AI and Jobs—The Primer From Our Short Videos

🟢 Beginner: The Economics Is on Your Side

Three economists at the University of Toronto—Ajay Agrawal, Joshua Gans, and Avi Goldfarb—have an easy way to understand what AI does to the economy. AI is a prediction machine. It predicts the next word, the next pixel, the most likely diagnosis and turns more and more problems into prediction problems. It makes prediction cheap.

When something gets cheap, the things you need alongside it get more valuable. When coffee gets cheap, cream and sugar get more expensive. So more people drink coffee, and demand for the complements rises. Prediction got cheap. The complements are action and judgment—how much more you can do now you have more predictions, what to do them, when to override one, how to weigh clean predictions against context the machine doesn't have.

This logic has held across every automation wave for two centuries, including the deep learning boom in the mid-twenty teens. Five ideas explain why.

William Baumol showed that judgment can't be made cheaper. A string quartet still takes four people and forty minutes to play Beethoven. When everything around it gets more productive, the thing that resists productivity gains gets more expensive. Your hairstylist takes the same time as fifty years ago but it costs ten times more. This is because their wages have to rise as everything else around them has gone up. It’s one reason why housing, healthcare, and education have gotten more expensive (although obviously not the only one). These sectors have resisted productivity gains.

Chad Jones at Stanford showed that a system is only as productive as its least productive essential part. Automate everything about a flight and the bottleneck moves to the ground crew turning the plane around. Even infinite AI productivity in software—literally infinite—adds about 2 percent to GDP because software is 2 percent of the economy. AI will change that number. Software's share of the economy is growing, and its leverage grows with it. But bottlenecks matter and we can be sure they haven't gone away. In fact, there is reason to suspect that AI will find more bottlenecks, faster than it can remove them. What removes a bottleneck? Humans who understand which ones matter most to the problem at hand and are empowered to make choices.

David Autor—whose work is some of the most important in labor automation—showed that the same technology produces opposite outcomes depending on what it automates. GPS took the expert part of taxi driving—the knowledge, the thing that took years to learn. Wages collapsed. Computers took the routine part of accounting—the bookkeeping, the data entry. Wages rose. Whether AI takes your judgment or your drudge work determines which direction you're heading.

And when prediction gets cheap, two things happen. You use more of it—that's Jevons Paradox. When houses got more energy efficient, people heated them more. When prediction gets cheaper, people run more predictions. Total demand goes up, not down.

Jevons has a limit though. When the output is commodity — interchangeable, generic, good enough, demand doesn't expand. It saturates. There are only so many social media graphics anyone needs. David Autor has been flagging this since ChatGPT launched. Illustrators, translators, copywriters, medical transcriptionists—when technology floods a market with output that used to require skill, and the market didn't actually want more of it, wages collapse. His concern is these people will be unlikely to find work that pays as well, because the expertise they spent years building is the thing that got automated. That's happening now across creative fields as the cost of producing a passable artifact drops toward zero.

There are only so many product descriptions anyone needs. Just like there are only so many stock images people will look at. AI made those things nearly free and demand hit a ceiling. That's what's happening to some jobs, like language translators, right now. The work that was always commodity got priced like commodity. The work that required judgment—the eye, the taste, the read on what a specific client actually needs—still follows Jevons. More demand, not less. The split between those two is the question you need to answer about your own work.

But cheap prediction also does something else. You also attempt things you never would have tried. Problems too local, too specific, too costly to justify solving before. Nobody was going to build software for a food bank coordinator's donation mismatch. The market was too small. That changes when the cost drops far enough. And this is the part of the AI story is under discussed. Right now, the entire conversation is about what AI takes away. The expansion of what becomes possible—new problems, new work, new value that didn't exist before—barely gets mentioned.

Five ideas relying on one mechanism—cream and sugar. The complement gets more valuable. This has held for two centuries. There are reasons it might not feel like it right now. The question is why the world doesn't look like what the theory predicts. If judgment is getting more valuable, why are companies cutting the people who have it? Why is the conversation all about efficiency and not about innovation? About replacement not about enhancement? Is something working against the economics?