Leopold Aschenbrenner — 2027 AGI, China/US super-intelligence race, & the return of history
episodePreviously titled “Leopold Aschenbrenner - China/US Super Intelligence Race, 2027 AGI, & The Return of History” — renamed by the publisher on Aug 3, 2026
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Okay. 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. Then he was on the OpenAI super alignment team, may it rest in peace. 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. 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.
Yeah. Well, I think in the shelter in Trenton episode, I mentioned that a lot of the things I've learned about AI, I've learned from talking with them. 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 get all this stuff on the record now. Great. 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.
Yeah, so unlike basically most things that have come out of Silicon Valley recently, AI is kind of this industrial process. The next model doesn't just require some code. It's building a giant new cluster. Now it's building giant new power plants. Pretty soon it's going to be building giant new fabs. And, you know, since ChatGPT, 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, continuing to go up like, you know, big tech CapEx is skyrocketing. It's funny because there's this crazy scramble going on, but in some sense, it's just the continuation of straight lines on a graph. There's this long run trend, basically almost a decade of training compute of the largest AI systems growing by about half an order of magnitude, 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 just play that forward half a year. So then 2024, that's a cluster that's 100 megawatts. That's like 100,000 H100 equivalents. That's costs in the billions. Play it forward two more years, 2026, that's a cluster that's a gigawatt. That's sort of a large nuclear reactor size. It's like the power of the Hoover Dam. 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 H100s equivalents, you know, 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. And then there's more inference GPUs as well, right? Most of, you know, once there's products, most of them are going to be inference GPUs.
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