The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:31:04 · Chase Lochmiller, Harry Stebbings
[00:31:04] S0: Yeah. I I... The interesting aspect to all this is that data centers are this massive employer. They're leading to this massive economic boom for the entire blue collar labor economy in The United States. This massive resurgence and reindustrialization of The United States. We're hiring people, you know, in factories, in the field, and these are tons of folks that, you know, are skilled trade workers. Right? People working with their hands to bring the infrastructure of intelligence to life. I think the interesting aspect that people are concerned that AI is gonna take jobs, but yet it has so far only created tremendous amounts of jobs and tremendous amounts of economic development. So, again, I think it's one of these moments where people are having an emotional reaction to something. But if you look at the facts of what's actually happening, the data is on the side of... Data centers are actually a very positive investment for the communities. If anyone's doing data center security, by the [00:32:01] S1: way, would love to invest in that business. It's a really good buy. It might be a more PE play, to be honest, not a venture play. It's a phenomenal business. Another part of your business is obviously providing GPUs and compute. Can you help me understand the economics of that layer? Like when you look at it, I'm a venture investor, how long does it take to pay back on the hardware?
00:32:01 · Harry Stebbings, Chase Lochmiller
[00:32:01] S1: way, would love to invest in that business. It's a really good buy. It might be a more PE play, to be honest, not a venture play. It's a phenomenal business. Another part of your business is obviously providing GPUs and compute. Can you help me understand the economics of that layer? Like when you look at it, I'm a venture investor, how long does it take to pay back on the hardware? [00:32:22] S0: I would say the price of a GPU hour, it's kind of like a traded commodity. You're even seeing kind of these commodity platforms creating compute as a tradable asset. And I think people are sort of thinking through this lens of like compute is gonna be this next great commodity. So, you know, there's there's a couple platforms like the Compute Exchange, Orn. You know, think there's a handful of others that are like creating these tradable futures products. So, you know, I kind of view it as... That's like a product that we're producing and, you know, there is like commodity price fluctuation in it. When I think about payback periods for this, I think when we are taking debt on against it, there's different ways we think about like managing that risk, and we sort of think about about a portfolio of different services and a portfolio of different returns that we're gonna get for this. [00:33:11] S1: What does that mean? So we have
00:33:11 · Harry Stebbings, Chase Lochmiller
[00:33:11] S1: What does that mean? So we have [00:33:13] S0: a range of different deals that we're doing with the compute we're buying. We are renting capacity on a long term basis, you know, call it five year contracts to credit quality customers that, you know, we expect to pay. Those are gonna pay back within, you know, that timeframe and they're gonna cash flow during that timeframe. There are shorter term contracts that we'll do at higher margins, but they're riskier because at the end of the contract, it's like, is there a renewal? Is there another thing that you're gonna, you know, earn from from that resource? And then finally, there's other services that we provide in package. So things like Crusoe Managed Inference, things like our serverless fine tuning product that actually helps people... That... That's more of a developer tool that ultimately enables developers to, you know, fine tune some of these open source models, these these leading edge open source models so that they can get better performance for maybe some domain specific task. Or actually as their applications are scaling, they can actually serve the tokens needed to, you know, from an inference basis to, you know, enable that application scaling. Those contracts are typically much shorter term in nature, and they end up being higher margin to Crusoe. [00:34:17] S1: And so you want a portfolio of those different margin profiles to make up what you deem is like a healthy margin across them blended?
00:34:24 · Chase Lochmiller
[00:34:24] S0: Correct. So one of the analogies I've used in the past is when you think about Crusoe and sort of our vertically integrated strategy, there's really like three products that we ultimately sell to customers where we're making money. We can sell data centers, we can sell GPUs, and we can sell tokens. Those are the three core products that we sell. And why does that matter? You know, if you look at a different market, I think that there's very interesting parallels for, you know, AI infrastructure and AI compute is the energy market. If you look at the oil and gas market and you look at the value chain from, you know, the wellhead to the product, you know, the gas station or, you know, the plastics that are being sold to people, there's a lot of different ways to make money across value chain. But typically, it gets broken down into the upstream business, which is drilling wells and and sort of, you know, producing oil. There's the midstream business of, like, transporting oil and gas to, you know, a refinery. And then there's sort of the downstream business, which is typically refineries, gas stations, basically getting it into these finished products that you're ultimately selling to customers. Well, what Crusoe is focused on doing is what we believe the opportunity is is to build an AI supermajor. Right? So there are these supermajors like Exxon and Chevron in the traditional oil and gas industry that are vertically integrated across upstream, midstream, downstream. Where that becomes actually important is that Exxon very famously doesn't hedge their oil exposure. And you say, how could that work, you know, during this, you know, very crazy commodity price cycles? Well, they're sort of inherently hedged by being vertically integrated. And I think that where the margin accrues is gonna move around, and I think you're seeing a very similar phenomenon play out in AI infrastructure. But when oil prices come down, Exxon makes less margin at the wellhead, but their margins in their downstream business actually go up. So, you know, because the oil prices are lower, the margins on gasoline, on plastics, on all these finished products, they actually go up. And I think we're seeing a similar phenomenon play out in AI where it's like, you know, our margins are gonna move around across electrical, data centers, chips, and services.
00:36:21 · Harry Stebbings, Chase Lochmiller
[00:36:21] S1: What's the best margin layer today? [00:36:23] S0: I mean, the best margin layer, managed GPUs, like managed compute clusters is like incredibly high margin, like right this instant. The reason for that is just a massive shortage of supply. [00:36:35] S1: There's a phenomenon or a kind of a statement of take or pay in this business. Can you explain take or pay to me and [00:36:42] S0: to anyone that doesn't understand it? Sure. So take or pay is essentially where a customer is signing up to pay for something whether or not they use it. So there are take or pay contracts in energy oftentimes where if I commit to 100 megawatts of capacity, I'm paying for 100 megawatts of capacity whether or not I use [00:37:02] S1: Are all of your revenues take or pay? [00:37:05] S0: The GPU rental agreements are typically take or pay. Yes. Because I [00:37:11] S1: think a lot of people are looking at going, well, the first sign of trouble would be a crack in consumer demand. And if that crack in consumer demand permeated down and it was take or pay and they went, ah, don't need to take now. Is that correct? [00:37:25] S0: I don't really think of it that way because the thing about AI is it's transformative for so many areas of the economy. It's not a single thing that's benefiting. [00:37:34] S1: And I first think crack would be consumer demand softening. [00:37:38] S0: Consumer demand and what, though? I think it'd be consumer demand across [00:37:41] S1: or consumer demand across chat chat GBT and core
00:37:46 · Chase Lochmiller
[00:37:46] S0: Yeah. I mean, I think we're honestly just scratching the surface. I think like what's exciting to me is seeing the step function improvements that we've seen from some of the more recent models that are fundamentally enabling new discoveries that weren't possible. Model capabilities are only going up. When people think about the utility of these models and of this type of infrastructure, it's the worst it's ever gonna be for the rest of the future of humanity. When you sort of take that perspective, it is inspiring to what's actually possible. If we saw consumer demand softening for one very specific thing, I couldn't be more optimistic or I couldn't be more bullish for like how this is gonna transform scientific discovery of new materials, of new drugs, of new things that are ultimately gonna make people's lives better. When you're buying chips at the scale that you are, you have to think about, like, life cycle. How do you think about chip depreciation risk? Well, the way we depreciate the assets today is we use a six six year depreciation cycle. That's sort of like the standard across the industry. Now one of our philosophies as a business is like, look, we are gonna be long infrastructure. We're gonna be long data centers. We're gonna be long energy. We're gonna be long chips. And so the more ways we have of turning those long investments into money, the better off our shareholders are gonna be and the better off our business is gonna be. And so that's actually what caused us to investing in this suite of managed AI services. And when you look at that business where it abstracts away the the actual underlying chip, the underlying compute from the service that people are receiving. It opens up new monetization engines that can persist for a much much longer period of time. And there's always gonna be demand for the frontier of silicon. There's always gonna be demand for the frontier of, you know, model capability. But there's also gonna be demand especially for for much cheaper alternatives. And, you know, being able to provide intelligence at a lower cost with a older generation of chip that may be slower is still gonna have value.