Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the All Thoughts podcast.
Speaker 2: I'm Tracy Alloway.
Speaker 3: And I'm Joe Wisenthal.
Speaker 2: Joe, AI is so hot right now, in the immortal
Speaker 2: words of Mugatu, AI is so hot.
Speaker 4: It is.
Speaker 3: Yes, it is really hot. You know, you hear something.
Speaker 3: There's a little bit of slowing down in some of
Speaker 3: the progress on the models, but the recent in video
Speaker 3: results speak for themselves. There is nothing that I've seen
Speaker 3: yet that would suggest that this macro trend, at least
Speaker 3: as an investment trend, and I'm not talking about stocks
Speaker 3: per se, is anywhere close to quote slowing down.
Speaker 2: Yeah, And the interesting thing is we seem to be
Speaker 2: more and more players, some new types of players that
Speaker 2: are getting into the space. So, you know, we have
Speaker 2: AI funds kind of launching left and right. And one
Speaker 2: of the newest players is a hedge fund called Magnetar
Speaker 2: and I know them like primarily for credit stuff. I
Speaker 2: think they were big in redcap trades for a while. Yeah,
Speaker 2: and now they're launching an AI fund, a VC fund,
Speaker 2: which is kind of unusual for this type of hedge
Speaker 2: fund to.
Speaker 3: Do totally, I mean I've heard of magnetar for a
Speaker 3: long time, obviously, going back to the early twenty tens
Speaker 3: at least, And look, I'm not surprised that various investors
Speaker 3: are looking for what is their distinct way into this space?
Speaker 3: And of course, look, we've done interviews with vcs of
Speaker 3: various nature and positions in the past, and so I
Speaker 3: guess you know, there's sort of two questions to my
Speaker 3: mind anytime we're gonna be talking to someone investing in
Speaker 3: early stage or any stage of AI, which is obviously
Speaker 3: what is the thesis is what's going to win out
Speaker 3: where we'll value a crew. But then from an investor perspective,
Speaker 3: given so many entrants into this space, specifically whether on
Speaker 3: the public equity side, whether on the private side, whether
Speaker 3: on the VC side or early stage, late stage, what
Speaker 3: do they, as a fund or an investor bring to
Speaker 3: the table or will be able to see that the
Speaker 3: other billions of dollars competing for AI profits do not see.
Speaker 2: I have a slightly different question, which is for these
Speaker 2: types of investors, like how much is it about how
Speaker 2: good the technology is that they're investing in versus how
Speaker 2: much is it about getting in the right position in
Speaker 2: the capital stack.
Speaker 3: So that's a great question.
Speaker 2: I think it's going to be really interesting to talk
Speaker 2: to someone who's coming from this perspective. And without further ado,
Speaker 2: we have the perfect guest we're going to be speaking with,
Speaker 2: Jim Prosco. He is a partner and senior portfolio manager
Speaker 2: on Magnetar's Alternative credit and fixed income team. Jim, Welcome
Speaker 2: to the show.
Speaker 5: Thank you, great to be here.
Speaker 2: So how does someone on a hedge funds fixed income
Speaker 2: team get into AI.
Speaker 4: Well, we have a long history of investments in private companies,
Speaker 4: really dating back to an increased focus after the Financial
Speaker 4: Crisis when spreads and yields got tighter and the private
Speaker 4: markets seem more interesting. And we've often partnered with platforms
Speaker 4: where we thought we could grow the platform and generate
Speaker 4: an interesting asset, either a pool of cash flowing assets,
Speaker 4: or help grow the company and participate in that growth
Speaker 4: and support them through financing and other things like we
Speaker 4: can support them through helping them with hiring or accounting
Speaker 4: or other systems they need, and just to help them
Speaker 4: grow generally. And so you know, I've been doing that
Speaker 4: a long time and we've been a number of areas
Speaker 4: like auto lending in Ireland, and then we've moved into
Speaker 4: various fintech companies. We were one of the first institutional
Speaker 4: investors in open Door before they went public. We're supporting
Speaker 4: and investing in a very interesting fintech that is financing
Speaker 4: restaurants right now, and so we felt we had experience
Speaker 4: in that space, and then that sort of overlapped with
Speaker 4: our relationship and our investment in core Weave, where we
Speaker 4: were the first institutional investor in core Weave in twenty
Speaker 4: twenty one. So we're very early in the trend of
Speaker 4: putting capital into the AI infrastructure space and that's just
Speaker 4: sort of grown as this whole market has grown to
Speaker 4: encompass literally everything. Now, you know, we continue to look
Speaker 4: for smart ways to invest, and you know, one of
Speaker 4: those ways we felt was what can we provide that's
Speaker 4: a value And one of the things we can provide
Speaker 4: besides the general help we can give a growth stage
Speaker 4: company is compute because that is the scarce resource right now,
Speaker 4: and that's where all the capital is going to the
Speaker 4: various parts of the value chain to deliver compute, and
Speaker 4: so there's a competition to get compute, and if you're
Speaker 4: a smaller company with limited capital or limited access to capital,
Speaker 4: it can be difficult to get that, and so that
Speaker 4: was sort of the value proposition we thought we could
Speaker 4: bring to bear.
Speaker 2: Joe, I have this vision in my head of vcs,
Speaker 2: like going into startups bearing baskets full of chips.
Speaker 3: Ah yeah, instead of just saying.
Speaker 2: That, like our pitch is the relationship and the coaching as.
Speaker 3: We have access to the chips or the energy plus chips.
Speaker 3: Just for point of clarification, listeners should know we've talked
Speaker 3: to Core. We've at least twice on the show, and
Speaker 3: it feels like in the AI space specifically, this is
Speaker 3: one of those names that's a very big deal, but
Speaker 3: not many people don't know it the way they know
Speaker 3: sayan in Nvidia at the very back end or Chatgypt
Speaker 3: at the very front end, but they build a lot
Speaker 3: of the data centers that are filled with Nvidia chips.
Speaker 3: I want to get more into the business model there
Speaker 3: because I have a lot of questions in the business
Speaker 3: of selling compute, etc. But talk a little bit more
Speaker 3: about you said your experience in the private side is
Speaker 3: like this expertise with platforms per se. And when I
Speaker 3: think of platforms, I think of companies that can acquire
Speaker 3: lots of other companies or a lot can be built
Speaker 3: onto them. Talk to us about how the platform specific
Speaker 3: expertise informs you're thinking with a core weave or any
Speaker 3: other AI investment that you're making now.
Speaker 4: So we've tried to put capital into companies that are
Speaker 4: trying to build their business in a particular space, and
Speaker 4: oftentimes that could be a space where they generate a
Speaker 4: cash flowing asset, like in the auto loan example, in
Speaker 4: the open door example, they were acquiring real estate, which
Speaker 4: was a hard asset. In that restaurant fintech example, they're
Speaker 4: acquiring restaurant credit. And so we've tried to support businesses
Speaker 4: that had some kind of asset or flow and work
Speaker 4: with them on a number of ways that we can
Speaker 4: add value. I think first and foremost is all these
Speaker 4: growth stage companies need financing, and I think we have
Speaker 4: great expertise from debt to equity, private to public, and
Speaker 4: we can be innovative in trying to bring you the best,
Speaker 4: most appropriate, lowest cost capital to these growth stage companies.
Speaker 4: And like I said, as well as.
Speaker 3: So just to be clear, just to understand in this context,
Speaker 3: what makes AI distinct, say from other waves of tech
Speaker 3: or what makes it distinct for say a magnetar is
Speaker 3: in part this distinct capital demand that was not perhaps
Speaker 3: as big of a deal during the SaaS wave of
Speaker 3: the twenty tens.
Speaker 4: Yes, so not not only a general capital demand, but
Speaker 4: in many cases, for many of these companies, a very
Speaker 4: specific demand to have capital to deploy with compute, and
Speaker 4: because they need this very specific scarce resource, helping to
Speaker 4: deliver that resource, and in particular helping to deliver that
Speaker 4: resource in a high quality way. Where you have a
Speaker 4: partner like core Weave that has I think there's a
Speaker 4: lot of evidence that they have the highest performing AI
Speaker 4: training cluster, and so that is really valuable to these
Speaker 4: companies that might otherwise struggle to get enough compute to
Speaker 4: further their business model.
Speaker 2: Speaking of Core We've I'm really curious how that conversation
Speaker 2: actually started because this was a new and novel thing.
Speaker 2: I don't think we had chip based loans before to
Speaker 2: my knowledge, and I keep hearing that asset based financing
Speaker 2: is going to be like this next big thing in
Speaker 2: private credit or it's the last real frontier in private credit.
Speaker 2: How did you come up with this idea this deal?
Speaker 4: Well, acid based financing is really a classic private credit
Speaker 4: tool and there's a number of examples. Just if you
Speaker 4: think about my example with the Irish auto lender. If
Speaker 4: you buy a loan for a car, so the Irish
Speaker 4: auto lenders generating car loans and those go and you
Speaker 4: buy them in a vehicle, you have primarily the security
Speaker 4: of the people paying on those loans, and so you
Speaker 4: get paid back by the cash flow of the borrowers
Speaker 4: paying their car loans back, but there's credit risk to
Speaker 4: that they could potentially stop paying, and in the case
Speaker 4: where they stop paying, then you have the cars collateral.
Speaker 4: And really that metaphor applies almost directly to GPUs, where
Speaker 4: if you're a company delivering high performance compute like Core
Speaker 4: we've has, you're contractually selling that compute to some counterparty
Speaker 4: that's going to use it in their case. You know,
Speaker 4: that's often a very large, very credit worthy hyperscaler, but
Speaker 4: not always. There could be smaller startups that have riskier
Speaker 4: business models, and in that case, primarily by funding the GPU,
Speaker 4: you're getting paid back with those controls actual cash flows
Speaker 4: on the use of the GPU. But in the case
Speaker 4: that company fails, then as backup you have the GPU itself. Now,
Speaker 4: the GPU isn't really like the car where you'll probably
Speaker 4: go out and sell it, but you get the time
Speaker 4: back on the GPU, which you can then resell to
Speaker 4: somebody else, and being a scarce asset, you can think
Speaker 4: about what value that would have in a future time.
Speaker 3: One difference that I could imagine with the GPU versus
Speaker 3: other forms of assets, say whether it's a car or
Speaker 3: say whether it's a house, is a certain here in
Speaker 3: twenty twenty four, still unpredictability about many things in the future.
Speaker 3: Will in video always be the gold standard so to speak?
Speaker 3: In AI chips maybe it looks like it, yes, but
Speaker 3: it doesn't seem guaranteed. How fast will the current generation
Speaker 3: of chips that are deployed degrade in value? I imagine
Speaker 3: there are fairly predictable sort of depreciation curves for cars
Speaker 3: that perhaps are more uncertain for chips. And then also
Speaker 3: the uncertainty of actual deployment given permitting and challenges with
Speaker 3: energy and the other operational things that have to do
Speaker 3: with a new company building a data center. Talk to
Speaker 3: us about modeling or at least thinking through some of
Speaker 3: the uncertainties with chips specifically, Well, depending.
Speaker 4: What stage you get involved, you have the breadth of
Speaker 4: all those different risks potentially. So if you're investing in
Speaker 4: high performance compute but it's a greenfield data center, then
Speaker 4: you have to think about all those things. You have
Speaker 4: to think about the delivering of the power. You have
Speaker 4: to think about the timing on all the components to
Speaker 4: get to the data center. If you're making what we've
Speaker 4: been talking about, which is sort of a GPU based loan,
Speaker 4: then usually that loan is based upon a running GPU
Speaker 4: and an existing high performance compute data center, so you
Speaker 4: don't really have to think about some of the earlier
Speaker 4: stage issues. You more have to think about how long
Speaker 4: is my contract, how good is my contract, What do
Speaker 4: I think the value of renting that chip out will
Speaker 4: be at the end of that contract. How much rent
Speaker 4: on that chip could I get if I had to
Speaker 4: re rent that in the middle of the contract. So
Speaker 4: it's more near term things on actually having a functioning
Speaker 4: GPU in the data center, But all those other things
Speaker 4: have to be financed too, and there's going to be
Speaker 4: innovative and large amounts of capital dedicated to financing those things.
Speaker 2: Setting aside the financing for a second, how hard has
Speaker 2: it been just to find physical space in data centers, well, it's.
Speaker 4: Been extremely scarce, and a lot of that is driven
Speaker 4: by the search for power. The data centers required for
Speaker 4: the new AI chips are much different than the old
Speaker 4: data center. So it isn't really cost efficient in most
Speaker 4: cases to go and take an old data center and
Speaker 4: try to retrofit it because the amount of power just
Speaker 4: a loan that has to go there is you know,
Speaker 4: transcending an order of magnitude more per rack of GPUs now,
Speaker 4: and so that's just you just can't really retrofit that efficiently.
Speaker 4: It's better to build your own building. And so it's
Speaker 4: really come down to things like permitting availability of power
Speaker 4: and time to get all your components, and you know,
Speaker 4: all these things have their own lead time. So it
Speaker 4: had an interesting back and forth to Brian on curing transformers.
Speaker 4: You know, all these little you know, nuances come into
Speaker 4: play when you have to build a data center. And
Speaker 4: so because power is really the limiting factor most of all,
Speaker 4: you're seeing a lot of moves towards where the power is.
Speaker 4: And it was recently an article on Bloomberg. I think
Speaker 4: about a company in Texas that owns a bunch of
Speaker 4: land that's now worth forty billion dollars, right, And that's
Speaker 4: because they're near all this renewable power. But that isn't
Speaker 4: the only thing. It's incredibly complex to operate this high
Speaker 4: performance compute. So then you have to think about if
Speaker 4: I try to build my data center out there where
Speaker 4: the power is. Can I get everything out there, including
Speaker 4: operational expertise?
Speaker 5: Right?
Speaker 4: Can I staff my data center with the kind of
Speaker 4: experts I need to run this kind of highly technical,
Speaker 4: high performance compute. And each generation is just getting more complicated.
Speaker 4: We're going to have liquid cooling on the next generation
Speaker 4: of Nvidia chips, probably immersion cooling right after that. It's
Speaker 4: very complicated, very expensive, and very difficult to scale. Much
Speaker 4: harder to do in a large size than it is
Speaker 4: to do in.
Speaker 5: A small size.
Speaker 2: Maybe Magnetar can finance a small modular nuclear reactor. No, seriously,
Speaker 2: because if you're financing the compute and securing that on
Speaker 2: behalf of companies that you want to invest in, you
Speaker 2: could go one layer down finance the energy.
Speaker 4: And we're certainly interested in that, and we have a
Speaker 4: history in investing in energy. We have investment right now
Speaker 4: and a developer of utility scale solar power in the
Speaker 4: US who has least some of that solar power to
Speaker 4: various hyperscalers. So that is certainly a space we're interested in.
Speaker 4: I was just in Miami meeting with a company that
Speaker 4: has a novel heat sink battery technology that they want
Speaker 4: to deploy to data centers that they're talking to a
Speaker 4: bunch of data center type companies about launching that product there.
Speaker 4: So there's a ton of interesting things, and just like
Speaker 4: every other part of this ecosystem, it's going to require
Speaker 4: an immense amount of capital.
Speaker 3: I guess, just since we're sidetracked on the energy component
Speaker 3: for now while we're here novel battery technologies, there's a
Speaker 3: lot of them out there. There's a lot of startups
Speaker 3: that have something novel and energy, and often one of
Speaker 3: the things that they talk about is this chicken and
Speaker 3: egg problem where they need capital, They need sort of
Speaker 3: financing of some sort or another to build this stuff,
Speaker 3: but the lenders don't really want to give it until
Speaker 3: there's demand, and no one's just going to promise to
Speaker 3: buy it until it's shown that it can work. Can
Speaker 3: you talk a little bit, I mean again, I know
Speaker 3: there's a little bit off track from GPUs themselves. But
Speaker 3: since you were talking about similar yeah, talk about the batteries.
Speaker 3: Can you talk a little bit about that dynamic as
Speaker 3: it affects solving the energy side of the equation?
Speaker 5: Yeah, for sure.
Speaker 4: And it has some overlap with the way you look
Speaker 4: at an AI company too. You know, if you think
Speaker 4: about the core things that we really want to look at,
Speaker 4: it's technology team and traction. So does their technology really work?
Speaker 4: That's first and foremost. You know, what is this product?
Speaker 4: Does it have some kind of advantage? And then traction
Speaker 4: like time to market.
Speaker 5: That's super important.
Speaker 4: I was just talking to isokon pool side and like
Speaker 4: to him, like those are the two most important things.
Speaker 4: Speed to product, speed to market, because it's a race,
Speaker 4: and even if you have the greatest technology, if you
Speaker 4: take too long, someone's going to be using something else.
Speaker 4: And that's certainly true in the energy space where energy
Speaker 4: is of critical importance. So I think that for these
Speaker 4: startups on the traction side, they really need some strategic
Speaker 4: partnerships because their cost of capital is very high.
Speaker 3: Strategic partnership is kind of like an existing company that
Speaker 3: has a demand. It also has a lot of cash
Speaker 3: and could theoretically be a buyer of their.
Speaker 4: Solution, yes, and really on the other side too, So
Speaker 4: for example, because their cost of capital is so high,
Speaker 4: there's certain things that it's hard for him to do.
Speaker 4: And one of the things that it's really hard for
Speaker 4: all these startups to do, and this was true and
Speaker 4: the recycling industry and other industries, is build a plant.
Speaker 4: Like very expensive, time consuming to build a plant. You
Speaker 4: don't really want to raise bc capital to build a plant,
Speaker 4: and so it's important to have a partnership on the
Speaker 4: manufacturing side too. And that was really like the first
Speaker 4: thing this battery startup that I just visited talked about
Speaker 4: is like getting that because you've got to be able
Speaker 4: to deliver your product and you have to deliver it
Speaker 4: on scale, and ideally you don't want to be wasting
Speaker 4: time building your own plant on that and then like
Speaker 4: you said, on the other end, you want to have
Speaker 4: a partnership with the users of the energy, which is
Speaker 4: all the people that either have data centers or use
Speaker 4: data centers or customers of data centers, and you want
Speaker 4: them to ideally put together an attract a financing relationship
Speaker 4: where you know, in some form or fashion they're front
Speaker 4: loading their payments to use so that you can use
Speaker 4: that capital to actually build a product that they meet.
Speaker 2: So Joe and I went to San Francisco a little
Speaker 2: while ago and we saw some cool things. I had
Speaker 2: my first ride in a way Moo, and we saw
Speaker 2: some cool battery related technology. We also saw a lot
Speaker 2: of vcs. Everyone very excited about AI. Obviously, they were
Speaker 2: also talking about the difficulty of chasing deals right now,
Speaker 2: how do you compete with those traditional vcs or are
Speaker 2: you just not competing with them directly because you're taking
Speaker 2: the slightly different GPU backed approach.
Speaker 5: You know, I think it's both.
Speaker 4: I think you're competing with them and to an extent,
Speaker 4: partnering with them. And that's the thing we had to
Speaker 4: ask ourselves before launching the fund, is what are we
Speaker 4: bringing to the bear that's value added? And in this case,
Speaker 4: we're bringing to bear the compute. And so often these startups,
Speaker 4: even if they're backed by a strong VC, can have
Speaker 4: a bit of a chicken and egg problem, which is
Speaker 4: they need compute to develop their product, and they need
Speaker 4: capital to buy that compute. But if they don't have
Speaker 4: the compute lined up and the price locked in, then
Speaker 4: the capital might be hesitant to go in because they'd
Speaker 4: be like, we could put our capital into you, and
Speaker 4: then it could take you an extra six months to
Speaker 4: get your compute, and by that time some competitors passed
Speaker 4: you by or the technology has changed. And on the
Speaker 4: other hand, because they're a startup, they don't really have
Speaker 4: the credit worthiness to just contract the compute. They most
Speaker 4: likely have to pay up front, and so we bridge
Speaker 4: that gap. And so if we go into a fundraising
Speaker 4: round where there's a bunch of vcs putting cash in,
Speaker 4: if they know that we're putting compute in alongside them
Speaker 4: and that the second the round closes that compute will
Speaker 4: be available to the company, that makes it easier to
Speaker 4: raise the cash part of it. So we are competing
Speaker 4: and we need that value added to be part of
Speaker 4: the equation. But also I think it helps them to
Speaker 4: raise from traditional vcs because we take that one risk
Speaker 4: off the table.
Speaker 3: How big is the market of companies that need compute,
Speaker 3: because there are plenty of AI companies that just build
Speaker 3: on top of an existing model like GPT or anthropics model,
Speaker 3: et cetera. How many companies are actually out there and
Speaker 3: who like not who are they specifically, but what are
Speaker 3: the types of companies for whom actual access to compute
Speaker 3: is an important part of their business.
Speaker 4: Yes, well, you know it starts, of course with the
Speaker 4: LM companies. You're using massive, huge, huge amounts of compute.
Speaker 5: But then if you look.
Speaker 4: At the rest of sort of the AI stack, there's
Speaker 4: a couple areas where you're going to need compute, and
Speaker 4: one is all the small model custom model companies, and
Speaker 4: small commute a lot of different things. So you can
Speaker 4: have some very small companies that are using a very
Speaker 4: targeted model, like say in a vertical stack, you might
Speaker 4: have a robotics company that is specifically training a model
Speaker 4: to run a robot in a particular situation, and that
Speaker 4: could be anything from a warehouse to doing surgery, right,
Speaker 4: and they need compute to train that model or another
Speaker 4: one which is huge and dominated by an existing big
Speaker 4: players autonomous driving, but there are other autonomous driving companies
Speaker 4: that are trying to be deployed at other automakers that
Speaker 4: need compute to train those models.
Speaker 5: Or weather models.
Speaker 4: There's some really good companies that we've talked to doing
Speaker 4: weather models. They need compute to train their model, and
Speaker 4: so that whole model layer, and then even on the
Speaker 4: app layer, they might be custom elements of small models
Speaker 4: that they have that sit on top of the big
Speaker 4: lms that they need some amount of compute for.
Speaker 5: So there's quite a range.
Speaker 4: You know, it's not everyone, you know, it's more in
Speaker 4: that model application layer, and you know, less in the
Speaker 4: infrastructure layer that need compute.
Speaker 2: So this is one thing I always wonder about AI investment,
Speaker 2: which is you have a lot of companies that are
Speaker 2: building on top of existing models, as Joe mentioned, And
Speaker 2: to some extent that makes sense because they can save
Speaker 2: a lot of money by doing it, and realistically, are
Speaker 2: you going to compete with Google or Microsoft? Probably not.
Speaker 2: But on the other hand, I always wonder if you're
Speaker 2: building on top of an existing model, how do you
Speaker 2: ring fence that business? Because my assumption is if AI
Speaker 2: gets better, maybe at some point the AI can replicate
Speaker 2: any AI model basically.
Speaker 4: So this is the first thing we always worry about
Speaker 4: is does some giant company already have this product in
Speaker 4: a closet with like twenty PhDs working on this and
Speaker 4: somebody I was just at this conference and somebody coined
Speaker 4: the phrase incumbent maximalist, And that's the man. You think
Speaker 4: the incumbents are going to do everything and no one
Speaker 4: else will ever succeed. And I think there's a few
Speaker 4: use cases. There's things where it's a very specific task
Speaker 4: that is hard to do well with a giant general
Speaker 4: model and probably isn't worth doing well. Like if you're
Speaker 4: focused on growing tens to hundreds of billions of dollars
Speaker 4: of revenue, you can't be distracted by trying to do
Speaker 4: every little thing. And we've seen this in previous tech
Speaker 4: revolutions as well, and so it can be something that's
Speaker 4: very focused on a space. We've seen legal accounting, sales.
Speaker 4: There's some great companies that have virtual employees that they're
Speaker 4: doing things that are very task specific. There's some companies
Speaker 4: doing text of language and language to text and other
Speaker 4: things for very specific applications. So you know that's one way.
Speaker 4: The other way is data. The greatest ring fence is
Speaker 4: to any AI company or business is data. Because you've
Speaker 4: seen as the performance of some of the lms has
Speaker 4: supposedly flattened out, a lot of that is because they've
Speaker 4: just used all the data, like they've trained on the
Speaker 4: whole Internet, there's nothing left and so now you have
Speaker 4: to have other ways to train or novel sources of data.
Speaker 4: So proprietary data is super valuable. And then there just
Speaker 4: could be areas where they're conflicted. They don't want to
Speaker 4: compete with their customers right now, although you know, competing
Speaker 4: with your customers is a great tradition in the tech space,
Speaker 4: but there could be situations where it's not worth it
Speaker 4: to them yet to compete with their customers. And so
Speaker 4: I think there's those different use cases where you know
Speaker 4: you're going to see a small number of companies succeed.
Speaker 3: I have a very stupid question, and actually I shouldn't
Speaker 3: even be asking you. I should have asked it the
Speaker 3: last time we talked to core Weave, but since you're here,
Speaker 3: I'm gonna take them all again, or on the question
Speaker 3: I didn't ask them. I know that Nvidia is an
Speaker 3: investor in core Weave, but even setting aside that specific relationship,
Speaker 3: the actual purchasing of chips, how does the pricing work
Speaker 3: and how much is it a de facto auction? Where
Speaker 3: As demand for chips boomed, in Vidia can expand its
Speaker 3: margin versus in Vidia aims for a stable margin over time,
Speaker 3: And I imagine this enters into your calculation to somewhat
Speaker 3: thinking about a core Weaves future capital requirements. How does
Speaker 3: that market for chips work?
Speaker 4: Well, I can't comment on the internal workings of Nvidia
Speaker 4: setting their prices.
Speaker 3: But is an investor in a buyer whatever you I'm
Speaker 3: a buyer of chips, how do I want to buy
Speaker 3: some chips?
Speaker 2: And now imagine it's like the container industry where you
Speaker 2: have to have a specific relationship and there's a shipping
Speaker 2: manager called Lars somewhere in northern Europe who holds the
Speaker 2: keys to the chips.
Speaker 4: Well, for any company using a resource, and it's certainly
Speaker 4: true of companies using compute right, it's always a cost
Speaker 4: benefit example. So there's great benefits to running your AI
Speaker 4: training on an Nvidia ecosystem on a network like Core
Speaker 4: weaves that's very fast and very reliable because you know,
Speaker 4: when you train a model, you stop every fifteen or
Speaker 4: thirty minutes to save your work, and if there's a
Speaker 4: failure in there, you have to go back to the
Speaker 4: last time you save your work and there's a huge
Speaker 4: loss on that. So there's benefits to using the best technology,
Speaker 4: but those are quantifiable, and if you're a particular kind
Speaker 4: of technology becomes too expensive, you'll see people diversify out right.
Speaker 4: I mean, there was just news the last two days
Speaker 4: about Anthropic and AWS and aws's new chips, So there's
Speaker 4: always some form of competition. I mean, in Vida is
Speaker 4: sitting in a unique place where they've really had a
Speaker 4: de facto monopoly on this, and I think their pricing
Speaker 4: is being set in a way to grow the market,
Speaker 4: right Like, they want to grow the market. I can't
Speaker 4: speak for them, but you wouldn't want to set the
Speaker 4: price of your product so high that you stifle the
Speaker 4: market's growth, right Like, growth is more than making an
Speaker 4: extra dollar on every widget, And so I think that's
Speaker 4: got to be a calculation, and certainly to date it's
Speaker 4: been fruitful in that this market has taken off like
Speaker 4: almost no market ever.
Speaker 2: I want to go back to the capital question, and
Speaker 2: most venture capital comes in the form of equity. You're
Speaker 2: doing something slightly different in my understanding. You're primarily going
Speaker 2: down the debt and sort of fixed income route. That
Speaker 2: seems so different because in my mind, when I think
Speaker 2: about bond investing, and we've said this a number of
Speaker 2: times on the show, it's all about avoiding losers, right Like,
Speaker 2: there's limited upside, but you don't want a bankruptcy that
Speaker 2: wipes out your investment, whereas equity the upside is basically uncapped.
Speaker 2: So it's about finding that one stellar out performer or
Speaker 2: that one lottery ticket. How do you square I guess
Speaker 2: the risk averseness of some of this debt financing with
Speaker 2: getting the huge upside that is potentially there from AI.
Speaker 4: Well, the amount of financing required for this whole AI buildout,
Speaker 4: which is on some immense scale of you know, people
Speaker 4: have talked about the Manhattan Project, the building of the Interstates.
Speaker 4: It's going to require capital in many forms for many things,
Speaker 4: and I think there's a lot of thinking going on,
Speaker 4: and you know, certainly we're part of that in deploying
Speaker 4: the most efficient capital to the different layers of this buildout.
Speaker 4: And so we've talked about a couple different things here.
Speaker 4: We've talked about financing GPUs. So if you're financing GPUs
Speaker 4: with debt, then you can really think through your downside protection,
Speaker 4: just like in the audio metaphor.
Speaker 2: Right, you have the collateral.
Speaker 4: You have the collateral, you have the contract. You can
Speaker 4: analyze the credit worthiness of the contract. You can look
Speaker 4: at how the leasing curves of prior chip generations have decayed.
Speaker 4: You have some real information there, you have a real asset,
Speaker 4: you have real contracted cash flows. Now in the VC fund,
Speaker 4: that's a lot different. In this case, this is true
Speaker 4: venture equity, and it's just that it's being deployed in
Speaker 4: a unique way where instead of cash, the compute has
Speaker 4: been contractually secured and it's just being exchanged for the
Speaker 4: equity directly, as I talked about before, saving that step
Speaker 4: and de risking the process of acquiring compute for these
Speaker 4: grow stage companies.
Speaker 2: So you are doing equity through the VC fund.
Speaker 5: The VC fund is equity.
Speaker 4: Yes, it would be part of typically but not always
Speaker 4: a part of a round that a growth stage company
Speaker 4: might be doing.
Speaker 2: Doing convertibles.
Speaker 4: So we can do virtually anything across the debt equity
Speaker 4: private public spectrum, and have in many cases in the
Speaker 4: AI fund itself. Most of the companies being gross stage
Speaker 4: are not really in a position to do debt, so
Speaker 4: I think for the most part, I would expect that
Speaker 4: those would all be venture equity investments.
Speaker 3: I gotta chuckle when you're like, oh, we've been in
Speaker 3: this space, it's way back, and then you said twenty
Speaker 3: twenty one, But it does really sort of.
Speaker 5: Speak to hell.
Speaker 2: It feels like a long time.
Speaker 3: Yeah, well, you know, I mean ched GBT, I think
Speaker 3: came out at the very end of twenty twenty two
Speaker 3: or maybe early twenty twenty three, and that was the
Speaker 3: big light bulb moment for a lot of people. So
Speaker 3: even being that active in a lot of this stuff
Speaker 3: a year earlier truly is early. That being said, things
Speaker 3: like core weave, things like data centers. The need for
Speaker 3: compute is very well understood right now in a way
Speaker 3: that perhaps in three years ago many people in the
Speaker 3: credit and financing space weren't thinking of is that a
Speaker 3: margin compressor for you? The fact that other entities, probably
Speaker 3: many with much more capital than Magnetar has everyone has
Speaker 3: now woken up to this opportunity of yes, there's going
Speaker 3: to be a lot of financing needs in AI. And
Speaker 3: do you see change in competition or spreads or anything
Speaker 3: like that.
Speaker 4: Well, I think it really depends on what you're financing.
Speaker 4: So there's a lot of capital that's gone into all
Speaker 4: these spaces, and certainly all across the stack.
Speaker 5: Of financing compute.
Speaker 4: You've seen a huge amount of capital come in, and
Speaker 4: you've seen all the giant investment companies providers of capital.
Speaker 5: Get involved and so.
Speaker 4: There's a lot of capital in there, but there's also
Speaker 4: like a huge need for capital, and it's very complex
Speaker 4: thinking about the structuring and getting the right capital and
Speaker 4: the right space. And so I think there's room to
Speaker 4: be innovative. And I've spent the last twenty years at
Speaker 4: Magnetar thinking about unique ways to source investments and deploy capital,
Speaker 4: and I think that really comes to bear on this.
Speaker 4: And because this whole market, like you said, is so new,
Speaker 4: and we've only had chat GPT for a couple of years,
Speaker 4: you know, you're seeing companies with all different ways of working.
Speaker 4: You know, we I talked to a company in the
Speaker 4: text of voice space at a conference last week and
Speaker 4: they actually were buying their own DGX servers themselves and
Speaker 4: just running on themselves in their own on premp site.
Speaker 4: And We're like, sure, like that's something we can finance.
Speaker 4: That's like a hard asset.
Speaker 5: But no one's really looking at that yet.
Speaker 4: Because most of the capital is so big, it has
Speaker 4: to go to the biggest thing. So you have your
Speaker 4: trillion dollar investment firm, which was a couple you're not
Speaker 4: going to want to deploy twenty to fifty million dollars
Speaker 4: in a one off thing. You're going to want to
Speaker 4: deploy tens of billions of dollars in the biggest thing,
Speaker 4: whether that's power, physical data centers, or GPUs.
Speaker 2: What's the pitch to your investors, to Magnetars investors, Because again,
Speaker 2: this is something I know you said you've been in
Speaker 2: the tech space for a while, but it's still something
Speaker 2: that feels fairly new. And when I think about AI,
Speaker 2: there's been so much excitement over it. Some people have
Speaker 2: been talking about whether or not it's in a bubble,
Speaker 2: and I think about a hedge fund, and that's all
Speaker 2: about uncorrelated returns and investing profitably through the cycle. I
Speaker 2: get that you might be promising very large upside to investors,
Speaker 2: but what is the hedge aspect of this.
Speaker 5: Well.
Speaker 4: As a firm, we've done many different products and many
Speaker 4: different strategies for many different investors over the years, and
Speaker 4: we've really been flexible in trying to deploy capital in
Speaker 4: the most most interesting areas that are going to have
Speaker 4: the best risk adjusted returns. And many of our investors
Speaker 4: have been with us through the whole life of the
Speaker 4: firm since two thousand and five and appreciate that. And
Speaker 4: so we've done both diversified investment strategies where we just
Speaker 4: thought the general pipeline of deploying structured capital has been great,
Speaker 4: and then we've also done things targeted at a particular
Speaker 4: asset when we thought that opportunity was great. And so
Speaker 4: in the case of the VC fund, the value proposition
Speaker 4: really is for the investor what it is for the company,
Speaker 4: which is, we're bringing something unique to these gross stage
Speaker 4: AI companies which will get us access to making investments
Speaker 4: and what we hope will be the best best of
Speaker 4: those companies with the best business models and the best teams.
Speaker 4: And so we're going to use the unique compute that
Speaker 4: we have and the way that we're going to exchange
Speaker 4: that for equity and deliver that to these companies as
Speaker 4: a way of getting access to investments in what's a
Speaker 4: very as you mentioned, very competitive environment where there's a
Speaker 4: lot of capital going into the space. And so I
Speaker 4: think for investors that want to participate in that kind
Speaker 4: of investment, in getting capital deployed into growth stage AI companies,
Speaker 4: you know, this is a very unique opportunity, and so
Speaker 4: we saw a lot of traction with that.
Speaker 3: When you come in as a VC investor in some
Speaker 3: of these startups, do you have to supply dollars or
Speaker 3: in some cases or all cases, is your ability to
Speaker 3: promise compute from day one enough for equity?
Speaker 4: It really varies, and there's investments we've made both inside
Speaker 4: and outside the fund, and it just depends on the situation.
Speaker 4: So there can be companies that we find super interesting
Speaker 4: but don't need compute, and in that case we could
Speaker 4: invest in those companies directly outside of the fund. For
Speaker 4: the fund itself, the proposition is equity for compute, and
Speaker 4: so the fund itself is focused on companies that really
Speaker 4: do need equity and are interested in equity. And I
Speaker 4: really do need compute and are interested in compute on
Speaker 4: corewaves network, and so that's the kind of companies that
Speaker 4: will invest in from the fund. But as Magnetar as
Speaker 4: a whole, we've been focused, like we talked about, on
Speaker 4: everything from energy, through infrastructure, through other AI companies that
Speaker 4: just don't happen to me compute right now.
Speaker 3: Then, just to this point, your ability to promise or
Speaker 3: give AI startups compute, this access to compute emerged via
Speaker 3: that initial relationship as a financier.
Speaker 2: This is what I was going to ask, which is
Speaker 2: how worried. Are you about competitors doing the same thing
Speaker 2: and providing GPU back debt or is it the case
Speaker 2: that because of your first mover advantage with core Weave,
Speaker 2: you can hold onto that advantage for a while.
Speaker 4: So for the fund itself, it was the unique relationship
Speaker 4: we had with coreweve where we felt they were the
Speaker 4: best provider of AI training compute and we were able
Speaker 4: to work with them to contract some of the very
Speaker 4: scarce resource of that and then have that available to
Speaker 4: deliver to these AI growth companies. And so that was
Speaker 4: really where we were able to put together something unique because.
Speaker 3: Day one that was understood to be part of the
Speaker 3: payoff of being a financing partner to Corewave.
Speaker 5: I wouldn't say from day one.
Speaker 4: I would just say it's part of the natural growth
Speaker 4: in their business and our growth in investing in the
Speaker 4: AI market and in being a partner with them. Everyone
Speaker 4: is both a partner and a competitor in this space,
Speaker 4: and you know, Nvidia has multiple ways that they invest
Speaker 4: in their customers, as do all the hyper scalers for example,
Speaker 4: And so it's really about are you providing something unique,
Speaker 4: something that's different, And you know, right now this moment
Speaker 4: in time. We feel like the size of the compute
Speaker 4: we're providing and the network we're providing it on and
Speaker 4: the way that we can provide it in real time
Speaker 4: is unique and is valuable to many companies. Now, look,
Speaker 4: there could be some companies that are getting their compute
Speaker 4: from somewhere else and it's just not a fit that's
Speaker 4: certainly going to happen. But I think there's many, many
Speaker 4: AI growth companies where this is very valuable to them
Speaker 4: to get the compute on Quorwy's network, and that's going
Speaker 4: to lead to a relationship with them.
Speaker 3: When Amazon makes a VC investment, it's in large part
Speaker 3: understood that it's the same sort of premise that they're
Speaker 3: going to invest in some software company and the money
Speaker 3: comes right back in because that company has AWS needs
Speaker 3: and so it comes back. Obviously, we know that the
Speaker 3: not only to the large legacy hyper scalers. Not only
Speaker 3: they're building their own models, many of them they're building
Speaker 3: their own silicon and Facebook has its own chips and
Speaker 3: talked about Amazon and Google has I forget what their
Speaker 3: whole thing is called. How do you think about them
Speaker 3: as competitors to core weave in these sort of pure
Speaker 3: chips and data center side. I know their partners, I
Speaker 3: know their customers, et cetera. But they are also pure
Speaker 3: competitors both to say a core weave and to say
Speaker 3: an n video.
Speaker 5: Yeah.
Speaker 4: Again, everyone's a partner and a competitor, you know. I
Speaker 4: think the difference.
Speaker 3: Google's as TPUs is their thing. Anyway, Sorry, keep going,
Speaker 3: I just couldn't.
Speaker 4: Yeah, I mean the difference, as Brian talked about, is
Speaker 4: the core Weave network was built for the ground up
Speaker 4: to be hyper efficient at running AI solutions, and so
Speaker 4: I think it's unique in that way, and I think
Speaker 4: that's why it's grown so fast. But certainly everyone else
Speaker 4: is trying to build their own out and there will
Speaker 4: be other people that will have in Vida GPU chips
Speaker 4: and that will include the hyperscalers. But you know, one
Speaker 4: of the things we've.
Speaker 5: Seen is that.
Speaker 4: This is very hard technology. So it's particularly hard to
Speaker 4: deploy at scale because you run into like real physics issues,
Speaker 4: you know, surface area to volume type issues of getting
Speaker 4: this much power to IRAQ with like how much cable
Speaker 4: does that take? How much cooling does that take? How
Speaker 4: do you run the software layer, like the software layer
Speaker 4: to control you know, a node of eight GPUs is
Speaker 4: going to be a lot different than if you're trying
Speaker 4: to run one hundred and twenty eight thousand GPUs. And
Speaker 4: so this problem gets more and more difficult, and you
Speaker 4: need better technology and you need highly skilled people, and
Speaker 4: so the bar is always moving. You know, there's always
Speaker 4: a next generation chips that's going to be super complicated. Certainly,
Speaker 4: the Blackwell deployments and the incremental new Blackwell generations are
Speaker 4: going to be ever more complicated and trigger to deploy.
Speaker 4: And you've seen issues already, right, You've seen hyperscalers and
Speaker 4: other competitors in the space have reliability problems or be
Speaker 4: behind schedule. Like it's not easy. It's a very complicated technology.
Speaker 4: You're not plugging your GPU into the wall and it's
Speaker 4: ready to run an AI model, and so like, I
Speaker 4: think there's going to be value accrewing to skill an
Speaker 4: efficiency and execution in the space, and you know that's
Speaker 4: going to last for a while.
Speaker 2: So some people draw an analogy between the current enthusiastic
Speaker 2: cycle for AI and the early two thousands period where
Speaker 2: we had a lot of enthusiasm for internet companies and
Speaker 2: telecoms and things like that. Do you see evidence of
Speaker 2: froth out there, or is it the case that because
Speaker 2: of the huge amount of initial capital invents that's needed,
Speaker 2: it's difficult to get I guess enough new entrance that
Speaker 2: this would become a bubble.
Speaker 4: Yeah, everything can become a bubble eventually in almost any
Speaker 4: industry that's highly capital intensive. Usually if there's excess returns,
Speaker 4: you'll see capital go into it until those returns aren't
Speaker 4: good anymore, and a lot of capital will go in
Speaker 4: before you figure out.
Speaker 5: That last part.
Speaker 4: But this is extremely early. Like if you look at
Speaker 4: the capital that went into the Internet and then how
Speaker 4: that value accrued to both the big tech companies and
Speaker 4: the startups. People have looked at numbers like three trillion
Speaker 4: dollars of equity value created with the large incumbents, but
Speaker 4: there was another five hundred billion created for the new startups.
Speaker 4: And we're just getting going here, right. We're just building
Speaker 4: out the kind of data centers, the kind of energy infrastructure.
Speaker 5: We're just starting to deploy products. If you talked to.
Speaker 4: Enterprises, they're just starting to implement the most obvious use
Speaker 4: cases for AI. So I think we're much too early
Speaker 4: to worry about a bubble. I talked to somebody at
Speaker 4: a hyperscaler and they were like, the last thing we're
Speaker 4: worried about right now is having too much compute.
Speaker 3: Last question for me, you say, we're early. There's still
Speaker 3: no signs of too much compute. Earlier in the conversation,
Speaker 3: you're like, this is a Manhattan project, scale project. Give
Speaker 3: us some flashy number. How much has been deployed in
Speaker 3: this area, and you know over the next ten years
Speaker 3: how much capital is going to be demanded for this
Speaker 3: space and how much will be needed.
Speaker 4: So one one number I saw was that in twenty
Speaker 4: twenty three, thirty seven billion dollars was deployed into AI infrastructure,
Speaker 4: and in two thousand and thirty three that number is
Speaker 4: going to be like four hundred and thirty billion in
Speaker 4: that year. So this is trillion dollar scale investment.
Speaker 2: Cool, You're cool, all right, Jim Presco, Thank you so
Speaker 2: much for coming on all thoughts. That was great.
Speaker 5: Thank you for having me.
Speaker 3: Thank you so much.
Speaker 4: Joe.
Speaker 2: There's two things that I hear consistently about AI, and
Speaker 2: one is it's going to need a lot of capital, yeah,
Speaker 2: which Jim spoke to. And then the other thing I
Speaker 2: always hear is well, at some point AI companies have
Speaker 2: to actually produce revenue, and I guess the question is, like,
Speaker 2: are they going to start producing revenue in time to
Speaker 2: pay back that massive capital need.
Speaker 3: Yes, it's very interesting because, look, I believe that there
Speaker 3: are companies that are getting productive value out of AI models.
Speaker 5: Like I believe that exists.
Speaker 3: But you know, you talk about hundreds of billions over
Speaker 3: the coming years and financing in the end that is
Speaker 3: going to have to come from profitable deployment to customers,
Speaker 3: and so like this to me is like, you know,
Speaker 3: still a bit uncertain. I do think the financing that
Speaker 3: we talked about is extremely interesting just in the context
Speaker 3: of this conversation.
Speaker 5: Yeah.
Speaker 2: Absolutely, the GPU backed loans, Yeah.
Speaker 3: Well, both the GPU backed loans and the opportunity that
Speaker 3: that affords company like Magnetar to make GPU capacity in
Speaker 3: lieu of cash for equity investments is extremely interesting. And
Speaker 3: so and then you get this second order effect. So A,
Speaker 3: you're providing something that other vcs can't because you are
Speaker 3: giving them access to compute on day one. And then
Speaker 3: b other vcs want to enter that deal because they
Speaker 3: know that they're going to be investing in a company
Speaker 3: that is not going to be have to scrambling for
Speaker 3: compute once they get that VC cash.
Speaker 2: It's a very sort of middle way approach because I
Speaker 2: think so far the way we've seen AI investment unfold
Speaker 2: is either it's the sort of picks and shovels approach
Speaker 2: where you invest in the chip companies themselves and the
Speaker 2: data centers, or it's you invest in the AI companies
Speaker 2: that are doing cool things. But this is kind of both.
Speaker 3: It is exactly both, and it sort of sounds like
Speaker 3: some combination of foresightful planning and also stumbling into a
Speaker 3: very good situation by which the firm's relationship with core Weave,
Speaker 3: dating all the way back to twenty twenty one, does
Speaker 3: now give them this a certain edge in the VCR.
Speaker 3: It's just a really it's this is a fascinating sort
Speaker 3: of open frontier in many respects.
Speaker 2: I still want to know who came up with the
Speaker 2: idea for chip based financing. Jim kind of evaded that
Speaker 2: part of the question, but I want to know what
Speaker 2: those initial conversations were Like.
Speaker 3: Yeah, it's also just interesting to think about that on
Speaker 3: some level, the analogies are like an Irish car lender, right,
Speaker 3: So it's like, on some level this is a very
Speaker 3: none and with technology that is highly uncertain. And then
Speaker 3: on the other hand, if you're invested in a Carlo
Speaker 3: and Company, you could sort of get it.
Speaker 2: Yeah, all right, shall we leave it there.
Speaker 5: Let's leave it there.
Speaker 2: This has been another episode of the Oudlots podcast. I'm
Speaker 2: Tracy Alloway. You can follow me at Tracy Alloway and.
Speaker 3: I'm Joe Wisenthal. You can follow me at the Stalwart.
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