Dario Amodei

The other direction, of course, is these reasoning models that do chain of thought and stop to think and reflect on their own thinking. In a way, that's another kind of synthetic data coupled with reinforcement learning. So my guess is with one of those methods, we'll get around the data limitation or there may be other sources of data that are available.

We could just observe that even if there's no problem with data, as we start to scale models up, they just stop getting better. It seemed to be a reliable observation that they've gotten better. That could just stop at some point for a reason we don't understand. The answer could be that we need to invent some new architecture.

There have been problems in the past with, say, numerical stability of models where it looked like things were leveling off, but actually when we found the right unblocker, they didn't end up doing so. So perhaps there's some new – optimization method or some new technique we need to unblock things.

I've seen no evidence of that so far, but if things were to slow down, that perhaps could be one reason.

So right now, I think most of the frontier model companies, I would guess, are operating roughly you know, $1 billion scale plus or minus a factor of three, right? Those are the models that exist now or are being trained now.

I think next year we're going to go to a few billion and then 2026, we may go to, you know, above 10 billion and probably by 2027, their ambitions to build $100 billion clusters. And I think all of that actually will happen. There's a lot of determination to build the compute to do it within this country. And I would guess that it actually does happen.

Now, if we get to 100 billion, that's still not enough compute. That's still not enough scale. Then either we need even more scale or we need to develop some way of doing it more efficiently, of shifting the curve.

I think between all of these, one of the reasons I'm bullish about powerful AI happening so fast is just that if you extrapolate the next few points on the curve, we're very quickly getting towards human level ability, right? Some of the new models that we developed, some reasoning models that have come from other companies,

They're starting to get to what I would call the PhD or professional level, right? If you look at their coding ability, the latest model we released, Sonnet 3.5, the new or updated version, it gets something like 50% on Sweebench. And Sweebench is an example of a bunch of professional, real-world software engineering tasks. At the beginning of the year, I think the state of the art was 3% or 4%.

So in 10 months, we've gone from 3% to 50% on this task. And I think in another year, we'll probably be at 90%. I mean, I don't know, but might even be less than that. We've seen similar things in graduate level math, physics, and biology from models like OpenAI's 01.

So if we just continue to extrapolate this in terms of skill that we have, I think if we extrapolate the straight curve, within a few years, we will get to these models being above the highest professional level in terms of humans. Now, will that curve continue? You've pointed to and I've pointed to a lot of reasons why, you know, possible reasons why that might not happen.

But if the extrapolation curve continues, that is the trajectory we're on.

Yeah, so I want to separate out a couple things, right? So, you know, Anthropic's mission is to kind of try to make this all go well, right? And, you know, we have a theory of change called race to the top, right? Race to the top is about trying to push the other players to do the right thing by setting an example. It's not about being the good guy.

It's about setting things up so that all of us can be the good guy. I'll give a few examples of this. Early in the history of Anthropic, one of our co-founders, Chris Ola, who I believe you're interviewing soon, he's the co-founder of the field of mechanistic interpretability, which is an attempt to understand what's going on inside AI models.

So we had him and one of our early teams focus on this area of interpretability, which we think is good for making models safe and transparent. For three or four years, that had no commercial application whatsoever. It still doesn't today. We're doing some early betas with it, and probably it will eventually. But this is a very, very long research bed and one in which we've

built in public and shared our results publicly. And we did this because we think it's a way to make models safer. An interesting thing is that as we've done this, other companies have started doing it as well. In some cases, because they've been inspired by it. In some cases, because they're worried that,

You know, if if other companies are doing this that look more responsible, they want to look more responsible, too. No one wants to look like the irresponsible actor. And so they adopt this. They adopt this as well. When folks come to Anthropic, interpretability is often a draw. And I tell them the other places you didn't go. Tell them why you came here. And then you.

You see soon that there's interpretability teams elsewhere as well. And in a way, that takes away our competitive advantage because it's like, oh, now others are doing it as well, but it's good for the broader system. And so we have to invent some new thing that we're doing that others aren't doing as well in the hope is to basically bid up the importance of doing the right thing.

And it's not about us in particular, right? It's not about having one particular good guy. Other companies can do this as well. If they join the race to do this, that's the best news ever, right? It's about kind of shaping the incentives to point upward instead of shaping the incentives to point downward.

Trying to. I mean, I think we're still early in terms of our ability to see things, but I've been surprised at how much we've been able to look inside these systems and understand what we see, right? Unlike with the scaling laws, where it feels like there's some law that's deriving these models to perform better,