Sergey Levine

you're making the coffee or something.

Whereas as they get more capable, as their ability to have common sense and a broader repertoire of tasks increases, then we'll give them greater scope.

Now you're running the whole coffee shop.

I mean my sense there too is that this is probably a single-digit thing rather than a double-digit thing.

But the reason it's so hard to really pin down is because as with all research, it does depend on figuring out a few question marks.

And I think my answer in terms of the nature of those question marks is I don't think these are things that require –

profoundly, deeply different ideas, but it does require the right synthesis of the kinds of things that we already know.

And, you know, sometimes synthesis, to be clear, is just as difficult as coming up with like profoundly new stuff, right?

So I think it's intellectually a very deep and profound problem and figuring that out is going to be like very exciting.

But it

I think we kind of like know like roughly the puzzle pieces and it's something that we need to work on.

And I think if we work on it and we're a bit lucky and everything kind of goes as planned, I think single digit is reasonable.

So I think there's a nuance here.

And the nuance is it becomes more obvious if we consider the analogy to the coding assistance, right?

It's not like the –

The nature of coding assistance today is that there's a switch that flips and suddenly, instead of writing software, suddenly all software engineers get fired and everyone's using LMs for everything.

And that actually makes a lot of sense that the biggest gain in productivity comes from experts, which is software engineers, whose productivity is now augmented by these really powerful tools.

It's a very subtle question.

I think what it probably will come down to is this question of scope.

The reason that LLMs aren't doing all software engineering is because they're good within a certain scope, but there's limits to that.