AI timelines hinge on an exponential, not an AGI label

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 3:33

   So all of this is coming from your timeline. Basically, it seems like you have a shorter timeline than most. And so you were feeling a sense of urgency to get out there because you think that this is imminent. Yes, I'm not sure. You know, I think it's very hard to predict, particularly on the societal side. So if you say, you know, when are people going to deploy AI or when are companies going to use, you know, X dollars of spend of AI or, you know, when will AI, you know, be used in these applications or when will it drive these medical cures?

2. 4:09

   that's kind of harder to say. I think the underlying technology is more predictable, but still uncertain. Still no one knows. But I think on the underlying technology, I've started to become more confident. There isn't no uncertainty about it. I think the exponential that we're on could totally peter out. I think there's maybe, I don't know, 20% or 25% chance that sometime in the next two years, the models just stop getting better for reasons we don't understand, or maybe reasons we do understand, like data or compute availability.

3. 4:45

   And then everything I'm saying just seems totally silly. And everyone makes fun of me for all the warnings I've made. And I'm just totally fine with that, given the distribution that I see. And so I should say that this is part, our conversation is part of a profile I'm writing about you. I've spoken with more than two dozen people who've worked with you, who know you, who've competed with you, and I'm going to link that in the show notes.

4. 5:08

   If anybody wants to read it, it's free to read. But one of the themes that has come through across everybody I've spoken with is that you have about the shortest timeline of any of the major lab leaders, and you just referenced it just now. So why do you have such a short timeline, and why should we believe in yours? Yeah, it really depends what you mean by timeline. So one thing, and, you know, I've been consistent on this over the years, is, you know, there are these terms in the AI world like AGI and superintelligence.

5. 5:40

   Like, you'll hear leaders of companies say, we've achieved AGI, we're moving on to superintelligence. Or, like, it's really exciting that someone stopped working on AGI and started working on superintelligence. So I think these terms are totally meaningless. I don't know what AGI is. I don't know what superintelligence is. It sounds like a marketing term. It is marketing. Yeah, it sounds like, you know, something designed to activate people's dopamine.

6. 6:03

   So you'll see in public, I never use those terms. And I, you know. I'm actually careful to criticize the use of those terms. But I think despite that, I am indeed one of the most bullish about AI capabilities improving very fast. The thing I think is real, that I've said over and over again, is the exponential. The idea that every few months we get an AI model that is better than the AI model we got before, and that we get that by investing more compute in AI models, more data, data, more new types of training models. Initially, this was done by what's called pre-training, which is when you just feed a bunch of data from the internet into the model. Now we have a second stage that's reinforcement learning or test time compute or reasoning or whatever you want to call it. I think of it as a second stage that involves reinforcement learning. Now both of those things

7. 6:57

   are scaling up together, as we've seen with our models and as we've seen with models from other companies. And I don't see anything blocking the further scaling of that. There's some stuff about how do we broaden the tasks. On the RL side of it, we've seen more progress on, say, math and code, where the models are getting pretty close to a high professional level and less on more subjective tasks. But I think that is very much a temporary obstacle. So when I look at it, I see this exponential and I say, look, people aren't very good at making sense of exponentials, right?

8. 7:36

   Like, you know, if something is doubling every six months, then, you know, two years before it happens, it looks like it's only one-sixteenth of the way there. And so we are sitting here in the middle of 2025, and the models are really starting to explode in terms of the economy, right? If you look at the capabilities of the model, they're starting to saturate all the benchmarks. If you look at revenue and you know anthropics revenue every year has grown 10x um uh every year we're kind of conservative and we say you know it can't grow 10x this time you know i i you know i never i never assume anything and actually always i'm very conservative in saying ah i think it's going to slow down on the business side but we went from zero to 100 million in 2023 we went from 100 million to a billion in 2024 and you know this year in this first half of the year we've gone

9. 8:26

   from $1 billion to, you know, I think as of speaking today, it's well above four, it might be 4.5. And so if you think about it, you know, suppose that exponential continued for two years. I'm not saying it will. But suppose it continued for two years, you know, you're like well into the hundred billions. I'm not saying that'll happen. I'm saying the situation is that when you're on an exponential, you can really get fooled by it.

10. 8:51

    Two years away from when the exponential goes totally crazy, it, you know, it looks like it's just starting to be a thing. And so that's the fundamental dynamic. You know, we saw that with the internet in the 90s, right, where it was like, you know, were getting fast. And over a few years, it became possible to basically build a digital global communications network on top of all this when it wasn't possible just a few years ago.

11. 9:21

    And almost no one except for a few people really saw the implications of that and how fast it would happen. And so that's where I'm coming from. That's what I think. Now, I don't know. If a bunch of satellites crashed, maybe the internet would have taken longer. If there was an economic crash maybe would have taken a little longer so we can't be sure of the exact timelines but i think people are getting fooled by the exponential and and not realizing how fast it it might be how fast