Odd Lots - How the Hedge Fund Magnetar Is Financing the AI Boom

Episode Date: December 9, 2024

AI software and the hardware that enables it have been hugely popular investments this year. But there have still been limiting factors on the sector, including a shortage of compute to power so many new start-ups. Investors don't want to finance companies that lack a signed contract for compute, and compute providers don't want to sign contracts for startups that haven't already secured funding. Now Magnetar, a hedge fund which started its first ever venture capital fund earlier this year, is trying to solve this "chicken and egg" problem by offering compute in exchange for equity. Magnetar was an early investor in the AI space, partnering with Coreweave and recently helping the hyperscaler to raise $7.5 billion. On this episode, we speak with Jim Prusko, partner and senior portfolio manager on Magnetar's alternative credit and fixed income team, about why the hedge fund is getting into venture capital and some of the new ways they're deploying money in the space. Read More: Magnetar Starts First-Ever Venture Fund, Targets Generative AI Become a Bloomberg.com subscriber using our special intro offer at bloomberg.com/podcastoffer. You’ll get episodes of this podcast ad-free and exclusive access to our daily Odd Lots newsletter. Already a subscriber? Connect your account on the Bloomberg channel page in Apple Podcasts to listen ad-free. See omnystudio.com/listener for privacy information.

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all investing is subject to risk vanguard marketing corporation distributor. Podcasts Radio News. Hello and welcome to another episode of the Odd Lots podcast. I'm Tracy Allaway. And I'm Joe Wisenthal. Joe, AI is so hot right now. In the immortal words of Mugatu, AI is so hot. It is, yes, it is really hot.

You know, you hear something, there's a little bit of slowing down and some of the progress on the models. But the recent Invidia results speak for themselves. there's nothing that I've seen yet that would suggest that this macro trend, at least as an investment trend, and I'm not talking about stocks per se, is anywhere close to, quote, slowing down. Yeah. And the interesting thing is we seem to be having more and more players, some new types of players that are getting into the space. So, you know, we have AI funds kind of launching left and right. And one of the newest players is a hedge fund called Magnitar. And I know them like primarily. for credit stuff. I think they were big in red cap trades for a while. Yeah. And now they're launching an AI fund, a VC fund, which is kind of unusual for this type of hedge fund to do. Totally. I mean, I've heard of Magnetar for a long time, obviously going back to the early 2010s at least. And look, I'm not surprised that various investors are looking

for what is their distinct way into this space. And of course, look, we've done interviews with VCs of various nature and positions in the past. And so I guess, you know, there's sort of two questions to my mind, anytime we're going to be talking to someone investing in early stage or any stage of AI, which is obviously what is the thesis, what's going to win out, where we'll value accrue. But then from an investor perspective, given so many entrants into this space, specifically, whether on the public equity side, whether on the private side, whether on the VC side, early stage, late stage, what do they as a fund or an investor bring to the table or will be able to see that the other billions of dollars competing for AI profits do not see?

I have a slightly different question, which is for these types of investors, like how much is it about how good the technology is that they're investing in versus how much is it about getting in the right position in the capital stack? So I think it's going to be really interesting to talk to someone who's coming from this perspective. And without further ado, we have the perfect guest. We're going to be speaking with Jim Prusco. He is a partner and senior portfolio manager on Magnitar's alternative credit and fixed income team. Jim, welcome to the show. Thank you. Great to be here. So how does someone on a hedge fund's fixed income team get into AI? Well, we have a long history of investments in

private companies really dating back to an increased focus after the financial crisis when spreads and yields got tighter and the private markets seem more interesting. And we've often partnered with platforms where we thought we could grow the platform and generate an interesting asset, either a pool of cash flowing assets or help grow the company and participate in that growth and support them through financing and other things like we can support them through helping them with hiring or accounting or other systems they need and just to help them grow generally. And so, you know, I've been doing that a long time and we've been a number of areas like auto lending in Ireland. And then we've moved into various FinTech companies.

We were one of the first institutional investors in Open Door before they went public. We're supporting and investing in a very interesting fintech that is financing restaurants right now. And so we felt we had experience in that space. And then that sort of overlapped with our relationship and our investment in Corweave, where we were the first institutional investor in Corweave in 2021. So we were very early in the trend of putting capital into the AI infrastructure space. And that's just sort of grown as this whole market has grown to encompass literally everything now. You know, we continue to look for smart ways to. to invest. And, you know, one of those ways we felt was what can we provide that's of value? And one of the things we can provide besides the general help we can give a growth stage company is compute, because that is the scarce resource right now. And that's where all the capital is going to the various parts of the value chain to deliver compute. And so there's a competition to get compute. and if you're a smaller company with limited capital or limited access to capital, it can be

difficult to get that. And so that was sort of the value proposition we thought we could bring to bear. Joe, I have this vision in my head of VCs like going into startups bearing baskets full of chips. Yeah, exactly. Instead of just saying that like our pitch is the relationship and the coaching aspect. We have access to the chips or the energy plus chips. Just for point of clarification, listeners should know we've talked to Corwey. at least twice on the show. And it feels like in the AI space specifically, this is one of those

names that's a very big deal, but not many people don't know it the way they know, say, an Nvidia at the very back end or chat GPT at the very front end, but they build a lot of the AI data centers that are filled with Nvidia chips. I want to get more into the business model there because I have a lot of questions in the business of selling compute, etc. But talk a little bit more about, you said your experience in the private side is like this expertise with platforms per se. And when I think of platforms, I think of companies that can acquire lots of other companies or a lot can be built on to them. Talk to us about how the platform-specific expertise informs your thinking with a core weave

or any other AI investment that you're making now. So we've tried to put capital. into companies that are trying to build their business in a particular space. And oftentimes that could be a space where they generate a cash flowing asset, like in the auto loan example. In the open door example, they were acquiring real estate, which was a hard asset. In that restaurant fintech example, they're acquiring restaurant credit. And so we've tried to support businesses that had some kind of asset or cash flow.

and work with them on a number of ways that we can add value. And I think first and foremost is all these gross stage companies need financing. And I think we have great expertise from debt to equity, private to public. And we can be innovative in trying to bring, you know, the best, most appropriate, lowest cost capital to these gross stage companies. And like I said, as well as... So just to be clear, just to understand it in this context, what makes AI distinct, say, from other waves of tech, or what makes it distinct for, say, a Magnetar is in part this distinct capital demands that was not perhaps as big of a deal during the SAS wave of the 2010s? Yes, and not only a general capital demand, but in many cases, for many of these companies, a very specific demand to have capital to deploy with compute.

And because they need this very specific scarce resource, helping to deliver that resource, And in particular, helping to deliver that resource in a high-quality way where you have a partner like CoreWeave that has, you know, I think there's a lot of evidence that they have the highest-performing AI training cluster. And so that is really valuable to these companies that might otherwise struggle to get enough compute to further their business model. Speaking of CoreWeave, I'm really curious how that conversation actually started because this was a new and novel thing. I don't think we had chip-based loans before, to my knowledge. And I keep hearing that asset-based financing is going to be like this next big thing in private credit or it's the last real frontier in private credit. How did you come up with this idea, this deal? Well, asset-based financing is really a classic private credit tool.

And there's a number of examples. Just if you think about my example with the Irish auto lender. If you buy a loan for a car, so the Irish auto lenders generating car loans and those go and you buy them in a vehicle, you have primarily the security of the people paying on those loans. And so you get paid back by the cash flow of the borrowers paying their car loans back. But there's credit risk to that. They could potentially stop paying. And in the case where they stop paying, then you have the cars collateral.

And really, that metaphor applies almost directly to GPUs where if you're a company delivering high-performance compute like Corleave has, you're contractually selling that compute to some counterparty that's going to use it. And in their case, you know, that's often a very large, very credit-worthy hyperscaler, but not always. There could be some smaller startups that have riskier business models. And in that case, primarily by funding the GPU, you're getting paid back with those contractual cash flows on the use of the GPU.

But in the case, that company fails, then as backup, you have the GPU itself. Now, the GPU isn't really like the car where you probably go out and sell it, but you get the time back on the GPU, which you can then resell to somebody else and being a scarce asset, that you can think about what value that would have in a future time. One difference that I could imagine with the GPU versus other forms of assets, say whether it's a car or say whether it's a house, is a certain here in 2024 still unpredictability about many things in the future? Will Nvidia always be the gold standard, so to speak, in AI chips?

Maybe it looks like a yes, but it doesn't seem guaranteed. how fast will the current generation of chips that are deployed degrade in value? I imagine there are fairly predictable sort of depreciation curves for cars that perhaps are more uncertain for chips. And then also the uncertainty of actual deployment given permitting and challenges with energy and the other operational things that have to do with a new company building a data center. Talk to us about modeling or at least thinking through some of the uncertainties with CHIP specifically?

Well, depending what stage you get involved, you have the breadth of all those different risks potentially. So if you're investing in high-performance compute, but it's a greenfield data center, then you have to think about all those things. You have to think about the delivering of the power. You have to think about the timing on all the components to get to the data center.

If you're making what we've been talking about, which is sort of a GPU-based loan, then usually that loan is based upon a running GPU in an existing high-performance compute data center. So you don't really have to think about some of the earlier stage issues. You more have to think about how long is my contract, how good is my contract, what do I think the value of renting that chip out will be at the end of that contract, How much rent on that chip could I get if I had to re-rent that in the middle of the contract? So it's more near-term things on actually having a functioning GPU in the data center.

But all those other things have to be financed too. And there's going to be innovative and large amounts of capital dedicated to financing those things. Setting aside the financing for a second, how hard has it been just to find physical space in data centers? Well, it's been extremely scarce, and a lot of that is driven by the search for power. The data centers required for the new AI chips are much different than the old data center. So it isn't really cost efficient in most cases to go and take an old data center and try to retrofit it because the amount of power just alone that has to go there is transcending an order of magnitude more per rack of GPUs now. And so that's just, you just can't really retrofit that

efficiently. It's better to build your own building. And so it's really come down to things like permitting, availability of power, and time to get all your components. And, you know, all these things have their own lead time. So it had an interesting back and forth, the Brian on curing transformers, you know, all these little, you know, nuances come into play when you have to build a data center. And so Because power is really the limiting factor, most of all, you're seeing a lot of moves towards where the power is. And it was recently an article on Bloomberg, I think, about a company in Texas that owns a bunch of land that's now worth $40 billion, right?

And that's because they're near all this renewable power. But that isn't the only thing. It's incredibly complex to operate this high-performance compute. So then you have to think about, if I try to build my data center out there, where the power is, can I get everything out there, including operational expertise, right? Can I staff my data center with the kind of experts I need to run this kind of highly technical, high-performance compute? And each generation is just getting more complicated. We're going to have liquid cooling on the next generation of Nvidia chips, probably immersion cooling right after that.

It's very complicated, very expensive, and very difficult to scale, much harder to do in a large size than it is to do in a small size. Maybe Magnetar can finance a small modular nuclear reactor. No, seriously, because if you're financing the compute and securing that on behalf of companies that you want to invest in, you could go one layer down, finance the energy. And we're certainly interested in that. And we have a history investing in energy. We have investments right now in a developer of utility scale solar power in the U.S., who has at least some of that solar power to various hyperscalers. So that is certainly a space we're interested in.

I was just in Miami meeting with a company that has a novel heat sink battery technology that they want to deploy to data centers, that they're talking to a bunch of data center type companies about launching that product there. So there's a ton of interesting things. And just like every other part of this ecosystem, it's going to require an immense amount of capital. I guess just since we're sidetrack on the energy component for now while we're here, novel battery technologies, there's a lot of them out there. There's a lot of startups that have something novel and energy.

And often one of the things that they talk about is this chicken and egg problem where they need capital. They need sort of financing of some sort or another to build this stuff. But the lenders don't really want to give it until there's demand. and no one is going to promise to buy it until it's shown that it can work. Can you talk a little bit? I mean, again, I know there's a little bit off track from GPUs themselves, but since you were talking about... Well, it's similar with AI, right?

Yeah, since you're talking about batteries. If you talk a little bit about that dynamic as it affects solving the energy side of the equation? Yeah, for sure. And it has some overlap with the way you look at an AI company, too. Yeah. If you think about the core things that we really want to look at, it's technology, team and traction.

So does their technology really work? That's first and foremost. You know, what is this product? Does it have some kind of advantage? And then traction, like time to market. That's super important. I was just talking to ISO-Cont at Poolside. And like, to him, like, those are the two most important things.

Speed to product, speed to market. Because it's a race. And even if you have the greatest technology, if you take too long, someone's going to be using something else. And that's certainly true in the energy space where energies of critical importance. So I think that for these startups on the traction side, they really need some strategic partnerships because their cost of capital is very high. The strategic partnership is kind of like an existing company that has a demand. It also has a lot of cash and could theoretically be a buyer of their solution. Yes. And really on the other side too.

So, for example, because their cost of capital is so high, there's certain things that it's hard for them to do. And one of the things that it's really hard for all these startups to do, and this was true in the recycling industry and other industries, is build a plant. Yeah. Like, very expensive, time consuming to build a plant. You don't really want to raise BC capital to build a plant. And so it's important to have a partnership on the manufacturing side too. And that was really like the first thing this battery startup that I just visited talked about is like getting that because you've got to be able to deliver your product and you have to deliver it on scale. And ideally, you don't want to be wasting time building your own plant on that.

And then like you said, on the other end, you want to have a partnership with the users of the energy, which is all the people that either have data centers or use data centers or customers of data centers. and you want them to ideally put together an attractive financing relationship where, you know, in some former fashion, they're front-loading their payments to you so that you can use that capital to actually build the product that they meet. Today's show is brought to you by Vanguard. To all the financial advisors listening, let's talk bonds for a minute. Capturing value and fixed income is not easy. Bond markets are massive, murky, and let's be real. Lots of firms throw a couple flashy funds your way and call it a day. But not Vanguard. At Vanguard, institutional quality isn't a tagline. It's a commitment to your clients.

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Go to FactorMeals.C.A. and use code fit. That's 11 meals, free shipping, and free sides for life. but only with the code fit at factormeals.ca. Factor, Canada's number one ready-to-eat meal delivery service. So Joe and I went to San Francisco a little while ago, and we saw some cool things. I had my first ride in a Waymo, and we saw some cool battery-related technology. We also saw a lot of VCs. Everyone very excited about AI, obviously.

They were also talking about the difficulty of chasing deals right now. How do you compete with those traditional VCs? or are you just not competing with them directly because you're taking the slightly different GPU-backed approach? You know, I think it's both. I think you're competing with them and to an extent partnering with them. And that's the thing we had to ask ourselves before launching the fund is, you know, what are we bringing to bear that's value added?

And in this case, we're bringing to bear the compute. And so often these startups, even if they're backed by a strong VC, can have a bit of a chicken an egg problem, which is they need compute to develop their product and they need capital to buy that compute. But if they don't have the compute lined up and the price locked in, then the capital might be hesitant to go in because they'd be like, we could put our capital into you, and then it could take you an extra six months to get your compute. And by that time, some competitor has passed you by or the technology has changed. And on the other hand, because they're a startup, they don't really have the creditworthiness to just contract the compute, they most likely have to pay up front.

And so we bridge that gap. And so if we go into a fundraising round where there's a bunch of VCs putting cash in, if they know that we're putting compute in alongside them and that the second the round closes that compute will be available to the company, then makes it easier to raise the cash part of it. So we are competing and we need that value added to be part of the equation. But also, I think it helps them to raise from traditional VCs because we take that one risk off the table. How big is the market of companies that need compute? Because there are plenty of AI companies that just build on top of an existing model like GPT or Anthropics model, et cetera. How many companies are actually out there and who, like, not who are they specifically, but what are the types of companies for whom actual access to compute?

is an important part of their business. Yes, well, you know, it starts, of course, with the LLM companies. You're using massive, huge, huge amounts of compute. But then if you look at the rest of sort of the AI stack, there's a couple areas where you're going to need compute. And one is all the small model custom model companies. And small can meet a lot of different things. So you can have some very small companies that are using a very targeted model, like say in a vertical stack.

You might have a robotics company that is specifically training a model to run a robot in a particular situation. And that could be anything from a warehouse to doing surgery, right? And they need compute to train that model. Or another one which is huge and dominated by an existing big player's autonomous driving. but there are other autonomous driving companies that are trying to be deployed at other automakers that need compute to train those models or weather models. There's some really good companies that we've talked to doing weather models. They need compute to train their model.

And so that whole model layer, and then even on the app layer, there might be custom elements of small models that they have that sit on top of the big LLMs that they need some amount of compute for. So there's quite a range. You know, it's not everyone. You know, it's more in that model application layer and, you know, less in the infrastructure layer that need compute. So this is one thing I always wonder about AI investment, which is you have a lot of

companies that are building on top of existing models, as Joe mentioned. And to some extent, that makes sense because they can save a lot of money by doing it. And realistically, are you going to compete with Google or Microsoft? probably not. But on the other hand, I always wonder if you're building on top of an existing model, how do you ring fence that business? Because my assumption is if AI gets better, maybe at some point the AI can replicate any AI model, basically. So this is the first thing we always worry about is does some giant company already have this product in a closet with like 20 PhDs working on this? And so,

I was just at this conference and somebody coined the phrase incumbent maximalist. Oh, nice. And that's when you think the incumbents are going to do everything and no one else will ever succeed. And I think there's a few use cases. There's things where it's a very specific task that is hard to do well with a giant general model and probably isn't worth doing well. Like if you're focused on growing tens to hundreds of billions of dollar of revenue, you can't be distracted by trying to do every little thing. And we've seen this in previous tech revolutions as well.

And so it can be something that's very focused on a space. We've seen legal, accounting, sales. There's some great companies that have virtual employees that they're doing, things that are very task-specific. There's some companies doing text to language and language to text and other things for very specific applications. So, you know, that's one way. The other way is data.

The greatest ring fence is to any AI company or business is data. Because you've seen as the performance of some of the LLMs has supposedly flattened out, a lot of that is because they've just used all the data. Like they've trained on the whole internet. There's nothing left. And so now you have to have other ways to train or novel sources of data. So proprietary data is super valuable. And then there just can be areas.

where they're conflicted. They don't want to compete with their customers right now, although you know, competing with your customers is a great tradition in the tech space. But there could be situations where it's not worth it to them yet to compete with their customers. And so I think there's those different use cases where, you know, you're going to see a small number of companies succeed. I have a very stupid question. And actually, I shouldn't even be asking you. I should have asked it the last time we talked to Corleave. But since you're here, I'm going to take a Mulligan, on the question I didn't ask them. I know that Nvidia is an investor in CoreWeave,

but even setting aside that specific relationship, the actual purchasing of chips, how does the pricing work? And how much is it a de facto auction, whereas demand for chips booms, Nvidia can expand its margin versus invidia aims for a stable margin over time? And I imagine this enters into your calculation to somewhat thinking about,

a core weave's future capital requirements. How does that market for chips work? Well, I can't comment on the internal workings of Nvidia, setting their prices, per se. But is an investor in a buyer? Whatever you, I'm a buyer of chips. How do we're, I say, I'm, I want to buy some chips. And I want to get in line. I imagine it's like the container industry where you have to have a specific relationship and there's a shipping manager called Lars somewhere in Northern Europe who holds the keys to the chips. Well, for any company using a resource, and it's certainly true of companies using compute, right, it's always a cost benefit example. So there's great benefits to running your AI training on an Nvidia ecosystem on a network like Coreweaves that's very fast and very reliable because when you train a model, you stop every 15 or 30 minutes to save your work. And if there's a failure in there, you have to go back to the last time you saved your work.

And there's a huge loss on that. So there's benefits to using the best technology. But those are quantifiable. And if you're a particular kind of technology becomes too expensive, you'll see people diversify out, right? I mean, it was just news the last two days about Anthropic and AWS and AWS's new chips. So there's always some form of competition. I mean, Nvidia is sitting in a unique place. where they've really had a de facto monopoly on this. And I think their pricing is being set in a way

to grow the market, right? Like, they want to grow the market. I can't speak for them, but you wouldn't want to set the price of your product so high that you stifle the market's growth, right? Like, growth is more important than making an extra dollar on every widget. And so I think that's got to be a calculation. And certainly to date, it's been fruitful in that. this market has taken off like almost no market ever. Eating well shouldn't be complicated, but somehow it turns into recipes, prep, clean up, and half your Sunday gone.

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I want to go back to the capital question. And most venture capital comes in the form of equity. You're doing something slightly different. In my understanding, you're primarily going down the debt and sort of fixed income route. That seems so different because in my mind, when I think about bond investing, and we've said this a number of times on the show, it's all about avoiding losers, right? Like there's limited upside, but you don't want a bankruptcy that wipes out your investment, whereas equity, the upside is basically uncapped. So it's about finding that one stellar outperformer or that one lottery ticket. How do you square, I guess, the risk-averseness of some of this debt financing with getting the huge upside that is potentially there from AI?

Well, the amount of financing required for this whole AI build-out, which is on some immense scale of, you know, people have talked about the Manhattan Project, the building of the interstates. It's going to require capital in many forms for many things. And I think there's a lot of thinking going on and, you know, certainly we're part of that in deploying the most efficient capital to the different layers of this buildout. And so we've talked about a couple different things here. We've talked about financing GPUs. So if you're financing GPUs with debt, then you can really think through your downside protection just like in the auto metaphor. Right. You have the collateral.

You have the collateral. You have the contract. You can analyze the creditworthiness of the contract. You can look at how the leasing curves of prior chip generations have decayed. You have some real information there. You have a real asset. You have real contracted cash flows. Now, in the VC fund, that's a lot different.

In this case, this is true venture equity. And it's just that it's being deployed in a unique way where instead of cash, the compute has been contractually secured and is just being exchanged for the equity directly, as I talked about before, saving that step and de-risking the process of acquiring compute for these gross-stage companies. So you are doing equity through the VC fund? The VC fund is equity, yes. It would be part of typically, but not always, a part of a round that a gross-stage company might be doing.

Are you doing convertibles? So we can do virtually anything across the debt equity private public spectrum and have in many cases. In the AI fund itself, most of the companies being gross stage are not really in a position to do debt. So I think for the most part, I would expect that those would all be venture equity investments. I got to chuckle when you're like, oh, we've been in this space that's way back. And then you said 2021, but it does really sort of speak to how. It feels like a long time. Yeah.

Well, you know, I mean, Chad GPT, I think came out at the very end of 2022 or maybe early 2023. And that was the big light bulb moment for a lot of people. So even being that active in a lot of this stuff a year earlier, truly is early. That being said, things like CoreWave, things like data centers, the need for compute is very well understood right now in a way that perhaps three years ago, many people in the credit and financing space weren't thinking of. Is that a margin compressor for you, the fact that other entities, probably many with much more capital than Magnitar has, everyone has now woken up to

this opportunity of, yes, there's going to be a lot of financing needs in AI. And do you see change in competition or spreads or anything like that? Well, I think it really depends on what you're financing. So there's a lot of capital that's gone into all these spaces. And certainly all across the stack of financing compute, you've seen a huge amount of capital come in and you've seen all the giant investment companies, providers of capital, get involved. And so there's a lot of capital in there, but there's also like a huge need for capital. And it's very complex thinking about the structuring and getting the right capital in the right space. And so I think there's room to be innovative. And I've spent the last 20 years at Magnetar thinking about unique ways to source

investments and deploy capital. And I think that really comes to bear on this. And because this whole market, like you said, is so new. And we've only had Chad GPT for a couple of years, you know, you're seeing companies with all different ways of working. You know, I talk to a company in the text the voice space at a conference last week. And they actually were buying their own DGX servers themselves and just running on themselves in their own on-prem site. And we're like, sure, like that's something we can finance. That's like a hard asset. But no one's really looking at that yet because most of the capital is so big. It has to go to the biggest thing. So you have your trillion dollar investment firm, which is a couple. You're not going to,

want to deploy 20 to 50 million dollars in a one-off thing. You're going to want to deploy tens of billions of dollars in the biggest thing, whether that's power or physical data centers or GPUs. What's the pitch to your investors, to Magnetars investors? Because again, this is something, I know you said you've been in the tech space for a while, but it's still something that feels fairly new. And when I think about AI, there's been so much excitement over it. Some people have been talking about whether or not it's in a bubble. And I think about a hedge fund, and that's all about uncorrelated returns and investing profitably through the cycle. I get that you might be promising very large upside to investors. But what is the hedge aspect of this?

Well, as a firm, we've done many different products and many different strategies for many different investors over the years. And we've really been flexible in trying to deploy capital in the most interesting areas that are going to have the best risk-adjusted returns. And many of our investors have been with us through the whole life of the firm since 2005 and appreciate that. And so we've done both diversified investment strategies where we just thought the general pipeline of deploying structured capital has been great. And then we've also done things targeted at a particular asset when we thought that opportunity was great. And so in the case of the VC fund, the value proposition really is for the investor, what it is for the company,

which is we're bringing something unique to these gross stage AI companies, which will get us access to making investments and what we hope will be the best, best of those companies with the best business models and the best teams. And so we're going to use the unique compute that we have in the way that we're going to exchange that for equity and deliver that to these companies as a way of getting access to investments in what's a very, as you mentioned, very competitive environment where there's a lot of capital going into the space. And so I think for investors that want to participate in that kind of investment in getting capital deployed into gross stage AI companies, you know,

this is a very unique opportunity. And so we saw a lot of traction with that. When you come in as a VC investor in some of these startups, do you have to supply dollars or in some cases or all cases is your ability to promise compute from day one? enough for equity? It really varies. And there's investments we've made both inside and outside the fund. And it just depends on the situation. So there can be companies that we find super interesting, but don't need compute. And in that case, we can invest in those companies directly outside of the fund. For the fund itself, the proposition is equity for compute. And so the fund itself is focused on companies that really do need equity and are interested in equity, and really do need compute

and are interested in compute on Coreweed's network. And so that's the kind of companies that will invest in from the fund. But as Magnetar as a whole, we've been focused like we talked about on everything from energy through infrastructure, through other AI companies that just don't happen to need compute right now. Then just to this point, your ability, to promise or give AI startups compute. This access to compute emerged via that initial relationship as a financier. This is what I was going to ask, which is how worried are you about competitors doing the same thing and providing GPU back debt?

Or is it the case that because of your first mover advantage with CoreWeave, you can hold on to that advantage for a while? So for the fund itself, it was the unique relationship. we had with CoriWeeb where we felt they were the best provider of AI training compute, and we were able to work with them to contract some of the very scarce resource of that, and then have that available to deliver to these AI growth companies. And so that was really where we were able to put together something unique. And from day one, that was understood to be part of the payoff of being a financing

partner to CoreWave? I wouldn't say from day one. I would just say it's part of the natural growth in their business and our growth in investing in the AI market and in being a partner with them. Everyone is both a partner and a competitor in this space. And, you know, Nvidia has multiple ways that they invest in their customers, as do all the hyperscalers, for example. And so it's really about, are you providing something unique?

something that's different. And, you know, right now, this moment in time, we feel like the size of the compute we're providing and the network we're providing it on. And the way that we can provide it in real time is unique and is valuable to many companies. Now, look, there could be some companies that are getting their compute from somewhere else and it's just not a fit. That's certainly going to happen. But I think there's many, many AI growth companies where this is very valuable to them to get the compute on Corwin's network, and that's going to lead to a relationship with them. When Amazon makes a VC investment, it's in large part understood that it's the same sort of premise that they're going to invest in some software company, and the money comes right back

in because that company has AWS needs, and so it comes back. Obviously, we know that the, not only do the large legacy hyper-scalers, not only are they building their own models, many of them, they're building their own silicon, and Facebook has its own chips and talked about Amazon, and Google has, I forget what their whole thing is called. How do you think about them as competitors to CoreWeave in the sort of pure chips and data center side? I know they're partners, I know their customers, et cetera, but they are also pure competitors, both to say a CoreWeave and to say an InVIDia. Yeah, again, everyone's a partner and a competitor. You know, I think the difference... Oh, Google's just TPUs is their thing.

Anyway, sorry, keep going. I just couldn't... Yeah, I mean, the difference as Brian talked about is the core weave network was built for the ground up to be hyper-efficient at running AI solutions. And so I think it's unique in that way, and I think that's why it's grown so fast. But certainly everyone else is trying to build their own out, and there will be other people that will have Nvidia GPU chips. and that will include the hyperscalers.

But, you know, one of the things we've seen is that this is very hard technology. So it's particularly hard to deploy at scale because you run into like real physics issues, you know, surface area to volume type issues of getting this much power to a rack with, like, how much cable does that take? How much cooling does that take? how do you run the software layer? Like the software layer to control, you know, a node of eight GPUs is going to be a lot different than if you're trying to run 128,000 GPUs.

And so this problem gets more and more difficult and you need better technology and you need a highly skilled people. And so the bar is always moving. You know, there's always a next generation chips that's going to be super complicated. certainly the Blackwell deployments and the incremental new Blackwell generations are going to be ever more complicated and tricker to deploy. And you've seen issues already, right? You've seen hyperscalers and other competitors in the space have reliability problems or be behind schedule. Like, it's not easy.

It's a very complicated technology. You're not plugging your GPU into the wall and it's ready to run an AI model. And so, like, I think there's going to be value accruing to skill and efficiency and execution in the space. And, you know, that's going to last for a while. So some people draw an analogy between the current enthusiastic cycle for AI and the early 2000s period where we had a lot of enthusiasm for Internet companies and telecoms and things like that. Do you see evidence of froth out there? or is it the case that because of the huge amount of initial capital investment that's needed, it's difficult to get, I guess, enough new entrance that this would become a bubble.

Yeah, everything can become a bubble eventually in almost any industry that's highly capital-intensive. Usually, if there's excess returns, you'll see capital go into it until those returns aren't good anymore. And a lot of capital will go in before you figure out that last part. But this is extremely early. Like if you look at the capital that went into the internet and then how that value accrued to both the big tech companies and the startups, people have looked at numbers like $3 trillion of equity value created with the large incumbents, but there was another $500 billion created for the new startups. And we're just getting going here, right? we're just building out the kind of data centers, the kind of energy infrastructure, we're just starting to deploy products, right?

If you talk to enterprises, they're just starting to implement the most obvious use cases for AI. So I think we're much too early to worry about a bubble. I talked to somebody at a hyperscaler, and they were like, the last thing we're worried about right now is having too much compute. Last question for me, you say we're early. there's still no signs of too much compute. Earlier in the conversation, you're like,

this is a Manhattan Project scale project. Give us some flashing number. How much has been deployed in this area? And, you know, over the next 10 years, how much capital is going to be demanded for this space and how much will be needed? So one number I saw was that in 2003, $37 billion was deployed into AI.

infrastructure. And in 2003, that number is going to be like $430 billion in that year. So this is trillion-dollar scale investment. Cool. Cool. All right, Jim Presco, thank you so much for coming on All Thoughts. That was great. Thank you for having me. That's fantastic. Thank you so much. Joe, there's two things that I hear consistently about AI, and one is it's going to need a lot of capital, which Jim spoke to. And then, The other thing I always hear is, well, at some point, AI companies have to actually produce revenue. And I guess the question is, like, are they going to start producing revenue in time to pay back that massive capital need? Yes, it's very interesting because, look, I believe that there are companies that are getting productive value out of AI models.

Like, I believe that exists. But, you know, you're talking about hundreds of billions over the coming years and financing. In the end, that is going to have to come from profitable deployment to customers. Right. And so, like, this to me is, like, you know, still a bit uncertain. I do think the financing that we talked about is extremely interesting, just in the context of this conversation. Yeah, absolutely. The GPU-backed loans.

Yeah. Well, both the GPU-backed loans and the opportunity that that affords a company like Magnitar to make GPU capacity in lieu of cash for, equity investments is extremely interesting. And so, and then you get this second order effect. So, A, you're providing something that other VCs can't because you are giving them access to compute on day one. And then B, other VCs want to enter that deal because they know that they're going to be investing in a company that is not going to be have to scrambling for compute once they get that VC cash. It's a very sort of middle way approach because I think so far the way we've seen AI investment unfold is either it's a sort of picks and shovels approach where you invest in the chip companies

themselves and the data centers or it's you invest in the AI companies that are doing cool things. But this is kind of both. It is exactly both. And it sort of sounds like some combination of foresightful planning and also stumbling into a very good situation by which the firm's relationship with CoreWeave dating all the way back to 2021 does now give them this a certain edge in the VCR. It's just a really, this is a fascinating sort of open frontier in many respects. I still want to know who came up with the idea for chip-based financing. Jim kind of evaded that part of the question, but I want to know what those initial conversations were like.

Yeah. It's also just interesting to think about that on some level, the analogies are like an Irish car lender, right? So it's like on some level, this is a very novel area and with technology that is highly uncertain. And on the other hand, if you're invested in a car loan company, you could sort of get it. Yeah. All right. Shall we leave it there? Let's leave it there.

This has been another episode of the Odd Lots podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Wisenthal. You can follow me at the stalwart. Follow our producers, Carmen Rodriguez, at Carmen Armin, Dashel Bennett at Dashbot and Kill Brooks. Thank you to our producer, Moses, and I'm. For more Odd Lots content, go to Bloomberg.com slash Oddlots, where we have transcripts

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