Long Strange Trip: CEO to CEO with Brian Halligan
00:17:02 · Chase Lochmiller
[00:17:02] S0: You know, I'm not really paranoid with competition. I mean, you know, in certain regards, like, you know, Crusoe is an infrastructure business, right? And there are... We don't view it as a winner take all category. We think there's going be a number of great businesses built here. And I think, you know, even people that... Even groups that people would paint as competition to us, I view it as, like, there's far more ways for us to work together than to work against each other. I think, you know, the the the greatest opportunity of our lifetime is building the infrastructure layer of intelligence. And it's, again, it's not going be a winner take all opportunity. And one kind of comparison that I've often made is that, you know, I kind of view this compute infrastructure layer of AI and or SI, I guess, as people are calling it now today. This infrastructure layer of of super intelligence is is really... There's a lot of effective comparisons to the oil and gas industry. And if you look at if you look at the value chain across the oil and gas industry, you know, it's typically broken down into three components. You have upstream business, where people are actually drilling holes in the ground and and and pumping wells of oil and gas. You have the midstream business, which is where you're taking those, those raw commodities and and transporting them from the wellhead to natural gas processing plant or the oil refinery. And then you have the downstream business, which is actually processing those raw outputs from the earth and then converting them into usable materials, whether it's gasoline or jet fuel or plastics or whatever it is, then you're ultimately selling that into the market. And within that stack, there's all these different margins that move up and down depending on the prices of oil, depending on the demand from consumers, depending on the demand from businesses. And for us, I view it as we're trying to build an AI supermajor in the same way that Exxon and Chevron are supermajors in the oil and gas industry. And with with that, you sort of have this embedded risk risk management where, you know, Exxon very famously doesn't hedge their oil exposure. Because when oil prices come down, their upstream business at the wellhead, those margins actually get compressed.
00:19:09 · Chase Lochmiller, Brian Halligan
But their downstream business at the refinery, those margins actually expand because the raw input costs costs actually go down. And, you know, they they they have these natural areas of collaboration versus competition where they're competing with other upstream businesses. But when they have a pipeline in an area, they're serving other upstream businesses that they may compete with. And I think Crusoe is kind of similar in that regard. We're willing to partner with businesses that we may compete with in other cases. But again, it comes back to this perspective that I just feel like there's so many more ways for us to help each other than for us to work against each other. [00:19:47] S1: Can I say something weird to you? [00:19:49] S0: Yeah, please.
00:19:49 · Chase Lochmiller, Brian Halligan
[00:19:49] S0: Yeah, please. [00:19:51] S1: In addition to my Sequoia work, I started an ocean fund called Propeller. And I heard a pitch yesterday from a company, and I'm not sure about this company, but... Or about this industry, is mining, you know, rare earth metals from the ocean. And same analogy, like, we're gonna do it end to end, and we're gonna build an... Exxon was the exact same company that they talked about as a super vertically integrated version. Speaking of that, we invested in Blue Energy, and I know you know Jake from Blue Energy building nuclear in the ocean. Can you talk about that? Like, you're... You've had a kind of a journey with the energy ops. It seems like you started in just, like, there's there's wasted energy out there, and we're gonna put it to work, and you're still doing it. But it sounds like the Abilene facility is a lot of just natural gas. Like, where where are you?
00:20:48 · Chase Lochmiller
[00:20:48] S0: At this point, you know, Crusoe is very focused on activating megawatts for intelligence, and that requires all sorts of energy resources and all sorts of different use cases. At times, it's natural gas. You know, we're we're focused on trying to find and and and catalyze innovative new energy solutions that can be things like Blue Energy. They're trying out new nuclear, you know, energy production technologies. We have another partnership with Oklo, that we announced recently, where we're trying to bring to life the first, SMR powered AI factory at the Idaho National Lab. We've done stuff with behind the meter wind, with behind the meter gas, with behind the meter solar, with batteries. We've many different things. We've gone to Iceland, right, where Iceland's this sort of like geological phenomenon that it exists, but it's this land of fire and ice where you have very abundant, low cost geothermal energy, very low cost hydropower, and not a lot of people and and very, you know, cheap, efficient cooling. And and bringing the demand for power there via compute has been our philosophy of, and and that's really what we mean when we say take an energy first approach to building compute infrastructure. Norway is another great market for us, where they have a lot of low cost hydropower that's being underutilized and can be built bigger than it is today. And so we don't think it's a silver bullet. We think compute infrastructure will be ubiquitous to serve the infrastructure layer of intelligence that's ultimately gonna power, global economic development. And so you need to use different energy solutions in different cases. And and so we're really excited about a lot of the innovation taking place in the in in the industry. And, you know, Blue Energy is another great example of, like, you know, can we leverage the same steam turbine technology that, you know, is is needed for gas generation to basically... That... That's needed for both gas generation and nuclear energy to be a sort of bridge between gas and and and baseload firm nuclear power. All of these things we think are great. We think a lot of the development in new battery technology is really exciting. Everything from what's happening with companies like Redwood Materials that are repurposing used electric vehicle batter...
00:23:16 · Chase Lochmiller, Brian Halligan
Batteries and used lithium ion batteries to long duration storage solutions like Form Energy and Antora, that we think will be cost effective hundred hour batteries. And so there's like a spectrum of whole different things, and we kind of view it as like we sort of need them all. The demand from intelligence for energy is voracious. And it's really going to accelerate the human progress as it has more access to energy. [00:23:43] S1: One of the things I find fascinating about you and your company is you started essentially in the Bitcoin mining industry with stranded energy, putting that to work. And it was a really good business. You were cranking with it. And it sounds like from the beginning, you sort of foresaw there's going to be a need for AI infrastructure, and you started building it out. And it rhymed. At some point, you decided we're going to sell off this. Most of our business is this Bitcoin mining business. When did you make that decision? How did you tell your board? Take us behind the scenes on when that became a good idea in your head. That that was a bold move.
00:24:27 · Chase Lochmiller
[00:24:27] S0: Well, so the thing was is that I think externally, a lot of people thought this was a pivot for us. But internally, and if you talk to any of our investors or any of our employees that have been here for a very long time, they'll tell you that, like, this was sort of the plan all along, was this energy first approach to computing. You know, I had been doing research in AI as a quant portfolio manager. And then I was at Stanford for grad school and working in AI and doing a lot of work in computer vision and natural language processing. And I was pretty AI pilled early days. Like, when you know, I was reading things... Like like, one of the more influential things on me was actually Tim Urban had this blog called Wait But Why. And he wrote this blog. Yeah. It's great. It's fantastic. Just like everybody should go out and read that. But there were a couple pieces that were particularly influential on me. There was one that was about the path to superintelligence. And in it, he espoused this thesis that these multilayer neural networks were essentially really good nonlinear modeling mechanisms that, as they had more data and more compute, they just got better and better at modeling whatever thing that you're trying to model, whether it's natural language or computer vision or whatever it was. And so I was sort of playing that out and felt like AI was this meta science that wasn't going to improve one thing. It was going to improve everything. Right? So I had this professor at Stanford named Andrew Ng, and he has this famous saying where he says, AI is like electricity. Right? Electricity is not good for one thing. Right? If you ask someone, what what is electricity good for? It's it's not like, oh, electricity is good to, like, turn on the lights. It's like, yes, it's good at turning on the lights, and it's also good at powering my computer and my car and heating my stove and, you know, all these different things that we really take for granted with electricity. And so it's not just like one thing that, you know, that AI is good for. It's really good for everything. And and, you know, I was... I had, like, high conviction of this fairly early days. And, you know, I felt like if data and compute were the bottlenecks, ultimately compute becomes an energy problem. And when you you sort of play that out and think about that at scale...