Tamay Besiroglu

And on top of that, even if you did automate the process of research, we think a lot of the software progress has been driven not by cognitive efforts, though that has played a part, but it has been driven by compute scaling.

You just have more GPUs, you can do more experiments to figure out more things, your experiments can be done at larger scales.

And

That is just a very important driver.

If you're 10 years ago, 15 years ago, you're trying to figure out what software innovations are going to be important in 10 or 15 years, you would have had a very difficult time.

In fact, you probably wouldn't even conceive of the right kind of innovations to be looking at because you would be so far removed from the

context of that time with much more abundant compute and all the things that people would have learned by that point.

So these are two components of our view.

Research is harder than people think and depends a lot on compute scale.

Right.

So I think one interesting thing is if you just look at these reasoning models, they know so much, especially the large ones, because, I mean, they know in literal terms more than any human does in some sense.

And, well, we have unlocked these reasoning capabilities on top of that knowledge, and I think that is actually what is enabling them to solve a lot of these problems.

But if you actually look at the way they approach problems, they...

Like, the reason what they do looks impressive to us is because we have so much less knowledge.

And the model is approaching the problems in a fundamentally different way compared to a human would.

A human would have much more limited knowledge, and they would usually have to be much more creative in solving problems because they have this lack of knowledge, while the model knows so much.

Like, you'd ask it some obscure math question where you need, like, some specific theorem from 1850 or something, and then it would just, like, know that if it's, like, a large model.

So that makes the difficulty profile very different.

And if you look at the way they approach problems, the reasoning models, they are usually not creative.

They are very effectively able to leverage the knowledge they have, which is extremely vast.