CoreWeave, Inc. (CRWV) Earnings Call Transcript & Summary

I think it's hard. I think building infrastructure is difficult. I think that some will be more successful than others. I think that it is a capital-intensive industry and capital-intensive industries will tend towards periods of proliferation and they will tend towards periods of consolidation. I think that during a time where the demand is so incredibly overwhelming, it is easier to launch a company. But the difference between launching a company and talking about a contract you signed and actually being able to operationalize and deliver and maintain that infrastructure, the more experience you have with it, the more you realize how difficult it is to do over time. And so I think there's going to be some interesting outcomes that are going to be associated with delivering this new infrastructure.

Absolutely. Okay. Let me ask you the supply-demand question in a slightly different way. So we've established that we're in a period of incredible supply constraints. At some point, and we can debate when that is, the industry will come back into supply-demand balance. Right now, CoreWeave gives us these amazing data points on contract renewals for H100s and pricing dynamics. How should we think about longer term, what happens when supply-demand normalizes? And the bear case we hear from investors all the time is, well, you have hyperscaler customers who can simply then pull back what they've "outsourced" to CoreWeave and retrench back in the stack. And so maybe just would love your thoughts on the longer term for CoreWeave in a normalized supply-demand environment.

So I think my last answer is the most like politically correct answer I could have given to that question. Look, CoreWeave has been leading the AI cloud space now since its inception. I would argue that largely CoreWeave has really built the space. What started out as a cost-plus environment where we were able to win contracts because we would go to large consumers of compute and say, hey, we'll build this for you and they would say, well, we're willing to pay cost-plus for it. That kind of built our business and allowed us to scale. And with scale in this business comes all kinds of incredible ways of driving economics and driving margin, which we're really excited about, and the market will see that continue to accrete to us over time. The other piece of it is just these companies, they entered into contracts with us. And when we IPO-ed, they were like, oh, you're never going to do that again. It was just a one-off. And then they came back and did it 5 more times with us, and then they did it across the industry. And the argument continues, oh, they're just going to pull it back. And that doesn't really -- those 2 things, right? It doesn't really match with the fact pattern, right? They build data centers and they could have pulled those back, but they didn't, right? They go through these periods where they build their own data centers and then they go through these periods where they go to third-party data center providers. So that's one thing. We do not believe that they are going to pull back, right? We just don't think that, that is the way that it's going to play out. But the second piece of it is what these contracts did for us was provide us with time, right? And with that time, you have seen us build a cloud that is specifically built to be able to support AI use cases. And with the software stacks, with the infrastructure, with the reputation for delivering this incredibly high-quality product, we have begun to win a broader and broader universe of clients. And so some of those contracts may come back to us. Some of those contracts may not. But the size and scale of the business, our ability to support enterprise, our ability to support governments, our ability to support our own products internally, our managed inference product, all of those things, those internal demand things, those are incredible paths to building a sustainable cloud. And we believe that is a necessary part of exiting this period of disequilibrium in a way that allows you to be successful and a hyperscale provider of the infrastructure that is required by the world for artificial intelligence. And we're really well positioned to do that.

Let's talk about building the cloud. So there are a number of pieces that I think are interesting. We could talk about managed inference, for example. There's a CPU pull-through piece to this as observability. Maybe just take a step back for us. What are the pieces that you think are most strategic to going from AI neo-cloud to full-blown long-term hyperscaler winner?

So the answer to that is we meet our clients where they are, right? And so when we go and we work with the client, they tell us what they want, right? And we can be extremely flexible about integrating, hey, we've got this particular type of storage we want to use. Okay, we'll build it into our system, not a big deal. So like it really depends on who the client is, what the workload they're trying to support will define what we bring to market. The attach rates, and I think I spoke about this maybe 2 earnings ago, like 75% of our clients are using 3 different services, right? 3 different silos within our product suite are already being consumed, right? That's exactly what you want as you're building the cloud. That is exactly the way that you measure how people are going to consume what you're doing. And I would argue that the percentages are even higher than what they look like because some of our clients are these really, really early-stage ventures that are not up to product # 2 and product #3 yet. They're still kind of like working it out. And so we really think that your -- you've got to build a suite of software services, whether it's storage or memory or networking and all the different things that people are going to consume and then go ahead and put it in front of them and say, look, we can help you build your company. We can help you deliver your service. We can help you be successful because of the infrastructure that you are going to be able to run your products on. And that has been an engine for us.

Absolutely. In terms of road map, anything you can share on where customers are pulling next?

We are -- we've always talked about the fact that we are client-led, right? And being client-led means that you spend time with your important clients and you talk to them about what they're trying to accomplish and you let them kind of help you build your products. So a lot of the storage solutions that we have were built specifically to help folks that had a very specific problem. When you look at the way that our road map has led us internationally, right, to ensure that we are both resilient from a capacity factor, but also able to serve different markets with low latency infrastructure. I mean like all of those things are really manifestations of our clients and what they need, and that really does inform the road map. I mean sitting at the center of the flywheel, having the conversations with all of these incredible entrepreneurs or existing companies that are integrating artificial intelligence into their workflows. And that is really, really fascinating, right? So just absolutely fascinating to see how that works.

Maybe the perfect case study is actually someone like a Caterpillar, which you were talking about earlier. Give us a sense for the flavor of the enterprise customer conversations that you're having today where customers will say, look, we have previously used 1 of 3 hyperscalers. And we're now incrementally moving net new workloads or even brownfield workloads over to CoreWeave. What does that look like in practice?

So what it looks like is when we bring a customer like that over, we try to wrap our arms around them and support them through the process, right? And so it really depends on who the customer is and what they're trying to build and what they're trying -- and we try to make it so that all of their resources are being focused on integrating AI to make their company better, faster, stronger, whatever -- however you want to talk about it. What we really are trying to do is simplify the process of ensuring that they have the most performant infrastructure to be able to train internal models, right, that they're able to serve their inference calls effectively. All of those pieces have to come together for us to be viewed as a viable alternative to one of the hyperscalers. And so the breadth of the services, like it is -- it's not endless, right? We really do say, hey, this is our lane and our lane is to be able to serve all the components that are required to drive artificial intelligence. And then we don't wander too far from it, right? Like the world doesn't need another solution for X, Y and Z. What it needs is the best solution for the AI workflow, and that's where we're focusing our energy.

Mike, you've already touched on the diversity of your customer base. Do you have a view or do you -- how do you think about the mix between frontier models and open source, open weights? Does that have a second derivative impact either on the health of your customer base or the health of your business?

So we're in a position -- when we think about like closed source versus open source, believing that this is not going to be a binary breakdown, right? There are going to be use cases for open source. There are going to be use cases for closed source. As a supplier of the cloud that is required to run these use cases regardless of whether it's open or closed, we're sort of indifferent, right? We want to make sure that our infrastructure is able to serve both sides of that fence equally well, equally as efficiently. And that's sort of how we've approached the problem. I think there are other people that have to spend more time deciding which models are appropriate for which workflows within their organization. But for us, it's the idea that we will provide the computing power that you need in order to be successful regardless of how you choose to allocate the infrastructure in terms of whether it's open source or closed source model. So we're sort of a cop-out, but we're sort of indifferent on that, right? Like it's just more demand for compute is better for our business.

The other part of the strategy that I wanted to ask you about is the relationship with NVIDIA on selling through pieces of software. Maybe bring us up to speed on some of your ability to take proprietary CoreWeave software and sell it outside of the immediate CoreWeave stack.

Yes. That was pretty cool. Yes, it was. It really was. We -- NVIDIA designated our software stack as a reference architecture. And that's super exciting for us because it really is a recognition of the quality of the solutions that we have built, right? And we're -- that was great. We had some really excited engineers that have put their blood, sweat and tears into building the infrastructure. And so it was a wonderful recognition that they thought the quality of what we have built is high enough to be designated a reference architecture. And so that was really great. What it means in reality is that the ability to provide third parties access to our software solution on their infrastructure allows us to get some leverage on other balance sheets. And that's a great thing, right? Like we can go into companies that want to own their own infrastructure, and we can provide them a software layer. It's called Omni is what we call it. But it also allows us to build infrastructure in jurisdictions that we maybe are not comfortable owning or delivering an asset-heavy solution to for whatever reason. And once again, that's a great way to make use of other entities' ability to take the risk around the physical infrastructure while still being able to generate returns and to expand our TAM for the products that we bring to market. And so that's been a very exciting part of our portfolio. We closed a deal on that a couple of months ago, and there's a whole bunch coming down the pipe that really look like that for various reasons why we would use that model instead.

I want to spend a couple of minutes here on capital structure because the appetite of the debt markets to own neo-cloud businesses ebbs and flows over time. Tell us a little bit about your plans for capital raising. How do you think about the optimal balance sheet structure for a company like CoreWeave?

Yes. Look, we took a strategy around building our company that is, in many ways, part and parcel for how you go about or how the world has historically gone about building infrastructure and capital-intensive business, right? If you're SpaceX and you're valued at $2 trillion and you can sell some equity, you sell some equity and you can go ahead and build whatever you need to build because $2 trillion is a lot. But if you're building organically, like we were, the question is, how do you raise the capital to build at a scale that allows you to be of relevance. And the answer to that was clearly the debt markets. And I think that the market struggles to understand what we have done in the debt markets. And the largest engine of our borrowing is occurring at an SPV level, where we are providing transparency through to the credit of the offtake. And that is why we were able to build structures that allowed the credit market to underwrite what we were doing because they were basically looking through and saying, okay, it's Microsoft on the other side of this transaction. The money is going to flow into the SPV and then pay back the debt before it goes back to CoreWeave. And that was an incredibly effective engine for raising capital at an order of magnitude that has rarely ever been done by a company as new to the market as CoreWeave. That was a wonderful way of enabling us to drive the scale that we needed to be able to serve our clients. We also raised some debt at the ParentCo. We raised convertibles up at the ParentCo. We have a very -- not a lot of religion around this stuff, right? Like we look at the space and say, what is the most effective way, the cheapest way, to raise the best possible capital to be able to execute on our road map. And that has been the defining North Star around the capital market structures that we use. And we have been the tip of the spear around the innovation. We did the first GPU transaction. The -- just kind of looking back in history, the last debt structure we did was the first time anybody has been willing to take -- or any of the lenders have been willing to take renewal risk. Like that's an important step forward to being able to access capital markets to be able to take advantage of the short-term contracts because now the lenders are taking renewal risk. The one before that, we borrowed -- we were able to get the [ venture rated ] so that we were able to borrow at A- credit, like that's unbelievable, right, to be able to borrow for a company like us to be able to borrow at such a low cost of capital was kind of a crowning event within the group that builds these capital structures. It was incredible. And so it puts our borrowing really at par or close to at par with many of the hyperscalers that are out there raising capital, and that allows us to operate more effectively, more competitively.

Mike, congrats on all the milestones. Please join me in thanking Mike for his time.