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

Okay. Great. Thank you, everybody. I'm Karl Keirstead. We've got Tim Arcuri, who covers NVIDIA and honored to have Nick Robbins of CoreWeave here. Today has been a fun day for me because this has been the theme of today, partly by design, but we had Navios on stage earlier today. We just got off stage with Crusoe and Lanci Lancium who are building out the next star gates. Now we've got Nick to talk through CoreWeave story shortly afterwards, I think in a couple of hours, we've got Blue Owl Capital and Magnetar to talk about how they're financing all of this build-out. So this has been a fun day actually to go deep into this whole GPU cloud build-out. Did you want to start with -- Nick's got some profound words he'd like to share at the very start.

Yes, this is the CYA. All right. Before we get started, I would like to remind you that CoreWeave may make forward-looking statements during today's fireside chat. Actual results may vary materially from today's statements. Information concerning risks, uncertainties and the other factors that could cause these results to differ are included in CoreWeave's SEC filings. We did it.

Thank you, Nick. Let's chunk this up a little bit. Let's talk about some demand-related questions. We'll talk about some supply issues as you're scaling up the infrastructure. Tim is obviously going to want to talk to you about GPUs and CPUs and stuff like that. But let's start on the demand side. So obviously, CoreWeave put up 134% revenue growth this past quarter. Your backlog is $55 billion. That's 10x your revenue run rate. Things appear to feel pretty good. You've begun diversifying more away from the earlier high concentration with Microsoft. Maybe, Nick, I can ask you to just describe to some extent, the demand you're seeing today. I mean it feels phenomenal, but you can go a little bit deeper.

Yes. I think the words we typically have used over the recent past to describe it have been -- they've ranged from insatiable to relentless to tremendous. It seems that we have, even within this year, kind of had a couple of step functions upward in demand, where I would say to start the year, demand was pretty relentless and then we found ourselves over the summer where seemingly all of our customers wanted -- or most of our customers wanted a whole lot more and a lot of potential new customers wanted a whole lot more. And yet where we find ourselves today, as we kind of said on our earnings call a couple of weeks ago, is it seems like that's taken another step upward, right? It's -- I think as we see more use cases for the compute that are delivering ROI that are transforming business industry world, right, more people want more. And frankly, we continue to find ourselves in the position of how do we bring on capacity quicker to service this demand because that continues to be the constraint on growth for our business.

Okay. Let's talk a little bit about one layer deeper and your judgment on where that demand is coming from. Is it that the model providers woke up and realized the merits of reinforcement learning, they needed more compute. Is it that everybody underestimated, let's say, the compute intensity per prompt on consumer AI products like ChatGPT, therefore, you need a lot more inference compute. What was the trigger for these step-ups in compute demand this year as best you can estimate?

I don't think there's a single one. I think it's many things all in one. I think scaling laws are continuing to hold in a way, right, where even on a pretraining basis, you want more. I think the proliferation, right, of post-training and other types of training that are very compute-intensive and hungry, right, catalyze or if like further accelerate that. And then on top of that, yes, you've seen -- from an inference perspective, the world see innovation there that is also more compute-intensive, namely reasoning models or change of that, right? And I suspect that as you see more AI labs and enterprises bring AI product to market, those products will continue to become more compute-intensive in hungry. And so you have all of these tailwinds pushing to the same bottleneck of -- they all point in the same direction of more compute.

Okay.

Maybe a year ago or 14 months ago, I think the world very simplistically thought of training is compute-intensive, you do it once, then you just ship a model, and it's just about latency and inference is very compute light. And it's just -- you ask a question, get an answer, right? Like 14 months ago or so, I think 1 was released and the world saw a new vector of inference. And I think the proliferation across those 3 things is the answer.

What about demand from a customer cohort? Obviously, the frontier model providers have been fueling a lot of this compute demand, much of it. But now we've got a number of smaller AI natives, the pool sides of the world, Cursor, et cetera, fueling demand. And at some point in time, the enterprise side will kick in. So where are we in that demand curve for -- maybe you think of it differently, but frontier model providers, AI natives and traditional enterprises like UBS?

Sure. I would say from a frontier AI lab perspective, it is more pleased, and it's a question of how do you access that greater compute and continue to grow your footprint, right? And I think that the runway there is very long based on where we are today. I don't -- maybe it won't always do this way, but it almost feels endless. I would say on the enterprise side, we are undoubtedly earlier in the journey, right? I would say it's clear with the -- away from hyperscalers and away from how folks like Google and Meta are clearly using AI and GPU compute to reaccelerate growth and drive returns in their business. But when we think about the UBSs, I think clearly, there's going to be a role to play for AI labs who are helping productize AI and sell and the growth that has been unbelievable, right, of OpenAI and Anthropic and businesses like that, that are adding billions and billions of billions of ARR in a given year at an unprecedented pace are validating that. But I would say enterprises are going to need some help to get there, right? We made an acquisition of a business called Monolith a couple of months ago. That was really centered around helping bring AI to the physical world. And what they do and what we'll be doing together, right, is focusing on more compute-intensive industries like industrials and in Monolith's case, autos that they have mature workloads and they have a clear use case in this -- one of them, for example, being battery optimization and experimentation, but they don't necessarily, you have the product to do it. And so -- we think about how we can deploy our own software and services to help accelerate that for some of the older world enterprises, while also serving the AI labs that are clearly driving that market ahead.

Okay. I'll ask you two more and then we'll turn to Tim. On the supply side, Nick, obviously, on this last call, you highlighted some pushouts. Now that's probably -- I'm sure standing up gigawatt scale AI campuses is one of the more complex go-live supply chain problems that anyone will ever see. So the notion that there could be a quarter delay due to partner delays, I don't think it's shocking to anybody. But can you describe perhaps your confidence in hitting those supply targets? And if there's any new supply bottleneck that seems to be bubbling up that might be interesting to flag for the group?

Yes. And -- you're absolutely right. Like the scale at which and the pace of which these data center and AI campuses are being built is just simply unprecedented, right? And there's not a playbook, right? Like the joke -- I don't know if it's joke, that I like the analogy I like to make is like you're asking someone to build like the Death Star LEGO set without the instructions, right? Like it's just like it's -- you don't know what you're doing, you figure it out, and it makes you better for the next one. I would say the supply chain issues that we're seeing are not new. We've been pretty artfully, I think, navigating those for the last couple of years, and we will continue to. And it's not a single thing, right? People ask, is it that there isn't enough labor? There isn't enough labor. People ask about long lead time equipment. There isn't enough long lead time equipment, and it's called long lead time in part because it takes a while to get it, right? And so we're seeing the confluence of those things. But again, we've scaled out clusters that now, I think, at 41 data centers across the North America and Europe in the last several years alone. We've been doing that while working through this and we will continue to do so. When it comes to the fourth quarter, we got on our third quarter earnings call, we were pretty disappointed that we had to revise guidance because something was slipping bit versus kind of the expectations, we had underpinning our guidance. A matter of weeks, I can count on my hands, right? But when it slips from quarter-to-quarter, it appears to be pretty acute. We talked about how the vast majority of that comes online in Q1. We are tracking very nicely versus what we said a few weeks ago on our earnings call. But I would say how do we keep working through it. I would say, self-build helps a bit. We've talked about our first couple self-build projects in Kenilworth in New Jersey and Lancaster, Pennsylvania. An externality of that has very much been that we've been investing this year and meaningfully growing our data center teams, data center technicians, project managers, et cetera. And that enables us to not only have more of a self-build capability, which is a small minority of what we do today, but it also allows us to have more boots on the ground in more places and take our own view of the timing and development of those sites, such that we can more transparently and accurately communicate those to our customers and folks like the people in this room.

Okay. Let me ask you one more. We'll hit you with a hot subject. So that's the AI bubble concerns that people have on their minds because I'm listening to you and you're talking about insatiable demand, you're expressing confidence in standing up significant amounts of supply. Yet we can contrast that with some thoughtful investor concerns that you and the whole GPU cloud infrastructure business are overbuilding, that the demand is not going to be there, Nick, in 3 years' time. So take that head on and give us your rebuttal.

Absolutely. First and foremost, disagree with the assertion that there's a bubble, right? Like when we look about -- look at the pace at which AI is being rapidly adopted and monetized, right? And I talked a few minutes ago about like the tremendous and like overwhelming growth, it seems that -- of course, we're experiencing, but that's a direct output of leading AI labs and how quickly they're growing. We're seeing what some -- how the hyperscalers are monetizing, and we're all saying there's just simply not enough capacity, right? I think people like to make a comparison to some other things like the dot-com bubble, where it's like -- and other people can yell at me if they disagree. But like from my perspective, it doesn't seem like you were really ever hearing in 1998 or 1999 you don't get it. There's just not in the fiber, right? Like it was kind of quite the opposite. We're building for something on the come. We are just trying to build what our customers are demanding from us right now. We are signing longer dated take-or-pay contracts for capacity that comes online in the next 9 to 12 months that they are committing to use for the next 5 years, right? The cash flows from that are paying for that CapEx. They're naturally deleveraging to pay down our debt and delivering us free cash flow, right? And that's leaving us in a position where we will have effectively depreciated and paid for infrastructure some cash flow and the opportunity to continue to monetize what we think is the most performance solution in the market going forward. But it's just a very little of what we do and nothing of what we do on the GPU CapEx side is speculative. We are building to demand. We are not trying to outpace it. In fact, we are struggling, and I think a lot of the hyperscalers have said, they are struggling just to keep up. I think that is a very different paradigm than some of the other kind of -- situations people claim to be analogous.

Yes. Got it. Okay. Helpful, Nick. Over to you, Tim.

Great. Thanks, Nick. So another debate you hear about is vendor financing. And you signed this deal with NVIDIA. And at the time, people thought, well, that's vendor financing, but it's not at all really. So can you actually talk about that?

Yes. It's -- it was -- you're absolutely right. And it came in -- we got some other questions about it, too, just based on headlines. And I think it's misunderstood, so thank you for asking. We announced a $6.3 billion partnership or collaboration with NVIDIA, I think, in September. And that looks like a customer contract, right? Like virtually all of our other customer contracts, save for a key exception, which is this concept of interruptibility, which allows us to say, NVIDIA, we're pausing your access to compute. We know you have use cases for it and you would love to have it, but we're going to pause it and we are going to go sell it to somebody else. Who that other -- somebody else is in this instance is the -- think of the small- and medium-sized companies or smaller AI labs that are not in a position today to commit to the 5-year contract that we like require to go buy the server for a frontier scale cluster, right? And that means that the barrier to entry is pretty high because if you can't -- if we don't think you can afford it, we're not going to go buy the servers and build the cluster on your behalf and give you the capacity, right? And so what we're seeing here is like it's almost like a product, right, where it's -- it's a win-win-win for us, for NVIDIA for the end customer who we interrupt NVIDIA with where, hey, if no interruption happens, which is unlikely to be the case anytime soon, that NVIDIA gets compute and they have a use case for it. But more importantly, we are able to go service and acquire the smaller customer who wants to be on our platform that we want on our platform that NVIDIA would, I'm sure, love to have on our platform because we do deliver the most performing compute in market, but that you just can't afford it. And so they get access and we don't sacrifice our discipline around CapEx, while being able to go out and acquire a customer that otherwise the barrier to entry would be too high. And hey, knock on wood, some of those customers grow and they graduate into their own direct contracts because they've scaled, but a big part of their scaling is likely because they have that access that we have unlocked through that partnership.

Right. So it's not like they do a deal just for the sake of moving GPUs. This is for the sake of expanding the TAM.

Absolutely, expanding the ecosystem and reducing that barrier to entry for those long tail of customers who aren't the world-leading AI labs who have seemingly limitless access to capital.

Great. And then another question that -- one thing that I like to look at to determine the supply-demand balance is I'd like to ask companies like CoreWeave, what the pricing is for the oldest instance that's being used for AI workloads, which in your case is Ampere?

Yes. It's certainly Ampere and then it's L40 and Hopper, right? And the pricing for each of those, Mike went on to you the day after earnings, in fact, we said we're virtually sold out of Hopper of Ampere of L40. And when you look at the pricing quarter-over-quarter-over-quarter, it's proven to be. We had our first, and I'll highlight it's our first. It's not an example. It's the only example we got of a large scale, so call it, 10,000-plus H100 cluster begin to approach contract expiry. So it's probably 2 quarters in advance. And the end customer there said, we want to go recontract this out for an extended period of time and they like using that for an inference use case. They saw a really attractive ROI on that cluster and said, we can -- we are comfortable continuing to pay a very similar price per GPU hour because we understand the ROI, it is tangible, and it is attractive to us. So let's go keep doing that.

Got it. And maybe just one last for me. There's this notion that you're going to wait around and you're going to pick and choose generations from NVIDIA. You're going to skip over one generation, not just CoreWeave, but just customers generally. How do you -- this idea that customers will, "Oh, I'm not going to buy Ruben, and I'm going to wait for Ruben Ultra, or I'm not going to buy Blackwell, I'm going to wait for Blackwell Ultra." How do you kind of think about that?

Yes. I think our experience has been more of highest end of we're going to buy some of this, and we're going to buy some of that, right. And we are pretty deeply entrenched with our customers and understand like, hey, you want to buy some Blackwell, but you're not going to buy everything in just Blackwell right now because you know you're going to buy some VR later, right, and probably some fine after that. And so we take pride in being the first to market with seemingly almost every generation of NVIDIA GPU technology over the last couple of years. I would expect that to continue based on the customer conversations we have. We think that the demand is pretty rampant across generations and people are being thoughtful around how much they buy this with that in the future. But we haven't seen someone say, "Oh, I'm going to skip over this one." It's more, I would say, of a timing perspective of like let's say we're beginning to talk about a data center that might come online in early 2027, then the customer has to make the decision of do I want to keep working with Blackwell because by then, I'll have scaled Blackwell clusters of configured software, by engineering, we'll devote time to it? Or do I want to get started and push that to VR. But haven't seen it as much as one or the other as much as how do we sequence the timing of everything.

Got it. Back to you, Karl.

Yes. I'll ask you 2 or 3 more, and then there might be time for 1 or 2 more for you, Tim. So on the fungible infrastructure question, let's dig in that a little bit because all of us listen to Satyam's pods. And he's very fond of saying that Microsoft is building a super fungible infrastructure, not for any single customer, any one location any single workload type, but others are. And therefore, they're absorbing a lot more risk than we are. Can you comment on what CoreWeave is doing?

I would say were fungible fleets, I think, have been something we've been pounding on the table on for the last several years, right? And part of that is location, right, where we have some larger campuses, we're working on in certain locations, and we have some other place -- smaller campuses where location, we think matters. And we think kind of customers want a mix of all of the above. It's not necessarily one or the other. But more importantly, right, it's the software, right? It's how we build the technology stack to fungibly serve across training and inference such that we don't -- our customer doesn't tell us what they're going to do. We enable them to do training one day and inference the next. And I do think that fungibility really matters. I do think the flexibility for us to be able to take a cluster and turn it into a bunch of small clusters or one massive cluster or to have one customer use it one day and a couple of weeks later, the next customer might be using it for something else. That's pretty critical, right, to the evolution of workloads and AI that will continue to develop in the coming years.

Okay. Let's talk about another aspect of the Street concern, and that's not -- it's not so much on will demand be there. It's more like will financing be there because Microsoft and Meta and Google don't need to lean on debt capital markets or vendor financing or GPU leasebacks to finance it. But CoreWeave does. And by the way, so does Oracle. And so there are several other emerging GPU vendors. So how would you describe as best you can, the state of the financing demand right now for building out these AI infrastructures. As I mentioned, we're going to have Blue Owl, Magnetar up here this afternoon. Trust me, we'll ask them. But...

Our partners.

What's your perspective? How healthy is it today? Are you seeing any pullback?

So I think taking a step back, CoreWeave has been built on, I think, 2 vectors of excellence, technological and engineering excellence and then excellence at navigating the capital markets and designing our customer contracts in a way such that they are maximally financeable, such that it's no secret, right, like the -- there's been some volatility, right, in the equity market, right, even in the bond market, right, in -- over the past few months. But I think you got to focus on how we primarily finance our business, right, which is these asset level delayed draw term market or maybe it's 3 years. Are the customers -- are the contracts written the right way. We know how to do that. We pioneered this market. It's can CoreWeave execute, right? I think we, over the last few years, have built a track record of excellence there. And frankly, our first delayed draw term loan when we were pioneering this market was for investment-grade credit. And people have gotten more comfortable with us and understand this is what we are best at. We've driven down that cost hundreds and hundreds of basis points to the point where over the summer, we financed an unrated customer at SOFR plus 400, where we're talking about 500 to 1,000 -- or 900, I guess, basis points of savings right there. And what we are continuing to see is the depth and breadth of that market is incredibly robust. If you understand how to, like I said, write those contracts, structure that debt, so it's self-amortizing and also build your backlog in a way such that you are being mindful of things like creditworthiness. We talked about how north of 60% of our revenue backlog at the end of the third quarter was investment grade, right?

From a technology standpoint, what is CoreWeave's enduring advantage when, let's say, any of your large contracts come up for renewal, the world is no longer supply-demand constrained, they pull that into first-party data centers, and you need to sink, or swim based on how good you are executing from a technology standpoint. Anybody can acquire data center space, cable together NVIDIA GPUs in a server -- I'm massively oversimplify. But what is your special sauce, Nick?

So I got to argue for one thing before I answer the question is. I think building supercomputers is a whole lot more complicated than buying some racks, bolting them into the ground and pushing the on button.

I understand. I oversea it...

Yes. And -- by the way, I think we have to continue to kind of explain that to the world. And I do think we're at a point in time in which the world might -- it might be more difficult to differentiate, right?

Yes. That's what [indiscernible] relative to others.

Yes. And I think -- so now to dive into the meat of your question, right, we have purpose-built this cloud from the ground up to deliver maximal performance. That has been an advantage of ours that has allowed us to grow our footprint and acquire customers at a rapid is underselling at rate. We are continuing to innovate right? We are developing new products and services that fit within GPU or AI cloud that are not germane to general compute, but are purpose-built for these types of workloads and what customers will increasingly need in the future. Take things like AI object storage, right? Like that's a product that fits into our broader storage business that we announced in the third quarter has grown to north of $100 million of ARR, growing like a weed. And that product was built in direct, I would say, response to the advantaged position we're in, which is that we are deeply entrenched with our customers on a technical level. We understand what their pain points are. And when we see something that is missing, we either go out and build it or buy it, right? And so take storage, right? I think we used to live in a world, right, where you were a single cloud customer. You're an AWS customer. I think you are now increasingly an AWS customer and an Azure customer and a CoreWeave customer and storage was not built for that world, right? You had data lock in, you had high latency, if you want to move it to another cloud, you had high egress fees and transaction fees. So what do we do? We built a product that's low latency with no egress or transaction fees and customer adoption and attach has been very attractive as we've gotten started in bringing that product to market. We are going to keep building those types of products and services such that you look 5 years from now and you say, "Wow, there's a corollary to what the hyperscalers did with the CPU cloud, but they did it specifically for this technology and this world."

Tim, do you want to take a some?

Sure. So there's also this debate, I would say, that's come up lately about alternatives to GPUs. And you build whatever the customer wants you to build. So can you speak to that? Do you have any demand for, number one, AMD GPU? And most importantly, what I'm asking for is, do you have demand for any ASICs such as TPUs? Do you have customers coming to you and saying, "Hey, I want to do development on TPU, so you go out and add any capacity on TPU?"

So you pointed out, we're customer-led in everything we do, right, whether it's entering a new geography or scaling out a different type of accelerator. I would say demand continues for us to be overwhelmingly for NVIDIA technology. I think if there comes a point in time, right, in which we start to hear something different from our customers in a scaled way, not just like a phone call here or there of what do you think of this, then that may change our behavior. But for now, kind of all signals that we get, get to, we need more NVIDIA GPUs, please.

And do you think that there's any -- as the inbound calls to you, do you -- I get the sense that there are some more inbound calls who people want to talk about, well, what if we wanted AMD or we wanted an ASIC.

No, we are...

[indiscernible] calls coming more commonly on that?

Not in any notable way. But I would say like we built this technology stack from the ground up to be fungible across silicon, right? Like we were prepared for a world in which our customers want different things, and we want to give our customers what they want, right? And that embodies what we do. That doesn't mean we don't explore, right, and make sure that we are prepared to work with other types of accelerators. But again, now like the pattern and the trend has been the trend, which is NVIDIA GPUs, please.

Great. I think that's all the time we've got down to 1 second. We squeezed every bit out of it we could. Nick, thank you for coming. Having CoreWeave here, I think, makes this conference phenomenal in terms of the GPU tracks that we've got here. So I appreciate your attendance.

Thanks for having me, guys.