The Exchanges

Q dynamic in the market is that because there is so much of a supply constraint in providing power or generation or whatever, um, you know, the, the supplier can demand more out of the customer. So yeah, is that becoming a challenge for the NeoClaus? Is it putting them at a competitive disadvantage in being able to build capacity relative to like the hyperscalers who obviously have big balance sheets?

A Yeah, and a pretty massive one. Um, you can just look at the numbers, you know, CoreWeave, they have, you know, three and a half gigawatts of contracted power. Contracted for that means signed leases for the most part, some self-built, mostly signed leases with third parties. And what you saw was that this number was about, if I remember correctly, 1.3 gigawatts in Q four, 20, 24. Uh, so they've scaled that up pretty fast. Uh, but since Q three, 25, they haven't really been able to secure more. And that sort of coincided with the overall tightening of financial conditions, where you saw a pretty massive bond sell-off, which impacted the likes of CoreWeave, Oracle, and many of these guys. And suddenly, sort of, the high-yield market froze to some extent, right? And that's also, you know, obviously related to the fact that this market is not that big, and basically, you know, they massively increased the supply on that market. Anyways, we get, we sort of get to where we are today, which is that it's getting pretty tough for these companies to get the financing Um, for all of these parts. And they're all, as you said, more and more capital intensive. Utilities now are asking multi-billion dollar commitments for gigawatts, uh, gigawatts of power. Turbines and so on and so forth is the same thing. So yes, pretty massive disadvantage. And again, like I go back to what I said earlier,…

AI assessment note: “Yeah, and a pretty massive one. Um, you can just look at the numbers”

Q a second first. When you think of the hyperscalers and how they approach power, provisioning power, getting enough power to build the infrastructure that they want for AI, do you think of them as being Fairly monolithic, and they are all approaching, basically, do they all have the same strategy in your mind, um, and they're just in a land grab, or do you see meaningful differences within that group?

A I think there's pretty meaningful differences, uh, company by company. You see really varying degrees of, uh, first of all, USA versus international, uh, appetite to sort of behind the meter versus grid connection, um, Sort of location of data center, how close to the end user versus sort of middle of nowhere be campuses. Um, so I would say pretty different overall. Uh, also, also with regards to the way they negotiate with utilities. Generally speaking, I think it's fair to say that Google is the most sophisticated company. Um, and on the energy side, they have sort of the biggest, you know, trading desks. They've choked some pretty big deals with utilities, as you probably know, for low flexibility, uh, kind of stuff. So they're definitely sort of at the frontier of innovating on, on the energy side. Another way you'd see this is when you look at the minutes of the conversations with officials in, you know, PJM, ERCOT, you always see Google's name. You generally see them more than others. So I would say probably the most sophisticated company is Google. But other companies, other companies have different strategies. For example, I would say Meta was probably the first among the four big guys to adopt behind the meter at bigger scale.

AI assessment note: “I think there's pretty meaningful differences, uh, company by company.”

Q you can tell me, like, where you think the comparison lies today. But historically higher capex, somewhat higher efficiency, um, Um, the main thing seems to be availability, which is, like, Bloom was not sold out till 2031, and so they were able to take advantage, and, and particularly with Oracle, it seems. But, like, how do you think about fuel cells in that cascading chain that you described before?

A Yeah, I, I think the, the biggest disadvantage that fuel cells have, um, is not really cost. It matters, but not so much these days. I can explain why, but generally not. Um, I would say it's, as a bridge power solution, it's really bad. Uh, because Blue Energy fuel cells, you know, they have to run extremely hot. And so, uh, if you want to use them as backup, uh, this basically takes two days, you know, to go from like zero to a hundred. Uh, whereas aero derivatives, as you know, can, you can scale up fairly fast, uh, Reciprocating engines can scale up fairly fast. And a lot of folks, um, the, the usual hope of behind the meter was that it's all going to be bridge power. Uh, is I'm going to deploy sort of these power plants for, you know, a year, two years, maybe three years, and then the good is going to come, and hey, maybe I'm going to use this as backup. Uh, in many cases, you see folks starting with sort of lower redundancy, no diesel gensets, like that. And so in some sense, Bloom is like the ultimate play on power constraints. Because it's to play an island at data centers. And if you do bloom, you're basically islanded for life. Uh, either that, or maybe you get good at some point, and then you move your fuel cells to some other location, uh, but you can't use them as backup. It's not a very efficient solution for backup purposes.

AI assessment note: “the biggest disadvantage that fuel cells have... as a bridge power solution, it's really bad”

Q but that's not entirely true, right? They are building their own capacity as well, and, and back to the point of the, like, energy teams, you know, both OpenAI and Anthropic have started to hire up energy teams. They're small and, small but mighty at this point compared to the hyperscalers, but what, what is your perspective on what OpenAI and Anthropic are doing from an energy perspective themselves, right?

A Yeah, so you've seen them, uh, work at different layers. Um, I, I think in a lot of time, they, they're looking at sites themselves. Uh, and you, you could argue, for example, Stargate was kind of a two-way street between OpenAI and Oracle, where, sort of, they were both involved in the decision-making process to get this done. Right. I, I think both of them evaluate a lot of, sort of, powered land, uh, sites. They look at their different options. They hire a bunch of people internationally, uh, as well. To look at how, how, how, how, what do these, what do these markets look like? Um, and then, you know, once they sort of find sites that they like, they can bring in partners, uh, whether it be Microsoft or Oracle or Cori and so on and so forth, right? I, I think overall, like, their, their biggest problem is that they, they just need a lot. Uh, they, they just need a lot. Um, and they have a financing constraint, uh, in the sense that they're obviously not investment grade. Um, and a lot of this is very capital intensive and it's a prompt capex that they just can't afford. Um, So the, the, that sort of slows them down in their ambition and desire to be more vertically integrated, uh, which creates, you know, very large market opportunity for hyperscalers. And again, as I was saying before, hyperscalers are essentially, you know, half of their business, Amazon, Microsoft, um, i…

AI assessment note: “I think in a lot of time, they, they're looking at sites themselves.”