprecedented scale of capital that is coming to bear on this. And because of that, there tends to be an incredible focus on the companies that are building on these most advanced chipsets.
And the truth of the matter is, even within those companies, they have a long tail of useful life to provide inference, horsepower, to work on other experiments, to do... less bleeding edge activity, but still needs to be done.
And yeah, I mean, rendering comes to mind as well. Or yeah, we're making images on nano banana. Like there will be a use for it. There is a moment in time where maybe the compute to power ratio doesn't make sense.
My expectation is obsolescence will be defined by the moment in time where the power in the data center for me, will be able to be repurposed for a higher margin than the existing infrastructure provides. And like I said, I fully expect this infrastructure to last in excess of six years.
But the standard in the space has really been used with one exception, which is Amazon, which is, yeah, it's six years. That seems like the right schedule. I'm not making it up. That's what everybody's using.
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Chapter 4: What role do GPUs play in AI and how has their use evolved?
corporate debt. I'm in the venture business. People are like, why should I be in venture when corporate debt pays so well? Corporate paper is so huge. I'm curious how this fits in and what interest rate people are paying on a billion dollars in infrastructure. What do they pay on that?
Yeah. So CoreWeave is...
really been the innovator around a lot of the financing engines that have come to bear on this we did the first gpu-based uh loans um and like i think it's important or i'm going to try to explain this in a way people can understand so what we do is we go out and we find a client let's use microsoft you brought them up before right and microsoft comes to us and says we'd like to buy some computer and we say okay great we're going to sign a contract once i have a contract in hand
Then what I do is I create something, it's not a particularly creative name, it's called the box. And what I do with the box is I take my contract with Microsoft and I put it in the box. I go to Jensen and I buy the GPUs, I put it in the box. I take my data center contract, I put it in the box. And now the box governs cashflow.
And it has a waterfall of cashflow that comes into it and goes out of it. And so the way it works is then I build the compute and then I deliver the compute to Microsoft and they pay the box. They don't pay me. It goes into the box. And the first thing it does is it pays the data center. It pays the power bill. It pays the interest in the principal.
And then whatever's left flows back to us, right? And so it is an incredibly well-structured, time-tested, pressure-tested vehicle to be able to borrow money against client paper and all of the other collateral around the deal, which is why CoreWeave, which is a company that many people haven't ever heard of, was able to go out and raise $35 billion in 18 months to build infrastructure at scale.
But what's important to understand is the economics in this box are such that within two and a half years of a five-year deal, we have paid for everything. The principal's been paid off. The principal has been paid off. The interest has been paid off.
The return into the box is such that we are able to generate returns to our company at the box level, which gives the most sophisticated lenders in the world, whether it's banks or private equity funds or whoever, confidence that they're going to be able to achieve the one rule of lending, which is give me my money back.
Yes, it works better when that happens.
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Chapter 5: How does Mistral AI plan to innovate in the AI space?
When I pulled my stocks up, it summarized the news in real time. And I was like, wow, this execution is great. And I kind of made you my front door to different models and it made it easier to check it. Then you came out with the comment browser and I was like, holy cow, I can give this a series of instructions. Go to my LinkedIn,
find everybody from this company, put them into a Google Sheet, and boom, you were the first out of the gate with that. And then just the last couple of weeks, I had been claw-pilled and using OpenClaw, but you came out with computer. And I started using computer, and boy, it's good.
It's a really strong start, allowing me to do repetitive tasks, very similar in some ways to co-work from Claude, or basically an engineer or developer using it. So... Are these the evolution of the company? And I should think about it that way. But how do you look at Perplexity now? You have a very loyal fan base. You're making a lot of money.
I don't know if you disclose it, but I think it's hundreds of millions to billions. You can tell us. But what is Perplexity in the face of, wow, Claude's having a great run. OpenAI still doing strong. Grok doing very well. Gemini coming on strong. There's like six or seven of you. And you just happen to be one of my top twos right now. Thank you.
First of all, thank you. Thank you so much. Perplexity has always been built for people who are always looking for the extra edge, the curious people. So it's very natural that you are one of our power users. One common theme for us for the last three and a half years is accuracy. Perplexity wants to be the company that's building the most accurate AI.
So when you want to give somebody answers, accuracy is very essential for building trust, because only then the user is going to ask the next set of questions. It turns out it was a great idea to give AI access to the internet to be accurate. So that's the Perplexity Ask product.
It turns out it's a great idea for AI to have full access to a browser so that it can be accurate when you task it to go do something that you would do yourself on a browser, agentic browsing, Comet. Now, the last phase is, it turns out it's a great idea for AI to be given a full access to a computer.
so that it can do whatever you do on a computer on its own, essentially becoming the computer itself, an orchestra of everything AI can do today, every single capability each individual AI model has, be it GPT or Claude or Gemini or anything else, An orchestra of all those capabilities, that's what perplexity computer is.
And all these sub-agents that are running inside computer are the musicians. The models are essentially the instruments. And they're like hundreds of models out there, each having their own specialization. Some are good at coding, some are good at writing, some are good at multimodal, visual synthesis, image generation, video generation, audio.
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Chapter 6: What is IREN's strategy for leveraging AI in their operations?
Nobody needs to manage separate billing across like a hundred different services, figure out what you can give access to and not access to. We take care of that. So it's a Steve Jobs way of doing it, end-to-end integration.
And how do you think about local models? I have started running Kimmy 2.5 on a Mac Studio. It's not as good as Claude or Gemini or Grok, but you can probably do about 80% there for free. Yeah. Essentially. Yeah. And so that's quite compelling considering some of my other bills, Claude, and stuff were getting expensive. So do you have one of those? You started testing on your local Mac studio.
I assume you have a Mac studio and you're doing this yourself. Yeah. Or now, I don't know if you saw Dell and NVIDIA announced a giant workstation. Yeah. Was it a 3800? Yeah. Something like that. Something like that with 750 gigs of RAM. So what do you think about the desktop going back to workstation slash server status?
I think it's very promising. My prediction is it'll initially start off as a sub-agent.
So whatever you need to go, like your tax returns, your personal photos, your emails, your calendar, all that stuff, those local apps, your personal notes, very personal notes, you can make sure that the models that access those tokens will be running on your local hardware if you want to, if you're that privacy conscious.
And more complicated stuff that accesses your data that's already on the server side, example, your Google Calendar, your Gmail, this is personal data still, but an AI runtime can access that through your connector, your Google Calendar connector, your Google Workspace connector. And that could run on the server side because anyway the data is on the servers, it's not even lying on your device.
So that sort of hybrid orchestration is where we are headed to. I don't think it's a dichotomy between fully local versus fully server. It's all about choice. And anyway, when you're on your phone, you don't care actually which server that workload's running from because it's not going to be able to run on your phone anyway. The chips need to exist on a Mac Studio or a Mac Mini or on the server.
Or this new Dell that's coming out. And I really think the idea of... spending $10,000 on a powerful desktop will appeal to people if it lowers their $500 a month Claude bill. This is an incredible savings. Plus you get the benefit of privacy and not educating the language models on your personal data.
Yes. And it's going to be like you're buying a refrigerator. your internet modem, like the cost for these will eventually go down. But it's not gonna feel like you're wasting your money. Every home has a lot of other sensors that runs your home. That'll also be part of this orchestration loop.
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Chapter 7: How do energy sources impact AI data centers?
Computer exists as a Slack bot right now that you can add to your Slack workspace on the enterprise plan. And our entire company works like that. People are talking more to computer on Slack than to other people.
In our first volley, we were sending reports in, but it wasn't interactive. That's perfect. So now you've got your company going in two different directions. This incredible consumer run you have. How many people are using the product every month? Several tens of millions. So tens of millions of people. That's very much similar to the trajectory of the Google and Yahoo consumer business.
Now you've got corporate. How are you doing on the corporate side? Thousands of companies?
It's the fastest growing business for us. It's growing faster than the consumer and revenue. And things like computer unlock entirely new possibilities. For example, we've saved more than $100 million for our Enterprise Max customers who are on the highest tier of enterprise. Explain what that is. What does it cost? $200 a month per person? So there are two tiers.
One is the Enterprise Pro, which is $40 a month. And there's the Enterprise Max, which is $400 a month. And on a computer, after you run out of your credits, you would pay for the tokens. You pay for the usage.
Are you making money on the $400 a month, $5,000 a year one? Or at this point in time, are people going so crazy?
One thing that Perplexity has is every revenue we make, unlike certain other wrapper companies, every revenue Perplexity makes has positive gross margins. Got it. Because we're not just selling tokens. Most of our revenue is recurring because people are paying a subscription fee. And because we route through multiple different models, we are very efficient in terms of how we spend on the tokens.
Because we have all this advantage with RAG and orchestration and search, we don't actually need to blow up the context window of the models. As a result of that, we have positive gross margins on all the revenue, every single penny we make. We make profits on that. Overall, the company is still yet to be profitable, but we're working towards that. You've had the opportunity to exit.
A lot of rumors, Apple, other people were like, hey, this is a great team. How many people on the team now?
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Chapter 8: What future developments are expected in AI technology?
And Dario, CEO of Anthropix, said recently in an interview that models are specializing. Towards the beginning of last year, people thought models are going to commoditize. But towards the end of last year, models started specializing. Even within coding, Cloud Code and Codex have very different capabilities. Our iOS engineers love using Codex. Our backend engineers love using Cloud Code.
So even within a specialization like coding, models have their own unique specialties. And there are many other use cases outside coding where different models are good at different things, which means the orchestra conductor that has no one model the horse in the race can win by providing a very unique value and service to the customer that each of these amazing names that you mentioned cannot.
And so you're buying tokens wholesale from them and then you'll charge customers to do it or do you think it's all...
We're going to take care of all that orchestration. Yeah. So you don't have to manage tokens across different models.
Because I authenticate a couple of my different accounts, my pro accounts, into Perplexity. But does it... I don't have enough knowledge to know if you're abstracting that and people can just search across them and it's part of their Perplexity subscription?
No, we're not bundling subscriptions into other AIs. Yeah. We just ping the models directly. Got it. What you get in us is the Perplexity orchestration. Got it. The harness. Right. So when models are kind of specializing, there's a bigger value in the one who knows how to build a great harness. Right. That can take the best in each model.
Does it auto route today or do you still have to drop down somebody's got to pick? It definitely auto routes the best model for each prompt. But we also give users the flexibility to pick whatever model they want.
What do you think of, I've seen a bunch of startups hack this together, but doing the same query across multiple. We built a thing called Model Council. Model Council, yeah.
Yeah, so that's one of the modes and perplexity where I saw Jensen say in one of the interviews that he puts the same prompt in five different AIs and sees what each of them says. Yes, everybody does that, yeah. But then you still have to apply a biological compute to read every answer and then figure out where they differ. It's like talking to five lawyers about your trust
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