Peter McCrory

I mean, it's kind of related to this idea that like you don't want to fully automate the process.

You want to be make sure that there are appropriate guardrails in the context of reading a paper.

If you only ever read the summary, you're sort of missing out on crucially important information and you need a mechanism.

Maybe that's a process like a peer or your manager is like reading over very carefully that whatever you've written.

Or in the business context, you need to have, in the API context, like structure and guardrails to ensure that the quality of the output sort of meets whatever threshold that you need.

And we see some evidence of that in how businesses deploy the tool.

I'm curious to know what was the detail in the Paul Romer paper and whether or not it kind of relates to this question of

AI's impact on long-run growth.

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.