Peter McCrory

Yeah, it's such a great phrase.

So much of our analysis in this report focuses on sort of task level efficiency gains that we see in our data.

You use Claude to write a report and you do it X times faster.

And we do this exercise towards the end of the paper where we say, OK, imagine that all of those

efficiency gains across tasks that we see in cloud.ai and our API traffic, imagine that that materializes fully within the economy over the next decade.

How much would that increase labor productivity growth each year?

We come up with a number in the baseline analysis of 1.8 percentage points using standard macro growth accounting.

If you're interested, read about Halton's theorem, some great papers by David McKay on that front.

But that is all focused on this question of what if we're more productive at the things we're

doing right now?

I think the long run question is exactly as you said, like, to what extent might this technology automate the process of innovation itself?

A great turn of phrase that I've heard Jonathan Haskell use is AI might very well be an innovation in the method of innovation itself, overcoming the burden of knowledge.

To become an expert economist, you need to

To get to the frontier, you need to spend many years sort of getting that narrow expertise.

But in principle, it might be the case that these large language models, they've read the corpus of human knowledge.

They can maybe span the space where new ideas, productive ideas, both for scientific applications and for business applications, that's where they exist.

So I think that is like a pressing question to which we don't currently...

analyze in our report and sort of something that's very much on my mind.

I think another way to think about it is, again, another shout out to Ben Jones.

This paper from fall of late last year