Dylan Patel — Deep dive on the 3 big bottlenecks to scaling AI compute

**Dwarkesh Patel** (0:00)
All right, this is the episode of My Roommate Teaches Me Semiconductors.

**Dylan Patel** (0:04)
It's also the sendoff for this current set.

**Dwarkesh Patel** (0:07)
Yeah, after you use it, I'm like, I can't use this again. I got to get out here.

**Dylan Patel** (0:11)
No sloppy seconds for Dwarkesh.

**Dwarkesh Patel** (0:14)
Okay, Dylan is the CEO of SemiAnalysis. Dylan, the burning question I have for you, if you add up the big four, Amazon, Meta, Google, Microsoft, their combined forecast of CapEx that you published recently this year is $600 billion. And given yearly prices of renting that compute, that would be like close to 50 gigawatts. Now, obviously we're not putting on 50 gigawatts this year. So presumably that's paying for compute that is going to be coming online over the coming years. So I have a question about how to think about the timeline around when that CapEx comes online. A similar question for the labs, where OpenAI just announced that they raised $110 billion. Anthropic just announced they raised $30 billion. And if you look at the compute that they have coming online this year, you should tell me how much it is. Is it not another 4 gigawatts total that they'll have this year? It feels like the cost to rent the compute that OpenAI and Anthropic will have this year to sustain their compute spend at $10, $13 billion a gigawatt. Those individual raises alone are like enough to cover their compute spend for the year. And then this is not even including the revenue that they're going to earn this year. So help me understand first, when is the time scale at which the big tech capex is actually coming online? And two, what are the labs raising all this money for if like the the yearly price of a one gigawatt data center is like $13 billion?

**Dylan Patel** (1:41)
So when you talk about the capex of these hyperscalers, right, on the order of $600 billion, and you look at the cross the rest of the supply chain, gets you to on the order of $1 trillion. A portion of this is immediately for compute going online this year, right? The chips and the other parts of capex that do get paid this year. But there's a lot of setup capex as well, right? So when we're talking about 20 gigawatts this year in America, roughly.

**Dwarkesh Patel** (2:08)
Incremental.

**Dylan Patel** (2:09)
Incremental added capacity. A portion of this is not spent this year. A portion of that capex is actually spent the prior year. And so when you look at, hey, Google's got $180 billion, actually a big chunk of that is spent on turbine deposits for 28 and 29 A chunk of that is spent on data center construction for 27 A chunk of that is spent on power purchasing agreements and down payments and all these other things that they're doing for further out into the future so that they can set up this super fast scaling, right? And this applies to all the hyperscalers and other people in the supply chain.
And so 20 gigawatts roughly deployed this year. A big chunk of that being hyperscalers, a chunk of that not being. And all of these companies, their biggest customers are Anthropic and OpenAI. Anthropic and OpenAI are in the 2 gigawatt and 2.5 gigawatt and 1.5 gigawatts roughly right now. They're trying to scale to much larger, right? If you look at what Anthropic has done over the last few months, 4 billion, 6 billion revenue added, and if we just draw a straight line, hey, yeah, they'll add another 6 billion dollars of revenue a month. People would argue that's bearish and that they should go faster. What that implies is that they're going to add $60 billion of revenue across the next 10 months, right? $60 billion of revenue at the current gross margins that Anthropic had, at least last reported by media, would imply that they have roughly $40 billion of compute spend for that inference for that 60 bill of revenue. That $40 billion of compute at roughly $10 billion a gigawatt, that rental cost means that they need to add 4 gigawatts of inference capacity just to grow revenue. And that's saying that their research and development training fleet stays flat, right? So, in a sense, Anthropic needs to get to well above 5 gigawatts by the end of this year. And it's going to be really tough for them to get there, but it's possible.

**Dwarkesh Patel** (4:02)
Can I ask a question about that? So, if Anthropic was not on track to have 5 gigawatts by the end of this year, but it needs that to serve both the revenue that's gone crazier than expected, and maybe it's going to be even more than that, plus the research and training to make sure its models are good enough for next year. Where is that going to come from?