CoreWeave Co-Founder on Sold-Out Compute and What the Market Gets Wrong about GPUs

Welcome back to the rundown, interview edition. Today, we are talking to Brennan McBeat, the co-founder and chief development officer at CoreWeeb. CoreWeeb has become one of the biggest players in the AI cloud space. Business is booming right now, but so are questions about their business model. So in today's conversation, we get into it all. We talk about how CoreWeeb makes money, how they differentiate themselves from the hyperscalers, why the company believes their debt load is manageable, how take or pay contracts work, and why older generation GPUs are holding up their value. This was a very interesting conversation. It got a bit nerdy and technical there in the middle, but I think you guys are going to really enjoy it. So let's get into it. All right guys, today we are joined by Brennan McBeat, the co-founder and chief development officer at CoreWeeb. Brennan, welcome to the rundown. Thanks. Thanks for your opportunity to join. Looking forward to it. Hey, I'm super excited for today's conversation. Before we really get into it, you know, I don't want to rehash the CoreWeeb origin story. I think a lot of people are familiar that the company pivoted from being a crypto miner to being an AI cloud provider. What I'm curious though is more about your role. What does a chief development officer do? Yeah, it's one of those titles. What are you developing? The development organization and what I run, it's capital origination, it's M&A, it's Vichips. Right, so our group raises all the money for the business. So in these public market debt and equity transactions that you see, we run M&A processes for the business and then we have a group that does, you know, direct into company venture investing as well. It's a group that's dominated by, you know, ex private equity, private credit investment banking guys were pretty much fully based in New York. We, we qualified as a, you know, pretty small strategic team that just goes and gets shit done. I love it. I love it. I'm kind of curious. You guys are at the forefront of AI infrastructure right now, spending billions of dollars in the process. I mean, just high level. What do you think is the hardest part right now for the company? Is it just, you know, getting the chips, building the physical shells of the data centers, getting the power, the labor, the financing, like, what's the hardest part right now? Yeah, at its point, that has evolved over time right at the very beginning. It was absolutely getting the chips and the allocation. I'm talking about 2021, 2022, time by, like, very early days as we were scaling the cloud. Then it became financing the business, right? Like, you have all this intense demand for a product. And that demand was really scaling quickly back in those days, but how do you go finance? And I'm sure we'll get into the financing in the business a little bit later. But we, it provided in Korean, these very innovative strategies and market to get that financing together to provide the growth vehicle for the business. Where is it today? Today, it really sits on just getting access to more powered shell, right? And powered shell we qualify as the data center itself. As you guys know, we predominantly lease data centers. We have some self development as well. But the reason why it's so hard today is because what we have access to now, we had to predict how much we needed two years ago, right? Like, you can't buy and get access to these leases in a slot basis, right? Like, 2026. So, that can't find any more leases out there, 2027. Pretty much gone at this point, right? Like, there's nothing there, like 2028, very competitive. And I'm talking, you know, what's the US based deployments? But for getting access to incremental data center space, that will be online so that we can move our infrastructure and our software technology into those sites. That's a tough thing to predict into the future. And I mean, I don't remember it two years ago when we were planning for 2026. Like, yeah, this is a lot of capacity. Like, is this right? And we were getting all the right signals from our client base that I think has evolved pretty materially from two years ago as well to keep growing, keep growing quickly. But, you know, if you hadn't. Extra 100 megawatts, extra 500 megawatts, whatever that number is today. It's gone in an instant. It's just so funny you say that. Like, I remember two years ago when like the, you know, the capex numbers really started going up across the board and everyone started freaking out about it. What's funny is that everyone still didn't invest enough because there still isn't enough compute because I think everyone was so worried about overbuilding at the time. And honestly, those capex numbers should have been double what they were back then. And maybe the compute constraint wouldn't be so bad. So, you guys have a tough job. I gotta say. Yeah, yeah. Look, I think that people are so freaked out by capex numbers, right? Huge numbers. We are moving at such an unbelievable velocity of technology and capital. It's truly some curcuing efforts that are happening across the industry, right? Like, it's truly phenomenal watching our peers at execute. It's phenomenal watching our team executes. The scale of these projects is so mind boggling when you go to these sites and the fact that we're able to bring this all together and deliver the intelligence, the infrastructure for the intelligence, creation of intelligence, like gaps. It's just such a cool thing to be a part of. Yeah, and so let's dig into some of the core weeds as actual business. You guys are coming off a big quarter, revenues doubling year over year, over $100 billion in backlog. Like you said, near-term capacity pretty much sold out. And then you guys are spending $30 plus billion on capex. The other eye-watering amount though was the debt on the balance sheet, right? I think over $30 billion. Why is that something that investors shouldn't freak out about? There's a number of things. Let's spend a few minutes on this. I think why don't we start with how does our debt work? How do we finance the business, right? I think that's a point that is a little bit misunderstood. We break the financing into two categories, right? There's a parent code or top-go financing. And then there's asset code financing. Asset code financing is where the overwhelming majority of our debt sits. And asset code, as the name in first, is where a lot of our assets are as well. So that's where GPUs predominantly sit, is down at Asset Go, right? And what we came up with, and this is all saluting to a few years ago, where we first brought these things in the market. This is where the kind of clutterized GPU financing comes from. And the idea we had, it's actually, you know, we kind of pulled it out of other infrastructure fanatics that other sectors use, like take look by natural gas. They use this type of financing to do their terminals. And the premise of it is, you go out there, you sign these multi-year take-or-pay agreements with your clients, right? Which means they must pay over that entire duration of the contract. And the contract is fixed economic terms, right? They pay the exact same rate every single month. It's for a fixed skew and fixed set of infrastructure, right? It's not like they can upgrade the infrastructure or change the volume or anything like that. And they, there's no variability in utilization for us in terms of our economics either, right? They pay the same amount of views. And the cost of the infrastructure is zero-present in the infrastructure. As we extract everyone uses the infrastructure. Right. So now you have this really attractive stream of payments out that you can, that you have contracture, right? We take that skew and payments and you pair it with the infrastructure. And you go clutterized, clutterized these things together. And the way it works is you go to the creditors, you say, right, here's our data center contract, here's our GPU update contract, here are the GPUs. When we've set it up to where revenue comes in to this facility, a box, sort of say, revenue comes in off the contract. It pays down first and foremost the debt, right? It amortizes. So it pays the principal and it pays the interest on the debt, radically, right? It's called self-amortizing instrument, right? The whole debt is paid off at the end, it's paid off throughout the term of the contract. Then the next stream of payments goes to data center operations, right? So your next dollars, pay off the operation of these GPUs. And then the final amount, the remaining amount, the remainder, as it's referred to, gets kicked back up to the parent pill, right? So if you break that down into like, that's the profit, right? That's the profit. Exactly. That's the profit that goes into the parent. So if you break this down into dollars, right? Like, if you have a dollar revenue that comes in, you have 75 cents that goes to paying the debt, paying the interest, and paying the data center operations, and then 25 cents, a contribution margin of 25 percent, goes up to the parent pill, and that's the profit. So it's a self-amortizing facility that takes care of its debt. In other words, like, if we didn't build another GPU cluster, didn't take on another contract, all of that data, as we go, gets paid off by the existing take or pay contracts that we have. And these are, you know, I think like four and a half to five years in duration, or so as weighted across our hundred and I think four billion dollars of backlog that we disclose at the end of Q2. It's been a wonderful way to finance the business, and we've liked this way of financing, because it doesn't require an immense amount of equity, right? Like, if we had to go do all equity for this instead, it dilutes the shareholders. And mentally, it's a way of financing business that doesn't require us to need upfront payments to rely on our clients to finance our business. Like, of course, like, you know, clients willing to give us 50, 75% plus upfront payments to finance everything, that works great as well. But, man, that's a tough way to build a business because it puts all the leverage to the client because they can show up some day and say, I don't want to give you enough upfront payment anymore, like, and then you're kind of luck with, well, how do you finance your platform and if there's something about credit markets, it's like that they deeply appreciate a track record of execution and participating in the credit markets. And that's something we've done really well. We've gone through a number of these facilities. You've successfully brought them online with successfully paying them down. And that's why the credit market continues to show up in larger and larger size for us at a decreasing cost of capital. Over time, it's a very important financing mechanism that I think is quite beneficial for the business. But that makes the question, though. So the take-or-pay contracts are great for you guys because the kind of de-risk is the debt, de-resses it for a core weave. But the follow-up question today is like, you still become reliant on your clients and that kind of brings in the question of whole circular financing. We're like, you're dependent on the hyperscalers to, you know, to design these take-or-pay contracts. They're willing to do it right now because there's a shortage of compute. They need that compute. They're willing to agree to these terms. But what happens in the future, three, four, five years from now, if we have a better compute-supplied demand balance, what happens then? Like, what if they're not willing to sign these take-or-pay contracts? Yeah, yeah. So the hyperscalers side, right, and this goes a little bit to customer diversification and everything. I think that's something that we took a lot of well-directed criticism for in the quarter leading up to our IPO last year, right? We had a lot of customer concentration with one hyperscaler. It makes sense then. Obviously, we've driven so much diversification across our business, right? And the sector said in enterprise, they said in AI lab, they said in hyperscaler, hyperscaler includes companies like Meta as well. And that, we announced, like, catapillar as a client this past quarter. I don't think people have that on their bingo cards, but that, like, catapillar amongst, like, catapillar's tons of existing hyperscaler like cloud relationships, right? You have to think about it. It's like, core week must be doing something so much better than the existing hyperscaler providers that catapillar is willing to move its workload requirements away from there in the core weeks. Can we talk more about that? Like, what is it? Like, what is it that core week does that's so different than, like, the AWS or the Azure or the Google Clouds of the world? Yeah, it varies, but, you know, it comes down to a simple premise that our hyperscale peers, they were optimized over a decade of engineering and investment work to host websites and store data. And it's a great job with that. Like, it's a phenomenal product they have for doing so, but it's an entirely different solution to run AI workloads and the analogy I've used in the past, which, you know, I still like a lot is, it's kind of like walking into Toyota and asking them, why can't you produce a model while. And Tesla is going to look at you and say, no, no, I'm sorry, Toyota is going to look at you and say, no, of course we can. They're going to take their camera, put it battery in it and say, here it is. It's called a Prius. But we know of those that are Prius and about a while, although both EV, they're entirely different products. Right? One product was built around being an electric vehicle. The other product was taking a traditionally produced vehicle, augmenting a bit and asking their clients to take compromises. Right? And those compromises are what the Hikers goes ask their clients to take with those platforms because it has these legacy components that at the end of the day, don't have the performance of Cory's product. And you asked like, all right, well, where's the credibility enough performance? Look at our client base, right? We have, I believe it's 10 of the 10 top AI labs on the planet or on Cory's platform. We have a number of the hikers killers on our platform themselves. We have enterprises signing up for Cory every day. I think, but the client base speaks to it. I think third party research speaks to it through fantastic organizations like semi analysis. And I think our supply chain speaks to it as well, right? Like the data center operators are choosing to work with us. The chip suppliers are choosing to work with us because of who we are in the quality of our product and market. Yeah, I mean, that makes sense. And I think that's something that, you know, maybe casual retail investors have a hard time understanding. They just see Cory, they see AI cloud provider, and then they're thinking like, well, how are they going to compete with the mammates like the AWS is in the Google Clouds and like, you know, and the other thing is, these hyper scale just have advantages when it comes to better financing terms and things like that because they're trillion dollar companies with massive cash flows, that's another disadvantage that, you know, that Cory would have when it comes to competing with them. It is a disadvantage, but it's one of the days so much progress on, right? Like I think two and a half, three years ago, we were financing at so for plus a 50 on like really attractive kind of body contacts, right? Now we're financing that at so for plus 225, what a massive contraction of cost of capital, right? And we have this flexibility now of, of financing through the credit markets of financing through equity of finance. financing through pre-payment, like we can choose based on the needs of our client as well, because if there's something I'm certain of, it's that the needs of the client is going to change, right? Like this, this reliance upon pre-payment as financing, of course, we can accept it. It's just we can't build a business that relies upon it. And, you know, the other thing on the, on the credit or the debt financing side, I want to hit on for I forget about it, it's the incremental level of institutional diligence that comes with that process, right? Let me have a second to make this point. equity investors, all they really see at the end of the day when a large G2 contract assigned is a TCV, right? Like $10 billion contract gets sent, right? They don't see any of the detail in there, like what does delivery of that infrastructure actually looks like? What are the SLI penalties? Like what happens if they don't bring up at a certain time? What are the cancel ability clauses that are in there? There's a lot of detail that's said to these contracts, a lot of detail that really matters to inform like just how much value is there in that TCV? In other words, how realistic is it that you're actually going to extract that full contract value? And when we go through this debt raising process, they see those contracts, right? They see every single detail that sits there, they see every detail that sits on the data center contract as well. Is that data center going to be available to take the GPUs to turn it into revenue, right? So you're getting this really intensive and criminal level of diligence that's occurring that just benefits the equity investor. Because an equity investor can look at our backlog in our TCV and say, I know that those contracts have gone through an excruciating level of diligence by people whose job it is to not lose a single author. Because that world is not allowed to lose, right? There are only going to underwrite things that they have a high degree of confidence and of execution and ability to make money not only for the leaders, but the business. We have to make money as well, right? It's not good for lenders for parentho or core weave to not make money that goes back to the 25% contribution margin that's out there. So I feel like it's just been a bit underappreciated of just how beneficial it is for the equity investors for us to have built such a sophisticated financing mechanism that is able to interact with the debt markets at an increasing scale and a decrease in cost of capital. You do make a good point there because you're right. I mean, these credit guys, they're not they're not just going to be lending anyone money if they're not like sold on the contract. They're doing their deep research on it. So that's a good point. I want to move move along and talk more about like some of the other things that were mentioned in the earnings call, which was like the useful life of the GPUs. I think that's been a lot very surprising is like some of these older GPUs like, you know, that are three, four, five years old are still maintaining their value. In fact, some of them are selling for higher than they were when they first came out, all right. Let me see how I want to ask this question. That's a fun one. Yeah. But I'm trying to understand is, did you expect that to happen? And is this I guess how much longer will these older GPUs maintain their value? Because I mean, new ones are coming out all the time, you know, if Jensen gets on stage once or twice a year has new products coming out. So how much longer can these older GPUs maintain their value, or is this just a function of like, you know, the supply constraint that we have right now? And like, kind of like what happened with COVID where used car prices were being sold for higher than what they were when they were new? Yeah. Yeah. I think the easy conclusion is it's just supply demand because without looking in details, which admittedly details are not readily accessible out there. But without looking at the details, details you say, well, people must be forced to use these older generation of GPUs. What you see within our platform every day with clients coming in is clients asking for these older generation GPUs. They want amperes, they want hopper and they want those platforms because those are the platforms that are most efficient for the workloads that they're running. Right? I think that there's been this misconception that everyone only wants the latest generation GPU and that there's one GPU to rule them all, right? There's one model to rule them all. And why would you have anything different than that? But the reality is something entirely different. The reality is that there is a very broad array of different sizes of models. And those different sizes and models all efficiently pair with different sizes or different generations of GPUs. Right? Like we advise you we'll come in for Ampere and we'll say we only have hopper available right now and we'll say, well, we don't want hopper. We only want Ampere because that's the most efficient GPU for our workload. Right? And I think that we see that all the way from enterprise to AI lab, diaper scale, clients. I believe that's the piece of information that the market is missing is that people want exposure to these different types of GPUs. And accordingly, it is completely changing the expectations of useful life. But we depreciate over six years. A number of our peers depreciate by them at six years. We're in there. We've always thought that that makes a lot of sense. What we're seeing today though, and as you mentioned in our Q2 earnings, we touched on this. We signed a three year A100 lease. Do I'm Q2? That pushes that A100 useful life out to the end of 2029. And by the way, we're signing that skew at the same prices that we were signing in early 2025 as well. Now the way to think about that, that means no pricing degradation on the A100, which is a 2020 skew from 2025 to 2029 puts it in nine years of life. We're seeing that across our platforms. And by the way, you've seen that in the cloud historically as well. I think at AWS, they still have Tesla's and Volta's online. Those are late 2010 skews. It's not like they're writing those things unprofitably, like they're writing money off that they have demand for them. You pull it down otherwise. So I think that this whole debate around useful life being two to three years, it's just not based in any semblance of empirical data. Like they might be based in hopes in prayers for whatever their thesis on the market is, but empirical data for a long time at this point, as supported at least six years useful life. And it's going for it. Well, I think the thought process is that the AI space is changing so fast. There's new stuff coming out all the time. And I think people think back to like what happened in the 80s and 90s, where these new CPUs were coming out so often. And that the old one, the old generation would be useless after six months to a year, because this new one was coming out. It was so much better. But that's not what the case is with AI, because these old ones are useful for other workloads. And not everyone needs a Ferrari when it comes to running their AI models. They can use an older Honda Civic or whatever the case may be. And that's what's going on with the AI's chips. Now, what I would say is really important is, regardless of which skew the client is on, they're getting access to the same quality of technology platform. That sits on nobleness. And the quality of technology platform is this incredibly robust management suite that ensures up-pilot GPUs. It allows for inference to be run on GPUs. It hardens the platform to the point where I think we have the best PCO like cool cost of ownership in the industry or close to it. It's another way of saying like, we have the most efficient platform for serving all of these skews on the market. And I mean, you should look at our engineering work. It's one by some of those brilliant people I've ever met in my life. And what they bring to market in the problems they solve for our clients every day is only driven by having access to this full suite of AI cloud infrastructure. And that stands from CPUs to memory, to storage, to the GPU infrastructure to high performance tablets to a distributed set of data centers is what we have 51 data centers in operation. Global. And we think that that makes sense because inference is going to be distributed. You're not going to run inference out of just one or two sites somewhere. To bring enterprise adoption to be truly global cloud adoption, you need a global platform. And that's what core events. So you mentioned inference. That's one thing I wanted to ask you about is we're seeing this change right now in the industry where a couple of years ago it was all about training models. I think that's one reason why everyone wanted the cutting-edge chips from Nvidia, because those were the best for training these massive models. Now we're moving towards the world of AI agents. And that's why CPUs are becoming more important. That's why agenting workloads are, you know, I think they've overtaken AI work training workloads. Are you seeing that on your end? And how does that impact your business? We're seeing that every day. It imprints this exploding. It presents the monetization of AI, right? I mean, the investment in AI was training. And core we've was known as a bill to platform for training for years because training training is exceptionally difficult. Stabilizing a 60, 60, 80,000 GPU fabric and enabling those types of training runs enormously difficult. And takes a incredibly robust suite of software solutions that we've developed in our entirely core requirements, are you to keep these infrastructure online available for our clients? But what we're seeing now is this shift towards inference for our clients. And the use case has changed, right? Clients are asking us for inference only or training only infrastructure. They just want AI infrastructure, right? And AI infrastructure can do everything. AI infrastructure allows you to run inference for 18 hours during the day as inference command is peaking and whatever region you're in. And then at night, it allows you to run training and fine tuning workloads on your platform. So that's interesting. So there's actually not a difference between like how a data center or a cluster is made for inference work workloads versus training workloads. Not on the core we platform. That might be in the industry. There's some inference only or training only platforms, but we build AI infrastructure. And what we're observing is this kind of loop process and AI loop that we refer into in our recent earnings call where clients are consistently shifting workloads from training and to fine tuning and inference and just going around and around as they collect more and more information, right? And we expect that that's going to be the new norm across AI. And again, I think we're just widely recognized as having the best platform to enable the scaling of that AI loop. Yeah, I think that's the thing that I'll be keeping my eye on is like, what is like this explosion of inference going to do? How does that change the economics of the AI cloud business? What does it do to like, does it require changes to these data centers that are halfway under construction right now? Does it need to make any do you need to make any applications to it? And I mean, it seems like for for core weave, that's not the case. You guys make AI infrastructure. So it's it works for all types of use cases. That's right. And you know, a little on that point of like what has changed in the data center is liquid cooling, right? Like that that was the biggest shift in data center construction because data centers previously were like no water, which makes sense. But you can't just like, you know, drill holes and walls and put pipes through it and say it's liquid cooling now. It's kind of a first principles engineer solution with liquid cooling. And you know, we have been very focused on all of our data center operations to the liquid cooling for a while at this point. What else happens in the data center from here? They just get more dense, right? You're moving up the the rack density of like how much power is going into the rack. But liquid cooling is incredibly efficient within those platforms that that's we don't expect further like major data centers shifts within the next few generations. Gotcha. Gotcha. Speaking of data centers, I'm going to end with two quick questions. One kind of a serious one. One more fun. The serious question is like, you know, there's been a lot of backlash now when it comes to data center buildouts throughout the country throughout, you know, throughout the world. There's talks of like data centers in space that kind of go around some of the backlash here. Does that impact your how you guys are thinking about data center buildout, your projections moving forward? I mean, how are you dealing with some of the recent commentary out there? Yeah. This doesn't impact our injections going forward, right? So at the end of the day, demand for AI is only increasing and is not being impacted by whatever may be happening at a state or county level. The demand is still there. I think all that really changes is where does it get built, right? Like a space maybe, right? Yeah, space, space. I think it's a big literal lift to get to space, but like the infrastructure is going to come online because the demand there, the ROI is there, right? And the ROI for our clients is massive, right? The ROI for us is incredibly attractive on these investments. I think it makes sense for states and counties to want to understand the development that's happened, but it's not going to stop the development on a national or global level because the demand profile is there, and thus it's a, where does it get built question, rather than will it get built question? Yeah, I think that's going to be the key question over the next six to 12 months. Last question, fun one. So again, you guys started off as a crypto mining company. You still keep track of crypto prices at all, is that just that? Because like are you still keeping track of Bitcoin on your home screen? Like what's your relationship with crypto these days? Yeah. I mean legitimately, we started this in 2018. I think we were focused on crypto for maybe maybe 18 months, right? By mid 2019, we were all in on developing and fundraising for cloud. Do I still follow crypto? Of course, like it's such an interesting asset class. I think Bitcoin became this institutional quality asset. It's, it's pretty amazing to watch something have scaled to there, but all by attention is focused on core weave and building the infrastructure that drives the creation of intelligence. I mean, you guys probably pulled off one of the greatest pivots in corporate history going from you know, crypto to AI cloud and time to perfectly. So congrats on that. And congrats on all the success. I appreciate you making the time today. I think I learned a lot. I think our audience had learned a lot. So hope you will have you back on soon. And you know, we're doing update on what's going on with core weave in, you know, six to nine months. Appreciate that today. Thank you. Thanks so much, Brennan. Well, all right. Guys, hope you enjoyed that conversation with Brennan, McBee. I thought this was a really interesting one because AI infrastructure is one of those areas where the bull case and the bear case are both pretty compelling. There's no question that demand for AI computers exploding, but there are also legitimate questions around dead and financing and competition and the supply and what the business will look like when the market eventually matures. Let me know in the comments on what you guys thought about today's conversation. Did you buy the core weave story or do you still have concerns about the neo cloud business model? Drop your comments on Spotify and YouTube. And while you're at it, consider giving us a five star rating as well. Know all that engagement really does help us out. And it helps other people find the show. Thank you guys so much for listening, watching and commenting. Shout out to Mike for all the work behind the scenes. And we'll see you guys back here tomorrow. Tammy regularly haul stuff to her kids at college. That's why she shops at BJ's wholesale club saving up to 25% off grocery store prices. Beef jerky, microwave meals, laundry pods, even they can't screw up. 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