Odd Lots: How CoreWeave Sees the Market for Compute Right Now

Sourced by podcast-ingest on 2026-06-08. Auto-transcribed via AssemblyAI (universal-2, en). Speakers identified by AssemblyAI Speaker Identification using the per-podcast host/regulars hints; the resulting label→name mapping is in the frontmatter. Duration: 50m. Episode page: https://omny.fm/shows/odd-lots/how-coreweave-sees-the-market-for-compute-right-now. Audio: https://podtrac.com/pts/redirect.mp3/tracking.swap.fm/track/UVBrz8bN8aM2Xe47PEPu/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/8a94442e-5a74-4fa2-8b8d-ae27003a8d6b/450514c3-2648-49a8-9794-b46000d1738a/audio.mp3.

Show notes (from RSS)

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 is seeing in the market for compute right now, including leases, Nvidia new Vera Rubin systems, demand for training versus inference, and the possibility of standardizing the market for compute.

Transcript

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Joe Weisenthal: Bloomberg audio studios podcasts radio news. Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.

Tracy Alloway: And I'm Tracy Alloway.

Joe 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 what 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 Because it just feels like the moment we do an episode, a few weeks later it may be out of date.

Tracy Alloway: We should just bite the bullet and do a weekly episode, transform lots more into a market update on compute.

Joe Weisenthal: We could do inference. I don't know, we'll have to workshop odd inference. No, no, no, we'd have to. But anyway, this is lots of inference. Lots of inference. This is like the story of the moment. And we know that a couple 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 budget. And there was even a headline of like Uber saying, like, okay, like $1,500 of max per employee. Like, don't spend more than that in a month on tokens. So, like, this is a very fast moving area.

Tracy 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, and it was citing an unnamed source, it's from Axios. So, you know. Oh, yeah, not entirely sure it's true, but reportedly it's a great headline. 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.

Joe Weisenthal: It's because everyone, it's 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 Argente, 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 will probably be a lot of investment made in sort of like optimal model routing, because some models are like 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, I have a feeling we're going to see a lot of investment in that area specifically.

Tracy 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.

Joe 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.

Tracy Alloway: So like, which is different to what it was like three years ago. Yeah, yeah.

Joe Weisenthal: 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.

Tracy Alloway: All right. Consider this our inference update.

Joe 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 with 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 neoclouds, 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 Brannon McBee, Coreweave's co founder and chief development officer. So Brandon, thank you so much for coming on odd lots.

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

Joe 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 there's 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, 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?

Brannon McBee: Yeah, I think you see headlines there that there are surprises of spend, etc. 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? And 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. 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 maxing approach. And I think that that is unsustainable. But do we see adoption in other sectors and how this can continue to spread out? 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, you know, 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, on what they're doing on, 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 weave solution in the market.

Tracy 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?

Brannon McBee: Yeah, it's shifted enormously towards a more diverse customer base. Right. We got a lot of flack for this in our ipo, right. Like, people were noting that we only had a handful of large clients, that our clients were like just the hyperscalers and AI lab or to. And I think that we have made tremendous progress in driving diversification. So I'd say it sits broadly 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. It's not these massive multibillion 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 court. 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 it 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 that 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. And all of a sudden that tells you, my God, like H1 hundreds could last six, seven, eight years. A1 hundreds are going to last longer. And it totally changes the entire conversation around depreciable 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 to use the appropriate model for their prompts. And that'll be solved by Model Rabbit, 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 a 1/ hundreds.

Joe 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 city 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 an LLM per se, but some type of internal, house specific model from which they want to run inference?

Brannon McBee: It's a great question. The scenario you presented, anthropic would be our client there. Got it. So what I'm highlighting, I want to correct the number I said earlier. Our financial service clients, and this is direct to those financial services, they're approaching 10 billion in backlog. Okay, so this would be, you know, a good example of this announcement we made recently is with Jane Street. Okay, 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

Joe Weisenthal: for a model that they're build. So it's a Jane street or Inference.

Brannon McBee: Right? It's training.

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

Brannon McBee: At the end of the day, we don't know what exact workloads these entities are running. Especially for entities like Jane Street, I would imagine that's highly secretive. 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.

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Joe Weisenthal: 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, so 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 from investing to push the pure model frontier, having the absolute best state of the art model versus investing in say better harnesses? Because a big reason we're excited and I'll talk about AI right now is really the excitement that happened over with Claude Code in 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.

Brannon McBee: I don't think that we're exposed to that decision making with the AI labs as 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. Now they're coming and saying, well actually we want it for five years. 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. 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.

Tracy Alloway: Oh yeah, what's the deal with Vera Rubin? Can you explain that to us?

Brannon McBee: Which aspect of it?

Tracy Alloway: What is it?

Brannon McBee: Oh yeah, basically, 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 predominantly in a MBL72 configuration which was Entirely 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 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, 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 Vera Rubin will be the next architecture that comes out. And we've started receiving testing racks for Vera Rubin.

Tracy 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.

Brannon McBee: 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, etc. 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.

Joe Weisenthal: Now 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 every single day 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. 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 the labs, they want to have advanced models too. And apparently it's a good model. And they announced the micro AI 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 the squeeze is there of aligning the model with custom silicon from your vantage point?

Brannon McBee: What, what we could offer is what we hear from our clients. Yeah, on that. And it's important to keep in mind we can run any type of silicon on our platform.

Joe Weisenthal: Okay.

Brannon McBee: 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 to 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 dedicated to that for I think over 15 years at this point 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, there's always been, think over the past few years, right. There's always been talk like what is it but 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 demand on a material basis for anything but that Nvidia compute. And that's what we are building today

Joe 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 in 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 the 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 Cerebras, the company that makes the gigantic plate and or, sorry, the gigantic, gigantic plate. And of course he did, but I mean, of course he was going to say, yeah, the, the Cuda moat is vastly overrated for inference. It 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.

Brannon McBee: 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. Okay. 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. Those customers are choosing Nvidia to work with inference. I think what you're going to 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 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 default facto choice for AI infrastructure. I think we're going to 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 that hasn't happened.

Tracy Alloway: Okay, 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?

Brannon McBee: Land usage specifically, I wouldn't say is as much of a concern. Having a powered shell is the bottleneck today. And let me qualify powered shell. Powered shell is effectively an empty data center that is energized. 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 the industry term for it. That is the bottleneck because of all of the supply chains 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, you have personnel. Right. Let's think about the electricians for these sites and getting the accreditation on electrician side to be able to participate in these builds. I mean, I think it's a five year plus apprenticeship to be able to go through that program. Right. We can't just make new electricians leveraging a supply chain. Right. Like 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. 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.

Joe Weisenthal: Yeah, right.

Tracy Alloway: Doesn't seem to be an issue anymore.

Brannon McBee: I would agree 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 don't get to do that and just go from 0 to 20 right out of nowhere. 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 power at this point. Right. Like a gigawatt like at the data center level with GPUs delivering into clients. And I think that there has been kind of a misunderstanding of the market where people are conflating the concept that like, you know, something on paper is 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 has been so successful. I mean, 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.

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

Brannon McBee: You know, I Haven't seen those charts in a while.

Joe 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 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 alieved 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 gear, 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, as we all learned from SVB was the risk of industry and depositor concentration. That if 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, et cetera, 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.

Brannon McBee: 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 specific. 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 is like single name clients. Right? Like there is a pretty significant number of single name clients we can go allocated out into. But 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 leading AI consumers and producers on the planet. I don't think we're supposed to be sporting just one or two companies.

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Joe Weisenthal: Game Night Rush or any night of the week really. Genius keeps every order moving from online ordering to your kitchen to the front counter. Big league reliability for any business.

Tracy Alloway: That's genius 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, 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 it, 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?

Brannon McBee: Seeing the inference aspect of it really emboldened investors. But like that was really just 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 other companies, but for us, 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 watch us deliver on these contracts. And the way, tell me if I'm going into too much detail here, but the 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 SPD's or we'll just call it a box, so to say.

Joe Weisenthal: And you, sorry, keep going. Mentioning boxes on lots of connotations, but keep going, keep going.

Brannon McBee: We put them into SPD's and these, these SD SPVs, they have the infrastructure, they have the data center costs and they have 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 debts 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 code, right? Like these are highly profitable agreements down in the SPB stack. And so you take that SPV out to the credit market and say, look at this instrument. That's a discrete set of contracts with counterparties, like entities you want to consume GPU, compute, you have the data centers within it, etc. And one of the latest ones we did was as we call it, DDTL4. This was a investment grade rated first of its class. No one had done this before for GPU financing, non recourse HPC infrastructure financing and got done at SOFR plus 225. 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. So we're kind of continuously making progress through these different stacks of capital, unlocking access to more and more types of investors. It's why you've seen this 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 hyperscaler to go credit these build outs. Right. It's, it's on core weave honestly to be building this path into how do you finance the AI hyperscaler effectively? And I think we've just done a terrific job of it over the past few years.

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

Brannon McBee: Yes, I was a commodity trader.

Joe 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, et cetera. What's your guess? Like, do you anticipate that there will be a sufficient ecology of hedgers and speculators such that there will be a liquid market for tradable compute?

Brannon McBee: I think it's a, it's a very much a timeline question that's out there short term. No. 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, 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 cineanalysis. 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 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 exact same GPU by the way, versus another GPU deployed in another facility. And so in order for something to be commoditized, 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.

Joe 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 have 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 changes in the performance of an H100 in one cloud versus another?

Brannon McBee: 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 GPUs. But. But the rest of it honestly is just how you operate the GPUs and that is the Core Weave software stack. That is how do you keep these GPUs online? Right? Like what happens if a 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 an immense suite of software solutions and infrastructure management solutions that we have built to have the best good put to have have the best MFUs in the industry. And that's none of that is off the shelf. Right. And so I wouldn't say it comes down to the strict components. That's kind of like a bare minimum starting point. Right. Like you have to start in DGX reference spec, but where does differentiation come from there? I mean that's the core reproduct you're describing right there.

Joe 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 MFUSE model flops utilization hardware metric for evaluating real world efficiency of LLMs training. So two new terms. I actually hadn't heard of MFUSE or good put before this, so I just learned two new terms today.

Tracy Alloway: We got to create a glossary. AI glossary.

Joe Weisenthal: Yeah, we do.

Tracy 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.

Brannon McBee: Yes, 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 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 build, operate, Provision, deliver these GPUs. It's getting more difficult. And 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 of infrastructure. That's where we sit.

Joe 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 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 prices. 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'm an inference user by the way. So I made a little machine learning model in one of my hobby projects and, and I provide inference over at Havelock AI or I'm a user of inference or whatever. I have a model, whatever.

Tracy Alloway: Why is it that impressive if you were providing inference?

Joe Weisenthal: I'm trying to. I guess I'm a consumer of inference. I use a. Anyway, why is it that I am 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 sign 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. Given what you always hear about like a utilization is slammed, why is it actually not that hard to find GPU capacity for someone like myself?

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

Joe Weisenthal: Okay.

Brannon McBee: Our clients are focused on the hundreds of thousands of GPUs.

Joe Weisenthal: I'm not there yet, but I'm not there yet.

Brannon McBee: Not yet. I'm sure it'll get there. Yes. And that's where 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 think that there is a lot more ability to go secure that sizing in the market.

Tracy 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.

Brannon McBee: Yeah, that.

Joe Weisenthal: That Tracy's asking for questions. Yeah, that's basically like questions that we can ask people when we're over there.

Brannon McBee: Yeah, that. That's likely going to be my response. Tracy is like, 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 gonna be able to help you with specific questions in that market, but I would imagine you're gonna 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 is like there is no solution in sight for being able to satiate 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.

Joe Weisenthal: You mentioned. So Tracy asked you about land use. You said you weren't. That really was 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 your capacity to build? 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?

Brannon McBee: 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 go through the checks on the diligence process to make sure you're going through it correctly. I, I think that there's misconceptions out there, like water usage.

Joe 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?

Brannon McBee: Yeah, no, I, I would say our greatest challenge is still just getting that delivery of our like the construction and all points and getting everything in there like that is truly more of the bottleneck that's in the market today.

Joe Weisenthal: Brandon, thank you so much for coming back on odd lots. We'll, we'll have you back next month for another market. No, or at least, or maybe in three years.

Tracy Alloway: Not three years.

Joe Weisenthal: Yeah, not three years.

Brannon McBee: But really that's an eternity.

Joe Weisenthal: Yeah, I know. Thank you so much.

Brannon McBee: Thanks guys. Appreciate it.

Joe Weisenthal: I'm very excited about whether compute futures will take off. I think that's an exciting like story. You know, it's not the biggest story in the world, but it is actually a very exciting story.

Tracy 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?

Joe Weisenthal: No, no, I mean it makes a lot of sense. This was also Lewis Hart's point that it's like the fun. 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.

Tracy Alloway: Yeah.

Joe Weisenthal: Then maybe it doesn't become commoditized. But I think that's like a fascinating question. Yeah.

Tracy Alloway: 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.

Joe 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 aspect 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 Trainium, Microsoft has its own hardware. Maybe even Jane Streets and the Hudson River Tradings 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, 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.

Tracy Alloway: Yeah. Why custom chips?

Joe 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.

Tracy Alloway: Yeah, we should. All right. Shall we leave it there in the meantime?

Joe Weisenthal: Let's leave it there.

Tracy Alloway: All right. This has been another episode of the All Thoughts podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

Joe Weisenthal: And I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our guest Brandon McBee at BrandonMcBee, follow our producers, Carmen Rodriguez at CarmenArmond, Dashiell Bennett at Dashbot, Kale Brooks at Kale Brooks and Kevin Lozano at Kevin Lloyd Lozano. And for more Odd Lodge 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.

Tracy 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 on Apple Podcasts and follow the instructions there. Thanks for listening.

Joe Weisenthal: Game Night Rush or any night of the week really Genius keeps every order moving from online ordering to your kitchen

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Joe Weisenthal: Big league reliability for any business, that's genius.

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