Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.
Speaker 2: I'm Joe Wisenthal.
Speaker 1: And I'm Tracy Alloway.
Speaker 2: Tracy, you know, we've done tons of course on like
Speaker 2: electricity and AI and data centers and all that stuff,
Speaker 2: but we've never actually done like a well, we've never
Speaker 2: talked to someone who is building data centers.
Speaker 1: Putting it all together, you mean.
Speaker 2: Yeah, putting it all together like what you know, just
Speaker 2: a bunch of you know, I've had consultants, so we
Speaker 2: talked to energy people, but like, how does this business
Speaker 2: of essentially, I guess, building a building, putting a bunch
Speaker 2: of chips in there, getting the electricity, and then in theory,
Speaker 2: selling all of that at a markup? Like, how does
Speaker 2: it actually work?
Speaker 3: You know?
Speaker 1: What I was reading recently This is kind of a tangent,
Speaker 1: but not really because we're talking about the physical and
Speaker 1: financial process of building these things. But I saw this
Speaker 1: is online. There's a guide to the like physical Planning
Speaker 1: around an IBM system three sixty from like nineteen sixty
Speaker 1: three or something, and it's two hundred and thirteen pages long.
Speaker 2: Have you read it yet?
Speaker 1: I did flip through it, there's like there's guidance on
Speaker 1: minimizing vibrations obviously, like temperature and humidity and stuff like that.
Speaker 1: I did not read the full two hundred pages, but
Speaker 1: I'm kind of thinking like if this is what if
Speaker 1: this is all the thinking that had to go into
Speaker 1: like one computer, albeit a supercomputer in the nineteen sixties,
Speaker 1: but like a pretty basic machine. When we look back
Speaker 1: on it now, how much planning and thinking has to
Speaker 1: go into building like these huge cloud servers and all
Speaker 1: their associated infrastry structure, both physical and software as well.
Speaker 2: No, totally, and you know, we you know, one of
Speaker 2: the ways that we've touched on this subject a little
Speaker 2: bit is in our conversations with Steve Eisman, who's been
Speaker 2: investing at least as far as we know, in a
Speaker 2: lot of these like industrial HVAC companies and electricity gear
Speaker 2: companies and stuff like that. So like companies that have
Speaker 2: actually been around for a really long time, sort of
Speaker 2: standard cyclical businesses, and then they've like caught the secular
Speaker 2: tailwind because with this boom in AI data center construction,
Speaker 2: suddenly there's this sort of continuous bid for all their
Speaker 2: gear and services.
Speaker 1: I'm going to start an anti vibration floor maker or something.
Speaker 1: Do you think that's a viable business? Does anyone care
Speaker 1: about vibrations anymore?
Speaker 2: I am certain that in various high tech environments you
Speaker 2: do not want to have vibrations. You know, you have
Speaker 2: like a valuable chips, you don't want them to be
Speaker 2: like degrading.
Speaker 1: Because people are walking around.
Speaker 2: Yeah, or just you know what, all the machine and
Speaker 2: all your air conditioners and equipment and all that stuff,
Speaker 2: you can't be having that stuff degrade.
Speaker 1: Well, the other interesting thing that's happening in the space now,
Speaker 1: So in addition to the physical challenge of building a
Speaker 1: bunch of this stuff, there's also the financial aspect of it.
Speaker 1: And I guess as AI becomes more and more of
Speaker 1: a thing, and clearly, as you laid out, there's a
Speaker 1: lot of enthusiasm around the space. At the moment, you
Speaker 1: are seeing a bunch of financial entities get interested as well.
Speaker 1: So obviously venture capital has been pouring money into the space,
Speaker 1: but we're starting to see some new types of financial
Speaker 1: investments in AI. And I'm thinking about one thing in particular,
Speaker 1: and it is the recent GPU or chip backed loan
Speaker 1: that was reported by the Wall Street Journal and I
Speaker 1: think we should talk about that aspect.
Speaker 2: Of it too, totally, because one of the things that's
Speaker 2: happening in tech is this big sort of shift from like, okay,
Speaker 2: we're all of your costs in the past, where a
Speaker 2: lot of them were sort of op x, the cost
Speaker 2: of engineers, et cetera. And now suddenly tech companies have
Speaker 2: to think about CAPEX for the first time, these big
Speaker 2: upfront costs that are in theory going to pay off
Speaker 2: for a long time, which in theory then changes how
Speaker 2: you should think about the financing model.
Speaker 1: Absolutely well, I am.
Speaker 2: Excited to say because we literally do have the perfect
Speaker 2: guest we're going to be speaking with, Brian Venturo. He
Speaker 2: is the chief strategy officer at core Weave. Corewave. For
Speaker 2: those who don't know, it's probably the company right now
Speaker 2: that people most associate with being at the heart of
Speaker 2: the AI data center boom. They have a bunch of
Speaker 2: in video chips, they have investments from in Nvidio right
Speaker 2: here in the sweet spot. As you mentioned, one of
Speaker 2: the interesting things that's going on is they not long
Speaker 2: ago announced a debt financing facility sit back basically by
Speaker 2: the GPUs that they would acquire, so literally the perfect
Speaker 2: person to understand like the business of these AI cloud
Speaker 2: data center So Brian, thank you so much for coming in.
Speaker 3: Thanks for having me. It's the second time I've been
Speaker 3: on the podcast.
Speaker 2: That's right. We talked to Brian years ago. It's interesting
Speaker 2: to think about at that time because I think that
Speaker 2: may have been like twenty twenty or twenty one, and
Speaker 2: the excitement then was that these chips could be used
Speaker 2: for crypto mining and other things like sort of distributed
Speaker 2: video editing and stuff like that, and then Ethereum stopped
Speaker 2: using mining. But it was sort of fortuitous timing because
Speaker 2: right around then AI went crazy and that's probably I
Speaker 2: don't know, in my view, maybe a higher use of
Speaker 2: these chips before we get to that. Do you worry
Speaker 2: about vibration in your data center?
Speaker 3: So everywhere that's close to a fault line is designed
Speaker 3: around that and is part of code. So you know,
Speaker 3: the engineering firms that help us build these data centers
Speaker 3: have taken all of that into account, and all of
Speaker 3: our racks are you know, seismically tuned to make sure
Speaker 3: that we can withstand the normal vibration from the Earth.
Speaker 3: So yeah, it's been something that's been in those annuals
Speaker 3: for a long time. Some of our hardwer manufacturers actually
Speaker 3: have vibration testing labs where they put the racks on
Speaker 3: top of a big kind of platform that shakes, and
Speaker 3: it's pretty dangerous and uncontrollable and hard to watch. But
Speaker 3: you know, there's people out there that have been solving
Speaker 3: this problem for decades.
Speaker 1: Now I missed the boat on that business choir. It
Speaker 1: sounds like it's been dealt with decades ago. Okay, well, actually,
Speaker 1: why didn't I start with a very simple question, which
Speaker 1: is when when you're looking at the business of core Weave,
Speaker 1: so a specialized cloud service provider, let's put it that way,
Speaker 1: what are the different components that you have to think about?
Speaker 1: You know, Joe kind of alluded to all these different
Speaker 1: ingredients that go into the business, but walk us through
Speaker 1: what those actually are.
Speaker 3: Sure, so, there's there's three pieces that as a management team,
Speaker 3: we think are incredibly critical to the business. The first is,
Speaker 3: you know, our technology services that we provide on top
Speaker 3: of the hardware, right and this is everything from the
Speaker 3: software layer through the support organization to you know, how
Speaker 3: we work with our customers. This isn't the type of
Speaker 3: thing that you just go plug in and it works.
Speaker 3: In these large supercomputer clusters, there may be two hundred
Speaker 3: thousand infinibank connections that connect all the GPUs together, and
Speaker 3: if one of those connections fails for whatever reason, the
Speaker 3: job will completely stop and have to restart from its
Speaker 3: previous checkpoints. So, you know, everything that we do on
Speaker 3: the software side and engineering side is to make sure
Speaker 3: these clusters are as resilient and performant as they possibly
Speaker 3: can be to ensure you know, our customers can run
Speaker 3: their jobs, you know, increase efficiency and get all of
Speaker 3: the kind of monetary value they can out of the chips.
Speaker 3: So technology piece is really hard. It's something that I
Speaker 3: think is very overlooked by the market, but it's just
Speaker 3: as hard as the two other kind of pieces that
Speaker 3: this business stands on. The second is, you know, the
Speaker 3: physical nature of the business in that you have to
Speaker 3: actually build and run these data centers and those hundreds
Speaker 3: of thousands connections inside the supercomputers. Like somebody has to
Speaker 3: go put those together and make sure they're clean and
Speaker 3: make sure they're labeled correctly to be able to remediate failures.
Speaker 3: And when you're building a thirty two thousand GPU supercomputer
Speaker 3: that is one of the fastest three computers in the planet.
Speaker 3: You know, you're running thousands of miles of cable inside
Speaker 3: a very dense space, right. These data centers are built
Speaker 3: very tiny to make sure that you can connect everything together,
Speaker 3: and that becomes a huge logistical challenge. So, you know,
Speaker 3: the data centerpiece, which we're going to talk more about today,
Speaker 3: is very challenging to design for the use case. And
Speaker 3: then the third piece is how the hell do you
Speaker 3: finance the whole thing? Right, And you know, we've been
Speaker 3: very successful in the financing aspect of this, but you know,
Speaker 3: whether you're financing technology operations or the physical build of
Speaker 3: these things, it is an incredibly capital intensive business and
Speaker 3: constructing those financial instruments to back our business is very hard,
Speaker 3: and we have to be very very thoughtful around who
Speaker 3: the counterparties are, how do we think about credit risk,
Speaker 3: how do our investors think about that credit risk, How
Speaker 3: do we deal with contingencies inside the contracts to make
Speaker 3: sure that they are financeable on the scale that we've
Speaker 3: done over the last eighteen months.
Speaker 2: Talk to us a little bit more. We could probably
Speaker 2: talk about data center financing credit and have have that
Speaker 2: be a whole episode, but when you think about you
Speaker 2: have to think about your counter party's credit risk. Talk
Speaker 2: to us a little bit about what you're who those are,
Speaker 2: what the type of entity is.
Speaker 3: Sure, so I'll get myself in trouble if I just
Speaker 3: start naming them off. Yeah, some of them are more
Speaker 3: public than others. You know, I'm going to refer to
Speaker 3: them as you know, hyperscale customers. We have AI lab customers,
Speaker 3: we have large enterprise customers. We've really constructed our portfolio
Speaker 3: of business around the idea that you know, if we're
Speaker 3: going to build ten billion dollars of infrastructure for somebody,
Speaker 3: we have to know there's a balance sheet we can
Speaker 3: lean into behind it, right, and we're the pace at
Speaker 3: which we've grown. You know, our customers are demanding scale
Speaker 3: so quickly that the credit of the counterparty is incredibly
Speaker 3: important to find the low cost of capital we have
Speaker 3: with these ADIT facilities we've announced, right, So you know,
Speaker 3: when people talk about how this is a credit facility
Speaker 3: backed by GPUs, it's not really backed by GPUs. It's
Speaker 3: backed by you know, commercial contracts with large international enterprises
Speaker 3: that may have triple a credit, right, So you know
Speaker 3: it's it's the framing of the.
Speaker 1: Aid receivables finance.
Speaker 3: Basically it's closer to trade receivables financing than it is Hey,
Speaker 3: we're going to go leverage up a bunch of GPUs
Speaker 3: and see what happens.
Speaker 1: Huh, okay, well walk us through the I guess like
Speaker 1: the sequence in some of these financing agreements. So you know,
Speaker 1: if a customer comes to you and they say, we
Speaker 1: want a certain amount of compute, can you do this
Speaker 1: for us? And you start going down the process of like, okay,
Speaker 1: what do we need to make this happen? What do
Speaker 1: those like financial agreements actually look like. And who's bearing
Speaker 1: the initial risk? Is it the customer? Is it you?
Speaker 2: Good question?
Speaker 3: So when we're approached by a customer, right, you know,
Speaker 3: the ask is typically going to be pretty pretty general,
Speaker 3: and they're going to say, hey, we're looking for facity
Speaker 3: in Q one of next year. What's the largest thing
Speaker 3: you can do? And you know, we take that effectively
Speaker 3: as a mandate of okay, hey, you know this customer.
Speaker 2: We're not business.
Speaker 3: But before you know, we're really comfortable with them, we
Speaker 3: know that we're going to get a contract done. We'll
Speaker 3: go out and we'll try to secure an asset to
Speaker 3: you know, to go build it. And we may have
Speaker 3: it in our portfolio already. We maybe it may have
Speaker 3: been a strategic investment that we made. But once we
Speaker 3: find the data center asset, that's when we go back
Speaker 3: to the customer and say, okay, like we can commit
Speaker 3: to doing this. This is the timeline. We'll structure a
Speaker 3: contract around it. Depending upon who the customer is. There
Speaker 3: may or may not be some credit support associated with
Speaker 3: it around the scaling of the you know, that asset,
Speaker 3: and then we'll get a commercial contract in place, and
Speaker 3: we will initially fund a large portion of that project
Speaker 3: off of our own balance sheet. Right. It's why you
Speaker 3: also see us raising equity, right, is we have to
Speaker 3: have the capital to accelerate the business. And then once
Speaker 3: we have that and we're making progress, you know, think
Speaker 3: about it as you're building real estate. Right, you have
Speaker 3: a construction loan and then you have a stabilized asset loan,
Speaker 3: and we basically fund the construction loan piece off of
Speaker 3: our balance sheet. When we get to a more stabilized asset,
Speaker 3: that's when we go out and kind of do that
Speaker 3: trade financing or trade receivables financing our with our partner lenders.
Speaker 3: You know, they worked with us before, they know that
Speaker 3: these things are going to stand up, They know how
Speaker 3: they perform, and at that point in time, it's it's
Speaker 3: pretty easy for them to underwrite that risk.
Speaker 2: It's funny. Tracy and I had coffee with someone yesterday
Speaker 2: who is sort of in the space I want docs here,
Speaker 2: And I was like, what should we ask Brian? And
Speaker 2: he's like, ask him why he won't let my company,
Speaker 2: why I'm still on the waiting list or something, or
Speaker 2: why he hasn't approved my company to use core weave.
Speaker 2: But what are some of the bars or the threshold?
Speaker 2: So you know, I apparently there's a lot of demand
Speaker 2: for compute these days. What does it take to get
Speaker 2: in the door and get access to some of your
Speaker 2: chips and electricity?
Speaker 3: So it's it's a great question. It's a question that
Speaker 3: we get all the time from our sales teams, right
Speaker 3: is you know, we're faced a lot with a sales
Speaker 3: team that is incredible at delivering product to customer and
Speaker 3: we don't have anything to sell. And it's kind of
Speaker 3: my job. As the strategy organization at Core, We've were
Speaker 3: responsible for two things. It's product and infrastructure. Capacity, and
Speaker 3: you know, I spend most of my time going out
Speaker 3: and finding those data centers and being able to support
Speaker 3: those deals and the growth that we had over the
Speaker 3: past twelve months. The company was pretty flat out right
Speaker 3: in building and delivering this infrastructure. You know, publicly on
Speaker 3: our documentation page it says that we have three regions.
Speaker 3: We'll have twenty eight regions online by the end of
Speaker 3: the year. I think we delivered eleven of them in
Speaker 3: Q one alone, Right, So we're building at a scale,
Speaker 3: you know, i'd say that almost larger than some of
Speaker 3: the three big hyperscalers. But in terms of how do
Speaker 3: you become a customer of Core, it's really relationship driven,
Speaker 3: right is. We want to make sure that we're going
Speaker 3: to be able to be successfu with our customers and
Speaker 3: have an engineering relationship and we're aligned on what they
Speaker 3: need and.
Speaker 2: We can deliver what they need.
Speaker 3: The last thing that we want is for somebody to
Speaker 3: walk in the door and say, hey, I need this
Speaker 3: for three weeks and two weeks into it, they're unhappy
Speaker 3: and we can't give them what they need to be successful.
Speaker 3: Right is, you know, our customers are making such large
Speaker 3: investments in this infrastructure, that we have to have, you know,
Speaker 3: a lot of conviction that we will be successful with
Speaker 3: them and provide a good experience. So it's not that
Speaker 3: we're trying to keep people out, it's we're trying to
Speaker 3: ensure positive experiences for people that we do bring on board.
Speaker 2: Do you build complete housed facilities or is it all
Speaker 2: you're going to bring your chips and expertise into an
Speaker 2: existing Tier one data center and essentially rent floor space
Speaker 2: from them.
Speaker 3: Yeah, so a year ago it was we were effectively
Speaker 3: just a co location tenant, and now we've gone a
Speaker 3: lot more vertical for some strategic builds where we're either
Speaker 3: a partner in the project where we own equity and
Speaker 3: the development company, or we're building the project ourselves. We've
Speaker 3: been scaling that team up over the past six months,
Speaker 3: and we had to at our scale to be able
Speaker 3: to guarantee outcomes. Right, is, we were in a position
Speaker 3: where we had data centers getting delayed with things that
Speaker 3: weren't communicated to us, and you know, we had to
Speaker 3: go build the capability to handle that situation and you know,
Speaker 3: make sure we can still deliver for our customers.
Speaker 1: One of the differentiators that you and some of your
Speaker 1: colleagues have emphasized previously, is this idea that you're designing
Speaker 1: the server clusters kind of from the ground up, whereas
Speaker 1: like other hyperscalers maybe are doing it on a sort
Speaker 1: of different mass scale. But can you walk us through
Speaker 1: like what is the benefit of doing it that way?
Speaker 1: And then secondly, does that end up being an impediment
Speaker 1: to I guess efficiencies or economics of scale and how
Speaker 1: customized Like do you really get here?
Speaker 3: So from a customization perspective, it's aggressive, right, And I
Speaker 3: say that because you know, our customers are involved in
Speaker 3: the design of you know, our network topology of the
Speaker 3: East West fabric for the GPU to GPU communication, for
Speaker 3: things like cooling. You know, I have customers that toward
Speaker 3: the data centers under construction process with me like once
Speaker 3: a week, and it's to the point that they're impacting
Speaker 3: how we build the base level networking products to ensure
Speaker 3: they have enough throughput to you know, meet their use
Speaker 3: case needs. Whereas in you know, what I what we
Speaker 3: call the legacy hyperscaler installations, It maybe they have a
Speaker 3: couple thousand GPUs that are in a data center that
Speaker 3: was really built for CPU computation or to provide services
Speaker 3: to ten thousand customers that is really with a much
Speaker 3: lower base expectation of what they're going to be doing. Right,
Speaker 3: So it's things around connectivity for storage, it's things around
Speaker 3: power and cooling, It's things around how they want to
Speaker 3: be able to optimize their workloads inside of the GPU
Speaker 3: to GPU communication. You know, we have some customers that
Speaker 3: even customize their infiniban fabrics and the size of those
Speaker 3: fabrics and how they connect together. So you know, we
Speaker 3: work with them to really understand what their use case is,
Speaker 3: where they're worried currently and in the future, and then
Speaker 3: design around that. So it's a pretty comprehensive program when
Speaker 3: we're building something from the ground up.
Speaker 1: And how much complexity does that introduce into the business
Speaker 1: and does it end up being a limiting factor on
Speaker 1: your growth or is demand just so strong at the
Speaker 1: moment that it's not really an issue.
Speaker 3: The customization that we do is typically going to be
Speaker 3: above what our base level offering is, meaning the environment
Speaker 3: will be more performant because the customer required it. So
Speaker 3: it's typically not going to be limiting to us from
Speaker 3: a future you know, revenue or resale perspective. It's going
Speaker 3: to make the asset more valuable. But you know, we're
Speaker 3: we're designing our reference builds for ninety nine percent of
Speaker 3: use cases, and we're trying to price it efficiently, and
Speaker 3: then when customer wants something above and beyond, you know,
Speaker 3: it impacts price. But for these installations it's probably deminimus, right,
Speaker 3: So you know, it doesn't really add a lot of
Speaker 3: complexity for us from a business perspective, so we're happy
Speaker 3: to do it.
Speaker 2: You mentioned that some of the hyperscalers, yes they have GPUs,
Speaker 2: but they like built in an environment for like legacy CPUs.
Speaker 2: Can you talk a little bit about a just the
Speaker 2: difference between the legacy architectures and the new one and
Speaker 2: then in the design, like what kind of bottlenecks you
Speaker 2: run into? Is there issues with labor like the types
Speaker 2: of people who know how to string these things together well,
Speaker 2: or other different cooling requirements for this type of compute
Speaker 2: environment that did not exist, Like what are what are
Speaker 2: the challenges in building out these sort of like fundamentally
Speaker 2: different environments.
Speaker 3: Yeah, so that that's changed also in the last twelve
Speaker 3: months in that you used to be able to take
Speaker 3: what was an enterprise data center and you know, creatively
Speaker 3: retrofit it to be capable of supporting the AI workloads
Speaker 3: to a certain density level. Okay, right, Like instead of
Speaker 3: filling up a cabinet, you could put two servers in
Speaker 3: a cabinet and you could meet the power and cooling
Speaker 3: requirements of the installation. It you use a lot more
Speaker 3: floor space, but it was doable. One of the incredible
Speaker 3: things about is that they're always pushing the boundary on
Speaker 3: the engineering side, and their next generation of chips is
Speaker 3: largely dependent upon much more aggressive heat transfer, and they've
Speaker 3: introduced liquid cooling to the reference architectures. So as liquid
Speaker 3: cooling comes in, it changes what type of data center
Speaker 3: is capable of doing this, and it truly requires that
Speaker 3: ground up redesign and almost greenfield only build to support it.
Speaker 3: Is you've gone from an environment where you could take
Speaker 3: an enterprise data center and deploy less servers per cabinet
Speaker 3: and get away with it to hey, nobody's ever built
Speaker 3: this before. It's at an incredible scale and it has
Speaker 3: to happen on a yearly cadence now, so the data
Speaker 3: center industry is in't a full sprint to figure out, Okay,
Speaker 3: how do we do this? How do we do it quickly?
Speaker 3: How do we operationalize it right? And you know that's
Speaker 3: kind of where I've been spending all of my time
Speaker 3: over the past six months.
Speaker 1: Can I ask a really basic question, and we've done
Speaker 1: episodes on this, but I would be very interested in
Speaker 1: your opinion, But why does it feel like customers and
Speaker 1: AI customers in particular, are so I don't know if
Speaker 1: addicted is the right word, but like so devoted to
Speaker 1: in Nvidia chips, Like what is it about them specifically
Speaker 1: that is so attractive? How much of it is due
Speaker 1: to like the technology versus say, maybe the interoperability.
Speaker 3: So you have to understand that when you're an AI
Speaker 3: lab that has just started and it is a it's
Speaker 3: an arms race in the industry to deliver product and
Speaker 3: models as fast as possible, that it's an existential risk
Speaker 3: to you that you don't have your infrastructure be like
Speaker 3: your Achilles heel. Right, And and Vidia has proven to
Speaker 3: be a number of things. One is they're the engineers
Speaker 3: of the best products, right. They are an engineering organization first,
Speaker 3: and that they identify and solve problems. They push the limits.
Speaker 3: You know, they're willing to listen to customers and help
Speaker 3: you solve problems and design things around new use cases.
Speaker 3: But it's not just creating good hardware. It's creating good
Speaker 3: hardware that's scales and they can support at scale. And
Speaker 3: when you're building these installations that are hundreds of thousands
Speaker 3: of components on the accelerator side and the infinband link side,
Speaker 3: it all has to work together well. And when you
Speaker 3: go to somebody like in Video that has done this
Speaker 3: for so long at scale, with such engineering expertise, they
Speaker 3: eliminate so much of that existential risk for these startups. Right.
Speaker 3: So when I look at it and I see some
Speaker 3: of these smaller startups saying we're going to go a
Speaker 3: different route, I'm like, what are you doing? Right? You're
Speaker 3: taking so much risk for no reason here? Right, this
Speaker 3: is a proven solution, it's the best solution, and it
Speaker 3: has the most community support, right, Like go the easy
Speaker 3: path because the venture you're embarking on is hard enough.
Speaker 1: Is it like the old what was that old adage?
Speaker 1: Like no one ever got fired for buying Microsoft? Is
Speaker 1: it like no, yeah, or IBM something like that.
Speaker 3: But the thing here is that it's not even nobody's
Speaker 3: getting fired for buying the tried and true and slower
Speaker 3: moving thing. It's nobody's getting fired for buying the tried,
Speaker 3: true and best performing and you know bleeding edge thing.
Speaker 2: Right.
Speaker 3: So I look at the folks that are buying other
Speaker 3: products and investing and other products almost as like they're trying.
Speaker 3: They almost have a chip on their shoulder and they're
Speaker 3: going against the mold just to do it.
Speaker 2: There are competitors to in video that they claim cheaper
Speaker 2: or more application specific chips. I think Intel came out
Speaker 2: with something like that. First of all, from the core
Speaker 2: weave perspective, are you all in on in video hardware?
Speaker 3: We are?
Speaker 2: Could that change?
Speaker 3: The party line is that we're always going to be
Speaker 3: driven by customers, right, and we're going to be driven
Speaker 3: by customers to the chip that is most performant, provides
Speaker 3: the best TCO, is best supported and right now and
Speaker 3: in what I think is the foreseeable future, like I
Speaker 3: believe that is strongly in video.
Speaker 2: Think about okay, maybe one day you guys IPO And
Speaker 2: I'm looking through the risk factors, and one of the
Speaker 2: risk factors, right, we have a heavy reliance on in
Speaker 2: video chips. There is a risk that a competitor thing,
Speaker 2: what would it take for one of these competitors that
Speaker 2: does ostensibly over cheaper or hardware or perhaps lower electricity
Speaker 2: consumption in your view, To make one of those risk
Speaker 2: factors real.
Speaker 3: I think that they'd have to be willing to quote
Speaker 3: unquote buy the market. And when I say that, I
Speaker 3: mean they'd have to subsidize their hardware to get a
Speaker 3: material market share. And from what I've seen, there's no
Speaker 3: one else that's really been willing to do that so far.
Speaker 2: And what about Meta with Piedtorch and all their chips.
Speaker 3: So they're in house chips. I think they have those
Speaker 3: for very very specific production applications, but they're not really
Speaker 3: general purpose chips, okay, right, And I think that when
Speaker 3: you're building something for general purpose and there has to
Speaker 3: be flexibility in the use case. While you can go
Speaker 3: build a custom AASIC to solve very specific problems, I
Speaker 3: don't think it makes sense to invest in those to
Speaker 3: go to be a five year ass set if you
Speaker 3: don't necessarily know what you're going to do with it.
Speaker 1: So you talked about the advantages of Nvidia hardware like
Speaker 1: the chips themselves, but one of the things you sometimes
Speaker 1: hear is that those same chips might perform differently in
Speaker 1: different clouds. So what is it that you can do
Speaker 1: to sort of boost the performance of the same chip
Speaker 1: in your structure or ecosystem versus say an AWS or
Speaker 1: someone like that.
Speaker 3: Sure, a great question. We do a lot of work
Speaker 3: around this internally and it's a big part of our
Speaker 3: technical differentiation. And what we call it internally is mission control.
Speaker 3: And mission control is effectively a portfolio of different services
Speaker 3: that we run on our infrastructure to make sure that
Speaker 3: these incredibly complex supercomputers are healthy and performant and are optimized,
Speaker 3: you know, where we take a lot of that responsibility
Speaker 3: off of our customer engineering teams, right, And it sounds
Speaker 3: like that might be an easy lift, but when you're
Speaker 3: running supercomputer scale, you know you need a team of
Speaker 3: fifty to do that, right, So we provide a ton
Speaker 3: of software automation around that, providing that health checking and
Speaker 3: observed ability to our customers. But it's also the engineering engagement, right,
Speaker 3: is you know, working with our customers to understand, Okay,
Speaker 3: what are you doing, what's the best way to optimize this,
Speaker 3: how do we you know, how did we design the
Speaker 3: data center to be more performant, to make sure your
Speaker 3: storage solution was correct, Your networking solution was correct. So
Speaker 3: it's not just a hey core we've provides like this
Speaker 3: one little thing that makes it better. It's the comprehensive solutions,
Speaker 3: starting from the data center design, through the software automation
Speaker 3: and health checking and monitoring, via mission control, via the
Speaker 3: engineering relationships that really add that value.
Speaker 2: Let's talk about electricity, because this has become this huge
Speaker 2: talking point that this is the major constraint and now
Speaker 2: that you're becoming more vertically integrated and having to stand
Speaker 2: up more of your operations. We talked to one guy
Speaker 2: formerly at Microsoft who said, you know, one of the
Speaker 2: issues that there may be a backlash in some communities
Speaker 2: who don't want, you know, their scarce electricity to go
Speaker 2: to data centers when they could go to household air conditioning.
Speaker 2: What are you running into right now or what are
Speaker 2: you seeing?
Speaker 3: So we've been very very selective on where we put
Speaker 3: data centers. We don't have anything in Ashburn, Virginia, right
Speaker 3: and the Northern Virginia market, I think is incredibly saturated.
Speaker 3: There's a lot of growing backlash in that market around
Speaker 3: power usage and you know, just thinking about how do
Speaker 3: you get enough diesel trucks in there to refill generators
Speaker 3: that they have a prolonged outage.
Speaker 1: Right.
Speaker 3: So I think that there's some markets where it's just
Speaker 3: like okay, like to stay away from that, and when
Speaker 3: the grids have issues and that market hasn't really had
Speaker 3: an issue yet, it becomes an acute problem immediately. Like
Speaker 3: just think about the Texas power market crisis back in
Speaker 3: I think it's twenty twenty one, twenty twenty, where the
Speaker 3: grid wasn't really set up to be able to handle
Speaker 3: the frigid temperatures and they had natural gas valves that
Speaker 3: were freezing off at the natural gas generation plants that
Speaker 3: didn't allow them to actually come online and produce electricity
Speaker 3: no matter how high the price was. Right. So there's
Speaker 3: there's going to be these acute issues that you know,
Speaker 3: people are going to learn from and the regulators are
Speaker 3: going to learn from to make sure they don't happen again.
Speaker 3: And we're kind of citing our our plants and markets
Speaker 3: where our data centers and markets where we think the
Speaker 3: grid infrastructure is capable of handling it right, And it's
Speaker 3: not just is there enough power, it's also on things.
Speaker 3: You know, AI workloads are pretty volatile in how much
Speaker 3: power they use, and they're volatile because you know, every
Speaker 3: fifteen minutes or every thirty minutes, you effectively stop the
Speaker 3: job to save the progress you've made, right, and it's
Speaker 3: so expensive to run these clusters that you don't want
Speaker 3: to lose hundreds of thousands of dollars of progress, So
Speaker 3: they take a minute, they do what's called checkpointing, where
Speaker 3: they write the current state of the job back to storage,
Speaker 3: and that checkpointing time, your power usage basically goes from
Speaker 3: one hundred percent to like ten percent, and then it
Speaker 3: goes right back up again when it's done saving it.
Speaker 3: So that load volatility on a local market will create
Speaker 3: either voltage spikes or voltage SAgs, and a voltage sag
Speaker 3: is what you see is what causes a brown out
Speaker 3: that we used to see a lot of times when
Speaker 3: people turn their cognitioners on and it's thinking through, Okay,
Speaker 3: how do I ensure that, you know, my AI installation
Speaker 3: doesn't cause a brown out when people are turning their
Speaker 3: you know, during checkpointing, when people are turning the air
Speaker 3: conditioners on. Like that's the type of stuff that we're
Speaker 3: thoughtful around, like how do we make sure we don't
Speaker 3: do this right. And you know, talking to engineerings and
Speaker 3: in Video's engineering expertise, like they're working on this problem
Speaker 3: as well, and there they've solved this for the next generation.
Speaker 3: So it's everything from is there enough power there? What's
Speaker 3: the source of that power? You know, how clean is it?
Speaker 3: How do we make sure that we're investing in solar
Speaker 3: and stuff in the area to make sure that we're
Speaker 3: not just taking power from the grid. To also when
Speaker 3: we're using that power, how is it going to impact
Speaker 3: the consumers around us?
Speaker 1: I want to ask you more about what in Nvidia
Speaker 1: is doing, but just on that note, what's the most
Speaker 1: important metric for evaluating a data center's quality or performance?
Speaker 1: Is it like days without brownouts or an interrupted power supply,
Speaker 1: or is it measures of efficiency like power usage effectiveness
Speaker 1: or something like that. If I'm serving a bunch of
Speaker 1: data centers, I want to pick a good one. What
Speaker 1: should I be looking for?
Speaker 3: So right now, the market's pretty thin, So right now.
Speaker 1: Options Okay, I imagine I'm like the biggest customer on
Speaker 1: earth and I can get in anywhere. What should I
Speaker 1: be looking for?
Speaker 3: So it's the first thing goes back to the electricity piece, right,
Speaker 3: is the grid stable? Is there enough power supply? You know,
Speaker 3: is there excess renewable generation in the area that doesn't
Speaker 3: have the ability to make it too downstream consumers? Right?
Speaker 3: A lot of the renewables that we have in the
Speaker 3: US are built in places that don't necessarily have the consumers.
Speaker 3: So you're citing these data centers in places where you
Speaker 3: have this excess supply, So that that's the first piece, right,
Speaker 3: is how good is the electricity supply? And how angry
Speaker 3: are the people around me going to be if I
Speaker 3: take it? Now? You go from there into everything else
Speaker 3: is kind of solvable, right, And the way that you
Speaker 3: design it, and if you're building a green field, it's okay.
Speaker 3: You know what type of ups systems am I putting in?
Speaker 3: Are they capable of handling that load volatility?
Speaker 2: You know?
Speaker 3: How am I thinking about my cooling solutions? There's been
Speaker 3: a big shift to liquid cooling, right, and liquid cooling
Speaker 3: from a PE perspective, isn't a thirty to forty percent
Speaker 3: decrease in electricity utilization like people think? It's more like
Speaker 3: sixty to seventy percent, right, And the reason for that
Speaker 3: is it's not just the efficiency of the data center plant.
Speaker 3: It's also that now if you're not cooling things with air,
Speaker 3: you don't have to run the fans inside the servers
Speaker 3: as well. And for these AI installations, because they're so dense,
Speaker 3: the fans consume a lot of energy. Right. So everything
Speaker 3: that we're building now is a combination of liquid and
Speaker 3: air cooling, right. And the liquid cooling piece has solved
Speaker 3: the PUE issue, right, And we're everything we're doing is
Speaker 3: trying to say, Okay, how much power can we use
Speaker 3: only for running our critical IT operations versus cooling the
Speaker 3: environment making sure the environment's running correctly from a resiliency perspective,
Speaker 3: And there's been big strides made there over the last
Speaker 3: whole months.
Speaker 1: Does colocation trump grid reliability? Like if I'm Elon Musk
Speaker 1: building some sort of new AI thing as I think
Speaker 1: he's doing in Texas, say like, am I just going
Speaker 1: to have to find a data center in Texas? Or
Speaker 1: how much flexibility do I have to use one further away?
Speaker 3: So great question, it's it's a different answer for different
Speaker 3: use cases at different times. And right now, you know,
Speaker 3: we were in the middle of this rush to train
Speaker 3: whether they're open source or proprietary foundation models at the largest,
Speaker 3: most valuable companies in the world, and they're mostly worried
Speaker 3: about access to contiguous compute capacity. Right, how much compute
Speaker 3: can I get in one location, all connected together so
Speaker 3: I can go faster than the next guy. But when
Speaker 3: the models are trained, they want that compute to then
Speaker 3: be local to their customer base, right, is how do
Speaker 3: they take it from the middle of nowhere and then
Speaker 3: go serve it in the metropolitan markets. And as the
Speaker 3: use cases are more distilled and they get more real time,
Speaker 3: think like the type ahead suggestions that you get in
Speaker 3: your Gmail account right as you're typing something, and it's
Speaker 3: getting better and better. It's you know, that's an AI
Speaker 3: model somewhere like predicting what you would want to say next,
Speaker 3: And they want to make sure that's delivered at human speed.
Speaker 3: So that human speed is a latency consideration. Right as
Speaker 3: you're citing those GPUs and you're citing that compute to
Speaker 3: be locals to the people that are using it. So
Speaker 3: that move has started probably four months ago where we
Speaker 3: saw customers finally becoming concern around latency for their serving
Speaker 3: use cases. So initially training people don't really care where
Speaker 3: it is cheap power, reliable grid. They just need it
Speaker 3: all contiguous and they need it fast. And then down
Speaker 3: the road as their applications find success, they're more worried
Speaker 3: about where the compute is for their customers.
Speaker 2: What are some of the areas that are going to
Speaker 2: be the next Northern Virginia when it comes to data
Speaker 2: center clusters.
Speaker 3: So I think we're seeing it in Atlanta already, where
Speaker 3: Georgia has paused or has attempted to pause some of
Speaker 3: their tax incentives around it because they want to make
Speaker 3: sure they do grid studies. I think that we're we're
Speaker 3: probably going to see it in some of the other hotspots.
Speaker 2: You know.
Speaker 3: You know, you see aws up in Oregon who is
Speaker 3: trying to find creative alternative ways to power their data
Speaker 3: centers from non grid generation to alleviate some concerns there.
Speaker 3: But you know, I think that the market has to
Speaker 3: solve this problem. And you know, you're starting to see
Speaker 3: some of the startups around nuclear generation in you know,
Speaker 3: the small reactors at the data center level. As people
Speaker 3: are you know, being thoughtful for five to ten years
Speaker 3: from now, do.
Speaker 1: You have any influence on the type of power being
Speaker 1: built in certain areas? You know, could you say to
Speaker 1: a utility company of some sort, we're here, we need
Speaker 1: access to energy, but we want it to come in
Speaker 1: a particular form.
Speaker 3: So you can. But you have to understand that the
Speaker 3: investment cycles and the physical build cycles for those are
Speaker 3: so much longer than you know how quickly our customers
Speaker 3: need infrastructure, right. So you may go to a market
Speaker 3: and say, hey, we're going to be here over the
Speaker 3: next ten years, we'd like you to install X y Z,
Speaker 3: you know, renewable, and they're happy to do it. It's
Speaker 3: just that you have to find a medium term solution
Speaker 3: while that's being built.
Speaker 2: I'm going to ask a question. So there was a
Speaker 2: news story, and maybe you won't comment on the news story,
Speaker 2: specifically about core Weave having made a one billion dollar
Speaker 2: offer for a bitcoin miner called core Scientific, apparently was rejected.
Speaker 2: According to things I've read in the news. Setting aside
Speaker 2: this deal, there's you know, there used to be a
Speaker 2: lot of crypto mining and then ethereum went from proof
Speaker 2: of work to proof of steak and that all basically
Speaker 2: disappeared overnight. There are still bitcoin miners. I never get
Speaker 2: the impression it's like that great of business. But whatever
Speaker 2: are there bitcoin miners that have latent value in the
Speaker 2: fact that they I mean, I know those chips don't
Speaker 2: the bitcoin mining chip, the actual acis don't work for
Speaker 2: AI because all they are is bitcoin mining chips. But
Speaker 2: are there by dint of their access to electricity, space,
Speaker 2: et cetera, is there a fair amount of latent value
Speaker 2: in the general physical structures that they've built for the mining.
Speaker 3: So I'm just not going to answer your question at all.
Speaker 3: I'm gonna go on a tangent.
Speaker 2: Okay, that's fine.
Speaker 3: So I think that when I think about core Weave
Speaker 3: and what our mission is, it's to find creative solutions
Speaker 3: to problems in in you know, various markets, and those
Speaker 3: various markets can be blocking for us and our customers to.
Speaker 2: Achieve our goals.
Speaker 3: So if power is a concern for us, and power
Speaker 3: availability and substations and substation.
Speaker 2: Transform, coin miners definitely have access to power.
Speaker 3: That that is true.
Speaker 2: I'm just stating fact you could keep doing it.
Speaker 3: So you know, as we go and we try to
Speaker 3: solve these problems, you know, we're going to go to
Speaker 3: places that others may not have thought of, and we're
Speaker 3: going to go do due diligence and I'm going to
Speaker 3: personally go and walk the sites and I'm going to
Speaker 3: you know, look through and see, okay, can we.
Speaker 2: Pull this off?
Speaker 3: And we're going to get our engineering partners in to
Speaker 3: help us design retrofits. And you know, we're going to
Speaker 3: do deals with the companies that we believe have the
Speaker 3: ability to provide us value.
Speaker 1: Since we're doing stuff in the news. This has been
Speaker 1: in the news for a while, so it doesn't really count.
Speaker 1: But the new Nvidia chips, the GB two hundreds, what
Speaker 1: will those do for core weave and when would you
Speaker 1: expect to get them?
Speaker 3: What will they do for us? It's more about what
Speaker 3: they're going to do for our customers, right, and I think.
Speaker 2: That they are.
Speaker 3: This is a great question. They are going to open
Speaker 3: up a lot of both training and inference use cases
Speaker 3: in the AI side that I think our customers have
Speaker 3: been blocked by UH with the existing generation in that
Speaker 3: you're now able to think seventy two of these GPUs
Speaker 3: together to work almost as one unit, and previously that
Speaker 3: was limited to eight. They have a much larger what's
Speaker 3: called the frame buffer, which is how much memory that's
Speaker 3: usable for their matrix operations. So you know, I think
Speaker 3: that we're going to see a lot of new use
Speaker 3: cases show up for this stuff, but I think it
Speaker 3: extends well beyond AI as well, and it's going to
Speaker 3: be a lot more useful for things like scientific computing.
Speaker 3: One of the things that has me really excited is
Speaker 3: the computational fluidynamics and I'm specifically thinking about the uses
Speaker 3: for that in F one under the new regulation in
Speaker 3: twenty twenty six. I'm excited for the new platform. I
Speaker 3: think in a year and a half people are going
Speaker 3: to be using it for things that are different than
Speaker 3: anybody expects today. And that's to me. The pace at
Speaker 3: which this is changing is the piece that's really cool.
Speaker 1: Wait, I'm sorry, I hate sports.
Speaker 2: What's the six? Explain how the invidio is.
Speaker 3: Yeah, So the F one platform, they have very tight
Speaker 3: restrictions around what type of compute and how much compute
Speaker 3: you can use to do aerodynamic testing in your cars,
Speaker 3: and you can either do real life testing in a
Speaker 3: wind tunnel or you can do it through CFD analysis.
Speaker 3: And what are the great uses for the you know,
Speaker 3: the Grace Blackwell and the Grace Hopper architectures. Impairing that
Speaker 3: Grace super chip with the GPU is they're great for
Speaker 3: CFD workloads, right, and the.
Speaker 2: DAFD stands for computational fluid dynamics yep, yep.
Speaker 3: And the regulations around the existing program in F one
Speaker 3: are they're only able to use CPUs. They have very
Speaker 3: like specific limitations around it. But there's been a lot
Speaker 3: of talk of that changing for twenty twenty six car models,
Speaker 3: and for me, like, that's pretty cool and I'm gung
Speaker 3: ho excited about possibly supporting that.
Speaker 2: That does sound very fun. I want to get back
Speaker 2: to actually the financing a little bit because I guess
Speaker 2: two questions. So the logic of why you would borrow
Speaker 2: money both I guess for the equal position of chips,
Speaker 2: and the chips are sort of collateral, but I understand
Speaker 2: they're not really chip back loans per se. A. Do
Speaker 2: you see your clients getting more into debt financing rather
Speaker 2: than equity financing. I mean, there's a whole generation of
Speaker 2: software companies from the Zerp era that was just you know,
Speaker 2: all equity and never had any debt at all, and
Speaker 2: they never really had to think about like their compute costs,
Speaker 2: or they did, but not as much. Do you think
Speaker 2: that will rise their own use of debt instead of
Speaker 2: equity in terms of their own financing. And another topic
Speaker 2: we talk about a lot on the show private credit,
Speaker 2: like there is there an emergence of an ecosystem of
Speaker 2: lenders for whom this is going to become a specialty
Speaker 2: of some sort.
Speaker 3: So the first piece of the question, I don't believe
Speaker 3: that the venture backed kind of AI lab startups will
Speaker 3: ever take on debt in this type of environment, largely
Speaker 3: because they don't have the collateral to back it. If
Speaker 3: they're buying cloud services to run their infrastructure. And you
Speaker 3: may see some that start to buy their own infrastructure
Speaker 3: and to do that themselves, but it is a herculean
Speaker 3: task to do this at scale. Right, There's a reason
Speaker 3: why clouds exist is that there's a lot of complexity
Speaker 3: that they abstract away. On the second question around are
Speaker 3: is there a private credit sector that's going to be
Speaker 3: built to do this? I think that it's more you're
Speaker 3: seeing public lenders that are extending into the private credit
Speaker 3: space because the opportunities are there. And I'm going to
Speaker 3: give you the party line answer that my CEO gives
Speaker 3: all the time is that you know, as we're thinking
Speaker 3: about financing our business, the biggest thing for us is
Speaker 3: our cost to capital, and we're always going to do
Speaker 3: the things that provide us the lowest cost of capital.
Speaker 3: And you know the lenders that we work with, including Blackstone,
Speaker 3: that have been so wonderful for us, you know, them
Speaker 3: extending on the private credit side as we go to
Speaker 3: the public markets because we're dragged there by cost of
Speaker 3: capital concerns, I would expect them to be involved as well, right, So,
Speaker 3: I think it's a continuation of the business they've been
Speaker 3: doing in the public markets, just kind of extending into
Speaker 3: this capital intensive business.
Speaker 1: Wait, what was I guess you can't get into specific details,
Speaker 1: but my impression was for these types of loans that
Speaker 1: the interest rate is usually higher than like a basic
Speaker 1: bank loan or say issuing a corporate bond.
Speaker 3: I would definitely say our cost of capital is lower
Speaker 3: than some of the corporate issuance is out there, Okay,
Speaker 3: but you know it's definitely higher than if our cost
Speaker 3: of capital today is definitely higher than if we were
Speaker 3: republican public entity.
Speaker 1: But specifically on the GPU backed loans, and I know
Speaker 1: you keep saying it's not really a GPU back loan,
Speaker 1: but that's sort of an uphill battle to call it
Speaker 1: trade receivables financing instead. It sounds so much better that way,
Speaker 1: I know, I know, but like on that in particular, Okay,
Speaker 1: there's collateral, so maybe that brings the overall like borrowing
Speaker 1: rate down. But on the other hand, it's kind of
Speaker 1: a new thing, new structure. How does that compare with
Speaker 1: more traditional types of finance.
Speaker 3: Yeah, so you know that every credit facility that we do,
Speaker 3: the cost of capital declines, and it's declining because it's
Speaker 3: the execution risk and the ongoing concern risk are reduced. Right.
Speaker 3: And you know, when we first did this, people like
Speaker 3: you guys are crazy. You have no history of execution.
Speaker 3: And as we've gone through and we've done it, like
Speaker 3: now there's a path that everybody that's underwriting these loans
Speaker 3: now understands. Okay, this is what happens, this is how
Speaker 3: it reforms, This is what we should expect from the customers.
Speaker 3: This is what we should expect from receivables. They get
Speaker 3: more comfortable, they're willing to do it at more aggressive rates, right,
Speaker 3: so that the risk premium associated with it has just
Speaker 3: decreased over time.
Speaker 1: Got it.
Speaker 2: I just have one last question I sort of touched
Speaker 2: on it earlier. But Okay, we know that power is scarce.
Speaker 2: We know that, you know, there's not an infinite number
Speaker 2: of Nvidia chips et cetera. Like those are quite scarce
Speaker 2: for the other stuff. You know, we've done episodes in
Speaker 2: the past like talking about like just generic electrical gear components,
Speaker 2: and we've certainly done a lot on like labor shortages.
Speaker 2: What are you seeing on that front sort of like
Speaker 2: simple gear and the sort of basic building blocks of
Speaker 2: a new construction and how difficult that is to acquire.
Speaker 2: Verse to say, if you were doing this, you know
Speaker 2: you started in twenty seventeen, I imagine a lot of
Speaker 2: the things were more plentiful back then.
Speaker 3: Yeah, so it's not even that they're less plentiful today
Speaker 3: than they were. You know, the lead times were always
Speaker 3: the lead times for this electrical gear. It's that there
Speaker 3: was capacity to go buy off the shelf, right there
Speaker 3: was inventory in the data center market. And the inventory
Speaker 3: is basically gone. And you know, I see deals today
Speaker 3: that get brought to me and there's seven people bidding
Speaker 3: on the same deal and they're all trying to sell
Speaker 3: it to like similar customers. So the market has gotten
Speaker 3: pretty thin. So now you're looking at it, going Okay,
Speaker 3: my only option here is for new built, and you're
Speaker 3: looking at lead times that haven't really shifted that much
Speaker 3: on things inside of the data center. The substation transformers
Speaker 3: are multiple years out, and part of that reason is
Speaker 3: that it takes a year for them to cure after
Speaker 3: they're manufactured. Like, there's no getting around that, there's no
Speaker 3: speeding that piece up.
Speaker 2: I mean, it takes a year.
Speaker 3: You when the transformer is built, that's taking on so
Speaker 3: much power that whatever the process is, it has to
Speaker 3: sit for a year and harden before it's able to
Speaker 3: take on that electrical load. So even if you went
Speaker 3: and said, hey, I'm going to build ten more of
Speaker 3: these this year, it's still a year away before you
Speaker 3: can use them.
Speaker 2: Huh right.
Speaker 3: And those are the types of things from a manufacturing
Speaker 3: perspective you just can't get around, and it takes time
Speaker 3: for the supply chain to catch up. But you know,
Speaker 3: the problems that I'm solving on a day to day
Speaker 3: basis in these builds isn't even around the substation transformers.
Speaker 3: It's around like small components that somebody missed it when
Speaker 3: they ordered the gear sixteen weeks ago. And now you
Speaker 3: have to go scramble and call in favors across the
Speaker 3: country of Hey, who has this part? I need it
Speaker 3: by tomorrow because I have fifty thousand GPUs that are
Speaker 3: blocked by this one little thing, right, So it's a
Speaker 3: lot of it is logistical and human coordination and solving
Speaker 3: dumb problems in real time.
Speaker 2: Ryan Venturro, thank you so much for coming on odd Laws.
Speaker 2: That was fantastic. Thanks for having me, Tracy. I'm really
Speaker 2: glad we did that conversation because there are a number
Speaker 2: of these sort of like big picture ideas in there
Speaker 2: that we've sort of hit on of course, about data
Speaker 2: centers and AI and electricity consumption, and it was really
Speaker 2: interesting to hear some of them. So, like, for example,
Speaker 2: just this idea of like northern Virginia is out and
Speaker 2: like needing this sort of hunt to find these spots
Speaker 2: in the country where there is ample electricity and basically
Speaker 2: nobody local is going to get upset at you for
Speaker 2: using it.
Speaker 1: Yeah, no one will come out with pitchforks. The thing
Speaker 1: that stood out to me from a bunch of these
Speaker 1: conversations at this point is the arms race aspect of it,
Speaker 1: and how urgent building out AI is for a lot
Speaker 1: of these companies, and then there seems to be this
Speaker 1: mismatch between the immediate need for scale and compute and
Speaker 1: energy now versus these really long timelines of actually building
Speaker 1: the stuff out and Brian mentioning the substation transformers taking
Speaker 1: a care of cure.
Speaker 2: I had no idea about that.
Speaker 1: I didn't know that either. But that's a really good example.
Speaker 2: That's super interesting, and of course now we have to
Speaker 2: do a how do you build a substation transform.
Speaker 1: How do you cure a substation transformer?
Speaker 2: Totally? I mean maybe this is probably something that electrical
Speaker 2: engineer is not interesting to them at all, But for me,
Speaker 2: I did not realize that there was this one year long,
Speaker 2: one year long curing process. You know, I think there
Speaker 2: are like a couple other things that now I want
Speaker 2: to talk more about, so I'm interested. I mean, like
Speaker 2: Coreweave is an in video company. It's not owned by Video,
Speaker 2: but you know it's joined at the hip in many respects.
Speaker 2: So how difficult is it going to be either for
Speaker 2: some other maker of chips, whether it's an Intel or
Speaker 2: some other maker of software environments, whether it's Meta and
Speaker 2: PyTorch going against Kuda or whatever, like that's a really
Speaker 2: interesting question to me, Like, you know, we have to
Speaker 2: do more essentially on like how much of a lock
Speaker 2: and video really has on this industry.
Speaker 1: Yeah, this seems to be the really big question. And
Speaker 1: then the other thing I was thinking about, and I
Speaker 1: know Brian emphasized this and other Core Weave executives have
Speaker 1: emphasized this before, but this idea that hyperscalers maybe are
Speaker 1: starting from a point of being disadvantaged because they have
Speaker 1: to retrofit all this old infrastructure for this new AI
Speaker 1: technology totally, and like I can see that. But on
Speaker 1: the other hand, these are insanely impressive companies. You are
Speaker 1: explicitly trying to compete against Core Weave in this business,
Speaker 1: and they're not going to stand still. And so I
Speaker 1: guess there's an open question over how much progress they're
Speaker 1: making or how fast that progress is actually happening.
Speaker 2: Right, Large companies always are going to have some challenges
Speaker 2: when there's like a new model or something. But these
Speaker 2: companies have all the money in the entire world, right,
Speaker 2: and they also have all you know, one of the
Speaker 2: things that Brian said is like they if they were
Speaker 2: if one of them are going to do it, they
Speaker 2: would have to go out and to buy a big
Speaker 2: chunk of the market, which again they have all the
Speaker 2: money in the entire world. So theoretically, whether it's the
Speaker 2: big companies and retrofitting the clouds or building new clouds,
Speaker 2: or you know a lot of them like a Google,
Speaker 2: even if they're for now using their TPUs internally primarily like,
Speaker 2: it does seem like in theory the opportunities out there,
Speaker 2: particularly with the the sky high amount you know, valuation
Speaker 2: that a company like in video is getting.
Speaker 1: Oh yeah, you mentioned the sky high valuation. That was
Speaker 1: something that also stood out to me, just on the
Speaker 1: financing side. So this idea of you know, the debt
Speaker 1: financing deal that they did, and I'm not going to
Speaker 1: call it trade receivables because.
Speaker 2: No one GPU backed loan.
Speaker 1: Yeah, no one will be interested when we start talking
Speaker 1: about trade receivables. But the GPU back loan. This idea
Speaker 1: that like, okay, it's a new structure, but the more
Speaker 1: you do it, the more the cost of particular capital
Speaker 1: starts to fall, the more the market gets comfortable with it.
Speaker 1: I mean, we can talk about whether or not it's
Speaker 1: priced correctly for a new type of unfamiliar risk, but
Speaker 1: it does seem like that might be a new avenue
Speaker 1: for the vast amounts of capital that are needed for
Speaker 1: this business.
Speaker 2: So one, it's interesting to think about the idea that, like,
Speaker 2: you know, I don't think it's like totally true. You
Speaker 2: know that if you need compute at scale for AI,
Speaker 2: that you don't just get to call up core weave
Speaker 2: and get it, and you actually have to prove that
Speaker 2: you're going to be a good customer and so like
Speaker 2: have something that is probably going to be sustainable, have
Speaker 2: the balance sheet capacity. So this even if the sort
Speaker 2: of software the end users aren't themselves raising debt, it
Speaker 2: does sound like they have to have a lot of
Speaker 2: equity upfront just so that they're perceived as a sustainable,
Speaker 2: viable customer for a company like corewev. I also thought
Speaker 2: on the electricity front, like obviously we talk all the
Speaker 2: time about just sort of the raw demand for electricity.
Speaker 2: But this idea what he said, and I hadn't heard
Speaker 2: anyone say it that the runs the modeling runs stop
Speaker 2: everyone do you say thirty minutes and have to be saved.
Speaker 2: Oh yeah, And so you have this big variability at times,
Speaker 2: and that creates its own specific issue because it's not
Speaker 2: just steady state flow of electricity and solving for that.
Speaker 2: That's probably another area in which the legacy data centers
Speaker 2: or cloud companies. Perhaps my guess would be that they're
Speaker 2: just sort of the demand is more constant and therefore
Speaker 2: something that would be a novelty for them.
Speaker 1: Just thinking about the financing more, I do kind of
Speaker 1: wonder how much of this is like AI built on
Speaker 1: top of AI on top of AI. Like, yeah, to
Speaker 1: the point where if if the bubble were to burst,
Speaker 1: or if funding was suddenly pulled from a bunch of
Speaker 1: these startups, like what would that mean for core weaves financing?
Speaker 1: And what would that mean for black Rock, which lent
Speaker 1: money based on the GPUs that the clients are taking on,
Speaker 1: who might not be there anymore. I don't know.
Speaker 2: By the way, have you ever looked at a chart
Speaker 2: of riot lockschain?
Speaker 1: Oh no, not for a while?
Speaker 2: Yeah, well, I mean they're still there as a minor,
Speaker 2: but like here we are in the midst of this
Speaker 2: pretty big crypto bal run. I mean, I guess it's
Speaker 2: cooled a little bit, but and that stock is done
Speaker 2: terribly so it's interesting to wonder, and apparently it doesn't
Speaker 2: seem like anyone's made a bid for them. But it
Speaker 2: is interesting to wonder, like, Okay, those chips are useless
Speaker 2: for AI because they don't work for that, but you know,
Speaker 2: they do have capacity and they do have electricity agreements
Speaker 2: already in place. So it does make you wonder whether,
Speaker 2: like some of the bitcoin mining companies which aren't really
Speaker 2: getting a very the market is not excited about them, clearly,
Speaker 2: even in the midst of this crypto bal run.
Speaker 1: Maybe they should go back to being a diagnostics company.
Speaker 1: That's what they were before, is it. I think so.
Speaker 1: I think they're one of the ones that changed their
Speaker 1: name and then like there something including blockchain, and then
Speaker 1: their shares went up enormously and now they're back down.
Speaker 2: Well they have been. Riot Platforms has been around, Okay,
Speaker 2: now I'm curious. Yeah, so it's a bitcoin mining company,
Speaker 2: but it's been the stock has been around since two
Speaker 2: thousand and three. So pretty clearly, uh, pretty clearly they
Speaker 2: were in some other business. I don't know what.
Speaker 1: Yeah, I'm looking on the terminal, it says Riot Blockchain,
Speaker 1: formerly Bioptics, has ditched the drug diagnostic machinery business for
Speaker 1: the digital currency trade.
Speaker 2: Well, there you go. So if you have some sort
Speaker 2: of computing power or something. I don't know what they
Speaker 2: were doing before, but maybe it is interesting to think about.
Speaker 2: Maybe some of the option value for some of these
Speaker 2: miners isn't there. Non is in all the infrastructure other
Speaker 2: than the bitcoin mining operation.
Speaker 1: Maybe we should put in a bid.
Speaker 2: Let's do it.
Speaker 1: We can crowdfund and start our own business. Okay, maybe
Speaker 1: we should leave it there.
Speaker 2: Let's leave it there.
Speaker 1: This has been another episode of the All Thought podcast.
Speaker 1: I'm Tracy Alloway. You can follow me at Tracy Alloway and.
Speaker 2: I'm Joe Wisenthal. You can follow me at the Stalwart.
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