Dario Amodei — The highest-stakes financial model in history
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So we talked three years ago. I'm curious in your view, what has been the biggest update of the last three years? What has been the biggest difference between what I felt like last three years versus now?
Yeah. I would say actually the underlying technology, like the exponential of the technology, has gone broadly speaking, I would say about as I expected it to go. I mean, there's like plus or minus a couple there's plus or minus a year or two here. There's plus or minus a year or two there. I don't know that I would have predicted the specific direction of code. But actually when I look at the exponential, it is roughly what I expected in terms of the march of the models from smart high school student to smart college student to beginning to do PhD and professional stuff, and in the case of co
I I wanna understand what that exponential looks like right now because the first question I asked you when we recorded three years ago was, what's up at scaling? Why does it work? And I have a similar question now, but I feel like it's a more complicated question because at least from the public's point of view, three years ago, were these well known public trends where across many orders of magnitude of compute, could see how the loss improves. And now we have RL scaling and there's no publicly known scaling law for it. It's not even clear what exactly the story is of, is this supposed to be
Yeah. So I have actually the same hypothesis that I had even all the way back in 2017. So in 2017, I think I talked about it last time, but I wrote a doc called the big blob of compute hypothesis. It wasn't about the scaling of language models in particular. When I wrote it, GPT-one had just come out, right? So that was one among many things, right? Back in those days, was robotics. People tried to work on reasoning as separate thing from language models. There was scaling of the kind of RL that happened in AlphaGo and that happened at DOTA at OpenAI and people remember StarCraft at DeepMind,
kind
of what we see there. And indeed, those have continued going. Like, you know, I think now it's been widely reported like, you know, we feel good about pre training. Like pre training is continuing to give us gains. What has changed is that now we're also seeing the same thing for RL, right? So we're seeing a pre training phase and then we're seeing like an RL phase on top of that. And with RL, it's actually just the same. Even other companies have published in some of their releases have published things that say, Look, we train the model on math contests, AIME or the kind of other things. And
You mentioned Richard Sutton in The Bitter Lesson. Yeah. I interviewed him last year and he is actually very non LLM pilled. And if I'm I don't know if this is his perspective, but one way to paraphrase this objection is something like, look, something which possesses the true core of human learning would not require all these billions of dollars of data and compute and these bespoke environments to learn how to use Excel or how to use PowerPoint, how to navigate a web browser. And the fact that we have to build in these skills using these RL environments hints that we're actually lacking this
Yeah. Yeah. So I think I think this kind of puts together several things that should be kind of thought of thought of differently. Yeah. I think there is a genuine puzzle here, but it it may not matter. In fact, I would guess it probably it probably doesn't matter. So let's take the RL out of it for a second because I actually think RL it's a red herring to say that RL was any different fr
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