A.I. progress is giving me writer’s block

It’s hard to write good articles when you have no idea if everything is about to change.

Who can plan for this? (Photo by Liubomyr Vorona)

Here’s an idea for an article that I had recently:

One of the most underrated aspects of education policy is the impact that second-wave feminism had on the K-12 workforce. It used to be the case that an enormous fraction of the smartest and most ambitious women in America were working as public school teachers, and were doing so at depressed wages because of limited opportunities for women to have white-collar careers. Some of this was formal, but a lot of it wasn’t. Jeannette Rankin entered Congress in 1917 and Elizabeth Blackwell graduated from medical school in 1849, so it’s not like women “couldn’t” have careers in politics or medicine before 1970. But they rarely did. And there wasn’t one specific formal policy change that unleashed the entire transformation of women’s professional opportunities. There were formal changes in public policy, of course, but the most important changes were the shifts in attitudes and social values over several generations.

And a second-order consequence of this was the steady erosion of human capital available in the teaching workforce.

And it seems likely to me that as artificial intelligence generates a sharp decline in the demand for major categories of white-collar work — a much more restrained claim than the idea that it will replace all jobs — we could see a reversal of that flow.

Large language models have many potential applications in the educational context, but it’s hard to see them operating as a replacement for a human teacher in the way that they could replace people who work in jobs that mostly involve typing on computers. That, in turn, would be an example of how even though the labor market disruptions associated with new technology can be painful, they also have upside. Automation of white-collar work isn’t just a productivity boost in those specific sectors; it could also lead a lot of the human capital that is currently deployed in fields like law and accounting to be redirected toward teaching young people, which would have its own benefits.

When I pitched this idea to Kate, though, she raised a good point: Couldn’t this same process significantly reduce the value of a traditional education? Similarly, when I asked Claude about this, it told me that the timeframes don’t line up correctly. It’s true that a downward shift in the relative earnings of white-collar professionals could improve teacher recruiting and retention. But that would be a long-term change, and while the change plays out the A.I. is going to keep advancing and create further change.

This becomes a problem not just with this specific pitch, but with essentially any article on a huge swath of topics that aren’t narrowly focused on the very short term. Questions about basically every medium-run policy debate collapse into arguments about the future trajectory of A.I.

Most A.I. debates are about the present

To see this, though, you need to see past most of the arguments that people are currently having about A.I. — arguments that are really about the present state of A.I. rather than its future.