AGI is still 30 years away — Ege Erdil & Tamay Besiroglu

**Dwarkesh Patel** (0:00)
Today, I'm chatting with Tamay Besiroglu and Ege Erdil. They were previously running Epoch AI and are now launching Mechanize, which is a company dedicated to automating all work. One of the interesting points you made recently, Tamay, is that the whole idea of the intelligence explosion is mistaken or misleading. Why don't you explain what you were talking about there?

**Tamay Besiroglu** (0:22)
Yeah, I think it's not a very useful concept. It's kind of like calling the industrial revolution a horsepower explosion. Like sure, during the industrial revolution, we saw this drastic acceleration in raw physical power, but there are many other things that were maybe equally important in explaining the acceleration of growth and technological change that we saw during the industrial revolution.

**Dwarkesh Patel** (0:43)
What is the way to characterize the broader set of things that the horsepower perspective would miss about the industrial revolution?

**Tamay Besiroglu** (0:50)
So I think in the case of the industrial revolution, it was a bunch of these complementary changes to many different sectors in the economy.
So you had agriculture, you had transportation, you had law and finance, you had urbanization and moving from rural areas into cities. There were just many different innovations that kind of happened simultaneously that gave rise to this change in the way of economically organizing our society. It wasn't just that we had more horsepower. That was part of it. But that's not the kind of central thing to focus on when thinking about the Industrial Revolution. And I think similarly for the development of AI, sure we'll get a lot of very smart AI systems, but that will be one part among very many different moving parts that explain why we expect to get this transition and this acceleration and growth and technological change.

**Dwarkesh Patel** (1:46)
Yeah. I want to better understand how you think about that broader transformation. Before we do, the other really interesting part of your world view is that you have longer timelines to get to AGI than most of the people in San Francisco who think about AI. When do you expect a drop in remote worker replacement?

**Ege Erdil** (2:05)
Yeah, maybe for me that will be around 2045 or...

**Dwarkesh Patel** (2:09)
Wow. Wait, and you?

**Tamay Besiroglu** (2:11)
I'm a little bit more bullish. I mean, it depends what you mean by drop in remote worker and whether it's able to do literally everything that can be done remotely or do most things.

**Ege Erdil** (2:20)
I'm saying literally everything.

**Tamay Besiroglu** (2:21)
For literally everything, yeah. I just shade, I guess, predictions by five years or by 20% or something.

**Dwarkesh Patel** (2:28)
Why? Because we've seen so much progress over even the last few years. We've gone from Chad GBT two years ago to now we have models that can literally do reasoning, are better coders than me. I studied software engineering in college. I did become a podcaster. I'm not saying I'm the best coder in the world.
But if you made this much progress in the last two years, why would it take another 30 to get to full automation of human brains? I said that wrong. You know what I'm saying. Full automation of remote work.

**Ege Erdil** (3:01)
Yeah. So I think a lot of people have this intuition that progress has been very fast. They just look at the trend lines and just extrapolate. Obviously, it's going to happen in 2027 or 2030 or whatever. They're just very bullish. And obviously, that's not a thing you can literally do. There isn't a trend you can literally extrapolate of, when do we get to full automation? Because if you look at the fraction of the economy that has actually been automated, it's very, by AI, it's very small. So if you just extrapolate that trend, which is something, say, Robin Hansel likes to do, you're going to say, well, it's going to take centuries or something. Now, we don't agree with that view. But I think one way of thinking about this is, like, how many big things are there? How many core capabilities, competences are there that the AI systems need to be good at in order to have this very broad economic impact, maybe 10x acceleration and growth or something? How many things have you gotten over the past 10 years, 15 years? And we also have this compute-centric view.

**Tamay Besiroglu** (4:05)
So just to double-click on that, I mean, I think what Ege is referring to is, like, if you look at the past 10 years of AI progress, we've gone through about nine or 10 orders of magnitude of compute, and we've got various capabilities that were unlocked. So you had, in the early period, people were solving gameplay on specific games, on very complex games. And that happened 2015 to maybe 2020, and Go and Chess and Dota and other games. And then you had maybe sophisticated language capabilities that were unlocked with these large language models, and maybe kind of advanced abstract reasoning. And coding and maybe math, that was maybe another big such capability that got unlocked. And so maybe there are a couple of these big unlocks that happened over the past 10 years, but it takes, you know, that happened on the order of once every three years or so, or maybe one every three orders of magnitude of compute scaling.