Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.
Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy, I'm envisioning this future where like we have to
Speaker 2: do a state of the sort of AI inference market episode,
Speaker 2: like once a month, you know, where it's like things
Speaker 2: are moving so rapidly and there's so much change either
Speaker 2: in terms of what models are using or what they're
Speaker 2: being used for, et cetera, that in the same way
Speaker 2: we would do, like you know, the occasional regular stock
Speaker 2: market episode or whatever, we would just do, Okay, what
Speaker 2: are we seeing right now in a inference trends because
Speaker 2: it just feels like the moment we do an episode,
Speaker 2: a few weeks later it may be out of date.
Speaker 3: We should just buy the bullet and do a weekly episode,
Speaker 3: transform lots more into a market update on compute.
Speaker 2: We could do inference in I don't know, we'll have
Speaker 2: to workshop inference. No, no, we'd have to. But anyway,
Speaker 2: this is lots of lots of inference. This is like
Speaker 2: the story of the moment, and we know that, you know,
Speaker 2: a couple of years ago, everyone was sort of dabbling
Speaker 2: around with various things and experimenting and using AI, like
Speaker 2: oh like write a poem for me about this, etcetera.
Speaker 2: That phase of AI is long over, and we know
Speaker 2: that companies specifically are spending a ton on compute, so
Speaker 2: much so that CFOs around the world are getting sticker
Speaker 2: shock about their compute budgets. And there was even a
Speaker 2: headline of like Uber saying like, okay, like fifteen hundred
Speaker 2: dollars of max per employee, like don't spend more than
Speaker 2: that in a month on token, So like this is
Speaker 2: a very fast moving area.
Speaker 3: Yeah, you're starting to get headlines about, I guess a
Speaker 3: corporate reckoning AI as more people experiment and spend money
Speaker 3: on it. The Uber headline that you mentioned apparently Uber
Speaker 3: burned through its entire twenty twenty six AI budget in
Speaker 3: four months basically, and like what's more important is the
Speaker 3: COEO was actually asking whether or not that was worth it,
Speaker 3: like whether they saw productivity gains or whatever as a
Speaker 3: result of that. The other very amusing headline that I saw,
Speaker 3: and it was citing an unnamed source. It's from Axios,
Speaker 3: so you know, oh yeah, not entirely sure it's true,
Speaker 3: but reportedly great headline. It was a great headline. An
Speaker 3: AI consultant told Axios that one of their clients recently
Speaker 3: spent half a billion dollars in a single month after
Speaker 3: failing to put usage limits on this.
Speaker 2: Yeah, it's because there's everyone that's like, oh, I just
Speaker 2: have a simple question. I want to look up our
Speaker 2: guests title. I'm going to use the most advanced model
Speaker 2: to do that, et cetera. I have a theory and
Speaker 2: we'll get into this with our guests that one of
Speaker 2: the things that will and we've talked about this with
Speaker 2: a Goldenman's Marco Ardenti, but one of the things I
Speaker 2: predict is that companies are like, clearly you know, they're
Speaker 2: going to keep using it more and more would be
Speaker 2: my guess. But there are probably a lot of investment
Speaker 2: made in sort of like optimal model routing. Because some
Speaker 2: models are like one hundred per query of what a
Speaker 2: frontier model is, probably a lot of people don't know
Speaker 2: like what is the sort of like efficient frontier model usage,
Speaker 2: and so actually routing the query to the sort of
Speaker 2: most efficient model. I have a feeling we're going to
Speaker 2: see a lot of investment in that area.
Speaker 3: Specifically, well, there's also just the question of whether or
Speaker 3: not the models get cheaper overall as they advance, right,
Speaker 3: and we have seen some I think Nvidia has a
Speaker 3: new system or chip out or something that is supposed
Speaker 3: to reduce token usage. We can get into that as well.
Speaker 2: And you know, we did that live episode recently with
Speaker 2: In Dunning of Hudson River Trading and he said a
Speaker 2: lot of interesting things in that, But one of the
Speaker 2: things he said is that the scarcity is increasingly like
Speaker 2: just the real estate component. Finding a suitable place to
Speaker 2: plug in your GPU, at least from his perspective, right now,
Speaker 2: is as much, if not more so, of a challenge
Speaker 2: than securing GPUs themselves, so like.
Speaker 3: Which is different to what it was like three years ago.
Speaker 2: Yeah, yeah, so just like where you plug it in.
Speaker 2: We know there's all the like the anti data center
Speaker 2: politics out there, so it's like, yeah, we got to
Speaker 2: take the pulse of.
Speaker 3: This market, all right, consider this our inference update.
Speaker 2: Yeah, well, I'm really excited to say we really do
Speaker 2: have the perfect guests. Someone we spoke to like truly
Speaker 2: feels like eons a good I think the first thing
Speaker 2: we ever connected with this company, They've always had a
Speaker 2: lot of chips. But I think the first time we
Speaker 2: ever linked up with this company was still in the
Speaker 2: era where people were excited about in video chips being
Speaker 2: used for like cryptomining and stuff like that. But we
Speaker 2: are now in this very different era and this is
Speaker 2: truly like one of the companies of the moment, and
Speaker 2: that is, of course core weave, one of the so
Speaker 2: called neo clouds, offering both training and inference services for
Speaker 2: all sorts of different AI workloads. I'm very excited to
Speaker 2: say back on the show, we have Brandon McBee, Coreweav's
Speaker 2: co founder and chief development officer, So thank you so
Speaker 2: much for coming on ALTS.
Speaker 4: Appreciate being invited back, guys, and that was a fantastic intro.
Speaker 4: We look forward to hitting these topics today.
Speaker 2: All right, here's my question. So we know that like
Speaker 2: at the tail end of last year and then in
Speaker 2: the first quarter of this year, it's everyone started using
Speaker 2: clog code and just there's clearly a key inflection moment
Speaker 2: for sort of like overall AI demand. And then we
Speaker 2: get into Q two and suddenly the CFO is, oh
Speaker 2: my gosh, we're spending this much on inference. We got
Speaker 2: to like figure things out just straight up like in
Speaker 2: the last month whatever. Do you see any signs of
Speaker 2: that happening yet of these companies which are all like
Speaker 2: still AI eager AI adopters trying to get a little
Speaker 2: bit of a handle and maybe slowing the rate of
Speaker 2: the rate of growth. Is that happening yet?
Speaker 4: Yeah, I think you see head lines there that there
Speaker 4: are surprises of spend et cetera. I'd say our interpretation
Speaker 4: of it is entirely look at the authentic and foundational
Speaker 4: demand that is out there right, Like, all we're really
Speaker 4: doing is talking about how much consumption there is of
Speaker 4: AI and use for it. And I think that that
Speaker 4: was a real question in the market twelve eighteen, twenty
Speaker 4: four months ago, is will there be demand FEI? Where
Speaker 4: is this inference demand that everyone's been talking about. And
Speaker 4: I think you're absolutely correct January or so with this
Speaker 4: kind of like next group of models that were coming out,
Speaker 4: everyone all of a sudden and all at once said
Speaker 4: this is what we've needed, like this is the real
Speaker 4: product breakthrough. But I think we're keeping in mind that
Speaker 4: product breakthrough was like for a limited set of people
Speaker 4: at the end of the day, right, we're talking like
Speaker 4: coding professionals, finance professionals, but it's a relatively small group
Speaker 4: of people that are using infrastructure at this normal scale.
Speaker 4: And so where we see this moving towards next is
Speaker 4: broader enterprise use, like likely not seeing this whole to
Speaker 4: connecting approach, and I think that that is unsustainable. But
Speaker 4: do we see adoption in other sectors and how this
Speaker 4: can continue to spread out? Absolutely? I mean, you know,
Speaker 4: on our end, I think we have ten over one
Speaker 4: billion dollar clients at this point, and our financial services
Speaker 4: client backlog is into tens of billions of dollars at
Speaker 4: this point. And so we're now talking about things outside
Speaker 4: of AI labs, outside of hyperscalers. And look, as you
Speaker 4: guys know, we support nine of the top ten AI
Speaker 4: labs on the planet, and if you exclude China and
Speaker 4: everything that's going on over there, Like, we have a
Speaker 4: lot of visibility into what people are doing, and we're
Speaker 4: not seeing any pullback on what they're doing on inference today.
Speaker 4: If anything, it just remains this unrelenting demand for access
Speaker 4: to the best technology solution in the market for running
Speaker 4: artificial intelligence, and that's core week solution in the market.
Speaker 3: Wait, say more about the customer mix now versus say
Speaker 3: three years ago. So you have hyper scalers, you've got startups,
Speaker 3: you've got various businesses. How has that, I guess composition
Speaker 3: shifted over time.
Speaker 4: Yeah, it's shifted enormously towards a more diverse customer base. Right.
Speaker 4: We got a lot of flat for this. In our IPO. Right,
Speaker 4: people were noting that we only had a handful of
Speaker 4: large clients, that our clients were like just the hyperscalers
Speaker 4: and AI lab or two. And I think that we
Speaker 4: have made tremendous progress in driving diversifications. So I'd say
Speaker 4: it's broadly cross three buckets today. Right. We had hyperscaleup
Speaker 4: clients who continue to grow with us. We have AI
Speaker 4: lab clients. As I said, nine of the top ten
Speaker 4: AI labs on the planet choose core REEF. And then
Speaker 4: we have this enterprise base. And the enterprise base just
Speaker 4: doesn't grab as many headlines as you would expect because
Speaker 4: it's not these massive, multi billion dollar contracts that are
Speaker 4: being signed. But I think in Q four alone, we
Speaker 4: added twice as many logos to our client base as
Speaker 4: we had ever done versus any previous court. Right, And
Speaker 4: that enterprise base is one that's growing so much. And
Speaker 4: there was a point you guys hit on in the
Speaker 4: intro that I think is really worth acknowledging, and it
Speaker 4: was this concept of model routing and the idea that like,
Speaker 4: not everyone needs just the latest model, that it's different
Speaker 4: types of models I can hit different use cases. And
Speaker 4: this is something we've been talking about for a while
Speaker 4: right as it relates to the infrastructure side of things
Speaker 4: as well, right, because you don't need that latest model
Speaker 4: for everything, and accordingly you don't need the latest piece
Speaker 4: of infrastructure to support every single inference or training query
Speaker 4: that's out there. You can kind of conceptualize this matrix
Speaker 4: of different sizes of workloads well to the different sizes
Speaker 4: of GPUs, and all of a sudden that tells you,
Speaker 4: my god, like h one hundreds could last six, seven,
Speaker 4: eight years, a one hundreds are going to last longer.
Speaker 4: And it totally changes the entire conversation around depreci full
Speaker 4: life of infrastructure, as that was a really popular topic
Speaker 4: during twenty twenty five. People were saying like, oh, this
Speaker 4: stuff will last two years, it's worth zero afterwards, in
Speaker 4: like we've never seen any semblance of that. Because of
Speaker 4: the point you guys are accurately making, which is users
Speaker 4: are going to need to find the way to use
Speaker 4: the appropriate model for their prompts, and that'll be solved
Speaker 4: by model round to your point, but that just further
Speaker 4: enables this concept that infrastructure is going to be used longer,
Speaker 4: and we see that every day in our portfolio, extending
Speaker 4: all the way back to A one hundreds.
Speaker 2: I just want to ask a specific question about the
Speaker 2: broadening out of the customer base. And you mentioned, for example,
Speaker 2: financial services clients. When you talk about, say a financial
Speaker 2: services client as being distinct client from one of the
Speaker 2: major AI labs, does that mean, well, you're saying so
Speaker 2: it's like I'm just making it up. Let's just say
Speaker 2: I don't know if these relationships exist. Let's say a
Speaker 2: city group has an enterprise licensed with an ANTHROPIC. Does
Speaker 2: that count as Anthropic as a customer or city as
Speaker 2: a customer. And when you talk about this broadening out,
Speaker 2: are there essentially more types of entities who are building
Speaker 2: some type of model, not necessarily an LLM per se,
Speaker 2: but it's some type of internal house specific model from
Speaker 2: which they want to run inference.
Speaker 4: It's a great question. The scenario you presented ANTHROPIC would
Speaker 4: be our clients.
Speaker 2: Okay, got it.
Speaker 4: So what I'm highlighting I want to correct a number
Speaker 4: I said earlier are financial service clients and this is
Speaker 4: direct to those financial services. They're approaching ten billion in backlog. Okay,
Speaker 4: so this would be you know, a good example of this,
Speaker 4: and that's when we made recently is with Jane Street. Okay, right.
Speaker 4: That's not Jane Street coming through Open AI or Anthropic
Speaker 4: to get to us. That is Jane Street coming directly
Speaker 4: to us and using our platform, and that for.
Speaker 2: A model that they're building. So it's a Jane Street No, no, no,
Speaker 2: I'm not saying it's setting inside training, but it would
Speaker 2: be inference of a model that it's the Jane Street's
Speaker 2: model of something rather than Jane Street's contract and enterprise
Speaker 2: relationship with one of the major labs.
Speaker 4: At the end of the day, we don't know what
Speaker 4: exact workloads these entities are running, especially for and he's
Speaker 4: like Jane Street would imagine that's highly secretive. Yeah. But
Speaker 4: the point I would say is more that this is
Speaker 4: not them coming through an AI lab yus. They are
Speaker 4: interfacing with and managing the infrastructure directly on our platform.
Speaker 4: And that's a really important distinction as we grow this
Speaker 4: diversified client base. And I again, I think that we've
Speaker 4: just done a wonderful job of executing.
Speaker 2: That over the past, as you've talked about, including in
Speaker 2: earnings releases, and as you can just tell from these
Speaker 2: huge token budgets, inference demand is booming, but model training
Speaker 2: is still important. But in addition to model training, to say, okay,
Speaker 2: relative if you have a pie chart, the part that's
Speaker 2: inference is getting bigger. But I assume the training is
Speaker 2: also growing as well. But I'm curious from the perspective
Speaker 2: of like say, the AI labs when they think about growth,
Speaker 2: has there been a subtle shift from investing to push
Speaker 2: the pure model frontier, having the absolute best state of
Speaker 2: the art model, versus investing in, say, better harnesses. Because
Speaker 2: a big reason we're excited, and i'll talk about AI
Speaker 2: right now, is really the excitement that happened over with
Speaker 2: Claude Code in the final quarter of twenty twenty five,
Speaker 2: and it's like, oh, this harness has really unlocked a
Speaker 2: bunch of capabilities. Has there been a shift in investment
Speaker 2: from rather than just the purest, most advanced model to
Speaker 2: let's invest more in tooling capacity and other things that
Speaker 2: allow companies and clients to get more juice from an
Speaker 2: advanced model.
Speaker 4: I don't think that we're exposed to that decision making
Speaker 4: with the AI labs as counterparties to us. The observation
Speaker 4: I would make in a behavior change for the AI
Speaker 4: labs is they want access to more infrastructure for longer duration, right,
Speaker 4: And I'll qualify it a little bit, which is a year.
Speaker 4: Two years ago, we were signing three year committed contracts.
Speaker 4: The type of contracts we sign are basically like take
Speaker 4: our pay contracts, which is the best way to finance
Speaker 4: the infrastructure that we are building for our clients. Last
Speaker 4: year it was four year contracts. Right. They were saying,
Speaker 4: we want explicit access to Hopper for four years or
Speaker 4: Blackwell for four years. Now they're coming and saying, well,
Speaker 4: actually we want it for five years. We don't want
Speaker 4: any interruption of use. We'll commit to the exact same
Speaker 4: economics throughout the full duration of the contract. You can't
Speaker 4: upgrade or change the infrastructure within it. You cannot cancel
Speaker 4: the contract. It for five years, and they want it
Speaker 4: at more scale. Right. The deployments are getting larger and larger,
Speaker 4: so that's probably the best characterization we can offer on
Speaker 4: decision making that AI labs are going through right now
Speaker 4: as they look from an infrastructure perspective, it absolutely seems
Speaker 4: like tooling is important, but scaling laws are still holding, right,
Speaker 4: Like your ability to advance your frontier model through accessing
Speaker 4: more infrastructure, its scale holds and that will hold through
Speaker 4: Vera Ruben we expect and seemingly it's not stopping anytime soon.
Speaker 3: Oh yeah, what's the deal with Arah Rubin? Can you
Speaker 3: explain that to us?
Speaker 4: Which aspect of it?
Speaker 3: What is it?
Speaker 4: Oh?
Speaker 2: Yes, basically yeah.
Speaker 4: So it's just Nvidio's next architecture that's coming out, right,
Speaker 4: Like the current architecture that we're deploying today is Blackwell.
Speaker 4: Blackwall comes. We deplay phronomenally in a NBL seventy two configuration,
Speaker 4: which was an entire architecture change from the right. If
Speaker 4: you recall Hopper game before Blackwell Hopper, you could deploy
Speaker 4: these forty two U racks, which was typically like eight
Speaker 4: GPUs in a server case. You would take it, plug
Speaker 4: it in largely air cooled as well. Right, we ran
Speaker 4: some liquid cooling just so we understood the requirements of
Speaker 4: liquid cooling because Blackwell for our deployments is overwhelmingly liquid
Speaker 4: cooled and its deployment configuration, and instead of eight GPUs
Speaker 4: and a forty two U configuration, it's in this larger
Speaker 4: seventy two GPU rack. It's like an entire chassis that's
Speaker 4: being brought in and it just looks entirely different in
Speaker 4: the data center. It's like this giant tower thing that
Speaker 4: you've seen in pictures floating around on x so. Vera
Speaker 4: Rubin will be the next architecture that comes out, and
Speaker 4: we've started receiving testing racks for Vera Rubin.
Speaker 3: But the basic idea is like the new configureuration makes
Speaker 3: the whole system more efficient, like more tokens per energy
Speaker 3: use and that sort of thing.
Speaker 4: Yes, yeah, I think that's kind of where you're getting
Speaker 4: to with it. But that doesn't necessarily mean going back
Speaker 4: to the point earlier that everyone only wants the latest
Speaker 4: generation of GPU. Right, we have massive demand for Ampeer, Hopper, Blackwell,
Speaker 4: et cetera. And it just varies by US case model
Speaker 4: and type of client as well. Like I would qualify
Speaker 4: that AI labs are probably the ones who are lining
Speaker 4: up first to secure access to the latest generation GPUs,
Speaker 4: whereas enterprise clients might be probably very focused on current generation,
Speaker 4: right like Hopper and Blackwell.
Speaker 2: Right now, I'm going to be honest for a second.
Speaker 2: You know, I try to keep up on a lot
Speaker 2: of things AI related, I really do, and every single
Speaker 2: day the ones that I got to do not keep
Speaker 2: like in my mind. If you asked me, like I
Speaker 2: liked it in the old days, when it was like
Speaker 2: one eighty six, two eighty six, three eighty six, four
Speaker 2: eighty six, Penniem and then like Penniam two, et cetera.
Speaker 2: There was just this numerical sequence that I could keep
Speaker 2: track of in my head. And so if someone asked
Speaker 2: me like Joe, like Vera, Ruben Hopper, Blackwell, what was
Speaker 2: the sequence, I'd be like, I gotta be honest with you.
Speaker 2: I like, don't exactly remember, and I will prioritize that
Speaker 2: at some point. But speaking of Silicon, so yesterday Microsoft
Speaker 2: came out with a big They're really they want to
Speaker 2: be in the game too. They don't want to just
Speaker 2: be connected the labs. They want to have advanced models too,
Speaker 2: and apparently it's a good model. And they announced the
Speaker 2: MAI Thinking one model, but they said it's optimized on
Speaker 2: the maya two hundred chip, which is their own chip.
Speaker 2: And this is a thing which is even again going
Speaker 2: back to our recent conversation we had even a place
Speaker 2: like Hudson River Trading is thinking about getting into the
Speaker 2: customized hardware game. How much juice for the squeeze is
Speaker 2: there of aligning the model with custom silicon From your
Speaker 2: vantage point.
Speaker 4: What we could offer is what we hear from our clients. Yeah,
Speaker 4: on that, And it's important to keep in mind we
Speaker 4: can run any type of silicon on our platform. Right.
Speaker 4: We are entirely customer led in what we build. Like,
Speaker 4: we don't go commit to CAPEX and specuatively hope people
Speaker 4: come and use infrastructure, right like, We wait until a
Speaker 4: client says, we want you to go do this specific build,
Speaker 4: here's what we want it to look like, and then
Speaker 4: we go commit to that CAPEX right more like a
Speaker 4: success based CAPEX approach. And the client isn't asking for
Speaker 4: anything but in nvidio infrastructure. And I think a large
Speaker 4: contributor to that is I mean, they built this incredible
Speaker 4: ecosystem around their chipset. They have been dedicated to that
Speaker 4: for I think over fifteen years at this point. Through
Speaker 4: the Kuda architecture and in video. From what we hear
Speaker 4: from more clients, that platform just remains the most efficient,
Speaker 4: the most scalable, the most reliable set of infrastructure that
Speaker 4: is in the market. Right, So I think others there's
Speaker 4: always been you think over the past few years, right,
Speaker 4: there's always been talking like what it is, but yeah,
Speaker 4: this other again and these other chips, and at the
Speaker 4: end of the day, like people are still using in
Speaker 4: video infrastructure, they're committing to in Vidia infrastructure for five
Speaker 4: plus year contracts in these billion, multi billion dollar commitments
Speaker 4: because they know that that is going to be a
Speaker 4: critical part of how they scale their business. We really
Speaker 4: don't see demands on a material basis for anything but
Speaker 4: that in Vidia compute and that's what we are building today.
Speaker 2: Obviously, just to push back on this a little bit,
Speaker 2: and I'm not really in any position to push back,
Speaker 2: I can only relate what past guests have said and
Speaker 2: my own reading. So what one of our guests said
Speaker 2: is that absolutely in Nvidia has the lock on model training,
Speaker 2: that if you want to train a model, that yes,
Speaker 2: in video chips are the older game in talent, but
Speaker 2: that for inference they're really his view, this is in
Speaker 2: Donning again his views, there really were options. And then
Speaker 2: of course we had someone who was much more biased.
Speaker 2: We interviewed the CEU of Sarah Brash, the company that
Speaker 2: makes the Gigantic plate and or sorry, the Gigantic pichanic,
Speaker 2: and of course he did but I mean, of course
Speaker 2: he was gonna say, yeah, the Kudo mote is vastly
Speaker 2: overrated for inference. It barely exists now. Of course, of
Speaker 2: course he's going to say that, so, like, you know,
Speaker 2: he's in a competitor, but we've also heard it from
Speaker 2: a user of inference, and intuitively it makes sense, like
Speaker 2: training is very complicated, all that stuff. But what you're
Speaker 2: saying is that from the customer standpoint, you see the
Speaker 2: demand for in Vidia on both the training and the
Speaker 2: inference as being steady, and that you perceive that advantage
Speaker 2: to be consistent through both aspects.
Speaker 4: So I believe in our last quarterly report, our CEO
Speaker 4: might qualify that inference workloads represent well in access of
Speaker 4: fifty percent of infrastructure utilization on our platform. Exact same
Speaker 4: infrastructure we do is for training. Yeah. Right, When going
Speaker 4: back to my commentive like it's very fungible between those
Speaker 4: different types of workloads those customers are choosing in Vidia
Speaker 4: to work with. Okay, I think what you're going to
Speaker 4: see is people will want to try at small scale
Speaker 4: other types of silicon, but the reliable, proven, and remains
Speaker 4: from our perspective, most efficient infrastructure to use is in
Speaker 4: video today. Does that change over time? Who really knows,
Speaker 4: But I think we've seen in video battling this concept
Speaker 4: for years, and every year they show up and like
Speaker 4: they remain the de facto choice for AI infrastructure. I
Speaker 4: think we're going to be one of the first people
Speaker 4: in the market to see it because that will be
Speaker 4: a tone shift change for our clients asking us to
Speaker 4: run something else that hasn't happened.
Speaker 3: Okay, So have the constraints on your business changed at all?
Speaker 3: So three years ago we were talking about GPUs and
Speaker 3: how hard they were to actually get. I imagine GPU
Speaker 3: securing GPUs is still competitive to say the least. But
Speaker 3: are you seeing other constraints emerge? Like Joe mentioned in
Speaker 3: the intro, just land usage just places to actually build
Speaker 3: data centers.
Speaker 4: Landed usage specifically, I wouldn't say is as much of
Speaker 4: a concern. Having a powered shell is the battleneck? Okay
Speaker 4: today and let me qualify ball poweredshell. PowerShell is effectively
Speaker 4: an empty data center that is energized. Right. It has
Speaker 4: all the power and associated components. I can come into
Speaker 4: it and deliver electrons into a reck, has the cooling
Speaker 4: system built within it, like it has a whole thing. Right.
Speaker 4: Poweredshell is the industry termed for it. That is the
Speaker 4: bottleneck because of all of the supply chains that come
Speaker 4: into that, right Like, not only you have electricity, do
Speaker 4: you have the land, et cetera, But you have the
Speaker 4: backup batteries, supplies, you have the transformers, you have personnel.
Speaker 4: Right they just think about the electricians for these sites
Speaker 4: and getting the accreditation on the electrician side to be
Speaker 4: able to participate in these builds. I mean, I think
Speaker 4: it's a five year plus apprenticeship to be able to
Speaker 4: go through that program. Right. We can't just make new
Speaker 4: electricians leveraging a supply chain, right like that that's a
Speaker 4: trade that you can't really scale efficiently. So that is
Speaker 4: absolutely the bottleneck for us. And I think our peer
Speaker 4: set that's out there right now, access to chips, I
Speaker 4: think we have a phenomenal relationship within video where we've
Speaker 4: just proven to be the best operator of this infrastructure
Speaker 4: on the planet. You know, a bottleneck that existed for
Speaker 4: us previously I think was access to financing.
Speaker 3: Yeah right, we an't know, doesn't seem to be an
Speaker 3: issue anymore.
Speaker 4: I would agree with that broadly, But that's years of
Speaker 4: work in execution that has delivered that ability for us.
Speaker 4: I mean, year to date, we've raised over twenty one
Speaker 4: billion dollars of financing for our business. Like you don't
Speaker 4: get to do that and just go from you know,
Speaker 4: zero to twenty right out of nowhere. And I think
Speaker 4: that's largely driven by our track record of execution. Right,
Speaker 4: our investors, our creditors can see this deep set of
Speaker 4: experience over the years of consistently delivering on these builds.
Speaker 4: I mean, we have over a gigawant in active power
Speaker 4: at this point, right, like a gigawat like at the
Speaker 4: data center level with GPUs delivering into clients. And I
Speaker 4: think that there's been kind of a misunderstanding of the
Speaker 4: market where people are conflating the concept that like, you know,
Speaker 4: something on paper is equivalent to being physically done and delivered.
Speaker 4: And all I can say is there's an enormous gap
Speaker 4: between you know, signing for power for delivery in twenty
Speaker 4: thirty versus actually delivering that into billable GPU hours, and
Speaker 4: that gap of execution is what has driven down our
Speaker 4: costly capital so aggressively. That gap is where our business
Speaker 4: sits and why it has been so successful. I mean,
Speaker 4: that's the secret Sauce is our ability to take these
Speaker 4: data center deployments and these customer relationships and deliver were
Speaker 4: billable GPU hours into them.
Speaker 2: You know, speaking of financing, I just want to say,
Speaker 2: you know, during last year, like maybe six months ago,
Speaker 2: that might have been the sort of near peak of
Speaker 2: the Michael Burry inspired These chips are like in the
Speaker 2: last two years stuff. And one of the viral charts
Speaker 2: that you would see on Twitter was the core We've
Speaker 2: CDs chart. Those have come way in, So it is
Speaker 2: it is you know, while yeah, that's right, that's the
Speaker 2: thing about CDs. No one never posts charts of credit
Speaker 2: default swaps when they're coming in. They people love to
Speaker 2: post them when they're blowing out. They have come in,
Speaker 2: So you know, that does speak to some of this
Speaker 2: point about these anxieties having been a leaved at least
Speaker 2: somewhat since the start of the year. You know, it
Speaker 2: occurred to me, like we're talking about credit default swaps,
Speaker 2: we're talking about financing. I'm sort of gearing up to
Speaker 2: write a big thing maybe, but I'm writing it in
Speaker 2: my head currently that there really are a lot of
Speaker 2: analogies between the business of data centers and the business
Speaker 2: of banking. And one of the things in banking is
Speaker 2: we all learned from SVB was the risk of industry
Speaker 2: and depositor concentration that you if you have all your
Speaker 2: depositors are either in like one depositor gets too big,
Speaker 2: or all your depositors are in the same industry, then
Speaker 2: you have this risk of like correlated withdrawals. And that's
Speaker 2: what obviously did in SVB. When you think about planning
Speaker 2: and you think about, okay, here's a investment, et cetera,
Speaker 2: how much does this come up sort of like thinking
Speaker 2: about I guess tenant uh diversification. Yeah, tenant diversification as
Speaker 2: something that you think about in your multi your planning.
Speaker 4: It's a critical aspect of it. Right, As I said
Speaker 4: earlier too, like this was a key criticism of us
Speaker 4: coming into our im right where we had that customer
Speaker 4: concentration in our revenue, and we have made enormous progress there,
Speaker 4: and I think the best way to think about it
Speaker 4: is we could take all of our unallocated capacity. And
Speaker 4: I say that very specific it's not unsold capacity and
Speaker 4: implying that there's no demand for it. It's unallocated. There's
Speaker 4: intense demand for we're figuring out where it should go,
Speaker 4: and that customer piece of it, I think, honestly, like
Speaker 4: we could allocate all that capacity to like single name clients, right, Like,
Speaker 4: there is a pretty significant number of single name clients
Speaker 4: we can go allocated out into. But I don't think
Speaker 4: that is the business we are supposed to be building here.
Speaker 4: I think the business we are supposed to be building
Speaker 4: is a diversified cloud that is supporting the leading AI
Speaker 4: consumers and producers on the planet. I don't think we're
Speaker 4: supposed to be supported in just one or two companies.
Speaker 3: When it comes to financing, Can you say a little
Speaker 3: bit more about what changed to make the market more
Speaker 3: comfortable with this, because like this is the big story
Speaker 3: in markets, just how much AI is now being issued
Speaker 3: through the corporate bond market. The equity market as we
Speaker 3: know is basically all big tech at the moment. Like,
Speaker 3: what changed on the part of investors was Was it
Speaker 3: just pure return and performance or were there I guess
Speaker 3: efforts to like make the contracts more robust or increase
Speaker 3: visibility into demand and that sort of thing.
Speaker 4: Seeing the inference aspect of it really emboldened investors, But
Speaker 4: like that was really just January, right, or maybe late
Speaker 4: Q four where you started seeing this just massive inflection
Speaker 4: of demand driven by inference for us, right, It's tough
Speaker 4: for me to speak about you know, other companies, but
Speaker 4: for us, like why why have we been underwritten at
Speaker 4: such scale and at a decreasing costly capital? I think
Speaker 4: it goes back to that track record of execution, right,
Speaker 4: is just the market has watched us execute and watch
Speaker 4: us deliver on these contracts. And the way then tell
Speaker 4: me if I'm going into too much detail here, but
Speaker 4: but the way that we finance our business. You kind
Speaker 4: of break it into two broad buckets, right, you have
Speaker 4: parent co financing and acid co financing. And acid co
Speaker 4: financing is where all of the GPUs you get financed. Right,
Speaker 4: It's where all of our client contracts sit. And we
Speaker 4: can take these financings and put them into SPDs or
Speaker 4: we'll just call it a box so to say. And you.
Speaker 2: Keep going lots of connotations, but keep going, keep we.
Speaker 4: Put them into SPDs UH and these these stvs, they
Speaker 4: have the emphas structure, they have the data center costs,
Speaker 4: and they have the debt agreements within them. And so
Speaker 4: you're able to pair this like five year take or
Speaker 4: pay contract to an amortization schedule on the debt and
Speaker 4: you have the revenue come into the box, pay down
Speaker 4: the amortization schedule, pay down the operating costs of the
Speaker 4: data center, and it still contributes a It has a
Speaker 4: twenty five percent contribution margin of profit up to the
Speaker 4: parent code. Right, Like these are highly profitable agreements down
Speaker 4: to the SPB stack. And so you take that SPB
Speaker 4: out to the credit market and say, look at this instrument.
Speaker 4: It's a discrete set of contracts with counterparties like any
Speaker 4: ease you want to consume GPU compute, you have the
Speaker 4: data centers within it, et cetera. And you know, one
Speaker 4: of the latest ones we did was as we call
Speaker 4: ddtail four. This was a investment grade rated first of
Speaker 4: its class. No one had done this before for GPU financing,
Speaker 4: non recourse HPC infrastructure financing, and got done it. So
Speaker 4: for plus two twenty five like that is a phenomenal
Speaker 4: cost of capital for us, And importantly, we were able
Speaker 4: to bring in the insurance charge of capital, which is
Speaker 4: a massive change of capital out there that is looking
Speaker 4: to do allocations into the space. So we're kind of
Speaker 4: continuously making progress through these different stacks of capital, unlocking
Speaker 4: access to more and more types of investors. It's why
Speaker 4: you've seen this move into the convertible note market, into
Speaker 4: the unsecured market as well, along with taking direct strategic
Speaker 4: equity investments. But for us, it's really important for the
Speaker 4: entire investor space to understand this business because this business
Speaker 4: largely didn't exist before, right, Like people weren't making loans
Speaker 4: into the hyper scale just to go credit these buildouts.
Speaker 4: Right it's on core weave honestly to be building this
Speaker 4: path into how do you finance the AI hyperscaler effectively?
Speaker 4: And I think we've just done a terrific job of
Speaker 4: it over the past few years.
Speaker 2: You used to be, in a prior lifetime a trader.
Speaker 4: Right, yes, as a commodity trader.
Speaker 2: So I'm curious, like you know, there's a lot of
Speaker 2: interest in and I don't know if it's going to
Speaker 2: materialize in GPU capacity trading and there's gonna be a
Speaker 2: new contract. We recently interviewed the CEO of Compute Exchange
Speaker 2: and they're very close to having something listed on the CME.
Speaker 2: From your perspective, because I don't have a view on
Speaker 2: this yet, the one like you see, like okay, a
Speaker 2: big AI company does a five year contract. As you say,
Speaker 2: the duration is lengthening, we're gonna lock this in. I
Speaker 2: don't know, like what the need is for tradeable compute
Speaker 2: in that environment, etc. What's your guest, like, do you
Speaker 2: anticipate that there will be a sufficient ecology of hedgers
Speaker 2: and speculators such that there will be a liquid market
Speaker 2: for tradable compute.
Speaker 4: I think it's a it's a very much a timeline
Speaker 4: question that's out there. Short term No, let me offer
Speaker 4: why no short term? And then I'd say maybe in
Speaker 4: the long term. And it all comes back to fungibility. Right.
Speaker 4: If you think about gold, the gold is defined by
Speaker 4: its chemical composition, right, and there's no question of of
Speaker 4: what is gold and not gold, et cetera. Compute really isn't, right,
Speaker 4: especially GPU compute GP compute today is not fungible. And
Speaker 4: I think that this is well understood by our client base,
Speaker 4: by our suppliers, by you know, third party consultants like
Speaker 4: syny Analysis, And it's it's this idea that an H
Speaker 4: one hundred deployed and one cloud doesn't have the same
Speaker 4: performance of an eight one in another cloud. And the
Speaker 4: metrics that people use are things like good put or
Speaker 4: model flop utilization MFUS, and there are these measurements of
Speaker 4: like how much more performance is one the exact same
Speaker 4: GPU by the way, versus another GPU deployed in another facility.
Speaker 4: And so in order for something to be commoditized, it
Speaker 4: has to be fungible, right, Otherwise there's just too much
Speaker 4: you know, murkiness, and there isn't like an exact data
Speaker 4: point in there.
Speaker 2: Can I push on that a little bit further? So,
Speaker 2: I mean, I think that seems like a reasonable view.
Speaker 2: Is the non fungibility related to configuration of like how
Speaker 2: they literally like the configuration of the GPUs within physically,
Speaker 2: Like what is it? Is it about power? I mean,
Speaker 2: I think they're all like, you know, there are plenty
Speaker 2: of places that will say you only have nine nines
Speaker 2: or however many nines you need in your industry or whatever.
Speaker 2: What is it in your view that would cause significant
Speaker 2: changes in the performance of an H one hundred in
Speaker 2: one cloud verse another.
Speaker 4: It could be in some part configuration. Right, we build
Speaker 4: everything the DJX reference back, which is the most outlined
Speaker 4: by video. It's the most performance way to build, operate,
Speaker 4: and deliver GPUs. But the rest of it, honestly is
Speaker 4: just how you operate the GPUs, and that is the
Speaker 4: core weave software stack. That is, how do you keep
Speaker 4: these GPUs online? Right? Like what happens if the GPU flails?
Speaker 4: Can you predict if a GPU is about to fail
Speaker 4: and swap in other infrastructure so that the client doesn't
Speaker 4: have downtime on that component. And there's an immense suite
Speaker 4: of software solutions that and infrastuctual management solutions that we
Speaker 4: have built to have the best good put to have
Speaker 4: the best MFUS in the industry. That's none of that
Speaker 4: is off the shelf, right, And so I wouldn't say
Speaker 4: it comes down to the strict components. That's kind of
Speaker 4: like a minimum starting point, right, Like you have to
Speaker 4: start in DJ's referencepect But where's differentiation come from there?
Speaker 4: I mean, that's that's the core re product you're describing
Speaker 4: right there.
Speaker 2: By the way, Tracy, I'm just looking up in terms
Speaker 2: of art. Goodput measures the fraction of peak hardware performance
Speaker 2: that the training job can extract. This is according to
Speaker 2: Google and mfu's model FLOPS utilization Hardware metric for evaluating
Speaker 2: real world efficiency of LEMA training. So two new terms.
Speaker 2: I actually hadn't heard of MFUS or good put before this.
Speaker 2: I just learned two new terms today.
Speaker 3: We got to create a gloss AI glossary. Yeah, I do, Brandon.
Speaker 3: When Joe asked you that question about compute markets earlier,
Speaker 3: you said it was a timeline question, which in my
Speaker 3: mind implies that it's inevitable, like it's just a question
Speaker 3: of how long it takes. But then when you describe
Speaker 3: the fungibility problem, it seems like this is an actual
Speaker 3: issue that will be very difficult to solve.
Speaker 4: Yes, I think that characterization is absolutely correct. Right, Like
Speaker 4: if you just general commodity theory and I traded natural
Speaker 4: as electricity agriculture products for over a decade, like it
Speaker 4: suggests that it should become that at some point, But
Speaker 4: what is the reality today? The reality is this stuff
Speaker 4: isn't getting easier to operate, right. We've moved from these
Speaker 4: kind of relatively simple forty two to uter air cold
Speaker 4: racks of Hopper to these immensely complex Blackwell deployments moving
Speaker 4: into Vera Rubin following that, like, it's not getting easier
Speaker 4: to build, operate, provision, deliver these reputs. It's getting more difficult,
Speaker 4: and I think until it starts becoming easier, you don't
Speaker 4: really have a path to commoditization. You will have to
Speaker 4: continue to prioritize working with the world class and world
Speaker 4: leading operators of infrastructure. That's where we sit.
Speaker 2: First of all, this is helpful, and I like that
Speaker 2: we're getting multiple respectives because I do think this is
Speaker 2: gonna be like one of the big questions for financial markets,
Speaker 2: because let's say if they took off, then you could
Speaker 2: imagine that might even improve financing conditions because then the
Speaker 2: lender can hedge against that. Yeah, so like there would
Speaker 2: probably be some good things for the industry if this
Speaker 2: took off. So I appreciate it's good to have your
Speaker 2: perspective on this. Why is it you know, I'm a
Speaker 2: I'm an inference I am an inference user, by the way,
Speaker 2: So I made a little machine learning model in one
Speaker 2: of my hobby projects, and I provide inference O, Havelock,
Speaker 2: dot AI or I'm a user of inference or whatever,
Speaker 2: I have a model whatever.
Speaker 3: Why is it that impressive if you were providing I'm
Speaker 3: trying to.
Speaker 2: I guess I have a consumer of inference. I use
Speaker 2: a anyway, Why is it that I am actually very
Speaker 2: easily able to get not a huge allocation of you like,
Speaker 2: GPU access? So I was like, how do I train
Speaker 2: this model? It's a model called bert the Google really
Speaker 2: east in twenty eighteen or twenty nineteen. I fine tuned
Speaker 2: it for my purposes and then literally using claud code,
Speaker 2: I was able to in ten minutes sign up. I
Speaker 2: started using this company called Modal, and I was able
Speaker 2: to start training a model. I was surprised that there's
Speaker 2: like and it didn't cost me very.
Speaker 4: Much and I have like no volume.
Speaker 2: But nonetheless, evidently there was a little GPU capacity out
Speaker 2: there that I could get and it cost me like
Speaker 2: five dollars or something for the whole thing. Given what
Speaker 2: you always hear about like a utilization is slammed, why
Speaker 2: is it actually not that hard to find GPU capacity
Speaker 2: for someone like myself?
Speaker 4: You know, I think it's the skill O difference right there,
Speaker 4: finding ones or tens of GPUs. I think that's way
Speaker 4: more accessible out there. Okay, our clients are focused on
Speaker 4: the hundreds of thousands of gps.
Speaker 2: I'm not there yet, but I'm not there yet, not yet.
Speaker 4: I'm sure you'll get there, yes, And that's where it
Speaker 4: kind of decommodetizes itself with scale as well. Right, Like,
Speaker 4: as you're in the hundreds of thousands component, there's just
Speaker 4: not that many deployments, right, It's handfuls of deployments at
Speaker 4: that size, But getting access to ones of GPUs, I
Speaker 4: think that there is a lot more ability to go
Speaker 4: secure that sizing in the market.
Speaker 3: So Joe and I are heading to Hong Kong very soon,
Speaker 3: and I expect that AI in China is going to
Speaker 3: be a big topic of conversation. How would you characterize
Speaker 3: I guess the difference between the US and the Chinese
Speaker 3: market at the moment. I'm sure this is something you
Speaker 3: think about, even though you don't participate in the Chinese
Speaker 3: market directly.
Speaker 2: Yeah, that's Tracy asking for questions.
Speaker 3: Yeahs Basically it's.
Speaker 2: Like questions that we can ask people when we're over there.
Speaker 4: Yeah, that's likely going to be my response, Tracy is like,
Speaker 4: we just do not participate in that market, I think
Speaker 4: that there's opportunity for us to be expanding. As you
Speaker 4: guys know, we operate in Canada, Europe. I think moving
Speaker 4: further east makes a lot of sense for us, But
Speaker 4: we're trying to be very methodical in the way that
Speaker 4: we expand, So unfortunately, I'm not gonna be able to
Speaker 4: healthy with specific questions in that market. But I would
Speaker 4: imagine you're going to encounter a lot of the same
Speaker 4: things that you're seeing in the US, which is just insatiable,
Speaker 4: unrelenting demand for AI And like you know, we just
Speaker 4: kind of keep coming back to this. It's like there
Speaker 4: is no solution in sight for being able to satiate demand, right,
Speaker 4: There's just too many supply chain there's no path to
Speaker 4: solving demand in the near term or even the medium term.
Speaker 2: Frankly, you mentioned sir Tracy has two about land use.
Speaker 2: You said really was an issue. But like the first
Speaker 2: time we talked to you in twenty twenty three or
Speaker 2: whenever that was, there was not a major growing movement
Speaker 2: of people who were just like anti data centers in America.
Speaker 2: Maybe there were a few fringed people, but it was
Speaker 2: not something that was on the mind of politicians and
Speaker 2: activists and so forth. You do see these headlines, you
Speaker 2: know about some projects really having been shelved. It was
Speaker 2: like a big one. Northern Virginia is a huge hotspot
Speaker 2: for it and there was a big project that was
Speaker 2: they pulled the plug on due to them they couldn't
Speaker 2: get an agreement with the local government. That must have
Speaker 2: affect you. What are you seeing in terms of like
Speaker 2: your capacity to build? How has it changed specifically in
Speaker 2: light of or have you seen a change? Would you
Speaker 2: be able to build faster in a world where this
Speaker 2: had never become a political hot button issue.
Speaker 4: I believe it has become that hot button issue. It's
Speaker 4: something that we're quite proactive about in market and I
Speaker 4: think you just kind of go through the checks on
Speaker 4: the diligence process to make sure you're going through it correctly.
Speaker 4: I think that there's misconceptions out there, like water usage.
Speaker 2: Yeah, setting aside, the misconcept like setting aside, I know,
Speaker 2: setting aside, the whole debate about but just in terms of,
Speaker 2: like operationally, what's it changed for you in terms.
Speaker 4: Of Yeah, no, I would say our greatest challenge is
Speaker 4: still just getting that delivery of our like the construction
Speaker 4: and all the goings and getting everything in there like
Speaker 4: that is truly more of the ball back that's in
Speaker 4: the market today.
Speaker 2: Brandon, thank you so much for coming back on odd lugs.
Speaker 2: I'll have you back next month for another market about it. No,
Speaker 2: or at least or maybe in three years, not three years,
Speaker 2: not three years.
Speaker 4: But really that's an eternity.
Speaker 2: Yeah, I know, thank you so much.
Speaker 4: Thanks guys, appreciate it.
Speaker 2: I'm very excited about whether compute features will take off.
Speaker 2: I think this is an exciting like story. You know,
Speaker 2: it's not the biggest story in the world, but it
Speaker 2: is actually a very exciting story.
Speaker 3: I've said this before. Even if you're not that interested
Speaker 3: in AI, this is a really interesting market structure story, right.
Speaker 3: It's based the creation of a brand new market and
Speaker 3: poses all these interesting philosophical questions about how you do that.
Speaker 3: And I thought Brandon's point about fungibility. I mean, that
Speaker 3: is a real issue, and it does seem like it's
Speaker 3: a challenging one to fix at the moment. I don't
Speaker 3: know if it's inevitable in the future, but who knows.
Speaker 2: No, No, I mean it makes a lot of sense.
Speaker 2: This was also Lewis Hart's point that it's like the
Speaker 2: fun you know, it's in the word commodity, right if
Speaker 2: it's if it's not a commodity, you're not going to
Speaker 2: get a commodity market for it. And of course a
Speaker 2: number of entities are betting that it will be commoditized.
Speaker 2: But if the if it's true that like you know,
Speaker 2: they're getting more difficult to work, that the technical demands
Speaker 2: on the influence provider, on the data center company are
Speaker 2: getting greater in order to get the maximum you know, juice,
Speaker 2: then maybe it doesn't become commoditized. But I think that's
Speaker 2: like a fascinating thing.
Speaker 3: Like if you do see those efficiency improvements and new
Speaker 3: designs and things like that, you could imagine that, like
Speaker 3: the demand is there for standardized GPU as well.
Speaker 2: Yeah, I don't know.
Speaker 3: Like I'm really torn. It feels like they should go
Speaker 3: either way.
Speaker 2: Well, and even in his answer, he talked about how
Speaker 2: they can figure their own GPUs to expect largely that
Speaker 2: Nvidia itself has come up with, so in theory, like
Speaker 2: there is aspect that everyone can match to. So that's
Speaker 2: like a really interesting that's a really interesting question. I
Speaker 2: also really want to do more on all of these,
Speaker 2: so Google his TPUs, Amazon his tranium. Microsoft has its
Speaker 2: own hardware. Maybe even Jane Streets in the Hudson River
Speaker 2: tradings will have their own hardware if they're not, like
Speaker 2: I want to understand better why, right, because like they
Speaker 2: presumably have some reason and they at least like the
Speaker 2: Microsoft will say, well, this will run better on our
Speaker 2: customers hardware. I want to understand why that would be.
Speaker 2: How much difference in performance is there? And then the
Speaker 2: degree to which demand materializes from users for non in
Speaker 2: video silica, And there's like a really big question.
Speaker 3: Yeah, why custom chips?
Speaker 2: Yeah, and what can you get out of that if
Speaker 2: you align model and chip to optimally work together. I
Speaker 2: have no idea, but I feel like it's an episode
Speaker 2: I would like to do.
Speaker 3: Yeah, we should, all right? Shall we leave it there
Speaker 3: in the meantime, Let's leave it there?
Speaker 4: All right?
Speaker 3: This has been another episode of the All Thoughts podcast.
Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.
Speaker 2: Follow our guest Brandon McBee at Brandon McBee. Follow our
Speaker 2: producers Carmen Rodriguez at Carmen armand Dash'll Bennett at Dashbod,
Speaker 2: kelb Brooks at Kelbrooks and Kevin Lozano at Kevin Lloyd Lozano.
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