0:09 Today, we are going to talk about the data centers that you guys are melting. The big theme of all of this is really this chart. This is the CapEx spend by the five hyperscalers on AI. And as you can tell, this is going top into the right and it's going top into the right fast. To put this in context, this is one of the biggest investments that we are making. This is bigger than space, bigger than our highway system, the Manhattan Project, second only to the US Defense budget.

0:48 And I'm so excited that we're going to break it down today with Chase Lochmiller, founder and CEO of Crusoe, who is arguably building it the best that we know. A quick introduction for Chase before we bring him on stage. Chase is obviously the founder and CEO of Crusoe, as you guys know. Less known fact and fun fact about Chase I learned very recently is that Chase is a very avid mountaineer, five out of the seven summits across the world, including Everest and Crusoe, is designed with mountaineering in mind.

1:22 There's a plan A, there's a plan B and then there's a plan C to the plan B. Chase, thank you so much for doing it with us. Please join us. Thank you. Thank you, thank you, thank you. Thanks for having me. Thanks for joining us. Yeah. So, Chase, tell us a little bit about what is this data center economy we're in the middle of. This is extra special for me. I went to grad school here at Stanford.

1:46 So it's fun being on the other side of the table, being back on campus. So I appreciate you guys taking an interest in what I'm working on now here at Crusoe. But I think in certain ways, like the data center itself is like this physical manifestation of this boom that we're seeing in AI adoption. It's the physical infrastructure that's required to power the GPUs, to operate the GPUs, to run these big compute workloads that are training new models, that are fine tuning models, that are operating large scale inference workloads to serve tokens to consumers and everybody that raised their hand today saying that they use Gemini, ChatGPT, or Cloud today.

2:28 So the data center is the physical infrastructure component that really enables all of this technology to proliferate and change people's lives. Amazing. Now there's a lot of different components that go into the data center phase. We've heard a thing or two about compute. We've heard a thing or two about memory and power and interconnects and labor and help us put into context all the different things that go into it, how should we contextualize it when we have our hyperscalers spend $650 billion building these data centers?

3:03 Where does that go and how much of that is going to you? That's a good question. When I really think about this infrastructure of intelligence and really the starting point that I start off with is like, what does it take to produce AI? Like, everybody's all obsessed about AI. It's like, what does it actually-- what's required to make AI? You have this basic equation, which is AI is the combination of data, algorithms, so back propagation, neural networks, transformer architectures, all these different algorithms that people have come up with to essentially statistically model the data sets, compute large amounts of compute, and particularly this high performance computing infrastructure through GPUs, where you're able to parallelize a lot of the workloads and do a lot of this tensor math and a high performance architecture, energy that's required to run those GPUs and then data centers, the physical buildings that house and operate all of this computing infrastructure.

4:03 So what actually costs money? Well, data-- sure it costs money. I mean, you have to buy data. It's like opened up this new opportunity for a lot of data labeling companies. Folks like scale AI, folks like [INAUDIBLE]. Even handshake has sort of gotten has sort of bridged into this. It's created a big opportunity for people to make money by producing data that's useful for AI. The algorithms, that sort of sits in a lot of the labs that are inventing new mechanisms.

4:34 And recursive learning techniques is a new trend and a lot of the different architectures that people are using to make better use of the data. But the compute energy and data centers, that's really what Crusoe folks focuses on is like, how do we actually build, operate, scale and make the best use of all of this infrastructure? And it tends to be actually the area where a lot of the money is being made or where a lot of the money is being spent.

4:59 Sorry, that CapEx chart that you showed. So what I would say is, the name of my presentation, I came up with was from electrons to tokens. So why are tokens actually valuable and given this isn't the econ department? Engineering. It's an engineering. OK, well. Some of you have taken economics. Of course. There's this economics model for production called the Cobb-Douglas model. So if you look at the growth in GDP, the growth in GDP is fundamentally the sum of three key components the change in labor, the change in capital, and this capital sort of includes both like physical capital like buildings and plants and equipment, as well as capital that's invested into an economy, and then the change in technology.

5:48 And technology basically makes labor more productive. I think is a good way of framing this. So why are tokens so valuable and why is this like a step change? Why is everybody making these huge investments, this CapEx chart that Apple showed? The reason for that is that for the first time in history, what we're able to do is we're able to actually create this sense of digital labor. When you give when you give an agent a task, when you give your cloud bot a task of going to doing something, go create a CRM for my new product that I just launched, that is literally digital labor that's being represented and that's being brought into the world.

6:30 And historically, labor is this thing that you could really only change via the birth rate. And it's got a 20-year lead time. It's a massive incubation period. You got to send them to schools and feed them and house them and whatever. Like all this stuff. I have three kids. It takes a long time to change changed delta L, naturally. But for the first time in history, what we're able to do is actually change this delta L digitally through the investment in buying data centers and buying GPUs, and really accelerating the growth in the economy by actually accelerating the growth in digital labor force.

7:08 So that's kind of like-- the premise I want to get across here initially is that the reason these investments are taking place, and the reason it's so broad based is that there's an opportunity to completely transform the economy by really accelerating the growth in GDP and fundamentally up leveling and improving people's quality of life by just seeing an unprecedented level of growth. So, I said, Crusoe sits at a couple different layers of the stack. Crusoe is a business, is a vertically integrated AI infrastructure business.

7:39 So we really try to-- because this is a new category of infrastructure, we've really taken the approach that we want to be able to unblock anything that really gets in our way of standing up this infrastructure of intelligence that's going to power the accelerating growth in GDP for the economy. So we think about it in two phases. One is like the bottom layers of the stack, which are basically energy development. So these data centers require lots and lots of energy.

8:08 And we're very thoughtful about taking this energy first approach in terms of going to areas that have access to abundant low cost energy resources. The next piece is really the data center piece, which is the actual physical buildings, which includes the building, the plant around it, all of the chillers. Because when you think about it, like from its most basic standpoint, a data center is a building that has power and cooling. And you can plug in computers.

8:37 That's pretty much what it is. And when you're doing it at very, very large scale, it becomes very, very complex and actually draws in the pinnacle of engineering and from across every single ecosystem from chemical engineering and cooling architectures and mechanical engineering and electrical engineering dealing with these high voltage power sources and very high density capacities and then also the computer science and the electrical engineering involved in the chip architectures and how compute gets run, how data gets transferred.

9:14 It really is the amalgamation and consolidation of like every form of engineering and one giant building that operates intelligence for the world. So it's a cool engineering problem. Yeah. On this chase, tell us a little about-- we hear the bottleneck in AI being compute four years ago to power, memory stocks are ripping right now, labor, then there's all these other components LAN-powered shell, et cetera, maybe even regulation.

9:46 Give us an overview of for the time that you've been doing this, which is just under a decade, that seems to shift. How has that traversed over time? Where is it today, and where do you see it going? What is the core bottleneck that is gating the growth of this? Like today, the core bottleneck is like energized data centers, like powered shells where you can plug-in chips and start operating a big GPU compute cluster. That today is the bottleneck.

10:14 But the bottleneck moves around a lot. Sometimes it's getting power to those data centers. Sometimes it's individual components that go into building the data centers, electrical equipment, switchgear, chillers, power Gen, chips. Chips is kind of softened as the bottleneck. I think access to chips has become more available. It's really finding places where you can put those chips and turn them on. So that's part of the reason that Crusoe has taken this is vertically-integrated approach is that bottlenecks move around, and being vertically integrated means you can do almost anything across the stack.

10:52 We're not in the chip business. That's like we're not in the chip business. We're not in the model business. But apart from that, we tackle, most challenges throughout the entire ecosystem. Right now maybe just to follow up on that, Chase, you started Crusoe with the core insight of Robinson Crusoe, with the insight that energy was one of the most scarce resources, at least in the Western world. And tell us a little about the-- maybe pick a site, maybe pick Abilene or one of the others that you can talk about.

11:21 Why did you start there? What was the scarce resource that you were solving for and why work backwards from energy? Why not from compute or memory or as you said, power shell? Well, I guess our insight was like markets are reasonably efficient when you look at things. And I think when there was this steady state growth in data center capacity as the Web 2.0 bubble or not bubble, but Web 2.0 trend unfolded and web applications were increasingly growing and people were more online.

11:58 It became this machine that was standing up new data centers. And they were happening in these big hubs. Markets like Northern Virginia come to mind as areas that run a large portion of the internet. I never wanted to be like a me too. Like, I'm the next data center developer in Northern Virginia. I'm going to build the next building there. That didn't seem like an appealing way to enter a new market and really make a splash.

12:20 So we said, look, there's going to be these new types of computing applications that are far different from serving web applications, things like artificial intelligence training, large workloads and back propagation, things like digital currencies that require tremendous amount of computing power for proof of work consensus mechanisms. Those require tons of energy and at scale energy becomes the bottleneck. So we said, can we find areas that aren't historical data center markets and go there and build data centers where we can instead of having to move the energy, we're actually moving data.

12:57 So we could actually co-locate in these areas. So giving an example here and we'll talk about the top two layers of the stack, both the deployment of GPU clusters as well as how to actually monetize that and serve intelligence with managed services. So we're going to talk about the bottom two layers of the stack here. So one first manifestation of that was what you see in this photo here. So this is a site that we've been working on since-- it started in June 2024, where we signed the first two buildings on the right-hand side of the screen that you see.

13:30 This is at this point, one of the largest AI computing campuses in the world. I think it might be the largest. Our insight here is in Abilene, Texas, many folks had never heard of until we put a shovel in the ground in Abilene. So why do we go to Abilene? Abilene is this area of West Texas that is consistently very windy and very sunny. And so a lot of renewable energy developers had gone there to build out large scale renewable energy production because they were incentivized by something called production tax credits, where they basically get paid by the government to produce clean electrons, and they have to sell them to someone independent of the price.

14:10 And what that resulted in was actually an overinvestment in renewable generation infrastructure in this West Texas market. And power prices were actually negative because there was no marginal buyer for this power, and there wasn't enough transmission to get the power to somewhere where it actually was useful. So we said, great, have we got a power hungry application for you? So we ended up working with the city of Abilene. If you look at the top there, there's this gray square.

14:42 And then below that a bigger gray square. So those are both substations. The top one is a 200 megawatt substation. The second, larger one is a one gigawatt substation. To put those numbers into context, like that gigawatt substation-- first of all, that's the largest privately owned substation in the United States. A gigawatt is-- I grew up in Denver. A gigawatt is basically what powers the whole city of Denver. So it's basically a city of Denver size worth of power to power computers.

15:16 It is a very large amount of power in one single location. And we were able to access it fundamentally because there was this abundant, low cost energy in this market that was actually having issues getting out, having transmission to get out of that market, created a massive opportunity for us to go in there and build large scale, cutting edge AI infrastructure. A couple of follow-up searches, who is this tenant for? Could you tell us is this ChatGPT, is this, Cloud, is this Gemini?

15:44 So the tenant of the-- If you look at this campus, there's eight buildings. So this building 1, building 2. That is the substation I was just referring to. And then you have building 3, 4, 5, 6, 7, and 8 up there. So those first eight buildings are all for Oracle and OpenAI. So it's basically what was known as Project Stargate, this first big project. So in order to help support this, we also built down here a natural gas power plant.

16:16 So this is roughly a 350 megawatt natural gas power plant to support the development and energize this giant computing cluster. It was also designed as to operate as one coherent cluster, which means that all of the chips across all of the data centers were interconnected on the same high performance back end network to be able to operate as one coherent workload. So you could run one training job that runs on all of the chips, on the entire data, on all the data centers together, which is really, really unique architecture.

16:50 To give you a sense of scale, because it's hard to see from a photo we had to build this parking lot over here. That parking lot is like-- it's a 5,000 car parking lot. And you can see it's totally full because we have roughly 9,000 people on site every single day that are working to bring this campus to life. There is an expansion planned to the South of the campus and see some of the dirt there.

17:20 That is for Microsoft. So the campus is 2.1 gigawatts in aggregate. So again, two Denver's worth of power to power all of this AI compute infrastructure that's going in here. 9,000 people, Chase, what is the population of Abilene? 9,000? Although there's another campus I'm going to show you that on site staff is larger than the population of the town. This is the Manhattan Project. The population of Abilene is 120,000.

17:51 So we're able to source, a lot of people initially from Abilene, but over time, we had to actually create a lot of these labor and retention incentives to get people that would move there to basically work, as short-term construction workers. Over time in the long-term there is like a steady job population that's operating these large computing clusters and power plants that are operating the infrastructure. That staff is somewhere in the neighborhood of like 2000 people just to put it in context, but it still becomes a very, very large job creator in this local economy where the population is roughly 120,000 people.

18:32 So-- Go ahead. No, no, please. While the audience is primarily engineering the classes, the economics of AI, so walk us through the metaphorical spend of $100. So if you had $100 or x dollars of spend, what is the distribution of it across the different layers? Yeah. Or maybe you were coming to it? I'm going to come to it. We're going to start with just initially the power plant and the data center side and then we'll go to the compute clusters and then we'll talk about-- these are tens of billions of dollars that are being invested in this.

19:07 How is anybody making any money? Where does the return happen? So we'll get to that as I progress through the slides. But the initial slide that Crusoe showed was this huge CapEx spend. A lot of those companies are Crusoe customers. We help serve all those customers when they're building out these big CapEx investments, and we help them build out this infrastructure of intelligence layer. With that comes-- I think there's a bunch of different components that go into this.

19:35 But electrical equipment is a huge component of this. So think of if you look at the-- there's over here, there's these small buildings with white roofs on them. Those are called power distribution centers. They take power from the high voltage or from the substation, which comes in at a medium voltage 34.5 kV, so 34,500 volts, and then it distributes it to that lineup. You see this where it says transformers.

20:07 There are transformers that line the entire left side of the building, and then the entire right side of the building. And what it's doing is distributing power to those transformers so it can step power down from 345 kV to 480 or 415. So that's like a big piece of like CapEx. It's a piece of equipment that we're investing in. It's going into building out the building. You also have all sorts of different cooling equipment, all of the mechanical equipment.

20:32 Think of you have this lineup of chillers. They look like RAM, but they're not. So those are all air cooled chillers, which is basically these wound pieces of copper pipe where you have this giant chilled water loop in the data center that's recirculating water, and it basically goes from cool water on the inlet side. It goes into the rack of GPUs, and there's a thermal transfer event from the GPU that's being energized and producing a lot of heat.

21:02 There's a thermal transfer event from the chip to the water, and then the water goes out through these chillers. You basically are blowing a bunch of air over these wound copper coils to exhaust the heat out of the system. So the water temperature steps back down and you have cold water to then cool the GPUs again. So again, that's another big investment. All of the plumbing that goes into this is really, really substantial. So we actually we have a ton of plumbers and pipefitters on site that are welding these big plumbing systems together.

21:34 Each building has about 1 million gallons of water in the building to cool these chips. But again, it's recirculating. So think there's this ongoing narrative that AI is taking all the water. We use, like zero water. We fill this system one time and then on an annual basis, we use about the same amount of water as a single family home. So it's a very limited water consumption usage, which is important in a market like Abilene in West Texas, where water is actually quite scarce.

22:02 So you can see the costs here. And I've normalized them on a per megawatt basis. But, things like power distribution centers, the UPS system, which is a battery system, uninterruptible power supply, you need this to smooth out the power that gets distributed from the substation into the actual chip. Other alternative battery systems that we're experimenting with cooling distribution units, these actually go in the data center itself, and they basically take the water in from the chilled water pipe and they distribute it to the individual racks of GPUs.

22:37 And then of course, you have a lot of these core components that go into this. There was nothing here before. There's a ton of steel. There's a ton of concrete. We have our own batch plan on site. So we're actually making concrete on site with people pouring concrete 24/7. All of the site work, all the labor that really goes into making this happen, it's tons of people, tons of man hours. And then on the power infrastructure side, we highlighted two things here.

23:02 One is the gas power plant, which I showed you in the previous slide. You see a little snapshot of it down here on the bottom. And then for some of the infrastructure we have diesel generators. So you can see these three gray roofed buildings that are in the middle there. Those are backing up power for the core network. So the way we've really thought about this problem is that not everything needs 100% sent five nines of reliability full backup.

23:28 But the core storage and networking systems do so that in the event of a full grid outage, in the event of a disaster scenario, we'll still be able to access the storage systems. If there's a checkpoint that we need to reference and move a workload to a new location, we can do that. So anyway, it's a lot of money that goes into this. And I wanted to give you a breakout the full power plant plus-- the full power plant plus like building costs.

23:56 And I want to highlight something for you guys because I showed that really big parking lot, that 5,000 car parking lot. Well, the bottom piece here is labor. So this is $4.7 million per megawatt. So that ends up being for a gigawatt or for megawatts-- for a gigawatt that becomes $4.7 billion. So this is literally money that's being invested in people to do jobs.

24:26 It's like literal-- it's a blue collar labor force that we're investing in to basically bring this infrastructure-- people working in construction, it's people on site that are building these things. And so it's a very, very substantial number when you look at it in the scheme. And that's an annual number. So-- When you ask me about bottlenecks-- this is a bottleneck. We don't have enough of these tradespeople. We don't have enough electricians. We don't have enough welders.

24:51 We don't have enough plumbers. We don't have enough construction workers because this is one project. There's many of these that are now cropping up. And there's actually a huge competition for labor. And so at Crusoe, we're trying to reinvent how we think about bringing a lot of this infrastructure to life to be able to navigate some of these really critical labor challenges. So if you look at soft costs, what does that include, that's things like insurance, that's things like financing costs like we borrow money with a construction loan and then we have to pay-- we have to service the debt for that construction loan.

25:28 That's things like siting and all of the different work we're doing with commissioning. The next piece is the gas plant. And again, this is probably 2 to $3 million per megawatt. I think what's important to realize about the gas plant is that those costs have gone up a lot. There's a small set of gas turbine manufacturers. You basically have GE, Vernova, Siemens, Mitsubishi Heavy Industries, Pratt and Whitney. Caterpillar has a company called solar. But a lot of these companies have been limited in the amount that they've actually expanded production capacity.

26:00 And so what's happened in a moment where everybody's trying to bring on new gas generation infrastructure to power their AI, compute clusters-- well, guess what? Prices have gone up a lot. So a gas turbine that used to cost $1 million-- a megawatt now costs $3 million a megawatt. So those prices have grown. And that's why you've seen-- I don't know how many people follow the stock market, but if you look at the stock price of GE Vernova-- it's been good to be a shareholder of GE Vernova.

26:26 So anyway, that's another piece of the stack. The tenant fit out, this is all the stuff that's in the actual data hall. So these are things like remote power panels, the hot oil containment systems, the fan walls, the cooling distribution units, all of this stuff that you need in the actual data hall, where the GPUs go to actually energize and power the GPUs. The electrical equipment, again, this touches all the different pieces from high voltage to low voltage.

26:54 This is things like power transformers, power distribution centers, medium voltage switchgear, low voltage switchgear. Think of your electrical panel in your home that if the lights go out, you go down, you flip a few breakers. It's like that but at the scale of a city, like all in a giant electrical room, the mechanical equipment. So all of the equipment from the chillers and all of the plumbing, all of the air handling units and the fan walls that sort of cool this stuff and have mechanical systems involved.

27:23 And then of course, the materials, the steel, the cement, all of the different components that go into making one of these large buildings happen. So anyway, that's what the full stack looks like. A couple of quick questions here, Chase. Where are the GPUs? Oh, that's on the next phase. OK. Yeah, so we'll get to that. So this is roughly $20 billion per gigawatt. And assuming a gigawatt took a year to come unlined, you would be paying 4 and 1/2 or $5 billion in salaries for labor per year.

27:51 That's for the construction period. This is like capitalized labor. So this is like-- Not OpEx CapEx? Yeah. This is not OpEx. Right. I'll show OpEx in another-- Great. And then one final question, is this number total, $20 million per megawatt or $20 billion per gigawatt going down over time or going up over time? Because obviously some components-- It's going up because there's so much demand. So things like gas generation, infrastructure, guess what? Prices have gone up.

28:17 Things like labor, if you're an electrician, your price has gone up because there's so much demand for your time. Great. So all these things you're seeing price inflation to varying degrees in each category of this infrastructure. So I wanted to show another cool project that we're doing just to say how we're taking this energy first approach and to your comment around how many people. So today I think we have 3,500 people at this site. It's in a town called Quad, Texas, which is like a hilariously poetic.

28:51 We didn't name the town, but Claude is a town of 1,500 people. We have 3,500 people working on this project. It happens to be close enough to Amarillo that we're able to tap into a lot of the working population in Amarillo. You can see it's an area that's very rich in renewables. It's one of the best places in the US to build wind because it's so consistently windy. So you can see that on-site wind farm that we have there on the top of the screen there where there's a very large wind farm that's producing power that directly feeds into the data centers.

29:20 We're able to firm up the power with something-- we call this across the meter. So basically the meters, the interconnection point into the grid. And we have behind the meter, which is like power that's on-site. But what we're doing is what I call across the meter, which is we have on-site generation through wind. There's a plan to build solar batteries and gas. So all of the above energy solutions to basically energize this campus. What we don't need, guess what?

29:45 We can sell into the grid, create an energy abundance that drops the cost for all local ratepayers. And when we actually need power, because we need to firm up the power, because we're doing maintenance on some of the generators or the wind's not blowing and the sun's not shining, we need to firm up the power, we can draw power from the grid. So it becomes this very mutually beneficial relationship of us investing in the power infrastructure and then leveraging the large distribution, transmission and other generators across the grid.

30:13 So it's a very cool project. I can't speak about who the customer is but it is a very big customer for this location. You asked about the GPUs. Great segue. All right. Who's making money in this guy? No if you didn't know already, it's got a building right over here. When we think about the IT CapEx per megawatt-- remember I just showed you this is for the whole data center and the power plant was roughly call it 20 million megawatt or rounding up.

30:38 When you look at the IT CapEx, this is basically the compute infrastructure that's going into the building. It's roughly 40 million per megawatt. And this is forward looking. This is next gen stuff. 30 million of that is going to the GPUs. That's why you always look so happy. I don't know. It's always smiling Jensen. And then where does the rest go? 4 million roughly to the networking. These are very complex networking systems, especially when you're thinking about the investment of interconnecting these GPUs together as one giant coherent cluster.

31:11 When you look at the latest generations of GPUs-- right here is the GB300 or GB200. I'm not sure, but what NVIDIA has come out with, there is actually a full rack design, which means that all of the GPUs, there's 72 GPUs in that rack, they're all interconnected on the same NVLink domain. So you can see the back copper plane there on the right side. They're all interconnected on this high performance back end networking domain, which enables AI researchers to do incredibly high performance tasks and enables a lot of incredible use cases to be able to share data across the NVLink domain.

31:52 But then you have to interconnect those racks together through another high performance back end network, typically InfiniBand or sometimes rocky, which is RDMA over connected ethernet. So that's where that four million per megawatt of spend is, that green bar of networking. The next thing is CPUs and storage. What's amazing, what we've been seeing recently is actually a massive shortage of CPUs. Why is that? With the boom in all of these agentic workflows with the boom and Claude, guess what?

32:23 You need a lot of CPUs to actually orchestrate those compute workloads. So you're seeing a lot of demand from everybody in the ecosystem to bring online a lot more CPUs. So about 3 million a megawatt for CPUs and storage. And then there's a bunch of in the room CapEx, which I think I might be double counting here, but a lot of that TFO that I sort of referred to earlier. It's roughly 3 million a megawatt, and then you have about 1 million in labor deployments, shipping, et cetera.

32:50 But you get to this roughly number of about 40 million per megawatt. One question here, Jensen did a podcast yesterday with Dwarkesh where he looked not so happy. Lots of debate around compute being a commodity. Yes or no, is this number going down over time? Is compute a commodity? What are you seeing in prices? It's tough to say. I mean, it's hard to say over how things look over the near term, medium term, and long-term. And it's possible that both are right.

33:19 Where it's like-- and it depends on the use case too. Like we see if you look at older compute, it's kind of commoditized as you like the H100 further back. I think one of the things that's absolutely not a commodity is scale. So if you do anything at really, really big scale, it's super hard to replicate and it's super hard to repeat. There's always going to be a cutting edge. So any of the newest stuff is always going to command a premium.

33:47 And that's like been the history of the IT industry. So we'll see how it kind of plays out. But I do think that folks-- look, capitalism is a powerful force. The invisible hand of capitalism is a very powerful force. So I do think that over time, margins do probably come down to more standard stabilized silicon margins, call it like, I don't know, 60% gross margin, which today NVIDIA is commanding like 80% gross margin, something like that.

34:19 I don't know. Competition is powerful. Yeah. Perfect. So I think it's important to understand you make this huge investment. We're talking initially, call it $20 million a megawatt for the data center and the power plant and another 40 million megawatt. To stand up this compute cluster, you've just spent 60 million per megawatt. So you build a gigawatt cluster, you just spent $60 billion. How are you going to make money? What's the pot of gold at the end of the rainbow?

34:49 People are buying this infrastructure to support all of these AI applications, to serve tokens to customers. And I think the reason I wanted to show this chart is actually-- this is a Bloomberg chart of basically each 100 spot pricing. And I think there's this question of how valuable is all this equipment and what timeline do you depreciate it over? What's the useful life of all this equipment? And I think people said, the next generation is going to come out and then this stuff's going to be completely useless.

35:23 Well, this chart tells the exact opposite story, which is that for each 100 that initially debuted in about three years ago, the pricing had come down. But with these boom that we're seeing in demand coming from agents, the price of H100 has actually come up and actually exceeded the price that folks were paying when these chips first came out, and that's something we're experiencing, experiencing firsthand on the ground in our crucial cloud business. Does the tangible outcome of this Chase-- right now most public companies depreciate their compute over five years.

35:57 Does this imply-- six is the standard. Does this imply it goes longer than six? I don't know. It's like my honest answer. It's like we're going to use compute so long as it's valuable to us or to someone else, so long as we can. And part of our strategy has been building services that abstract away the layers of compute. So you don't know if you're using an A100 and H100, an MI300, nor should you care. What you care about is the actual service that you're getting from that.

36:29 Just like when you log in to Zoom or Google Meets or teams, you're not thinking about, wait, is this like an Intel, Ice Lake CPU, or is this like an AMD. It's like you don't care what the chip is that's running. You care about the service that you're getting and the fact that you're able to log into this video chat and be able to hear and speak to the other person on the other side. Makes sense.

36:54 So we think the application scaling is really going to abstract away a lot of the core infrastructure. And I think we'll see how valuable stuff is over the course of time, but I think it's probably wrong. And then this is a similar chart that's semi analysis, who many of you are probably familiar with published. And this is for Blackwell's. So we see the pricing for Blackwell's following a very, very similar trend with this agent breakthrough in end of year.

37:24 So when we look at this as a holistic picture. We're bringing it all together. We have the data center. We have the power plant, and we have the chips. The upfront CapEx you're looking at is close to 60 million per megawatt. Again, I think I double counted something in there. This is the first time I'm going through these slides. But it's roughly 60 million per megawatt. And then when you look at the ongoing OpEx of this plant, it's a little over 1 million per megawatt.

37:52 It's actually pretty limited OpEx. And this is for things like your power, your insurance, some of your labor on site. That's like repairing and replacing cables and GPUs that fail and a number of other things, but call it like one to $2 million per megawatt. So what is your revenue if you're just renting out those chips? And I was using that chart before to show you rough pricing that you could rent an H100 for. What is your revenue per megawatt?

38:19 It's roughly 15 million per megawatt. So you're making this upfront capital investment of $60 million a megawatt. You're getting $15 million a megawatt in annualized revenue for just renting access to the infrastructure. Now, how does this become a good business? I think a lot of it comes down to how do you measure the depreciation of all the different bars that make up this CapEx numbers. And I think that's the critical question analysts on Wall Street are asking is like, how long is this building going to be valuable for?

38:52 How long is this chip going to be valuable for? How long is this power plant going to be valuable for? What's the right depreciation curve? but you're looking at-- from this, call it a four-year payback period for this huge investment. On a revenue basis. And what are the rough-- Well, this is what I'm saying. You have the OpEx stripping out the OpEx. Whatever, great. But then there's other labor that's not included here, which is like all the engineering workforce, et cetera.

39:17 So there's more OpEx than this. Again, this was put together this afternoon. Makes sense. Roughly four years. Yeah. Yep. But what's another way to actually improve the value that you're actually delivering to customers? Again, I sort of spoke about this vertically-integrated strategy that Crusoe has. When we deploy chips, one product that we offer is that managed compute cluster, for the engineer or developer that really wants to manage the infrastructure themselves, manage the compute nodes, run a big training workload, interact with individual virtual machines or a large managed Kubernetes cluster of compute.

39:54 But for folks that actually just want to interact with the model-- if you're actually hosting a model, again, the title of this slide deck is from electrons to tokens. So how do you get to tokens and where's the value uplift that you get from that? When you add in this managed services layer where you're actually serving the model, you're hosting a model and actually providing an endpoint for a customer to basically hit that API endpoint and actually serve those ChatGPT or those anthropic queries that everybody sending on their phones or laptops, you end up improving the margins quite a bit, adding call it another 15, anywhere from whatever, 5 to 15 million per megawatt.

40:36 So you end up with, in a very optimistic case, call it $30 or 30 million per megawatt per year. So you end up with a two-year payback. That's a dramatically better outcome. Makes sense. Perfect. If you have no other slides, I might roll you through a couple of questions that the class had and then open it up for questions. So-- Yeah, Go ahead. Did you have more? It's fine. This is just some pictures of some deployments that we had that were just showing the walls and whatnot.

41:07 And I'll talk about actually the inference scaling and where we actually to try to bring down those labor costs. Crusoe actually designed something we call Crusoe spark, which is our modular, self-contained, modular AI data center that we manufacture in these centralized locations where we can bring down the labor costs and we can actually bring down the infrastructure cost quite a bit. I call it, 30% to 50% savings depending on overall cost. So like that 19 million a megawatt.

41:38 We can actually bring down pretty dramatically. What's the capacity in terms of size? Is this like gigawatt a couple megawatt or? So each unit for our air cooled architecture is 500 kilowatts. Got it. So about-- And this is the air cooled design. And I have a video here actually of them deployed in the field. I don't know if this is going to run, but no-- OK, well, it doesn't matter. And then we have a liquid cooled version, that's two megawatts.

42:04 But you can deploy them in fleets, which actually opens up a lot of net new power opportunities, which is a pretty neat solution. Amazing. Amazing. Well, in the interest of time, I can't think of a better person to ask the question about to ask you. You have seen probably every layer of the stack, from chips to power to gas to labor to networking, if you had to pick a layer of the stack and in particular a company and in particular a stock, what would you go long what would you go short and why?

42:39 [LAUGHTER] I hope everybody's taking notes. What would I go long or short? I will call you in a year from now. Yeah. See how that did. Man, that's tough. Other than Crusoe, of course. Yeah, I mean, I'm turbo long Crusoe, but I do think that oftentimes getting these things right is difficult when you look at the time horizon.

43:09 I do think that my bear case is actually there's that huge investment that I showed across the electrical stack, there's so many components that go into the electrical stack. Because what you're doing is you're taking power from this high voltage substation that maybe powers being at 345 kV. So there's this new line that's going in Texas, that's 765. So it's very, very high voltage power that then goes through this transformation process where you're stepping it down to medium voltage, you're stepping it down to low voltage, you're distributing it.

43:43 There's tons of cable, tons of stuff. I think the data center is fundamentally going to drive a lot of innovations in the whole electrical stack and leverage a lot of solid state electronics and solid state transformers and power electronics. And I think it puts in jeopardy a lot of these companies that fundamentally have not innovated that much in the last 100 years. So this is like Eaton, Schneider, a number of other companies, which I think will-- and the reason I say it's very difficult to put a timeline on this is that those companies, I think, will do very well in the near term.

44:23 They are on the critical path right now. They're going to do super well in the near term and their big partners of mine. So I hate saying that I'm negative on them. But over the long-term if they don't innovate-- I think that whole piece of the stack is going to dramatically come down in cost because of innovators that are building out this next version of the electrical stack. And there's going to be huge shifts to 900 volt DC and all these different aspects.

44:47 So that's an opportunity for the electrical engineers in the room? The electrical engineers in the room absolutely. I think it's a huge opportunity. Power electronics, like, how do you get power from 765 kV to 900 volt DC in the rack. I think innovating on that problem is a super, super huge opportunity for people. OK. Any picks on the long side? But if not, I have another question for you. I mean I'm like bullish so many things.

45:17 Maybe some other thing on the short side, I do think that open source is winning. Not open source is winning, but open source will do well and take more from the closed source model players. Yeah. Fascinating. Elon space data centers. Yes. Bullish, bearish, real fantasy happening in our lifetime or not. Data centers in space. [LAUGHTER] So I'm actually I'm very interested in this.

45:49 And at Crusoe, we've established a partnership actually with another player in this ecosystem called Starcloud. That's actually launched the first H100s into space. There's a lot of things to about it. I just walked you through this whole stack of areas that I'm spending billions of dollars. All the concrete foundation, guess what? You don't need that in space. All the permitting, all the approvals you need on the power side, guess what? You don't need any of that in space.

46:15 A lot of the core networking pieces, what I didn't get into is the millions and millions of strands of fiber that go into one of these data centers, and all of the technicians that you have to have to plug all this stuff in. Guess what? In space, you use optics for everything. So everything is basically optically interconnected. And that's all very interesting. It's also very hard. I think that thermal management piece is very challenging. And I also think the ongoing operations piece is very challenging.

46:45 So in these data centers, when you're operating these big, large scale, interconnected compute clusters, things fail, GPUs fail, they have to be reseated in their compute tray, sometimes they have to be made and sent back to the vendor, Like, guess what? You're not sending an astronaut into space to take a chip and send it back to Jetson. That just isn't going to happen. So you're going to have a natural deprecation that will create challenging economics. And then I mean, a lot of it rides on does Starship fundamentally-- Cost of payload.

47:18 Yeah. Does payload cost come down by two orders of magnitude. I don't know. I mean, he has a better idea than I do on that. My philosophy on this is probably not material in the next five years and probably not material for 10 years. But I think over a longer period of time, I think data centers in space are going to play a major role in the future of intelligent infrastructure. In a couple of weeks, the SpaceX S-1 is going to be available for everybody here to read, and so we'll see what his time estimate is.

47:46 We know your time estimate next year. That's right. That's right. And final question, you're a Stanford alum, if you were here right now, what advice would you have for students who are making decisions about what to study, where to focus and no tougher time than now to make the decision? I have this philosophy that it's not like the exact things you learn in school don't matter that much. It's like I don't want to be disparaging to that or whatever, but they're important.

48:18 But it's more like this process of learning. And like in my experience, it is like, one of our core philosophies-- in one of our core values at Crusoe you talked about one earlier, which is thinking like a mountaineer, one of our other core values at Crusoe is actually living on the infinite growth loop. This notion that nobody's a finished product, everybody's a work in progress. If you can get better, if you can learn more, if you have that tenacity to know how to improve yourself every single day, over time you get this exponential compounding, which is really the most valuable asset that any of us can have.

48:56 So really, it's about investing in the process of hard work, of grit, of grinding, and then actually leveraging a lot of the tools because I don't know what the world's going to look like five years from now with the mass adoption and utilization of AI, where we all have the workforce of a million people at our fingertips. It's fundamentally going to change work. It's going to change the way everybody operates. So again, I would focus less on what and I would focus more on the how and leveraging of AI tools to run and live your life.

49:33 I think, the advice I'd give to students. Awesome. Chase, thank you so much for doing this. Yeah, thank you. [APPLAUSE]