The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:12:17 · Chase Lochmiller
[00:12:17] S0: It's interesting investors that you spoke to validated this claim. But from the outside, it does appear to be a pivot. Internally, when we started the company, the goal was always to build an AI platform. That was what I had sort of spent my career working on with AI machine learning, you know, algorithms. And, you know, I've been a big user and early adopter of of deep learning for, you know, certain tasks. And I felt like it was this meta science that ultimately was gonna, you know, transform every every industry. And, you know, at that scale, compute becomes the bottleneck. Compute and data become the two bottlenecks. And, you know, what is the bottleneck for compute? Energy. So that was kind of the premise of starting the company. Now, we obviously weren't dedicating all of our resources towards building an AI platform day one. We were dedicating actually the majority of our resources towards monetizing waste energy with Bitcoin mining. That was kind of the the philosophy of of of starting the company and finding and seeking out these low cost abundant energy resources that we could monetize with compute. Bitcoin was the best monetization engine we had at the time, but we were building this AI platform from very, very early days. So when you think about at what point do I shift the resource allocation to this other thing that maybe I'm working on on the side, I think about the world in a very Bayesian approach, right? I don't think the future is deterministic. I think it's very probabilistic, and I sort of have a view of what the future is gonna look like. It's more certain if if you go out just a little bit of time. If you go out, you know, a further amount of time, you know, it gets a little bit hazier, but I still have a view of sort of what what things are gonna unfold. And what was happening with this AI platform was it was sort of a project that Chase had devoted some time and resources and engineering towards internally, and we were committed to, you know, exploring this pathway. But we had launched the Crusoe Cloud Platform in early twenty twenty two with our first set of paying customers. You know, before that we were working with a lot of researchers at MIT and Stanford and, you know, different groups trying to understand what's actually valuable for AI researchers, like from an infrastructure platform.
00:14:26 · Chase Lochmiller, Harry Stebbings
So we finally launched in early twenty twenty two. But the moment that changed everything was obviously 11/30/2022, the launch of ChatGPT. Right? This was like the tweet heard around the world, the, you know, the moment that just shifted everything in terms of like, okay, AI is here and, you know, there there are really interesting, you know, use cases that are gonna explode from from sort of this moment. And that was kinda like a moment where my view of the world shifted quite a bit. How so? I just felt there was gonna be far more demand for AI compute infrastructure solutions, and, you know, we were very well positioned by actually having launched that platform at that point. [00:15:03] S1: What did you do then differently immediately as a result?
00:15:07 · Chase Lochmiller
[00:15:07] S0: After the ChatGPT moment happened, we had sort of watched this evolution of the data center to support Bitcoin. And if you look at the history of Bitcoin mining, it's it's pretty fascinating. Right? It started with, like, hobbyists on their laptops in this sort of decentralized way, and then people started to use GPUs, and then people started to lease data center capacity from, you know, more traditional colocation facilities that have, like, five nines reliability. People started to develop ASICs. And then at some point, people realized, you know what? We don't need five nines reliability to mine Bitcoin. We can do it with three nines. Oh, wait. No. What about one nine? Like, how much does that reduce the actual data center cost? And they were able to eliminate something like 98% of the total data center cost with like these, like, chicken coop type data centers. You know, very, very low cost, no redundancy, but, you know, ultimately brought down the payback period for these investments in Bitcoin mining. And, you know, we felt always that something similar was gonna play out in AI. And we're long term partners of NVIDIA, we'd been working with NVIDIA. And I was looking with them at their roadmap, you know, many years ago, and I was like, okay. Our current generation of chip is, you know, a 100 and watts or 200 watts. And, you know, the next generation is gonna be like 300 watts. And then maybe, you know, there's gonna be a future generation that's maybe 600 watts per chip. And I was like, okay, well, you know, if... As the power density goes up, it's gonna change what the data center looks like to actually support these things. And we also had a perspective on the compute workload. The workload itself for AI, it didn't need to be in these centralized facilities. I think so long people have been anchored on these, like, centralized hubs for data center computing infrastructure. So Northern Virginia is, the hub that runs most of the Internet. Most web applications run out of Northern Virginia. But it didn't have to be that way with AI because, you know, so much of the time to serve a neural network is actually the compute that's happening in the data center, not the time to get to the data center. And so that, I felt like, opened up new geographies, and we felt like infrastructure to support AI wasn't gonna be centralized.
00:17:15 · Chase Lochmiller, Harry Stebbings
It was gonna be distributed where energy was low cost and abundant. And so when we think about things that we did differently, it was like investing more heavily in how to design and develop the data center to support AI, and investing heavily in how do we find and source energy resources to support the scaling of AI. [00:17:33] S1: I I wanna kind of unpack those a little bit and kind of going back to some form of structure that I I did plan, I promise. That's great. If we start on data centers, we then have Crusoe Cloud, Managed Inference, energy kind of runs throughout it all. I'll kind of pick that apart too. On the data center side, we always hear about the supply constraints today. How truly supply constrained are we?
00:17:54 · Chase Lochmiller
[00:17:54] S0: Where it's actually manifesting is there are not places to plug in GPUs. So that's ultimately the supply constraint. There's just not places where you can plug in GPUs and turn them on and run AI workloads. So how does that actually manifest across the supply chain? You know, the supply chain to support large scale AI data centers is sort of like a game of whack a mole. At different points, different bottlenecks come into play, and I think that's actually one of the critical aspects of being vertically integrate... Integrated is that we're able to navigate a lot of those critical bottlenecks and situations. I'll give you an example. When we set out to build the first two buildings in Abilene, it was a, you know, a little over 200 megawatts of compute capacity. We had sort of committed to doing this in one year, and the next closest bid was two and a half years. There were 34 other data center developers, and you know, but speed was of the essence. So I said, sure, we can do it in year. One of the key bottlenecks was what's called a power distribution center. Where, you know, you receive medium voltage power and then you... So at around 34.5 KV, and then you distribute it to low voltage transformers that ultimately go into the data center and power the racks. When we went out and canvassed the market for vendors that were supplying these medium voltage power distribution centers, the lead time for this one component was a hundred weeks. And I said, well, I don't have a hundred weeks. I committed to doing this in a year. So I went to our internal team, and we had consciously vertically integrated electrical manufacturing. We said, how quickly could we make this ourselves? The team did a bunch of work, sourced components to make these things, and we were able to do it in twenty eight weeks. And so being able to navigate those types of key bottlenecks by actually having the resources internally gives you ultimately a lot more flexibility to unblock these key bottlenecks, number one. And number two, it gives you a perspective on, like, what are... What is the true cost of a lot of these different things that are being built. Elon has this very like interesting framing of this.
00:19:52 · Chase Lochmiller, Harry Stebbings
He calls it the idiot index in terms of, know, what what is the what is the cost of raw materials, and then what is the end product relative to that cost of, you know, just the raw raw materials going in. By being vertically integrated, we have a perspective on, like, the end to end cost of everything going into building and operating an AI factory. [00:20:10] S1: How much extra margin do you juice out by having that internal build process yourself? Like, is [00:20:16] S0: the idiot index chasm? Look. Electrical manufacturing is not like an ultra high margin business. Like, today, it's actually spreads have gone up quite a bit. Margins have gone up quite a bit just because they're so short on supply. But it's not where like the critical alpha of... It's not the main reason we're doing it. It's like stacking margin. It's more [00:20:35] S1: Availability and getting out.