Peter McCrory

Claude is able to do that sort of thing reasonably proficiently very quickly, and Opus 4.5 will be able to do that even better.

I think that this is a very insightful observation about how the nature of work might be changing and how the sort of capabilities unequally affect sort of early career workers versus later career workers.

And, you know, when I worked as a research assistant at the Fed to continue this example for my life, I learned a lot on the job from doing very basic tasks.

Sometimes it was more involved like a literature review.

I actually worked with an economic historian to transcribe historical records, exactly this data entry job, but in the context of economic research.

And Claude and other frontier models are increasingly capable of doing that type of work.

And the question is, will businesses sort of continue to invest in younger workers to equip them with those tacit skills that they had previously learned from these basic set of tasks?

I think it's not entirely clear.

clear.

That's partially why we're putting out this data, so that we can track in real time what are the actual effects in the labor market.

We have some suggestive evidence, of course, from this nice paper, Canaries in the Coal Mine, from researchers at Stanford that document that early career workers in high AI-exposed roles have had

worse employment trajectories in recent years.

But, you know, we have alternative evidence as well.

And so we're still in the early stages.

A quick note.

So I think I'll sort of respond to this through the lens of one of the primitives that we introduced.

And it was sort of to try to get at this idea where we asked,

how many, you know, ask Claude effectively to estimate how many years of formal education would someone need to have to understand the prompt?

And how many years of formal education would you need to understand Claude's response?

It turns out that