Neocloud contracts differ from hyperscalers’ self-build plans

Transcript

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

1. 20:51

   want to be clear about one thing dylan was talking about there in terms of termination versus what's going on with meta and the neoclouds which is that generally speaking these hyperscalers or you know large frontier labs who are signing up with contracts for the neoclouds do not have arbitrary cancellation rights on like a monthly basis where they can just pull the plug and the contract's done. Generally speaking, they're signing five-year, six-year term links, and they're off taking the full capacity. And they have a set hourly rate and total contract value that's very attractive to the neocloud providers because those guys have gone out and secured land and power and built a site and even, in some cases, purchased the chips prospectively before they've actually gone and done that contract. What Dylan's referring to with the potential to slow down or pause or

2. 21:35

   cancel projects, it's more with respect to the self-build sites, which is to say like Meta has massive campuses that are going to serve as more of a base load of capacity going into the future, which they can either choose to buy chips for or just leave as an empty shell for years if they want to slow down and not go as hard as they can. And generally speaking, construction costs, while very expensive, are a small percentage of the total cost of a new chip project like in other words the concrete and the steel and the walls is quite you know it's important and big and you know very a real challenge to get it done but on a dollar basis like the hbm and the optics and the gpus themselves are the the bigger portion of the bill um and so you know what it's what it's looking like right now is that all of these big

3. 22:25

   companies are really needing to contend with the massive growth that they're expected to pursue by their demand from their customer base. Either that's Meta or OpenAI or Anthropic who have demand on their consumer-facing applications like a ChatGPT or CloudCode or Codex or even Muse from Meta, things like that. In order to serve that demand, they need GPUs and it's profitable to serve the demand. So they need to ramp up and be prepared to roll this thing out to the billions of monthly active users on the meta.

4. 22:57

   Different, you know, surfaces that they serve and approaching a billion of monthly active users for ChatGPT and hundreds of millions for cloud code and things like that. So anyway, with that distinction in mind, what's the risk with project cancellation or a downturn or things like that? I mean, in order for us to forecast some sort of change in the demand supply curve there, you'd really need to see demand slow.

5. 23:28

   There are hard constraints on how many chips you can produce, how many data centers you can build, and how fast this stuff is going to come online. And as of right now, all of the modeling we can do basically shows that demand outstrips supply. And so is demand 10% more than supply right now? Is it 50%? is it infinite i mean we we don't get to answer that question because we are not able to catch up to demand with the supply of gpus that you can bring online right now and so when that question gets answered if it gets answered if we find limits to the demand for knowledge work to the demand for intelligence from the economy then we'll we'll be able to figure out who needs to slow at what time where and how that plays out yeah but so far i guess those limits haven't been found so um all right uh before we get into cluster max one last topic i want to cover uh