Will Bryk

Yeah, it's great.

He had a tweet about this a couple, I think a couple years ago, where it was like the trend is towards like small, smaller raw intelligence modules using tools.

And that trend will like, that's an important trend.

Because like, you have, you know, you have a limited, like the cost of the model is determined by the number of weights.

That, you know, determines the cost of the inference.

And if those weights are... If you're wasting those weights on, like, all sorts of information about the world, like the capital of France or, you know, this random blog that you read, like, you're just wasting tokens.

Sorry, you're wasting weights.

Those weights should be focused only on, like, intelligent processing.

And you could probably get to models that are, like, one billion, even less than a billion parameters that are extremely hyper-intelligent and completely...

unknowledgeable.

It's like Einstein who never saw the world.

That's kind of like the way I think about it.

And then it uses tools that are very cheap and efficient.

And that's a much more efficient world that will help solve this compute shortage that is affecting everybody.

I mean, the rally is definitely starting.

I don't have the exact... It's like trends are everywhere.

When does it become noticeable?

Yeah, I would say by end of 2026, it's very noticeable.

Yeah, at a high level, a lot of the big ideas in training LLMs apply equally well to training search models.

So, for example, we do pre-training of embedding models.