Invest Like the Best with Patrick O'Shaughnessy
· Patrick O'Shaughnessy, Matthew Smith
[00:00:00] S0: Who are the big losers, do you think, in this future? [00:00:00] S1: Well, sadly, the biggest losers of this will be the US consumer, to the point where you take what I'm saying and if we're even partially right, electricity prices rise, which you can see some of on the forward curves in these different markets. As electricity prices rise, you start to think about the trade-off. Are we going to export natural gas to foreign buyers? Are we going to use it for AI compute, or are we going to try to keep consumer electricity price bills low? It's an awful trade-off. I think it will probably start to contribute more to the public dialogue. The NIMBYism that we're seeing already pop up in some places. We think AI is tremendously transformational. We're not anti-AI, but it consumes a lot of power, and we need to really focus on the 2030 to 2035 period. And the US consumer's probably going to pay the bill in the meantime. Please note that most of the solutions being proposed by the government are to consume more gas because everybody believes it's plentiful. Bring your own generator or generation. BYOG is a thing today. That's what the hyperscalers are being asked to do to site their data center in a certain lat and long. That means more gas, not less. And so every time you read a press release from Bloom or from— [00:00:00] S0: Think more gas.
· Patrick O'Shaughnessy, Matthew Smith
[00:00:00] S0: Think more gas. [00:00:00] S1: And you can use the energy efficiency of each one of those units and understand exactly how much more incremental gas beyond the base case that I just shared is dangerously tight. Another loser, and I want to be respectful, but some of the biggest winners so far, at least in the stock market, have been the manufacturers of gas turbines or distributed power gensets. And when you think about those companies, it's been somewhat boom and bust. In the early 2000s, there was a boom to build as many gas plants as we could. The capacity was overbuilt and the industry really languished for a long time until now. And you've had tremendous profitability and equity returns come from these companies over the last two years. But as you look at 2028, 2029, most of them are adding more capacity again, just like they did in the early 2000s. [00:00:00] S0: What kinds of companies are these?
· Patrick O'Shaughnessy, Matthew Smith
[00:00:00] S0: What kinds of companies are these? [00:00:00] S1: Just like Caterpillar. Caterpillar, I think, is doubling their Solar Turbines capacity between now and the end of '29, which I would judge is at the exact wrong time when people may be questioning whether they even want to deploy those assets because the gas is much more expensive than they planned. Bloom Energy, they've been topical recently because of other things that folks are talking about, the rare earths they use in their manufacture, for instance. But for us, we don't think that Bloom Energy's assets at two gigawatts or more will be able to get natural gas in competition with all of the other assets that are being deployed that will consume gas given the scarcity that we see. So those are two, we'll call it manufacturers of distributed generation or BTM generation that we think are probably more poorly positioned than investors appreciate. It really doesn't make sense to us, beyond 2029-2030, to build large-scale natural gas generation until we ramp up production meaningfully and you can make sure we have the security of deliverability of supply of gas that's consistent with our model. We could see orders slow very meaningfully for natural gas generating assets, even at large scale, as '26 progresses. And those could be some of the losers — it may not make sense to use gas for power generation for new or incremental assets after a certain point.
· Patrick O'Shaughnessy
[00:00:00] S0: It seems like this whole memory shortage thing that we're going through right now, that hyperscalers might also be in trouble here if this is a key input to what they're doing. Do you think that's a big problem for them?
· Matthew Smith
[00:00:00] S1: So as we've been socializing this a little bit, trying to learn more and have people poke holes, I've shared this with one of your recent guests and he listened to this. He said, well, this sounds like DRAM two years ago. Slowly at first and then all at once. The lack of investment in capacity expansion is going to come up to bite us. And I think that's where the analog starts. When we think about the way this plays out and other analogs, that's probably the best one. And when we think about the cost of the hyperscalers, right now energy's budgeted to be about 10% of their cost. Depreciation's the highest. Memory and other things factor into that as well, but total cost of energy is supposed to be about 10%. If you plug in all of this compute and it's gas-powered, and we think gas could double or triple structurally, even without weather, it could end up being 20 or 30% of the cost of compute by 2029. We do think it becomes a much more material issue. Now, the levelized cost of energy, LCOE as it's referred to, takes into account CapEx, it takes into account cost of fuel. Everybody who is making decisions in this moment is using the forward curve for natural gas, which is flat. A little backwardated, a little contango, mostly flat out to the 2030s in the mid-3s. That is a very attractive low-cost fuel for the hyperscalers to commit to when they're focused on solving everything else. Like, how do I get compute in place to manifest this revenue growth at Anthropic or elsewhere? For us, we're just focused on modeling objectively — when you plug in this compute and this power gen source here and there, how exactly does it pull on the system of companies that we focus on?
· Patrick O'Shaughnessy
[00:00:00] S0: If you were forced to play devil's advocate in all of this and come up with the set of circumstances such that this is all much ado about nothing and we're sitting here in 2030 and gas costs $3, what do you think is the most likely reason? Is it data center power requirements are much lower because we make performance breakthroughs, or AI demand isn't what we think it's going to be like? What is this most sensitive to such that it might be wrong?