Stanford MS&E435 Economics of the AI Supercycle | Spring 2026 | Building AI Factories

Thank you. Thank you, thank you, thank you. Thanks for having me.

This is extra special for me. I went to grad school here at Stanford. 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. So the data center is the physical infrastructure component that really enables all of this technology to proliferate and change people's lives.

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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. 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. 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.

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. 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. 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. 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. 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. 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. 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. 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.

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. 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. 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. 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.

The population of Abilene is 120,000. 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. So--

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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.

Yeah. This is not OpEx. Right. I'll show OpEx in another--

It's going up because there's so much demand. So things like gas generation, infrastructure, guess what? Prices have gone up. Things like labor, if you're an electrician, your price has gone up because there's so much demand for your time.

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. 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. 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? 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. 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. 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. 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. 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? 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. But you get to this roughly number of about 40 million per megawatt.

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. 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. 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. I don't know. Competition is powerful.