Leopold Aschenbrenner — 2027 AGI, China/US super-intelligence race, & the return of history

**Dwarkesh Patel** (0:00)
, today, I'm chatting with my friend, Leopold Aschenbrenner. He grew up in Germany, graduated valedictorian of Columbia when he was 19 And then he had a very interesting gap year, which we'll talk about. And then he was on the OpenAI Super Alignment Team, may it rest in peace.
And now he, with some anchor investments from Patrick and John Collison and Daniel Gross and Nat Friedman, is launching an investment firm. So Leopold, I know you're off to a slow start, but life is long and I wouldn't worry about it too much. You'll make up for it in due time.
But thanks for coming on the podcast.

**Leopold Aschenbrenner** (0:37)
Thank you. You know, I first discovered your podcast when your best episode had, you know, like a couple hundred views. And so it's just been, it's been amazing to follow your trajectory. And it's a delight to be on.

**Dwarkesh Patel** (0:48)
Well, I think in the Schultor in Trenton episode, I mentioned that a lot of the things I've learned about AI, I've learned from talking with them.
And the third part of this triumvirate, probably the most significant in terms of the things that I've learned about AI has been you. We'll have a lot of the stuff on the record now.

**Leopold Aschenbrenner** (1:04)
Great.

**Dwarkesh Patel** (1:06)
Okay, first thing I had to get on record, tell me about the trillion dollar cluster.
But by the way, I should mention, so the context of this podcast is today, there's, you're releasing a series called Situational Awareness. We're going to get into it. First question about that is, tell me about the trillion dollar cluster.

**Leopold Aschenbrenner** (1:20)
Yeah. So, you know, unlike basically most things that have come out of Silicon Valley recently, you know, AI is kind of this industrial process.
You know, the next model doesn't just require, you know, some code, it's building a giant new cluster. You know, now it's building giant new power plants, you know, pretty soon it's going to be building giant new fabs.
And, you know, since Chatchity, this kind of extraordinary sort of techno capital acceleration has been set into motion. I mean, basically, you know, exactly a year ago today, you know, NVIDIA had their first kind of blockbuster earnings call, right? Where it like went out 25% after hours and everyone was like, oh my God, AI, it's a thing. You know, I mean, I think within a year, you know, NVIDIA data center revenue has gone from like, you know, a few billion a quarter to like, you know, 20, 25 billion a quarter now, and, you know, continue to go up like, you know, big tech capex is skyrocketing. And, you know, it's funny because it's both, there's this sort of this kind of crazy scramble going on, but in some sense, it's just the sort of continuation of straight lines on a graph, right? There's this kind of like long run trend, basically almost a decade of sort of training compute of the sort of largest AI systems growing by about, you know, half an order of magnitude, you know, 0.5 booms a year.
And you can just kind of play that forward, right? So, you know, GPT-4, you know, rumored or reported to have finished pre-training in 2022 You know, the sort of cluster size there was rumored to be about, you know, 25,000 H100s, sorry, A100s on semi-analysis.
You know, that's roughly, you know, if you do the math on that, it's maybe like a $500 million cluster. You know, it's very roughly 10 megawatts.
And, you know, just play that forward, half a new one a year, right? So then 2024, that's a cluster that's 100 megawatts. That's like 100,000 H100 equivalents. You know, that's, you know, costing the billions. You know, play it forward, you know, two more years. 2026, that's a cluster that's a gigawatt. You know, that's sort of a large nuclear reactor size. It's like the power of the Hoover Dam. You know, that costs tens of billions of dollars. That's like a million H100 equivalents. You know, 2028, that's a cluster that's 10 gigawatts, right? That's more power than kind of like most US states.
That's, you know, like 10 million H100 equivalents. You know, it costs hundreds of billions of dollars. And then 2030, trillion dollar cluster, 100 gigawatts, over 20% of US electricity production, you know, 100 million H100 equivalents. And that's just the training cluster, right? That's like the one largest training cluster, you know? And then there's more inference GPUs as well, right? Most of, you know, once there's products, most of them are gonna be inference GPUs. And so, you know, US power production has barely grown for like, you know, decades, and now we're really in for a ride.