How CoreWeave Sees the Market for Compute Right Now

When we last spoke to Brannin McBee, the co-founder and chief development officer of cloud company Coreweave, his business was not yet public and sourcing GPUs was a key constraint on growth. But three years later, things look pretty different. CoreWeave IPOed and has been raising money in the bond market too, as well as signing more deals with chipmaker Nvidia. In fact, investors have basically been throwing money at all-things-AI. But there are persistent bottlenecks to further growth. Chip supply is still scarce, but so are transformers and electricity. In this episode, we catch up with Brannin on everything he's seeing in the market for compute right now, including leases, Nvidia's new Vera Rubin systems, demand for training versus inference, and the possibility of standardizing the market for compute. Read more:Trump Officials Worry US Loophole Let Chinese Firms Buy Nvidia Blackwell ChipsBroadcom Slides Most Since January 2025 on AI Outlook Miss Only http://Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at bloomberg.com/subscriptions/oddlots Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Tracy Alloway?

  There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis. But sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused, and brief. Search Barclays Brief wherever you get your podcasts.

• 0:26Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw, innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting

• 1:08Speaker 01

  and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company, and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at chatgpt.com.

• 1:52Speaker 01

  Bloomberg Audio Studios. Podcasts, radio, news.

• 2:07Joe Weisenthal?

  Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.

• 2:12Tracy Alloway?

  And I'm Tracy Alloway.

• 2:13Joe Weisenthal?

  Tracy, I'm envisioning this future where like, we have to do a state of the sort of AI inference market episode like once a month, you know, like where it's like, things are moving so rapidly and there's so much change, either in terms of what models are using, or what they're being used for, et cetera, that in the same way, we would do like, you know, the occasional regular stock market episode, or whatever, we would just do, okay, what are we seeing right now in AI inference trends? Because it just feels like, the moment we do an episode, a few weeks later, it may be out of date.

• 2:47Tracy Alloway?

  We should just bite the bullet and do a weekly episode. Transform lots more into a market update on compute.

• 2:54Joe Weisenthal?

  We could do inference, I don't know, we'll have to workshop.

• 2:57Tracy Alloway?

  Odd inference?

• 2:58Joe Weisenthal?

  No, no, we'd have to, but anyway, this is like.

• 3:02Tracy Alloway?

  Lots of inference.

• 3:02Joe Weisenthal?

  Lots of inference. This is like, the story of the moment, and we know that, you know, a couple of years ago, everyone was sort of dabbling around with various things, and experimenting, and using AI, like, oh, like, write a poem for me about this, et cetera. That phase of AI is long over, and we know that companies specifically are spending a ton on compute, so much so, that CFOs around the world are getting sticker shock about their compute budgets. And there was even a headline of like Uber saying like, okay, like, $1,500 a max per employee, like, don't spend more than that in a month on tokens. So like, this is a very fast-moving area.

• 3:43Tracy Alloway?

  Yeah, you're starting to get headlines about, I guess, a corporate reckoning with AI as more people experiment and spend money on it. The Uber headline that you mentioned, apparently Uber burned through its entire 2026 AI budget in four months, basically. And like, what's more important is the COO was actually asking whether or not that was worth it, like whether they saw productivity gains, or whatever, as a result of that. The other very amusing headline that I saw in. It was citing an unnamed source. It's from Axios. So, you know.

• 4:15Joe Weisenthal?

  Oh, yeah.

• 4:16Tracy Alloway?

  Not entirely sure it's true, but reportedly.

• 4:19Joe Weisenthal?

  It was a great headline.

• 4:19Tracy Alloway?

  It was a great headline. An AI consultant told Axios that one of their clients recently spent half a billion dollars in a single month after failing to put usage limits on.

• 4:29Joe Weisenthal?

  Yeah. It's because everyone is like, oh, I just have a simple question. I want to look up our guest's title. I'm going to use the most advanced model to do that, etc. I have a theory, and we will get into this with our guest, that one of the things that will, and we've talked about this with Goldman's Marco Argenti. But one of the things I predict is that companies are like clearly, you know, they're going to keep using it more and more would be my guess. But there are probably a lot of investment made in sort of like optimal model routing, because some models are like a hundredth per query of what a frontier model is. Probably a lot of people don't know like what is the sort of like efficient frontier model usage. And so actually routing the query to the sort of most efficient model.

• 5:13Joe Weisenthal?

  I have a feeling we're going to see a lot of investment in that area specifically.

• 5:17Tracy Alloway?

  Well, there's also just the question of whether or not the models get cheaper overall as they advance, right? And we have seen some, I think Nvidia has a new system or chip out or something that is supposed to reduce token usage. We can get into that as well.

• 5:30Joe Weisenthal?

  And, you know, we did that live episode recently with Ian Dunning of Hudson River Trading. And he said a lot of interesting things in that, but one of the things he said is that the scarcity is increasingly like just the real estate component. Finding a suitable place to plug in your GPUs, at least from his perspective right now, is as much, if not more so of a challenge than securing GPUs themselves. So like.

• 5:57Tracy Alloway?

  Which is different to what it was like three years ago. Yeah.

• 6:00Joe Weisenthal?

  Yeah. So just like where you plug it in, we know there's all the like the anti data center politics out there. So it's like, yeah, we got to take the pulse of this market.

• 6:07Tracy Alloway?

  All right. Consider this our inference update.

• 6:09Joe Weisenthal?

  Yeah. Well, I'm really excited to say we really do have the perfect guest, someone we spoke to like truly feels like eons ago. I think the first thing we ever connected with this company, they've always had a lot of chips, but I think the first time we ever linked up. This company was still in the era where people were excited about Nvidia chips being used for like crypto mining and stuff like that. But we are now in this very different era and this is truly like one of the companies of the moment and that is of course CoreWeave, one of the so-called Neo Clouds, offering both training and inference services for all sorts of different AI workloads. I'm very excited to say back on the show, we have Brandon McBee, CoreWeave's co-founder and chief development officer. So Brandon, thank you so much for coming on Odd Lots.

• 6:52Speaker 03

  Appreciate being invited back, guys. And that was a fantastic intro. We look forward to hitting these topics today.

• 6:58Joe Weisenthal?

  All right, here's my question. So we know that like at the tail end of last year, and then in the first quarter of this year, everyone started using Claude Code and just this clearly a key inflection moment for sort of like overall AI demand. And then we get into Q2 and suddenly the CFO is like, oh my gosh, we're spending this much on inference. We gotta like figure things out. Just straight up, like in the last month, whatever, do you see any signs of that happening yet of these companies, which are all like still AI,

• 7:36Joe Weisenthal?

  eager AI adopters, trying to get a little bit of a handle and maybe slowing the rate of the rate of growth? Is that happening yet?

• 7:47Speaker 03

  Yeah, I think you see headlines there that there are surprises of spend, et cetera. I'd say our interpretation of it is entirely, look at the like authentic and foundational demand that is out there, right? Like all we're really doing is talking about how much consumption there is of AI and use for it. And I think that that was a real question in the market 12, 18, 24 months ago is will there be demand for AI? Where is this inference demand that everyone's been talking about? I think you're absolutely correct. January or so with this kind of like next group of models that were coming out, everyone all of a sudden and all at once said, this is what we've needed.

• 8:32Speaker 03

  Like this is the real product breakthrough. But I think it's worth keeping in mind that product breakthrough was like for a limited set of people at the end of the day, right? We're talking like coding professionals, some finance professionals, but it's a relatively small group of people that are using infrastructure at this enormous scale. And so where we see this moving towards next is broader enterprise use, like likely not seeing this whole token acting approach. And I think that that is unsustainable. Yeah. But do we see adoption in other sectors and how this can continue to spread out?

• 9:12Speaker 03

  Absolutely. I mean, you know, on our end, I think we have 10 over $1 billion clients at this point, and our financial services client backlog is in the tens of billions of dollars at this point. And so we're now talking about things outside of AI labs, outside of hyperscalers. And look, as you guys know, we support nine of the top 10 AI labs on the planet. If you exclude China and everything that's going on over there, like we have a lot of visibility into what people are doing and we're not seeing any pullback on what they're doing

• 9:53Speaker 03

  on inference today. If anything, it just remains this unrelenting demand for access to the best technology solution in the market for running artificial intelligence. And that's core we've solution in the market.

• 10:09Tracy Alloway?

  Wait, say more about the customer mix now versus say three years ago. So you have hyperscalers, you've got startups, you've got various businesses. How has that, I guess, composition shifted over time?

• 10:21Speaker 03

  Yeah, it's shifted enormously towards a more diverse customer base, right? We got a lot of flack for this in our IPO, right? People were noting that we only had a handful of large clients, that our clients were like just the hyperscalers and AI lab or two and.

• 10:42Speaker 03

  I think that we have made tremendous progress in driving diversification. So I'd say it sits probably across three buckets today, right? We have hyperscale clients who continue to grow with us. We have AI lab clients. As I said, nine of the top 10 AI labs on the planet choose Core Reef. And then we have this enterprise base and the enterprise base just doesn't grab as many headlines as you would expect, right? Because it's not these massive multi-billion dollar contracts that are being signed. But I think in Q4 alone, we added twice as many logos to our client base as we had ever done versus any previous quarter.

• 11:26Speaker 03

  Right? And that enterprise base is the one that's growing so much. And there was a point you guys hit on in the intro that I think is really worth acknowledging and was this concept of model routing? And the idea that like, not everyone needs just the latest model, that it's different types of models. I can hit different use cases. And this is something we've been talking about for a while, right? As it relates to the infrastructure side of things as well, right? Because you don't need that latest model for everything. And accordingly, you don't need the latest piece of infrastructure to support every single inference or training query that's out there. You can kind of conceptualize this matrix of different sizes of workloads relative to different sizes of GPUs.

• 12:12Speaker 03

  And all of a sudden, that tells you, my God, like H100s could last six, seven, eight years. A100s are going to last longer and it totally changes the entire conversation around the precious life of infrastructure. As that was a really popular topic during 2025, people were saying like, oh, this stuff will last two years. It's worth zero afterwards. And like, we've never seen any semblance of that because of the point you guys are accurately making, which is users are going to need to find the way.

• 12:48Speaker 03

  To use the appropriate model for their prompts and that will be solved by model rating to your point, but that just further enables this concept that infrastructure is going to be used longer. And we see that every day in our portfolio extending all the way back to A100s.

• 13:06Joe Weisenthal?

  I just want to ask a specific question about the broadening out of the customer base. And you mentioned, for example, financial services clients. When you talk about, say, a financial services client as being distinct client from one of the major AI labs, does that mean what you're saying? So it's like, I'm just making it up. Let's just say, I don't know if these relationships exist. Let's say a Citigroup has an enterprise license with an Anthropic. Does that count as Anthropic as a customer or Citi as a customer? And when you talk about this broadening out, are there essentially more types of entities who are building some type of model, not necessarily in LLM per se, but some type of internal

• 13:50Joe Weisenthal?

  house specific model from which they want to run inference?

• 13:54Speaker 03

  It's a great question. The scenario you presented Anthropic would be our client.

• 13:58Joe Weisenthal?

  Okay, got it.

• 13:59Speaker 03

  So what I'm highlighting, I want to correct a number I said earlier, our financial service clients, and this is direct to those financial services, they're approaching 10 billion in backlog.

• 14:10Joe Weisenthal?

  Okay.

• 14:10Speaker 03

  So this would be, you know, a good example of this and not something we made recently is with Jane Street.

• 14:14Joe Weisenthal?

  Okay.

• 14:14Speaker 03

  Right. That's not Jane Street coming through OpenAI or Anthropic to get to us. That is Jane Street coming directly to us and using our platform. And that

• 14:23Joe Weisenthal?

  For a model that they're building, so it's a Jane Street

• 14:26Speaker 03

  Or inference, right? It's training.

• 14:28Joe Weisenthal?

  No, no, no, I'm not saying setting aside training, but it would be inference of a model that it's the Jane Street's model of something rather than Jane Street's contract and enterprise relationship with one of the major labs.

• 14:42Speaker 03

  At the end of the day, we don't know what exact workloads.

• 14:46Joe Weisenthal?

  Okay.

• 14:46Speaker 03

  These entities are running, especially for entities like Jane Street. I would imagine that's highly secretive.

• 14:51Joe Weisenthal?

  Yeah.

• 14:52Speaker 03

  But the point I would say is more that this is not them coming through an AI lab to us. They are interfacing with and managing the infrastructure directly on our platform. And that's a really important Distinction as we grow this diversified client base. And I, again, I think that we've just done a wonderful job of executing on that over the past year.

• 15:32Tracy Alloway?

  There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis. But sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused, and brief. Search Barclays Brief wherever you get your podcasts.

• 15:58Speaker 04

  Running a business means keeping pace with the latest tech. You put a lot into building your enterprise software stack and scrapping it to integrate AI is out of the question. That's why IBM Technology works with all the apps, data, and models you need. It's another way IBM helps you build on the technology you rely on. Let's create smarter business. IBM. Visit ibm.com to learn more.

• 16:28Joe Weisenthal?

  As you've talked about, including in earnings releases, and as you can just tell from these huge token budgets, inference demand is booming, but model training is still important. But in addition to model training, should you say, okay, if you have a pie chart, the part that's inference is getting bigger, but I assume the training is also growing as well. But I'm curious from the perspective of like, say, the AI labs. When they think about growth, has there been a subtle shift from investing to push the pure model frontier, having the absolute best state of the art model versus investing in, say, better harnesses?

• 17:08Joe Weisenthal?

  Because a big reason we're excited and all talk about AI right now is really the excitement that happened with Claude Code. The final quarter of 2025 and it's like, oh, this harness has really unlocked a bunch of capabilities. Has there been a shift in investment from rather than just the purest, most advanced model to let's invest more in tooling capacity and other things that allow companies and clients to get more juice from an advanced model?

• 17:40Speaker 03

  I don't think that we're exposed to that decision making.

• 17:45Joe Weisenthal?

  Okay.

• 17:45Speaker 03

  What the AI labs has counterparties to us. The observation I would make in a behavior change for the AI labs is they want access to more infrastructure for longer duration, right? And I'll qualify that a little bit, which is a year, two years ago, we were signing three-year committed contracts. The type of contracts we sign are basically like take or pay contracts, which is the best way to finance the infrastructure that we are building for our clients. Last year, it was four-year contracts, right? They were saying, we want explicit access to Hopper for four years or Blackwell for four years.

• 18:29Speaker 03

  Now they're coming and saying, well, actually we want it for five years. And we don't want any interruption of use. We'll commit to the exact same economics throughout the full duration of the contract. You can't upgrade or change the infrastructure within it. You cannot cancel the contract. We want it for five years. And they want it at more scale, right? The deployments are getting larger and larger. So that's probably the best characterization we can offer on decision making. That AI labs are going through right now as they look from an infrastructure perspective. It absolutely seems like tooling is important, but scaling laws are still holding.

• 19:05Joe Weisenthal?

  Yeah.

• 19:06Speaker 03

  Right? Like your, your ability to advance your frontier model through accessing more infrastructure at scale holds. And that will hold through Vera Rubin, we expect, and seemingly it's not stopping anytime soon.

• 19:21Tracy Alloway?

  Oh yeah. What's the deal with Vera Rubin? Can you explain that to us?

• 19:24Speaker 03

  Which which aspect of it?

• 19:26Tracy Alloway?

  What is it?

• 19:27Speaker 03

  Oh, yeah.

• 19:27Tracy Alloway?

  Basically.

• 19:28Speaker 03

  Yeah, so it's just Nvidia's next architecture that's coming out, right? Like, the current architecture that we're deploying today is Blackwell. Blackwell comes, we deploy phenomenally in a MBL 72 configuration, which was an entire architecture change from deployment, right? If you recall, Hopper came before Blackwell. Hopper, you could deploy these 42U racks, which was typically like eight GPUs in a server case. You would take it, plug it in, largely air-cooled as well, right? We ran some liquid cooling just so that we understood the requirements of liquid cooling, because Blackwell, for our deployments, is overwhelmingly liquid-cooled in its deployment configuration. And instead of eight GPUs in a 42U configuration,

• 20:14Speaker 03

  it's in this larger 72 GPU rack. It's like an entire chassis that's being brought in. And it just looks entirely different in the data center. It's like this giant tower thing that you've seen in pictures floating around on X. So Verarubin will be the next architecture that comes out. And we've started receiving testing racks for Verarubin.

• 20:42Tracy Alloway?

  But the basic idea is like the new configuration makes the whole system more efficient, like more tokens per energy use and that sort of thing?

• 20:51Speaker 03

  Yes, yeah, I think that's kind of where you're getting to with it. But that doesn't necessarily mean, going back to the point earlier, that everyone only wants the latest generation of GPU, right? We have massive demand for Ampere, Hopper, Blackwell, et cetera. And it just varies by use case, model, and type of client as well. Like, I would qualify that AI labs are probably the ones who are lining up first to secure access to the latest generation GPUs. Whereas enterprise clients might be very focused on current generation, right? Like Hopper and Blackwell right now.

• 21:35Joe Weisenthal?

  I'm going to be honest for a second, you know, I try to keep up on a lot of things AI related, I really do. And it's every single day. It's hard. The one thing I do not keep like in my mind, if you asked me like, I liked it in the old days when it was like 186, 286, 386, 486, Pentium, and then like Pentium 2, etc. At least there was just this numerical sequence that I could keep track of in my head. And so if someone asked me like, Joe, like Vera Rubin, Hopper, Blackwell, what was the sequence? I'd be like, I gotta be honest with you. I like, don't exactly remember. And I will prioritize that at some point. But speaking of silicon, so yesterday, Microsoft came out with a big, they're really, they want to be in the game too. They don't want to just be connected to

• 22:19Joe Weisenthal?

  labs. They want to have advanced models too. And apparently it's a good model. And they announced the M A I Thinking One model. But they said it's optimized on the Maya 200 chip, which is their own chip. And this is a thing which is even, again, going back to our recent conversation, we had even a place like Hudson River Trading is thinking about getting into the customized hardware game. How much juice for this squeeze is there of aligning the model with custom silicon from your vantage point?

• 22:53Speaker 03

  What we could offer is what we hear from our clients.

• 22:56Joe Weisenthal?

  Yeah.

• 22:56Speaker 03

  On that. And it's important to keep in mind, we can run any type of silicon on our platform.

• 23:03Joe Weisenthal?

  Okay.

• 23:03Speaker 03

  Right. We are entirely customer led in what we build. Like, we don't go commit to CapEx and speculatively hope people come and use infrastructure, right? Like, we wait until a client says, we want you to go do this specific build. Here's what we want it to look like. And then we go commit to that CapEx, right? It's more like a success based CapEx approach. And the client isn't asking for anything but Nvidia infrastructure. And I think a large contributor to that is, I mean, they built this incredible ecosystem around their chipset. They have been dedicating that for, I think, over 15 years at this point

• 23:42Speaker 03

  through the CUDA architecture. And Nvidia, from what we hear from our clients, that platform just remains the most efficient, the most scalable, the most reliable set of infrastructure that is in the market. Right? So I think others.

• 24:01Speaker 03

  There's always been, I mean, if you think over the past few years, right? There's always been talk of like, what is it? But what about this other silicon and these other chips? And at the end of the day, like, people are still using Nvidia infrastructure. They're committing to Nvidia infrastructure for five-plus-year contracts in these billion, multi-billion-dollar commitments because they know that that is going to be a critical part of how they scale their business. We really don't see demands on a material basis for anything but that Nvidia compute, and that's what we are building today.

• 24:38Joe Weisenthal?

  Obviously, just to push back on this a little bit, and I'm not really in any position to push back. I can only relay what past guests have said and my own reading. So what one of our guests said is that absolutely Nvidia has the lock on model training, that if you want to train a model, that yes, Nvidia chips are the only game in town, but that for inference, there really, his view, this is Ian Dunning again, his view is there really were options. And then, of course, we had someone who was much more biased. We interviewed the CEO of Cerberus, the company that makes the gigantic plate and, or sorry, the gigantic chip. Gigantic plate. And of course, he did, but I mean, of course, he was going to say, yeah, the

• 25:20Joe Weisenthal?

  CUDA mode is vastly overrated for inference and barely exists. Now, of course, of course, he's going to say that. So like, you know, he's in a competitor. But we've also heard it from a user of inference. And intuitively, it makes sense, like, training is very complicated and all that stuff. But what you're saying is that from the customer standpoint, you see the demand for Nvidia on both the training and the inference as being steady and that you perceive that advantage to be consistent through both aspects?

• 25:53Speaker 03

  So I believe in our last quarterly report, our CEO Mike qualified that inference workloads represent well in excess of 50% of infrastructure utilization on our platform.

• 26:06Joe Weisenthal?

  Okay.

• 26:07Speaker 03

  It's the exact same infrastructure they use for training as well, right? Going back to my comment of like, it's very fungible between those different types of workloads.

• 26:16Speaker 03

  Those customers are choosing Nvidia to work with.

• 26:19Joe Weisenthal?

  Okay.

• 26:19Speaker 03

  On emprints. I, I think what you're gonna see is people will want to try at small scale other types of silicon, but the reliable, proven and remains from our perspective, most efficient infrastructure to use is Nvidia today. Does that change over time? Who, who really knows? But I think we've seen Nvidia battling this concept for years and every year they show up and like they remain the de facto choice for AI infrastructure. I, I, I think we're gonna be one of the first people in the market to see it. Because that will be a tone shift change from our clients asking us to run something else.

• 27:04Joe Weisenthal?

  Hmm.

• 27:04Speaker 03

  That hasn't happened.

• 27:05Joe Weisenthal?

  Okay.

• 27:06Tracy Alloway?

  So have the constraints on your business changed at all? So three years ago, we were talking about GPUs and how hard they were to actually get. I imagine GPU, securing GPUs is still competitive, to say the least. But are you seeing other constraints emerge like Joe mentioned in the intro? Just land usage, just places to actually build data centers?

• 27:29Speaker 03

  Land usage specifically, I wouldn't say is as much of a concern. Having a powered shell is the bottleneck.

• 27:37Tracy Alloway?

  Okay.

• 27:38Speaker 03

  Today. And let me qualify powered shell. Powered shell is effectively an empty data center that is energized.

• 27:45Joe Weisenthal?

  Yeah.

• 27:45Speaker 03

  Right? It has all the power and associated components. I can come into it and deliver electrons into a rack. Has the cooling system built within it, like it has a whole thing, right? Powered shell is, is the industry term for it. That is the bottleneck because of all of the supply chains that, that come into that. Right? Like not only do you have electricity, do you have the land, et cetera? But you have the backup battery supplies, you have the transformers, um, you have personnel, right? Let's just think about the electricians for these sites and getting the accreditation on the electrician side to be able to participate in these bills. We have to get a five-year plus apprenticeship to be able to go through that program, right?

• 28:30Speaker 03

  We can't just make new electricians leveraging a supply chain, right? Like that, that's a trade that you can't really scale efficiently. So that is absolutely the bottleneck for us. And I think our peer set that's out there right now. Access to chips. Um, I think we have a phenomenal relationship with Nvidia where we've just proven to be the best operator of this infrastructure on the planet. You know, a bottleneck that existed for us previously, I think, was access to financing.

• 29:02Tracy Alloway?

  Yeah.

• 29:03Speaker 03

  Right. We all know.

• 29:04Tracy Alloway?

  Doesn't seem to be an issue anymore.

• 29:06Speaker 03

  I, I, I would agree um with that broadly, but that's years of work and execution that has delivered that ability for us. I mean, year to date, we've raised over $21 billion of financing for our business. Like you, you don't get to do that and just go from, you know, zero to 20. Right. Right out of nowhere. And, and I think that's largely driven by our track record of execution, right? Our investors, our creditors can see this deep set of experience over the years of consistently delivering on these builds. I mean, we have over a gigawatt in active uh uh power at, at this point, right?

• 29:51Speaker 03

  Like a gigawatt, uh like activate a center level with GPUs, you know, delivered into clients. And I, I think that there's been kind of a misunderstanding in the market where, where people are conflating the concept that like, you know, something on paper is the equivalent to being physically done and delivered. And all I can say is there's an enormous gap between, you know, signing for power for delivery in 2030 versus actually delivering that into billable GPU hours. And that gap of execution is what has driven down our cost of capital so aggressively. That gap is where our business sits and why it's been so successful.

• 30:35Speaker 03

  I mean, that, that's the secret sauce is our ability to take these data center deployments and these customer relationships and deliver billable GPU hours into them.

• 30:47Joe Weisenthal?

  You know, speaking of financing, I just want to say during last year, like maybe six months ago, that might have been the sort of near peak of the Michael Burry inspired. These chips early in the last two years stuff. And one of the viral charts that you would see on Twitter was the CoreWeave CDS chart. Those have come way in. So it is, it is, you know.

• 31:11Speaker 03

  I haven't seen those charts in a while either.

• 31:13Joe Weisenthal?

  Yeah, that's right. That's the thing about CDS. No one ever posts charts of credit default swaps when they're coming. They people love to post them when they're blowing out. They have come in. So, you know, that does speak to some of this point about these anxieties having been allevied at least somewhat since the start of the year. You know, it occurred to me, like, we're talking about credit default swaps, we're talking about financing. I'm I'm sort of gearing up to write a big thing, maybe, but I'm writing it in my head currently, that there really are a lot of analogies between the business of data centers and the business of banking. And one of the things in banking is we all learned from SVB was the risk of industry and depositor concentration, that you if

• 31:57Joe Weisenthal?

  you have all your depositors are either in like, one depositor gets too big, or all your depositors are in the same industry, then you have this risk of like correlated withdrawals. And that's what obviously did in SVB. When you think about planning, and you think about, okay, here's investment, etc. How much does this come up? Sort of like, thinking about, I guess, tenant. Diversification. Yeah, tenant diversification as something that you think about in your multi-year planning.

• 32:27Speaker 03

  It's a critical aspect of it, right? As I said earlier, too, like, this was a key criticism of us coming into our IPO last year, right? Where we had that customer concentration in our revenue. And we have made enormous progress there. And I think the best way to think about it is, we could take all of our unallocated capacity, and I say that very specifically, it's not unsold capacity, implying that there's no demand for it, it's unallocated. There's intense demand for it. We're figuring out. Where it should go and that customer piece of it, I think honestly, like we could allocate all that capacity to like, single name clients, right? Like there is a pretty significant

• 33:09Speaker 03

  number of single name clients we can go allocate it out into. But I, I, I don't think that is the business we are supposed to be building here. I think the business we are supposed to be building is a diversified cloud that is supporting the, uh, uh, leading AI consumers and producers on, on the planet. I don't think we're supposed to be supporting just one or two companies.

• 33:48Speaker 04

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• 34:18Tracy Alloway?

  When it comes to financing, can you say a little bit more about what changed to make the market more comfortable with this? Because like this is the big story in markets. Just how much AI is now being issued through the corporate bond market, the equity market as we know is basically all big tech at the moment. Like what changed on the part of investors? Was, was it just pure return and performance? Or were there, I guess, efforts to like, make the contracts more robust or increase visibility into demand and that sort of thing?

• 34:52Speaker 03

  Seeing the inference aspect of it really emboldened in investors, but like that that was really just, you know, January, right? Or maybe late Q4 where you started seeing this just massive inflection of demand driven by inference. For us, right? It's tough for me to speak about, you know, other companies, but for us, like, why, why have we been underwritten at such scale and at a decreasing cost of capital? I think it goes back to that track record of execution, right? It's just the market has watched us execute and watched us deliver on these contracts. And the way, tell me if I'm going into too much detail here, but, but the

• 35:33Speaker 03

  way that we finance our business, you kind of break it into two broad buckets, right? You have parent co-financing and asset co-financing. And asset co-financing is where all of the GPUs get financed, right? It's where all of our client contracts sit. And we can take these financings and put them into SDBs, or we'll just call it a box, so to say. And

• 36:00Joe Weisenthal?

  Uh oh.

• 36:00Speaker 03

  You

• 36:02Joe Weisenthal?

  Sorry, I'm sorry. Keep going. Mentioning boxes is dangerous. On lots of connotations, but keep going, keep going.

• 36:09Speaker 03

  We put them into SDBs. And these, these SDBs, they have the infrastructure, they have the data center costs, and they have the, the debt agreements within them. And so you're able to pair this, like, five-year take-or-pay contract to an amortization schedule on the debt, and you have the revenue come into the box, pay down the amortization schedule, pay down the operating costs of the data center, and it still contributes a, it has a 25% contribution margin of profit up to the parent co, right? Like, these are highly profitable agreements down in the SDB stack. And so you take that SDB out to the credit market and say,

• 36:53Speaker 03

  Look at this instrument, like it's a discrete set of contracts with counterparties, like entities who want to consume GPU compute. You have the data centers within it, et cetera. And, you know, one of the latest ones we did was, as we call it DDTL four. This was a investment grade rated, first of its class. Yeah. No one had done this before for GPU financing, non-recourse HPC infrastructure financing. It got done at SOFR plus two twenty five. Like tha- that is a phenomenal cost of capital for us. And importantly, we were able to bring in the insurance tranche of capital, which is a massive tranche of capital out there that is looking to do allocations into the space.

• 37:37Speaker 03

  So we're kind of continuously making progress through these different stacks of capital and locking access to more and more types of investors. It's why you've seen us move into the convertible note market, into the unsecured market as well, along with taking, you know, direct strategic equity investments. But for us, it's really important for the entire investor space to understand this business. Because this business largely didn't exist before, right? Like, people weren't making loans into the hyperscale. Yeah. To go credit these build outs, right? It's on core weave, honestly, to be building this path into how do you finance the AI hyperscaler effectively?

• 38:22Speaker 03

  And I think we've just done a terrific job of it over the past few years.

• 38:27Joe Weisenthal?

  You used to be, in a prior lifetime, a trader, right?

• 38:30Speaker 03

  Yes, I was a commodity trader.

• 38:32Joe Weisenthal?

  So I'm curious, like, you know, there's a lot of interest in, and I don't know if it's going to materialize, in GPU capacity trading. And there's going to be a new contract. We recently interviewed the CEO of Compute Exchange, and they're very close to having something listed on the CME. From your perspective, because I don't have a view on this yet. On the one, like, you see, like, okay, a big AI company does a five-year contract. As you say, the duration is lengthening, we're going to lock this in. I don't- Know like what the need is for tradable compute in that environment, etc. What's your guess? Like, do you anticipate that there will be a sufficient ecology of hedgers and speculators

• 39:16Joe Weisenthal?

  such that there will be a liquid market for tradable compute?

• 39:20Speaker 03

  I think it's a it's a very much a timeline question that's out there. Short term, no. And let me offer why no short term. And then I'd say maybe in the long term. And it all comes back to fungibility, right? If you think about gold, gold is defined by its chemical composition, right? And there's no question of what is gold and not gold, etc. Compute really isn't, right? Especially GPU compute. GPU compute today is not fungible. And I think that this is well understood by our client base, by our suppliers, by, you know, third-party consultants like Sydney Analysis.

• 40:04Speaker 03

  And it's it's this idea that an H100 deployed in one cloud doesn't have the same performance of an H100 deployed in another cloud. And the the metrics that people use are things like good put or model flop utilization MFUs. And there are these measurements of like, how much more performant is one G, the the exact same GPU, by the way, versus another GPU deployed in another facility. And so in order for something to be commoditized, it it has to be fungible, right? Otherwise, there's just too much, you know, murkiness and there isn't like an exact data point in there.

• 40:42Joe Weisenthal?

  Can I push on that a little bit further? So, I mean, I think that seems like a reasonable view. Is the non-fungibility related to configuration of like how they literally like the configuration of the GPUs within physically? Like, what is it? Is it about power? I mean, I think they all like, you know, there are plenty of places that will say, you know, we have nine nines or however many nines you need in your industry or whatever. What is it in your view that would cause significant change? Is in the performance of an H100 in one cloud versus another.

• 41:17Speaker 03

  It could be in some part configuration, right? We build everything to DGX reference spec, which is the most outlined by Nvidia. It's the most performant way to build, operate and deliver.

• 41:29Joe Weisenthal?

  Okay.

• 41:30Speaker 03

  GPUs. But but the rest of it, honestly, is just how you operate the the GPUs. And that is the core weave software stack that is how do you keep these GPUs online, right? Like, what happens if the GPU fails? Can you predict if a GPU is about to fail and swap in other infrastructure so that the client doesn't have downtime on that component? And there's there's an immense suite of software solutions that and and infrastructure management solutions that we have built to have the best good put, to have the best MFUs in the industry. And that's none of that is is off the shelf, right? And so I I wouldn't say it comes down to the strict components. That's kind of like a bare

• 42:14Speaker 03

  minimum starting point, right? Like, you have to start in DGX reference spec. But where's differentiation come from there? I mean, that's that's the core we product you're describing right there.

• 42:24Joe Weisenthal?

  By the way, Tracy, I'm just looking up terms of art. Good put measures the fraction of peak hardware performance that the training job can extract. This is according to Google and MFUs, model flops, utilization, hardware metric for evaluating real world efficiency of LLM training. So two new terms. I actually hadn't heard of MFUs or good put before this. So I just learned two new terms today.

• 42:47Tracy Alloway?

  We got to create a

• 42:48Joe Weisenthal?

  A glossary.

• 42:49Tracy Alloway?

  AI glossary.

• 42:49Joe Weisenthal?

  Yeah, we do.

• 42:50Tracy Alloway?

  Brandon, when Joe asked you that question about compute markets earlier, you said it was a timeline question, which in my mind implies that it's inevitable. Like, it's just a question of how long it takes. But then, when you describe the fungibility problem, it seems like this is an actual issue that will be very difficult to solve.

• 43:10Speaker 03

  Yes, I I think that characterization is absolutely correct, right? Like, if you just take general commodity theory, and I traded natural gas, electricity, agriculture products for over a decade, like.

• 43:23Speaker 03

  It suggests that it should become that at some point. But what is the reality today? The reality is this stuff isn't getting easier to operate. Right? We've moved from these kind of relatively simple 42U air-cooled racks of Hopper to these immensely complex Blackwell deployments, moving into Vera Rubin, following that. Like, it's not getting easier to to build, operate, provision, deliver these GPUs. It's getting more difficult. And I, I, I think until it starts becoming easier, you don't really have a path to commoditization. You will have to continue to prioritize working with the world-class and world-leading operators

• 44:08Speaker 03

  of infrastructure. That's where we sit.

• 44:11Joe Weisenthal?

  First of all, this is helpful, and I like that we're getting multiple perspectives because I do think this is going to be like one of the big questions for financial markets. Because let's say if they took off, then you could imagine that might even improve financing conditions because then the lender can hedge against. Yeah, so like, there would probably be some good things for the industry if this took off. So I appreciate, it's good to have your perspective on this. Why is it, you know, I'm, I'm an inference, I, I, I am an inference user, by the way. So I made a little machine learning model in one of my hobby projects. And I provide inference over at havelock.ai, or I'm a user of inference or whatever. I have a model, whatever. Why is it that I?

• 44:54Tracy Alloway?

  It'd be impressive if you were providing inference.

• 44:56Joe Weisenthal?

  I'm trying to, I guess I'm a consumer of inference. I use a, anyway, why is it that I'm actually very easily able to get, now, not a huge allocation of you, like GPU access? So I was like, how do I train this model? It's a model called BERT that Google released in 2018 or 2019. I fine-tuned it for my purposes. And then literally using Claude code, I was able to in 10 minutes, set up, I started using this company called Modal, and I was able to start training a model. I was surprised that there was like, and it didn't cost me very much, and I have like no volume, but nonetheless, evidently, there was a little GPU capacity out there that I could get, and it cost me like $5 or something for the whole thing.

• 45:42Joe Weisenthal?

  Given what you always hear about like utilization is slammed, why is it actually not that hard to find GPU capacity for someone like myself?

• 45:51Speaker 03

  You know, I, I, I think it's the, the scale difference right there. Finding ones or tens of GPUs, I think that's way more accessible out there.

• 46:01Joe Weisenthal?

  Okay.

• 46:01Speaker 03

  Our clients are focused on the hundreds of thousands of GPUs.

• 46:05Joe Weisenthal?

  I'm not there yet, but I'm not there yet.

• 46:08Speaker 03

  Not yet. I'm sure you'll get there. Yes. And that's where it, it kind of decommoditizes itself with scale as well, right? Like as you're in the hundreds of thousands component, there's just not that many deployments, right? It's handfuls of deployments at that size. But getting access to ones of GPUs, I, I think that there is a lot more ability to, to go secure that, that sizing in the market.

• 46:34Tracy Alloway?

  So Joe and I are heading to Hong Kong very soon and I expect that AI in China is going to be a big topic of conversation. How would you characterize, I guess, the difference between the US and the Chinese market at the moment? I'm sure this is something you think about, even though you don't participate in the Chinese market directly.

• 46:52Speaker 03

  Yeah, that, that's.

• 46:53Joe Weisenthal?

  Tracy's asking for questions.

• 46:55Tracy Alloway?

  Yeah, that's basically it.

• 46:56Joe Weisenthal?

  It's like, it's like questions that we can ask people when we're over there.

• 46:58Speaker 03

  Yeah, that, that's likely going to be my response, Tracy, is like, we, we just do not participate in that market. I think that there's opportunity for us to be expanding. As you guys know, we, we operate in Canada, Europe. I think moving further east makes a lot of sense for us, but we're trying to be very methodical in the way that we expand. So I, unfortunately, I'm not going to be able to help you with specific questions in that market, but I would imagine you're going to encounter a lot of the same things that you're seeing in the US, which is just insatiable, unrelenting demand for AI. And like, you know, we just kind of keep coming back to this. It's like, there is no solution in sight for being able to satiate.

• 47:43Speaker 03

  Demand, right? There's just too many supply chain. There's no path to solving demand in the near term, or even the medium term, frankly.

• 47:52Joe Weisenthal?

  You mentioned, so Tracy asked you about land use. You said you weren't, that really wasn't an issue. But like, the first time we talked to you in 2023, or whenever that was, there was not a major growing movement of people who were just like anti-data centers in America. Maybe there were a few fringe people, but it was not something that was on the minds of politicians and activists and so forth. And you do see these headlines, you know, about some projects really having been shelved. There was like a big one, Northern Virginia is a huge hotspot for it. And there was a big project that was, they pulled the plug on due to some, they couldn't get an agreement with the local government. That must affect you. What are you seeing in terms of like your capacity to build?

• 48:34Joe Weisenthal?

  How has it changed specifically in light of, or have you seen a change? Would you be able to build faster in a world where this had never become a political hot button issue?

• 48:45Speaker 03

  I believe it has become that hot button issue. It's something that we're quite proactive about in market. And I think you just kind of go through the checks on the diligence process to make sure you're going through it correctly. I think that there's misconceptions out there, like water usage.

• 49:04Joe Weisenthal?

  Yeah, setting aside the misconception, like setting aside, I know, setting aside the whole debate about, but just in terms of like operationally, what's it changed for you in terms of your plan?

• 49:14Speaker 03

  Yeah. No, I would say our greatest challenge is still just getting that delivery of power, like the construction and all the components and getting everything in there. Like that is truly more of the bottleneck that's in the market today.

• 49:29Joe Weisenthal?

  Brandon, thank you so much for coming back on Odd Lots. Well, I'll have you back next month for another market.

• 49:34Speaker 03

  We'll love it.

• 49:34Joe Weisenthal?

  No, or at least, or maybe in three years.

• 49:37Tracy Alloway?

  Not three years, surely.

• 49:38Joe Weisenthal?

  Yeah, not three years, but really.

• 49:40Speaker 03

  That's an eternity.

• 49:41Joe Weisenthal?

  Yeah, I know. Thank you so much.

• 49:43Speaker 03

  Thanks, guys. Appreciate it.

• 49:57Joe Weisenthal?

  I'm very excited about whether compute features will take off. I think this is an exciting like story, you know, it's not the biggest story in the world, but it is actually a very exciting story.

• 50:07Tracy Alloway?

  I've said this before, even if you're not that interested in AI, this is a really interesting market structure story, right? It's basically the creation of a brand new market and poses all these interesting philosophical questions about how you do that. And I thought Brandon's point about fungibility, I mean, that is a real issue and it does seem like it's a challenging one to fix at the moment. I don't know if it's inevitable in the future, but who knows?

• 50:36Joe Weisenthal?

  No, no, I mean, it makes a lot of sense. This was also Lewis Hart's point that it's like the, you know, it's in the word commodity, right? If it's, if it's not a commodity, you're not going to get a commodity market for it. And of course, a number of entities are betting that it will be commoditized. But if the, if it's true that like, you know, they're getting more difficult to work, that the technical demands on the inference provider on the data center company are getting greater in order to get the maximum, you know, juice, then maybe it doesn't become commoditized. But I think that's like a fascinating question.

• 51:13Tracy Alloway?

  But at the same time, like if you do see those efficiency improvements and new designs and things like that, you could imagine that like the demand is there for standardized GPU as well. So I don't know, like, I'm really torn. It feels like it could go either way.

• 51:26Joe Weisenthal?

  Well, and even in his answer, he talked about how they configure their own GPUs to a spec largely that Nvidia itself has come up with. So in theory, like there is a spec that everyone can match to. So that's like a really interesting, that's a really interesting question. I also really want to do more on all of these. So Google has TPUs, Amazon has Tranium, Microsoft has its own hardware, maybe even Jane Streets and the Hudson River Trading will have their own hardware. If they're not like, I want to understand better why, right? Because like they presumably have some reason and they at least like the Microsoft will say,

• 52:11Joe Weisenthal?

  well, this will run better on our customized hardware. I want to understand why that would be. How much difference in performance is there? And then the degree to which demand materializes from users for non-Nvidia silicon is like a really big question.

• 52:28Tracy Alloway?

  Yeah, why custom chips?

• 52:30Joe Weisenthal?

  Yeah, and what can you get out of that? If you align model and chip to optimally work together? I have no idea, but I feel like it's an episode I would like to do.

• 52:39Tracy Alloway?

  Yeah, we should. All right, shall we leave it there in the meantime?

• 52:42Joe Weisenthal?

  Let's leave it there.

• 52:42Tracy Alloway?

  All right, this has been another episode of the Odd Lots Podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

• 52:48Joe Weisenthal?

  And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our guest, Brandon McBee, at Brandon McBee. Follow our producers, Carmen Rodriguez at Carmen Armand, Dashell Bennett at Dashbot, Cale Brooks at Cale Brooks, and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lots content, go to Bloomberg.com/oddlots or the daily newsletter and all of our episodes. And you can chat about all these topics 24/7 in our Discord, discord.gg/oddlots.

• 53:14Tracy Alloway?

  And if you enjoy Odd Lots, if you want us to do an episode on custom chips, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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