Peter McCrory

macro dynamic DSGE modeling, for example, sitting down and reading the methodological paper and just like forcing yourself to work through and understand it, it turns out that maybe it's like not as scary as you first thought.

The great hope of this technology is that it can actually help to facilitate and sort of accelerate the acquisition of skills.

So once you've done that first pass of something that's very challenging,

go back and interact with Claude to help clarify things that don't make much sense.

So actually, one of the primitives that we introduce in the report is, are people using it for professional, personal, or for coursework?

And we actually see that coursework is one of the

more common.

I mean, it is relatively common around a quarter of all usage globally and more so in lower income countries.

People are using Claude to sort of complement their education for educational purposes and skill and expertise acquisition.

It's an interesting question and sort of has sort of sparking interest in a number of threads.

I'll say briefly, and then I want to return to this Paul Romer point about endogenous growth theory and related growth theory, because I think it's like a really important point for thinking about what's on the horizon with this technology.

So one thing that we do see in our data when you compare the API versus the cloud.ai interactions is that businesses give less autonomy to cloud to complete the task, like less decision-making ability, which suggests that, uh,

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.