The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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.
00:39:46 · Chase Lochmiller, Harry Stebbings
And so, you know, abstracting away some some of the difficulties by actually, you know, having a suite of services that can monetize these chips for longer will ultimately, we believe, extend the depreciation cycle beyond six years. [00:39:59] S1: What do you [00:39:59] S0: think everyone gets wrong about chip buying, chip depreciation that they should know? I think when we started to make really substantial purchases with hoppers, and this was in 2023, the feedback we got was like, well, we don't even know if this is gonna be valuable after year three. Right? Here we are three years later, and the prices being charged for utilizing hoppers is higher than the rates that were being charged three years ago when they were when they were brand new. So early financings was, you know, you had to get paid back very, very quickly, and you had to have like a lot of lot of debt service coverage. So I think people underestimate the ingenuity of applications and application developers of turning compute capacity into value and into valuable services for the economy. [00:40:48] S1: I spoke to Lee Jacobs before, and he was talking to me about the risk mindset that you had to ordering millions of dollars of GPUs from NVIDIA well before it was cool, as you put it. My question to you is how on earth do you think about forecasting demand today? Is it just I'll take as much of it as I can?
00:40:48 · Harry Stebbings, Chase Lochmiller
[00:40:48] S1: I spoke to Lee Jacobs before, and he was talking to me about the risk mindset that you had to ordering millions of dollars of GPUs from NVIDIA well before it was cool, as you put it. My question to you is how on earth do you think about forecasting demand today? Is it just I'll take as much of it as I can? [00:41:04] S0: No. I mean, we build up forecasts from having conversations with customers. I think we are a very customer centric organization. We we wanna deliver ultimately solutions for customers. The challenge today is that increasingly customers are being asked to forecast their demand further and further out because the timeline to bring online AI infrastructure is extending. And, you know, that's problematic for the industry writ large. One of the things that Crusoe is focused on is actually trying to reduce that time to token or time to delivery of a managed cluster of GPUs by actually deploying small modular manufactured AI data centers that can deliver more just in time AI infrastructure, particularly for smaller clusters. And we think that's gonna be transformative for the industry. We think it's a very important shift in terms of... You know, especially, like, when you look at earlier stage companies, earlier stage startups that are being asked to commit to compute in 2028, it's like that is an eternity from now. Right? You know? This is this is like a [00:42:02] S1: We might have RSI by then. Right? That might change it. Exactly. Exactly. So Have you ever had a point moment in your career where the future is so unknown?
00:42:02 · Harry Stebbings, Chase Lochmiller
[00:42:02] S1: We might have RSI by then. Right? That might change it. Exactly. Exactly. So Have you ever had a point moment in your career where the future is so unknown? [00:42:13] S0: I feel... I actually don't feel like it's super unknown. I actually feel like, you know, we've had this breakthrough in overall model development cycles. You know, I think the scaling laws have continued to prove out. I think we've reached... You know, we keep moving the goalposts of what is what is artificial intelligence, what is AGI. You know, you think about the the Turing test. When I took my very first computer science class in high school, the Turing test was heralded as this as this critical notion of true machine intelligence, and it seemed like an impossible task at that point in time. We blew past the Turing test, and almost no one even celebrated. You think about, you know, the model capabilities you have today, and now, you know, we're solving Millennium Prize problems. Right? This was something, you know, when I was an undergrad at MIT, these Millennium Prize problems sort of came about, and it was like this, wow, these are these unsolved problems where there's a real financial bounty out there. Like, you can earn a million dollars if you can solve one of these problems. And they sat there for decades unsolved. Now we have AI solving these these these critical problems.