Peter McCrory

And so I think this is similar to the type of analyses that Meter has put out looking at task duration or task horizon and relating that to the reliability of frontier models.

But it is conceptually different along two important dimensions.

One, it's not just a sort of a laboratory controlled analysis.

It's people are choosing to bring

which tasks they bring to Claude, and they are bringing tasks where they believe Claude will be most capable, especially on Claude.ai where there's this multi-turn oversight interaction where you give Claude a task, you see the output, you give it some feedback, and you have this oversight that can increase the reliability of what the model is able to complete in a short amount of time relative to how long it would take if you had to do it alone.

I think compiling a literature review, a substantive literature review, is an example of a task that would take a research assistant.

So I'm speaking a bit from my own experience working at the Fed when I was younger.

That would take me multiple days to put together and compile nicely for the senior economists I was working for.

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.