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