AI Efficiency Is Repricing The Compute Market | Steve Hou

**Felix** (0:00)
Nothing said on Forward Guidance is a recommendation to buy or sell any investments or products.
All right, everybody, welcome back to another episode of Forward Guidance. And joining me today is repeat guest of the show, Steve Hou, who just joined me a couple of months ago, right at the tail end of when you were at Bloomberg. But now you're at Silicon Data, head of research. You are the man behind some of the most important charts and indices in the world of AI right now. There's a lot to get into, but yeah, really excited to have you back now under your new role at your new company. So congrats on starting there. And yeah, great to have you back, Steve.

**Steve Hou** (0:40)
Yeah, thank you, Felix. Great to see you always. And thank you for having me back on.

**Felix** (0:45)
Yeah, awesome. For those that don't know about Silicon Data and what you guys do, we'd just love to hear a bit about like why you made the shift to joining them, what you guys do, and yeah, what's how you think about the state of AI right now after.

**Steve Hou** (1:01)
So yeah, I left Bloomberg about two months ago, joined Silicon Data almost two months ago, but on the day.
And Silicon Data is a company that tries to bring data to physical AI compute market. And I think the easiest thing to think about is we are trying to bring futures contracts in derivatives to the AI physical compute and allow people to hedge the essential risks that are involved with this now enormous and evolving AI compute market that's hundreds of billions, if not trillions in size. And there's a lot of risks that's being held in equity form and in a fixed income form. And in our view, sometimes probably not perfectly efficiently. And there's a lot of, I think, risks that's now involved with AI compute and GPU income. Data can be handled with traditional financial instruments that is well on the historical financial markets or capital markets. So to the extent that on the natural hedging side of the data providers, other providers, compute providers, all the companies that are looking to buy compute, compute futures contracts is a very natural way to hedge out that risk. And maybe actually, in fact, to help you be a bit bolder in terms of at the outset, how much compute you actually acquire. So you don't find yourself being, I think, overexposed or maybe not having enough, which seems to have been the case with some of the AI labs.
I joined the company because I felt like, Kai, I have been working on public markets, benchmarking systematic indices at Bloomberg for six years. This feels like a natural way to become exposed to the AI sector. Same sort of similar set of skills that apply to AI.

**Felix** (3:01)
Yeah. Awesome. So why don't we get into, we'd love to just hear a bit about, like why is hedging and futures contracts a necessity for the AI compute build out? Like who are the ideal target customers, and what are they trying to hedge, and why? Like is it the hyperscalers? Is it the frontier models? Is it just downstream companies that are utilizing these models and are trying to hedge out their inference demands? Like what does that landscape look like?

**Steve Hou** (3:34)
So, I mean, the demand for it should really come from all major participants in this market that in the same way you would expect in the crude market or even agriculture historically, wherever futures contracts first evolved, as this market eventually become more, I think, fragmented, right? Right now, the AI compute market is very much dominated by a couple of big players, a couple of big sellers, very, very granular, right? So you've got the two major labs, OpenAI and Throatpick, that account for ostensibly half of the AI compute demand. And then you've got, on the other hand, the hyperscalers that are providing most of the compute and building most of the compute.
As we have now seen with the rise of very powerful open models and enterprise AI use, actually, if you think about how enterprises will ultimately adopt AI, they're going to actually not just basically pick a winner. This debate of who is going to emerge as the platform which everyone is going to build on top of, I think that's already been settled. Nobody is going to win it outright, and companies don't feel comfortable, and the labs themselves, I think, it's just not. So what's going to happen is that orchestration layer is going to accrue a lot of the value. Companies are going to retain their sovereignty by basically making models more substitutable.