The Exchanges

Q already some new, new data to me, so good to hear that. Um, but I wanted to, again, like, just get into what it, what it takes to build these things, uh, these data centers. Um, you're, you're assembling them with incredible speed, uh, so I just want to hear a little bit about, like, On the ground. Uh, what does it take to put together, uh, these data centers?

A So the, um, historically, uh, You know, let's say two years ago, we were able to go out and buy capacity or lease capacity that was much further through the development cycle, right? They were basically, the shell already existed. It was a fit out construction process, which means going in and installing like the last, last pieces of the cooling infrastructure, cabinets, conveyance for all the cabling, all the hundreds of miles of cabling we have in these things. Um, but it's shifted over the past year is that now we're doing much more, uh, bespoke in-house design, right? To make sure that we're meeting the needs of what our customer's deployment is going to be. Right? So it's everything now from, okay, how is the cooling and electrical distribution designed? Um, how are we ensuring electrical redundancy and reliability? Uh, you know, how are we cooling the air-cooled side of these things? Because you have liquid cooling, there's still a component of it that has to be cooled with air.

AI assessment note: “everything now from, okay, how is the cooling and electrical distribution designed?”

Q what a lot of the infrastructure has been used for build, scaling these models, throwing more compute at them, throwing more data, making the models bigger, and then the idea is that the models get better. So are you seeing most of your demand in the training side of things? Or has it gone to inference where like companies are actually using the models, uh, and deploying them into production?

A It's a great question. And I think it, it talks to the split or this kind of delineation of where the market's been for the last three years and where it's going. Um, you know, our customer base for the last three years has primarily been the largest AI labs and enterprises that are building the capabilities of AI. Right. It's now shifted from the people building those capabilities to the people that want to use those capabilities to change business outcomes. And this is where all the enterprise adoptions coming from. Um, you know, it's, uh, one of my favorite services out there is lovable, right? You go to lovable. You can build any app you want. There's a chat bot that helps you go through it. Um, you know, we're finally starting to see people chain together these capabilities to build real products that solve problems. And our business for the last three years has really been around the creation of those capabilities and has very quickly shifted to include not just the creation of them, but the deployment of them and use in business practices. All right. So, um, one of the things that I didn't expect was that, uh, what looked like training two years ago is how inference was going to look today. Right. Is that You're still dependent upon, uh, highly connected storage. Um, you know, your backend networks become critical to this because the models are so large. So there's reall…

AI assessment note: “shifted to include not just the creation of them, but the deployment of them”

Q But can I ask, how have you set up the company to make sure that you're not the distressed asset when the contract, if the contract happens?

A Is everybody last year talked about how customer concentration and exposure to Microsoft was a bad thing, but they have a better balance sheet than the U S government. Right? Like I'm not worried about them performing in their longterm obligations to us. Like, that's basically the best possible position we can be in, and we've been super thoughtful about the way that we choose which customers to work with and how we manage the credit exposure so that we're, like, we're certain that the investments we make will be paid back, and if you look at the people that are providing us the, the debt to do those projects, like Blackstone, right, they're the, some of the most sophisticated people in the world, and for their underwriting committee, uh, committees to come in and say, yes, I wanna do this, and I wanna scale it up as aggressively as possible, like, You're telling me you're gonna pitch some financial analyst against John Gray? I'm gonna go with John Gray.

AI assessment note: “we've been super thoughtful about the way that we choose which customers to work with”

Q Why does, why does Corweave need to exist? Why? I mean, we're talking about these big companies like Microsoft, like why wouldn't they just build their own data centers? Why are they licensing it from a third party?

A So it's a great question. Um, there was a void in this market, right? And how there's a couple pieces here. Um, the biggest clouds in the world today are built off the cash engines of peripheral businesses. Right. Google's built on search. Amazon's built on retail. Microsoft was, Microsoft was built on enterprise software. Uh, we came pretty much out of nowhere. Right. And our, the, the moment in time for us to be able to get ourselves into this position was driven by crypto. Right. You mentioned earlier that we came out of, you know, Ethereum mining. Um, we were able to leverage the revenue from Ethereum mining to go out and build and deploy additional scale so that when crypto went away, we had the infrastructure in place and we hopefully had enough clients that we became like, we are an escape velocity. Right. So, um, you know, we recognize that compute was going to be valuable. We didn't necessarily know at the time what it was going to be valuable for. Like, I don't think Mike and I ever had this idea of like, There's gonna be this hundreds of billions of dollars a year in capex for AI, but, um, you know, we had the thesis that compute is, uh, going to be incredibly valuable and we wanted to own a lot of it. And we looked at that compute resource as an option, like, and we said, okay, what are the best things that we can do with this? And that's how we've always approached…

AI assessment note: “we had the infrastructure in place and we hopefully had enough clients”