Luis Garicano - Why the Most Valuable Work Is the Hardest to Automate

Not all jobs are equally vulnerable to automation, and the difference may have less to do with skill level than with the structure of the job itself. My guest today is Luis Garicano, professor at the London School of Economics, former member of the European Parliament, and author of Messy Jobs, one of my favorite recent books. We discuss what AI does and doesn't do to work. Here's our talk. Welcome to The Economics of Work. I'm excited to welcome Luis Garicano, professor at LSE, former member of the European Parliament, and author of Messy Jobs, which, in my opinion, is the best and most important recent book on AI and work. Luis, welcome to the show.

Ben, that's really kind of you to say. I appreciate it. And it's really a pleasure to be here.

Yeah, it was really excellent and very timely. So I got to start by asking you, like, what is a messy job and why would someone want one?

Okay, so to easily define it, let's define the opposite, which is a clean job. A clean job is a job that is a single task that is verifiable, that you perform basically that task repeatedly, and it's subject to reinforcement learning. The more you do the task, the more it gets improved. And eventually AI kind of takes over that simple.

Do clean jobs exist? I

think clean jobs exist. Many jobs are different orders of mind of clean. If you think of trucks, for example, I always thought like a truck. My co-author Tom Hubbard wrote a few articles about trucks. And I always thought the truck drivers did quite a bit of the back-end and front-end, but they actually don't. Somebody loads the truck, somebody tells them the route, they get the route on the screen, they just drive through the road. So the truck seems as close to a single-task clean job as you can get. Interesting. One that is not protected by the messiness, is protected by all the things that we can discuss. So the single-task jobs are only protected by... regulation and potentially consumer taste or consumer demand or the demand, the technological demands on that single task. On the other hand, the messy jobs are bundles of tasks, which include relational, which include commitment, which include judgment. that cannot be easily split apart. So we talk about the strong bundles and weak bundles. And strong bundles are when this coordination goes between the different tasks that are sufficiently big that having automation on one task doesn't make it kind of split apart.

Wait, so let's say there's a job that has lots and lots of tasks, but they are easily split apart. So they're weekly bundled. Is that messy or clean? Like, how would you characterize the example? I mean, it's a... It's on the spectrum.

We wouldn't say it's a... messy job but it won't remain in that way so it's not the kind of jobs that we are recommending the prototypical person you were asking me to okay so it's a it's a these are all these are all tasks it's a weak bundle and as a result messy jobs are strong bundles so as a result this will kind of be split by ai it's not surviving we talk the key concept to think about this is the autonomy threshold So the autonomy threshold that we describe in the book is for a certain level of autonomy, for a