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Sara Frueh interviews David Autor on the subject: “How Is AI Shaping the Future of Work?” (Issues in Science and Technology, January 6, 2026). Here are some snippets that caught my eye, but it’s worth reading the essay and even clicking on some of the suggested additional readings:

How broadly are AI tools already being used at work?

At least half of workers, at this point, are using it in their jobs, and probably more. In fact, more workers use it on their jobs than employers even provide it because many people use it even without their employer’s knowledge. So it’s caught on incredibly quickly. It’s used at home, it’s used at work, it’s used by people of all ages, and it’s used now equally by men and women and across education groups.

The problem when people are paid for expertise–but the expertise becomes outdated

People are paid not for their education, not for just showing up, but because they have expertise in something. Could be coding an app, could be baking a loaf of bread, diagnosing a patient, or replacing a rusty water heater. When technology automates something that you were doing, in general, the expertise that you had invested in all of a sudden doesn’t have much market value. … And so my concern is not about us running out of jobs per se. In fact, we’re running out of workers. The concern is about devaluation of expertise. And especially, even if, again, we’re transitioning to something “better,” the transition is always costly unless it happens quite slowly. And that’s because changes in people’s occupations is usually generational. You don’t go from being a lawyer to a computer scientist, or a production worker to a graphic artist, or a food service worker to a lawyer in the course of a career. Most people aren’t going to make that transition because there’s huge educational requirements to making those types of changes. So it’s quite possible their kids will decide, “Well, I’m not going to go into translation, but I will go into data science,” but that doesn’t directly help the people who are displaced.

How is the “China shock” of rising imports from China in the early 2000s likely to be different from the current AI shock?

There are important differences. One of those differences is that the China shock was very regionally concentrated. It was, as I mentioned, in the South and the Deep South, in places that made textiles and clothing and commodity furniture and did doll and tool assembly and things like that. So it’s unlikely that the impacts of AI will be nearly as regionally concentrated. And that makes it less painful because it doesn’t sort of knock out an entire community all at once. We’ve lost millions of clerical and administrative support jobs over the last few decades, but nobody talks about the great clerical shock. Why don’t they? Well, one reason is there was never a clerical capital of the United States where all the clerical work was done. It was done in offices around the country. So it’s not nearly as salient or visible. And it’s also not nearly as devastating because it’s a relatively small number of people in a large set of places. So that’s one difference. The other is that AI will mostly affect specific occupations and roles and tasks rather than entire industries. We don’t expect entire industries to just go away. And so that, again, distributes the pain, as well as the benefits, more broadly.

Can the new AI tools be steered toward collaborating with people to improve their output, rather than displacing existing jobs?

So what does steering it mean? It means using it in ways that collaborate with people to make their expertise more valuable and more useful. Where are there opportunities to do that? They’re dispersed throughout the economy. One place where this could be very impactful is in healthcare. Healthcare is kind of one out of five US dollars at this point, employs a ton of people. It’s the fastest-growing, broadly, employment sector, and there’s expertise all up and down the line. We could, using these tools, enable people who are not medical doctors, but are nurses or nurse practitioners or nurses aides, for example, or x-ray techs, to do more skilled work, to do a broader variety or depth of services using better tools. And the tools are not just about automating paperwork, it’s about supporting judgment because professional expert work is really about decision making where the stakes are high and there’s not usually one correct answer, but it matters whether you get it approximately right or approximately wrong. And so I think that’s a huge opportunity. …

Another is how we educate. We could educate more effectively. We could help teachers be more effective in providing better tools. We could also provide better learning environments using these tools. Another is in areas like skilled repair or construction or interior design or contracting, where there’s a lot of expertise involved. Giving people tools to supplement the work they do could make them more effective at either doing more ambitious projects, doing more complex repairs, or even designing and engineering in a way where they would be able to do tasks that would otherwise require higher certification.

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