Compute boom risks depend on how providers finance and sell capacity

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 14:10

   Everyone in the markets feels like is looking for reasons to doubt the compute trade, and you've seen it with CoreWeave, you've seen it with the neoclouds in particular and the debt markets. I'd be curious to hear what you make of the moment wherein you had, I think, kind of alluded to it earlier. But you all are in a different spot, obviously, but I do think there's systemic risk a little bit that everyone's trying to figure out, like, how bad is it?

2. 14:34

   Yeah. I don't think it's systemic risk, actually. I do think that the risk is localized because the bets that people are making are materially different. And everybody has to go kind of make their own bets. I think when you've completely debt financed at a relatively high rate, you have a much smaller error bars as to like when your cash flows are going to land. You also have much smaller error bars when you have high concentration of customers and concentration of workloads.

3. 15:02

   And so if you go and look at the workloads that are in many of the NeoClouds, they're really large training clusters for the most part. And those really large training clusters are largely being bought by a small number of customers. And they're all, you know, debt financed at relatively high rates. That is not the business that we're in. We, you know, we both have data centers and computers, but other than that, like the business is actually quite different.

4. 15:26

   Most of our business is spread across the millions of enterprises and startups and small, medium-sized businesses and governments that are running on top of us. And much of that business is core compute. It's compute clusters. It's HPC workloads. It's websites. It's backup storage. It's databases. It's, you know, a lot of that kind of workload. And then the AI workload, the majority of our AI workloads are inference today.

5. 15:52

   Yeah. And so that inference is effectively just, if you really look at inference today, it is just another building block as part of an application, right? Your applications all just have AI embedded in them. It might be AI that's helping somebody serve better ads. It might be AI that's helping you, you know, auto-suggest something in your email. It's, you know, there's a whole bunch of things that it might be doing.

6. 16:12

   It might be AI that's helping traders figure out, like, what their, you know, like, stock market trade is going to be. But it's just part of the application that they're using. And so that is a very distributed workload. And so even if the usage is concentrated on Anthropic and OpenAI and a handful of model providers, the actual end users are thousands and thousands and thousands and thousands of folks out there running inference for us.

7. 16:34

   And so the systemic risk of like one or two companies not doing well, even if it's one of those model providers, it'll fall over to another. Like people aren't going to stop doing inference. They'll find a different model to use. One might be an OpenOids model. It might be open. We're going to have computers. You can put it on those computers. And so for us, that risk is very different. You all have very large investments in OpenAI and Anthropic,