Illia Polosukhin

Some of the adoption is being dragged by just, you know, there's a lot of industries who are still using pen and paper.

Yes, AI is getting better at, you know,

computer vision as well and you can you can potentially use it with that but uh obviously if your workflow is not even like digital you can't even like ai is not going to be that much help yeah totally um but i think like generally that that the we are kind of definitely on exponential and so um as i said we're living we're living in interesting times

You realize this is like a, probably a trillion dollar question, right?

Yeah.

I guess selling the subscription is that, uh, uh,

So, I mean, obviously I have my hypotheses.

Sure.

And I've kind of expressed some of them.

I mean, like a few years ago, RL was one of them.

And kind of we saw really kind of improvements coming from reinforcement learning.

I definitely think like there is only that much you can stuff, you know, random articles into a model until, you know, it stops learning.

But what we see is if you take some size of the model, like 8 billion parameter, the quality of that model keeps improving.

Meaning like at the same scale of the parameters, we're getting better at how we train them.

And so that to me is the main, like the way I look at these things is like, hey, let's fix the size and see the progression there.

And that progression defines to me the kind of, are we getting better at improving these models?

Yeah, I mean, well, bigger the problem is it's hard to compare apples to apples, right?

Like, hey, is this model better than what it was three months ago?

It is on some metrics, but if you spend 10x more compute, is that actually the improvement we're looking for?

So to me, the kind of...