20VC: The Future of Datacentres: What You Need to Know | Why Everyone Gets GPU Depreciation and AI's Energy Costs Wrong | Who Really Makes Money From AI & Why Most Moats Don't Exist with Chase Lochmiller

Chase Lochmiller is the Co-founder and CEO of Crusoe. Crusoe builds and powers the data centres that companies use to train and run AI. It has raised approximately $6.4 billion in equity, including its latest $3.9 billion Series F at a $30.9 billion valuation. Its backers include NVIDIA, Founders Fund, Gavin Baker's Atreides Management, Mubadala Capital and Valor Equity Partners. AGENDA: 04:40 What Does Climbing Everest Teach You About Building a Company? 17:40 What's Really Stopping Us From Building Enough AI Data Centres? 25:50 Are Data Centres Really Driving Up Your Energy Bills? 28:05 Will Half of Planned AI Data Centres Never Get Built? 32:10 How Quickly Can a GPU Pay for Itself? 38:35 Will Your GPUs Become Obsolete Before You've Paid Them Off? 44:20 What Does It Take to Produce the Cheapest Intelligence? 49:50 Will Companies Building Their Own Models Eat Into OpenAI's Business? 51:55 Can You Build a $30B Company and Still Be a Great Dad?

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  So absent having a purpose, I was like, man, I guess I should get rich. Like, that's maybe the next best thing to like having a monastic calling that you're devoted to. The infrastructure to support AI wasn't going to be centralized. It was going to be distributed where energy was low cost and abundant. Energy prices actually come down. It's actually the opposite of the narrative that's being told. People are very emotional about data centers. There's really like three products that we ultimately sell to customers and where we're making money. We can sell data centers, we can sell GPUs, and we can sell tokens. Well, you know, the GPU is actually the most valuable thing in the entire data center. Most moats are illusion. Most moats don't exist.

• 0:39Harry Stebbings?

  This is 20 VC with me, Harry Stebbings. Now, my job is to bring you the most relevant interviews in technology. Right now, we are in a race for data centers, for compute capacity, for energy, and there is not a more pertinent guest that could join me in the hot seat today than Chase Lochmiller from Crusoe Energy. They provide several different layers of this very important business, which is they provide the data centers, they can provide the compute, and then they can provide the inference, three of the most valuable pillars of this value chain. He sits down today to join me following their $3.9 billion Series F, which was raised at a $30.9 billion valuation. Get the notebooks out. This one went very granular into the build out that's required over the next few years

• 1:22Harry Stebbings?

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• 2:02Harry Stebbings?

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• 2:46Harry Stebbings?

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• 3:31Harry Stebbings?

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• 4:16Harry Stebbings?

  for free today at framer.com/20vc for 30% off a Framer Pro annual plan. That's framer.com/20vc. Rules and restrictions may apply. You have now arrived at your destination. Chase, I am so excited for this, dude. I told you before, I have been stalking the shit out of you. I even heard from performance coaches. This has been an amazing experience doing the research, so thank you so much for doing this with me.

• 4:43Speaker 00

  I'm pumped to be here, man.

• 4:44Harry Stebbings?

  I am just going to start with a totally weird one that I never expect to start with. Mountaineering. I hear that you are an experienced and skilled mountaineer. I haven't had that often in the hot seat. What do you learn from mountaineering that you think makes you better as an entrepreneur?

• 5:00Speaker 00

  It's a great question. We've actually ingrained mountaineering into the culture of Crusoe, and we have a core set of company values. One of our core values is actually to think like a mountaineer. And so what does that mean? You know, mountaineering is a practice where things are subject to change, and you need to be prepared for change. With it, it really embeds this sense of like cultural resilience that I think is really, really important for a business like ours that is doing stuff both in the digital world as well as the physical world. But to further the analogy here, for mountaineering, you come up with a plan to basically set out from your camp, go some at the peak, come back safely, and you know, take your photo, and your glory is written in the history books. But you have to be prepared for things to go wrong, right?

• 5:44Speaker 00

  You have to be prepared for the weather to change. You have to be prepared for gear to fail. You have to be prepared for your partner to get sick or an avalanche to occur. And the idea is really having, here's a Plan A, and then I have a Plan B, a Plan C, a Plan D. I have plans all the way down in case different things go wrong. You know, additionally, like, you have to have a sense of willingness to endure difficult challenges and difficult times. You know, this notion of enduring the expedition. The expedition can be long and hard and arduous, and you have to really dig deep at those moments of pain to work through those challenges. That's a different aspect of thinking like a mountaineer. And then, you know, another big aspect is thinking with a safety culture, having this

• 6:24Speaker 00

  notion of safety as a huge priority, which is important when you're doing things at the scale we're doing from a construction standpoint, thinking with the safety mindset of, you know, safety first, second, and third. There's this saying in mountaineering that getting up to the top is optional, getting down is mandatory. We try to practice that safety culture here at Crusoe and really try to avoid any sort of physical entries or challenges with digital attacks on the platform as well.

• 6:51Harry Stebbings?

  I have so much respect for mountaineers. It's one of those ones where I almost can't get my head around the complexity and the mental fortitude it takes to get through the change of plans, the weather, the partner that's sick, the endurance, as you said.

• 7:05Speaker 00

  Yep.

• 7:05Harry Stebbings?

  I met an entrepreneur yesterday that climbed Everest, and he showed me pictures of the dead bodies along the way. I mean, to see that is just so harrowing.

• 7:13Speaker 00

  Yeah, it's tragic, just kind of, you know, seeing when people take unnecessary risks. Because, you know, actually, spending a lot of time in, within high-altitude mountaineering culture and communities, you do see oftentimes people taking unnecessary, very careless risks. It's actually quite shocking. Oftentimes, tragedy is avoidable. It is preventable. And, you know, I think having that mindset of thinking like a mountaineer and planning ahead is what enables mountaineers to do it for a long period of time.

• 7:42Harry Stebbings?

  Can I ask, you said about resistance to change. What change did you find the hardest in your life?

• 7:48Speaker 00

  I think maybe one of the most difficult moments of change for me was, in my life, I always had sort of the next thing planned before I left the previous thing. So what I mean by that is, when I left high school, right, I knew exactly where I was going to college before, you know, the end of high school. When I left college, I knew exactly where I was going to work and, you know, what I was going to do. And when I left my first job, I knew I was, you know, going to work and going to grad school, and I had a job lined up even after that. When I left to go climb Mount Everest in 2018, I did not have a plan after that. You know, I had been, had a reasonable amount of financial success in my life to that point, but I didn't have the thing planned.

• 8:31Speaker 00

  And actually, having that void, that sort of emptiness, really was both challenging and empowering for me because it felt like I could do anything. It was sort of like a blank slate. I had thoughts of like, maybe I'm just going to invest my own money and just kind of like enjoy the rest of my life and not do much. But I could also start a company. And if I were going to start a company, what are the most challenging things that I could, you know, think about tackling that are interesting and that I would be excited about pursuing on a daily basis? And that was kind of like, the emergence of Crusoe sort of came through that chapter of difficulty and challenge of just nothingness, right? Just having an empty slate.

• 9:09Harry Stebbings?

  My job is to find signals that will determine a potentially great entrepreneur. And one thing that I find now is actually often richer entrepreneurs are better entrepreneurs because they're not worried about downside protection and they just see what is possible. How do you think starting Crusoe with a little bit of money and success already changed how you think about building it?

• 9:29Speaker 00

  Oh, I think it absolutely changed the way I think about building it. There's a bunch of like psychological aspects to it. I mean, you could look at like Maslow's hierarchy of needs, you know, and just like, it is empowering to know that like if I take risk and this all goes to zero, I'm still going to be fine. I'm going to be able to cover my rent and, you know, feed my family and cover these base needs that I may have in my life. When I was in university, I mean, I was convinced I was going to become a theoretical physicist and likely a professor. And I was spending time doing research in basic science, right? So I was, I was doing research in physics, both at MIT and then at Los Angeles National Lab. So early on in that journey, I was like pretty committed to living this monastic life of

• 10:11Speaker 00

  like not really earning a bunch of money, but just having this life of discovery where I get to go discover the mysteries of the universe and that that would be my, my, my life's work and pursuit. At some point I realized it's a very slow moving industry, this industry of discovery, you know, it may be changing today because of AI, but at least at that point in my life, it was a very slow moving industry and I felt like I wanted to do something very fast paced. And I felt like I had sort of lost my life's purpose. Like I was like, okay, I don't, I don't feel like I have a purpose anymore. So absent having a purpose, I was like, man, I guess, I guess I should get rich. Like that's maybe the next best thing to like having a, you know, a monastic calling that you're devoted to. You know, I was looking around for different ways to, to make money and this headhunter

• 10:54Speaker 00

  found me and recruited me into this field of quantitative finance. And it was like, hey, you're really good at solving math problems. If you come work in this industry, they just pay you a lot of money to, you know, solve math problems. And I said, this sounds great. Sign me up. But I think once that need was met of like, okay, I have enough money. Okay, what, what next? And, and I think to your original question of like, you know, starting from this position of strength of, you know, being an entrepreneur and actually having had some financial success, it's empowering to take bigger swings, empowering to take bigger risks, which can ultimately lead to bigger outcomes.

• 11:30Harry Stebbings?

  The final one before we actually get into what I plan to get into.

• 11:32Speaker 00

  Yeah.

• 11:33Harry Stebbings?

  It's just, it just picking up on you said kind of the endurance element of mountaineering. Endurance is great in some respects, but it can also lead you to continue doing something you maybe shouldn't. And sometimes there is a right time to change course rather than persevere. You went through a very big change in the business, seemingly so from the outside. A lot of your investors said you always had the foresight actually that it would lead to where we are today, but seemingly it's like a Bitcoin phase one and then phase two is AI.

• 12:00Speaker 00

  Yeah.

• 12:01Harry Stebbings?

  I don't want to kind of really actually get into that maybe weirdly, but I want to ask your advice, which is when asked, how do you advise someone when to endure, keep going, persist versus when to change tact and go down a different route?

• 12:16Speaker 00

  It's a great question. It's interesting investors that you spoke to validated this claim, but you know, 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'd 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 going to, 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

• 13:01Speaker 00

  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, I don't, 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 going to look like.

• 13:43Speaker 00

  It's more certain if you go out just a, 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 going to 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 2022 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 a, an infrastructure platform.

• 14:24Speaker 00

  So we finally launched in, in early 2022, but the moment that changed everything was obviously November 30th, 2022, the launch of ChatGPT. Right? This was like the, 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 going to explode from, from sort of this moment. And that was kind of like a moment where my view of the world shifted quite a bit.

• 14:52Harry Stebbings?

  How so?

• 14:53Speaker 00

  I just felt there was going to 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.

• 15:03Harry Stebbings?

  What did you do then differently immediately as a result?

• 15:07Speaker 00

  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 co-location facilities that have like five nines of 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

• 15:50Speaker 00

  like these like chicken coop type data centers, um, you know, very, very low cost, no redundancy, but, you know, ultimately brought down the payback period for this investments in Bitcoin mining. And, you know, we felt always that something similar was going to play out in AI. And we're long-term partners of Nvidia and we'd been working with Nvidia and I was looking with them at the roadmap, you know, many years ago. And I was like, okay, our current generation of chip is, you know, 150 watts or 200 watts. And, you know, the next generation is going to be like 300 watts. And then maybe, you know, there's going to 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 going to 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.

• 16:35Speaker 00

  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 like 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 the infrastructure to support AI wasn't going to be centralized. It was going to 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

• 17:20Speaker 00

  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?

• 17:33Harry Stebbings?

  I, I want to kind of unpack those a little bit, kind of going back to some form of structure that I, I did plan, I promise.

• 17:39Speaker 00

  That's great.

• 17:40Harry Stebbings?

  If we start on data centers, we then have Crusoe Cloud, managed inference, energy kind of runs throughout it all. So 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?

• 17:54Speaker 00

  Where it's actually manifesting is there are not places to plug in GPUs. So that's ultimately the supply constraint. There, there's just not places where you can plug in GPUs and turn them on and run AI workloads. So how does that, 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 integrated, 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.

• 18:37Speaker 00

  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 a year. One of the key bottlenecks was what's called a power distribution center. It's, it's where, you know, you receive medium voltage power and then you, so at around 34, 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.

• 19:18Speaker 00

  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 28 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, 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. He calls it the idiot index in terms of, you 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 materials going in?

• 20:02Speaker 00

  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.

• 20:10Harry Stebbings?

  How much extra margin do you juice out by having that internal build process yourself? Like, what is the idiot index chasm?

• 20:18Speaker 00

  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

• 20:35Harry Stebbings?

  Availability and getting

• 20:36Speaker 00

  Availability, being able to deliver on time. And then also, it gives us a critical innovation platform. By being able to, you know, start from first principles and say, okay, we can bring in raw materials and we can build anything we want in the world. What is the thing that we want to build to ultimately serve the workload? And so when you look at the campus in Abilene, the inspiration for that was like, okay, we want to build a one-gigawatt-scale computer, where you can interconnect a giant cohesive cluster of GPUs that can operate one cohesive workload across the entire campus. And what does it look like to actually build that thing from the ground up? That's how, you know, the cores are designed, that's how the GPUs are designed. That's, you know, all of it is designed to run on one cohesive RDMA fabric.

• 21:19Speaker 00

  When we think about scaling of inference, you know, 2026 is really about the scaling of utilization of AI infrastructure, utilization of LLMs and foundational models for useful tasks. You know, it's been this era of, you know, scaling of agents and tokens, token utilization. And, you know, during that moment of scaling infrastructure, as people are finding utility in these AI models, it's driving a lot more demand for inference. It's the era of the agent, the era of inference. And for that, you don't need a gigawatt-scale computer to serve these models. You can do it with a much smaller cluster. What matters is actually, how quickly can I go from not having anything to being able to deliver tokens? Like, so what is my time to token?

• 22:01Harry Stebbings?

  I love the analogy you said there of it's like whack-a-mole. What is the biggest supply constraint that we have today?

• 22:07Speaker 00

  Energy is definitely a key constraint. Power that's available for compute is a key bottleneck. Alongside that, labor is a very key bottleneck. There are a finite amount of skilled trade workers, electricians, welders, plumbers, construction workers in the United States. And then, when you overlap, trying to find, if I have access to energy in this unique place, can I get the labor to that place to actually bring the AI factory to life? You know, that is a big challenge.

• 22:37Harry Stebbings?

  With the greatest of respect, if power and labor are the two biggest constraints or challenges, the UK is totally fucked. We have energy prices that are four times higher than anyone else, and we don't have labor. And we have humans, they just don't work. You in the US have both, but significantly more expensive than China, if we're honest.

• 22:56Speaker 00

  Power is not significantly more expensive.

• 22:58Harry Stebbings?

  Really?

• 22:58Speaker 00

  No.

• 22:59Harry Stebbings?

  You don't think that the government are subsidizing data centers in China with much cheaper energy to power that, I'm asking him, not telling?

• 23:07Speaker 00

  There are subsidies going in place, but in the United States, we are globally competitive with power pricing. Labor, I'll give you, labor in China is far cheaper than the United States.

• 23:16Harry Stebbings?

  What about policy and regulation? Everyone I speak to is like, oh, it's the red tape, it's the policy and regulation that stops us in the US being able to build out. Is that a problem in the way that it's told to me?

• 23:27Speaker 00

  I wouldn't classify it as a problem. I would classify it as something that needs to be navigated. You know, when we think about policies, we want good policies to be in place. We don't want just the Wild West, everybody out doing whatever they want, building whatever they want with no care for anybody else in the world. You know, these are big investments that are being made, they have to be done thoughtfully. We are supporters of the right policies being in place. But it does cause friction in terms of developing things faster. That's actually one reason we also like the idea of manufacturing a lot more of this infrastructure, as opposed to making everything a giant construction project.

• 24:03Harry Stebbings?

  If I were to put you in charge of policy to enable and encourage this ecosystem to flourish, what would you do, what would you do differently?

• 24:10Speaker 00

  The thing about policy is it's hyper-local. Politics are local. And what people care about is, they care about, are their jobs being created? Is this going to drive up my energy costs? Is this going to take all of my water? Is this going to pollute the air so that, you know, my kids get, suffer from some disease as a result? Like, you know, they care about, they care about, are the data centers being good stewards and good citizens of the community? And are they actually creating value for the community? And then, what are other ways that the data centers are sort of giving back? And so, you know, this comes in the form of things like tax revenues and things like that. So, I think one of the challenges for AI data centers broadly right now is that, I think there's a lot of misinformation out there about what the impact of a data center is.

• 24:54Speaker 00

  The number of people that have told me that data centers use all the water in the world is crazy. It's also just false. When you look at all of these modern AI factory designs, they use pretty close to zero water. One of our giant buildings in Abilene, Texas, call it 140 megawatts of power, is like what's budgeted for each of those buildings, on the first eight buildings. They use about the same water annually as about 10 single-family homes. The primary water usage there is like, the staff using the bathroom, washing their hands, us watering the plants on the property. It's very, it's very de minimis water usage. And there is water in the building. We are using water to cool the GPUs. The critical aspect here is, we've actually designed a closed-loop architecture,

• 25:39Speaker 00

  so the cold water is entering into the racks, and then it's going out to a chiller outside, and we're sort of exhausting the heat.

• 25:46Harry Stebbings?

  So the water requirements argument is just wrong?

• 25:49Speaker 00

  The water requirements argument is just wrong, yes.

• 25:52Harry Stebbings?

  Okay, what about energy prices going up?

• 25:54Speaker 00

  So, I think this is an important aspect for the data center industry to get right and to storytell effectively. We like to look at data for these things. When you look at markets where data centers have made investments and have built big data centers, typically, energy prices for communities have come down. Because there, it catalyzes more investment in energy generation technology, in energy generation capacity. And you end up with more megawatts being amortized over the same transmission and distribution infrastructure. And as a result, people's energy costs end up coming down. When you look at some of these big builds that need a lot of net new generation capacity, we are very supportive of, you know, the data center industry

• 26:36Speaker 00

  helping to bring online new power production in order to support those energy requirements.

• 26:42Harry Stebbings?

  So actually, that too is misinformation. Energy prices won't go up for local towns and villages.

• 26:48Speaker 00

  The data has shown that energy prices actually come down. It's actually the opposite of the narrative that's being told.

• 26:53Harry Stebbings?

  So what is a legitimate concern?

• 26:55Speaker 00

  Anytime there is a big construction project, it does cause other aspects, right? There's benefits, there's people with money in town that, you know, are spending. And, you know, if you talk to any local business owner in Abilene, times have never been better, right? The amount of money being spent at restaurants and coffee shops and hotels and all the local services is, you know, is more than it's ever been in the history of Abilene. The challenge is, with that comes traffic. With it comes some dust from construction and some, you know, just noise that, you know, is involved with a very large construction project. But that will go away, and there will be long-term, permanent jobs in place that, you know, is continuing to support the local community. And there's tremendous tax revenue, right?

• 27:39Speaker 00

  If you look at the tax revenue that's coming from this, we're going to account for more than a third of the tax revenue in Taylor County and Abilene the town. And this is transformative for local services, like the police, the fire department, the roads, the schools. For the school system, we're more than doubling the tax receipts that are going to the schools.

• 27:57Harry Stebbings?

  I completely agree with the ripple effect that you see. I think it's one of the most worrying things when we see the amount of millionaires leaving the UK. People don't consider the ripple effect of them leaving in all of these different ways. We're seeing a lot of data centers that are planned not being built out. We've seen very prominent people say, oh, we expect 50% of data centers that are planned not actually to be built and ready to go. What percent of data centers do you think that are planned will not actually go online?

• 28:22Speaker 00

  50% seems reasonable. There's, it's one of these things, you know, it comes back to thinking

• 28:27Harry Stebbings?

  You can't give me a decimal point? Why do you bother? Let's end it here. Neil told me you give me precision.

• 28:36Speaker 00

  You know, it's one of these things where, coming back to this notion of thinking like a mountaineer, when you're going through these planning processes, a lot of things can go wrong.

• 28:45Harry Stebbings?

  What's the number one thing that goes wrong at Provence?

• 28:47Speaker 00

  Getting permits, getting entitlements, getting land. You know, you're trying to acquire a certain piece of land, and, you know, the person you're trying to buy it from ultimately doesn't want to sell. Getting a large load interconnection agreement done with the local utility. Getting an air permit if you're bringing online new generation.

• 29:02Harry Stebbings?

  With the greatest of respect, I don't come back to this, and I'm not an agent of the CCP, I promise. But in China, I mean, they'll move the 3 million people out the dam for you with a forklift.

• 29:11Speaker 00

  Yeah.

• 29:12Harry Stebbings?

  And they'll just give you a green light on day one.

• 29:14Speaker 00

  Yes, it's a benefit to the data center developers if you're viewing it through a purely Machiavellian lens, yes.

• 29:21Harry Stebbings?

  I am a venture capitalist. I give zero shits about the sentimentality of your home. You will be rehoused somewhere better. Again, charity is not our endeavor. Okay, when we look at these data centers, now they're being used as like a political instrument in a way that we didn't expect. Does that worry you?

• 29:44Speaker 00

  Yeah, I think I've never wanted to be the main character of a political policy debate. And, you know, data centers have come front and center to the upcoming midterms in the United States, and they've become this very polarizing topic. Why that is, I think they are kind of this, I think there is concern around, you know, this era of AI. And I think people are legitimately concerned around, like, am I going to have a job in the future? Am I going to have, be able to provide useful work to the world that, you know, I'm going to be able to get compensated for? So, I do think that that is a concern on people's minds. And data centers are sort of this physical manifestation of AI. And putting all of that to side, like, the biggest aspect is, people are very emotional about data centers.

• 30:25Speaker 00

  I think when you look at the facts of what data centers are doing, from job creation, from driving down energy costs, from being water-neutral, from, you know, generating long-term tax revenue that is transformative for the communities that we're investing in, I think the facts are all on our side.

• 30:41Harry Stebbings?

  Do you not slightly blame, with respect, Dario and a lot of other leaders who've said for the last few years, we're going to replace all jobs, we're going to replace all jobs, and then surprisingly, they hate you?

• 30:51Speaker 00

  Yeah.

• 30:52Harry Stebbings?

  Gosh, cake or death, cake please. I wasn't expecting that. Oh, crikey.

• 30:59Harry Stebbings?

  You won't have a job, you won't have a job. Oh, you don't want the data set up.

• 31:04Speaker 00

  Yeah, 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 in re-industrialization in the United States. We're hiring people in factories, in the field, and these are tons of folks that 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 going to take jobs, but yet it is 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

• 31:48Speaker 00

  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.

• 31:59Harry Stebbings?

  If anyone's doing data center security, by the way, would love to invest in that business. It's a really good, it might be a more P E 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 like, I'm a venture investor, how long does it take to pay back on the hardware?

• 32:22Speaker 00

  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. I think people are sort of thinking through this lens of like, compute is going to be this next great commodity. So there's a couple of platforms like the Compute Exchange, ORN, I think there's a handful of others that are like creating these tradable futures products. So I kind of view it as, that's like a product that we're producing and 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

• 33:06Speaker 00

  and we sort of think about a portfolio of different services and a portfolio of different returns that we're going to get for this.

• 33:11Harry Stebbings?

  What does that mean?

• 33:13Speaker 00

  So we have a range of different deals that we're doing with the compute we're buying. We are renting capacity on a long-term basis, call it five-year contracts to credit quality customers that we expect to pay. Those are going to pay back within that timeframe and they're going to 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 renewals? There another thing that you're going to earn 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's more of a developer tool that ultimately enables developers to

• 33:54Speaker 00

  fine-tune some of these open source models, 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 from an inference basis to enable that application scaling. Those contracts are typically much shorter term in nature and they end up being higher margin to Crusoe.

• 34:17Harry Stebbings?

  And so you want a portfolio of those different margin profiles to make up what you deem as like a healthy margin across them blended?

• 34:24Speaker 00

  Correct. So one of the analogies I've used in the past is, when we think about Crusoe and sort of our vertically integrated strategy, there's really like three products that we ultimately sell to customers and 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? If you look at a different market, I think that there's very interesting parallels for 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 the wellhead to the product, the gas station or the plastics that are being sold to people, there's a lot of different ways to make money across the value chain. But typically it gets broken

• 35:06Speaker 00

  down into the upstream business, which is drilling wells and sort of producing oil. There's the midstream business of like transporting oil and gas to 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 during this very crazy commodity price cycles? Well, they're sort of inherently hedged by being vertically integrated. And

• 35:50Speaker 00

  I think that where the margin accrues is going to 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, their downstream business actually go up. So 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, our margins are going to move around across electrical, data centers, chips, and services.

• 36:21Harry Stebbings?

  What's the best margin layer today?

• 36:23Speaker 00

  I mean, the best margin layer, managed GPUs, like managed compute clusters is incredibly high margin, like right this instant. The reason for that is just a massive shortage of supply.

• 36:35Harry Stebbings?

  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 to anyone that doesn't understand it?

• 36:44Speaker 00

  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 that.

• 37:02Harry Stebbings?

  Are all of your revenues take or pay?

• 37:05Speaker 00

  The GPU rental agreements are typically take or pay. Yes.

• 37:10Harry Stebbings?

  Because I 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?

• 37:25Speaker 00

  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, it's not a single thing that's benefiting.

• 37:34Harry Stebbings?

  So you don't agree that the first crack would be consumer demand softening?

• 37:38Speaker 00

  Consumer demand in what, though?

• 37:40Harry Stebbings?

  I think it'd be consumer demand.

• 37:41Speaker 00

  Like ChatGPT or?

• 37:42Harry Stebbings?

  Consumer demand across ChatGPT and Core.

• 37:46Speaker 00

  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 going to 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 going to transform scientific discovery of new materials, of new drugs, of new things

• 38:30Speaker 00

  that are ultimately going to make people's lives better.

• 38:33Harry Stebbings?

  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?

• 38:41Speaker 00

  Well, the way we depreciate the assets today is we use a 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 going to be long infrastructure. We're going to be long data centers, we're going to be long energy, we're going to be long chips. And so the more ways we have of turning those long investments into money, the better off our shareholders are going to be and the better off our business is going to 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 actual underlying chip, the underlying compute from the service that people are receiving, it opens up new monetization engines

• 39:22Speaker 00

  that can persist for a much longer period of time. And there's always going to be demand for the frontier of silicon, there's always going to be demand for the frontier of model capability. But there's also going to be demand especially for much cheaper alternatives. And being able to provide intelligence at a lower cost with a older generation of chip that may be slower is still going to have value. And so abstracting away some of the difficulties by actually having a suite of services that can monetize these chips for longer will ultimately, we believe, extend the depreciation cycle beyond six years.

• 39:59Harry Stebbings?

  What do you think everyone gets wrong about chip buying, chip depreciation, that they should know?

• 40:05Speaker 00

  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 going to be valuable after year three. Right? Here we are three years later and the price is 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 had to get payback 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.

• 40:48Harry Stebbings?

  I spoke to Lee Jacobs before and he was talking to me about the risk mindset that you had ordering millions of dollars of GPUs from Nvidia, well before it was cool, as he 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?

• 41:04Speaker 00

  No. I mean, we build up forecasts from having conversations with customers. I think we are a very customer-centric organization. We want to 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 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 going to be transformative for the industry. We think

• 41:49Speaker 00

  it's a very important shift in terms of, 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? This is, this is like a.

• 42:02Harry Stebbings?

  We might have RSI by then.

• 42:04Speaker 00

  Exactly.

• 42:06Harry Stebbings?

  That one might change it.

• 42:08Speaker 00

  Exactly, exactly.

• 42:09Harry Stebbings?

  Have you ever had a moment in your career where the future is so unknown?

• 42:14Speaker 00

  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 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?

• 42:59Speaker 00

  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.

• 43:19Harry Stebbings?

  I agree with you. My question to you is, we see these swarms of rogue agents doing things that are nefarious. We see whole industries that are being put to question. My girlfriend's a lawyer. Six months ago, she didn't use Lagora. Now she hasn't written a document in five months. There is just such uncertainty across everything. We have RSI that could come and, and change absolutely everything.

• 43:38Speaker 00

  So sure, I get the comment that there's uncertainty around what the future of work will look like in certain ways. But I also feel like it's, it's actually not uncertain that AI is going to be a critical underlying engine for work and productivity that is going to shift things quite a bit, quite dramatically. And so it's more about that there is certainty that AI is establishing capabilities that are at human level or greater than human level in terms of overall productive capacity. So sure, is that going to change things? Yes, that is going to change things. So, so there's uncertainty in the change, but not, to me, it's certain that there will be change. Maybe that's a different way of putting it.

• 44:18Harry Stebbings?

  Can I just, we, we mentioned the managed inference being a great part of your business. Gavin Baker, one of your investors, has said that the lowest cost producer of intelligence wins. When you think about that, like, what's the unit? Is it like dollars per token, per training run? What is that unit for you?

• 44:35Speaker 00

  Dollars per token is a, is a good measure in terms of this, but it's, it's not necessarily, you know, there is token efficiency. So, you know, just not all tokens are created equal. So it's not like every single token has the same, you know, unit of value, but I think it is a good indicator. I do think that, you know, there are different things that folks optimize for in terms of utilization, whether it's throughput. So the amount of tokens per second you're able to generate for, for, for a very specific model use case, as well as time to first token, so latency, time to first token, time to last token, you know, how quickly you're able to actually serve this. And a lot of that comes down to, it's a confluence of, of different things, right? So there's, you know, what is the cost that go into that? Obviously by being vertically integrated and owning the infrastructure, you're able to stack a lot of margin across the process.

• 45:20Speaker 00

  So it gives you more flexibility in terms of how you actually, you know, bill for that. Additionally, I think utilization of the GPU itself is a critical aspect of being able to deliver tokens ultra efficiently and shine on metrics of like latency and throughput. And so how does that actually manifest? Well, you know, the GPU is actually the most valuable thing in the entire data center, right? It is like the most expensive thing. So it's the thing you want to make sure you're keeping busy. If it's sitting idle, that's just, that's just money burning, right? If you think about that from, from that perspective, I think one of the key aspects is actually the management of memory and the management of the, you know, of the KV cache specifically. So the KV cache is the key value cache.

• 46:04Speaker 00

  It's really like, you know, you can kind of think about, you know, when tokens are fed into a, a large neural network, you can compute the output tokens by basically running this feed forward process and running all these matrix multiplications. That takes time. You may also already know the answer of that output token before you put the, so if you, if you've actually put, put those same tokens in before, you actually know the answers and you can look them up in this lookup table. The KV cache can get quite large and so it can expand well beyond the amount of, you know, memory you have on chip in the HBM. And so actually being able to manage that both across multiple different GPUs, HBM layers, as well as system memory, DRAM, as well as things like MVME, so any, any sort

• 46:47Speaker 00

  of, you know, solid state drive that you have on the system. And then sort of expanding that into the object storage layer of your cluster and being able to really, really efficiently move data around is actually a critical aspect to being able to serve inference and be able to keep those GPUs busy so that you're able to really shine on those metrics of latency and throughput.

• 47:08Harry Stebbings?

  What does no one see about this layer of the value segment that you provide that everyone should see? What do we not know?

• 47:15Speaker 00

  Well, I think the big aspect is that not all inference providers are created equal. I mean, you, you have like, you know, great open source projects like, you know, SG Lang and VLLM, but you know, there are ways to, you know, that, those are, are very generalized or generalizable.

• 47:31Harry Stebbings?

  When you look at like an inference provider like Fireworks.

• 47:33Speaker 00

  Fireworks is great. They've done an incredible, incredibly great job.

• 47:36Harry Stebbings?

  To what extent do they compete with you and cannibalize your managed inference business?

• 47:41Speaker 00

  Look, I, I think there are multiple layers across the stack that Crusoe competes with.

• 47:46Harry Stebbings?

  What do they have that you don't by being specialized?

• 47:49Speaker 00

  I think there are certain workloads that, you know, Fireworks, you know, does an incredibly great job serving. I think, you know, the user experience, I think is something that is unique across all of these different inference platforms that, you know, people may like more on, on one than the other. There's a suite of different tools that, you know, people are, you know, there, there's a whole bunch of different features that people basically provide across, you know, the inference ecosystem.

• 48:13Harry Stebbings?

  But there's nothing like your, ah, I wish I was then because then we could do this.

• 48:17Speaker 00

  I sort of come back to this notion of, you know, the oil and gas analogy where the, if you look at the value chain, there are different places where you can compete and there are different places where you can collaborate. When Exxon is pumping wells at the wellhead, they may compete with another upstream oil and gas company to, to, you know, most efficiently drill and operate wells. But they may sell to those same customers if they're building a midstream pipeline to transport oil from the wellhead to the refinery. They may provide capacity in that pipeline to other folks that they're competing with. And so again, I, I think it comes down to this perspective that like, I just view there are so many more ways for us to work with folks across the AI infrastructure stack than compete with them.

• 49:01Harry Stebbings?

  Can I just ask you, when you look at like open versus closed, given the purview that you have from the managed inference perspective, what do you see about distribution of dollars of tokens across open versus closed, given that unique perspective you have?

• 49:14Speaker 00

  I do think that people are spending more money on closed source frontier models than they are on open source, but they are generating more tokens on open source than closed source. We think open source is actually going to be very important, very valuable. I think it's important for, from a data sovereignty standpoint, folks wanting to own their own models, own their own intelligence. I think there's far more private data that's untapped that can actually lead to massive performance gains.

• 49:42Harry Stebbings?

  Do you think we will exist in a world where many companies, both mid-market and large enterprise, will have their own models with their own data and that will actually cannibalize a lot of frontier model business?

• 49:53Speaker 00

  I think it's possible, but I do think there's always going to be demand for the frontier. Like the frontier is-

• 49:58Harry Stebbings?

  I'm not saying there's not, but you're looking at your Harveys and your Rams and your, you name it. And you're an investor in McColl, you're an investor in Fireworks, and both of those enable that. They propagate that as a marketing message for sure. And I'm just wondering, is that true?

• 50:13Speaker 00

  I do think that the model capabilities today, I think there's massive room for improvement by incorporating more private repositories of data. You can do that with the frontier models that are closed source. You can also do that with open source and own the model yourself. I think it's going to be some combination of those things in the future. I don't think it's going to be like one approach is going to be the dominant approach. I, I know, I know there's a lot of exciting stuff happening between folks like cognition, post-training their own model, you know, with a lot of their own data. You know, Harvey kind of maybe trying to take a similar approach, you know, from a legal perspective. There are a lot of these amazing, you know, domain-specific model, you know, approaches to things. The frontier labs are also doing that as well, right?

• 50:57Speaker 00

  So they're also looking at that and they're trying to basically increase the surface of, of knowledge.

• 51:02Harry Stebbings?

  What does the managed inference market look like in five years?

• 51:05Speaker 00

  The key aspect is that you want to provide an easy way for companies to access intelligence. You know, it could be their own intelligence, right? It is hosting these custom bespoke models that are very domain-specific for their specific use case. And it may be post-trained on a lot of their, you know, own private data. But, you know, the key aspect to managed inference is like, it abstracts away a lot of the infrastructure complexity of managing this service across a lot of different GPUs, a lot of different data repositories, a lot of different models in terms of like how, how the queries get routed and, and which models they use.

• 51:46Harry Stebbings?

  I would love to do a quick fire round with you because there's many questions that I want to ask that I'm also aware that you have to get on with your life. Okay. So if we start with one, parenting. I heard from so many of your friends and investors, you're an unbelievable dad as well. If you were to advise me on like, how do I crush it at work and be a great dad? Very important. What are the must do's? What are the must not do's?

• 52:09Speaker 00

  It's really about prioritizing it. Spending times with my kids is, is like my, one of my greatest joys in life. It is-

• 52:17Harry Stebbings?

  I get it, dude, but like you can't, like you're raising $3.9 billion.

• 52:20Speaker 00

  No, sure. It's, it's such a cliche, but having kids is the best thing in the entire world. And, you know, I have a very busy schedule. I have a very challenging, you know, I have a lot of commitments at work. I'm on the road a lot, but it's about prioritizing things. Oftentimes what, what that means is like, I end up taking worse flights, right? I take a lot of red eyes just so I don't miss a bedtime with my kids. I'm home almost every weekend. You know, those weekends are very committed to coaching soccer or helping with swim or, you know, whatever activity that my kids are very passionate about. And then I think carving out very specific times that, you know, people at the company know I'm very unreachable, right? So if I'm home in, in, in the Bay Area, everybody knows that I'm unreachable from 7:00 to 8:00 AM.

• 53:05Speaker 00

  It's like, I'm not taking any meetings at that point because I'm making my kids breakfast, I'm getting them ready for school, getting them pumped up for the day, and, you know, I'm just unreachable in days when I'm, when I'm home.

• 53:15Harry Stebbings?

  What do you believe that many people disagree with you on and makes you unpopular?

• 53:20Speaker 00

  I guess right now that data centers are a good asset for communities and they should be celebrated in the communities where we build them. And again, this comes down to all of these benefits that we feel like we can provide, whether it's, you know, economic, whether it's energy, whether it's the investments we're making in communities from a school perspective, from a-

• 53:39Harry Stebbings?

  Do you worry about the income inequality and the wealth inequality that we're seeing?

• 53:43Speaker 00

  Uh, no, I don't worry about income inequality or wealth inequality because I think there's something inherently human about like, hey, I have this and that other person has that. And, you know, why don't I have that? There is like a bit of like a jealous neighbor type thing. But I do think that it's natural when you have these huge things that are driving economic progress, where everybody's actually benefiting far more than if we didn't have this investment happening.

• 54:13Harry Stebbings?

  They are, but three IPOs are the cumulative value of 45 years of IPOs. The concentration of value is unprecedented.

• 54:21Speaker 00

  Right, but think about all the value that's being created for the world. The market cap of the company is one is one thing. But actually, if you look at the value that's being created by, you know, abundant intelligence that, you know, is going to drive massive productivity gains for the global economy, it's many fold of the market cap that's being created.

• 54:38Harry Stebbings?

  What have you changed your mind on in the last 12 months?

• 54:41Speaker 00

  I think maybe something that I've changed my mind on is what creates ingrained long-term competitive advantages. I think a lot of, you know, we try to think of VCs love to ask about what's your moat? Like, what's your, you know, what's your long-term moat? What's going to create, you know, sustained above-market returns for you? I think what I've changed my mind on is like, most moats are illusion. Most moats don't exist. Most of them are ephemeral and especially during a moment of accelerating technological progress and capabilities of these models. I think a lot of it just comes down to being able to move quickly and adapt efficiently to the ever-changing chessboard that's in front of us.

• 55:23Harry Stebbings?

  I think everything starts off as a moatless wrapper. And it's the value that you build on top of it in segments.

• 55:30Speaker 00

  Yeah.

• 55:30Harry Stebbings?

  Will Crusoe be public by the end of 2028?

• 55:33Speaker 00

  I'm not sure. The company itself is requires a lot of capital to build data centers, to build AI factories, to deploy large-scale clusters of GPUs, to build out that infrastructure layer of intelligence upon which, you know, so much global economic value is going to be created. That requires a lot of capital. Being being public, there's a lot of advantages to being public and being able to access, you know, scaled capital resources. So we do think that ultimately the company is probably better off in the public markets. It's just a matter of like, when does that make sense for us?

• 56:04Harry Stebbings?

  Who do you not have on your board that you would love to have on your board?

• 56:10Speaker 00

  Good question.

• 56:11Harry Stebbings?

  I would love to have Zach.

• 56:12Speaker 00

  Yeah, um, I would say Michael Dell. He's not going to join my board. I just have tremendous admiration for Michael, just as a human being. I think he's an incredible person. He's built an incredible business. He's very, he's got incredible business instincts. He's a great family man. I just really look up to him a lot.

• 56:30Harry Stebbings?

  I spoke to Zach before this.

• 56:32Speaker 00

  Zach's an awesome guy.

• 56:33Harry Stebbings?

  He told me you're a douche.

• 56:37Harry Stebbings?

  Oh, bugger, sorry, I was anonymous. I didn't see that. That's so funny. Totally get that. Final one. What would the chase of 2018 find most unbelievable about the Crusoe of today?

• 56:51Speaker 00

  I think the chase of 2018 would find the fact that this all worked just so unbelievable because it was big, bold and ambitious, you know, to build out this AI cloud platform. The energy-first narrative would would come to fruition and energy truly would become the bottleneck to scaling the layer of intelligence. That it would have happened at this scale, I think, is it would have been almost unbelievable eight years ago. And when I think back to that era, you know, there were there were certain people that like gave me great advice, like during that period. And I had this a dear friend that, you know, was an entrepreneur. And, you know, I think his company at the time was about 150 people. And, you know, the, when you're first starting a company, the idea of having

• 57:34Speaker 00

  150 people working for you is like an almost unthinkable number of people. Like, it's like a, how am I ever going to have 150 people work for me? And, you know, today Crusoe is approaching 2,000 people, which is just like, you know, even when I say it, it sounds like an insane number of people that have bet these ultra-productive years of their career on Crusoe. And, you know, they're they're with us building in the trenches every single day. I just don't take it for granted, but people have invested their time with us. I think it's like such an important investment that these people have made in sort of making the bet that this is an important thing that I want to spend these years working on.

• 58:12Harry Stebbings?

  I've so loved having you on. As I said, it was such a joy stalking you and hearing the many stories that I did hear about you. So thank you so much for joining me. And this has been fantastic.

• 58:22Speaker 00

  Thank you so much for having me. It's great.

• 58:26Harry Stebbings?

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• 59:09Harry Stebbings?

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• 59:51Harry Stebbings?

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