Andrej Karpathy — AGI is still a decade away

**Dwarkesh** (0:00)
Today, I'm speaking with Andrej Karpathy. Andrej, why do you say that this will be the decade of agents and not the year of agents?

**Andrej Karpathy** (0:07)
Well, first of all, thank you for having me here. I'm excited to be here. So the quote that you've just mentioned, it's the decade of agents, that's actually a reaction to an existing, pre-existing quote, I should say, where I think some of the labs, I'm not actually sure who said this, but they were alluding to this being the year of agents with respect to LLMs and how they were gonna evolve. And I think I was triggered by that because I feel like there's some over-predictions going on in the industry. And in my mind, this is really a lot more accurately described as the decade of agents. And we have some very early agents that are actually extremely impressive and that I use daily, you know, Claude and Codex and so on. But I still feel like there's so much work to be done. And so I think my reaction is like, we'll be working with these things for a decade, they're gonna get better and it's gonna be wonderful. But I think I was just reacting to the timelines, I suppose, of the implication.

**Dwarkesh** (0:58)
And what do you think will take a decade to accomplish? What are the bottlenecks?

**Andrej Karpathy** (1:02)
Well, actually make it work. So in my mind, I mean, when you're talking about an agent, I guess, or what the labs have in mind, and what maybe I have in mind as well, is it's, you should think of it almost like an employee or like an intern that you would hire to work with you. So for example, you work with some employees here. When would you prefer to have an agent like Claude or Codex do that work? Like currently, of course, they can't. What would it take for them to be able to do that? Why don't you do it today? And the reason you don't do it today is because they just don't work. So like they don't have enough intelligence, they're not multimodal enough, they can't do computer use and all this kind of stuff. And they don't do a lot of the things that you've alluded to earlier. They don't have continual learning. You can't just tell them something and they'll remember it. And they're just cognitively lacking and it's just not working. And I just think that it will take about a decade to work through all those issues.

**Dwarkesh** (1:44)
Interesting. So as a professional podcaster and a viewer of AI from afar, it's easy to identify for me, like, oh, here's what's lacking. Continual learning is lacking or multimodality is lacking. But I don't really have a good way of trying to put a timeline on it. Like, if somebody's like, how long will continual learning take? There's no prior I have about like, this is a project that should take five years, 10 years, 50 years. Why a decade? Why not one year? Why not 50 years?

**Andrej Karpathy** (2:16)
Yeah, I guess this is where you get into a bit of, I guess, my own intuition a little bit and also just kind of doing a bit of an extrapolation with respect to my own experience in the field. So I guess I've been in AI for almost two decades. I mean, it's going to be maybe 15 years or so, not that long. You had Richard Sutton here who was around, of course, for much longer. But I do have about 15 years of experience of people making predictions of seeing how they actually turned out. And also I was in the industry for a while and I was in research and I've worked in the industry for a while. So I guess I kind of have just a general intuition that I have left from that. And I feel like the problems are tractable, they're surmountable, but they're still difficult.
And if I just average it out, it just kind of feels like a ticket, I guess, to me.

**Dwarkesh** (2:58)
This is actually quite interesting. I want to hear not only the history, but what people in the room felt was about to happen at various different breakthrough moments. What were the ways in which their feelings were either overly pessimistic or overly optimistic? Should we just go through each of them one by one?

**Andrej Karpathy** (3:16)
Yeah, I mean, that's a giant question because, of course, you're talking about 15 years of stuff that happened. I mean, AI is actually so wonderful because there have been a number of, I would say, seismic shifts that were like, the entire field has suddenly looked a different way, right? I guess I've maybe lived through two or three of those, and I still think there will continue to be some because they come with some kind of almost surprising irregularity. Well, when my career began, of course, when I started to work on deep learning, when I became interested in deep learning, this was just by chance of being right next to Jeff Hinton at University of Toronto. Jeff Hinton, of course, is the godfather figure of AI. He was training all these neural networks and I thought it was incredible and interesting, but this was not the main thing that everyone in AI was doing by far. This was a niche little subject on the side. That's maybe the first dramatic seismic shift that came with the AlexNet and so on. I would say AlexNet reoriented everyone and everyone started to train neural networks, but it was still very per task, per specific task. Maybe I have an image classifier or I have a neural machine translator or something like that, and people became very slowly actually interested in basically agents, I would say.