Conversations about AI and labor often focus on the dichotomy between automation, or replicating tasks that people perform, and augmentation, or enabling people to do things they previously couldn’t. (Charter has also written about this distinction, arguing in favor of the latter.) “But task automation and labor augmentation are not polar opposites,” write economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb in a piece in Science Magazine. They argue that task automation can augment workers, and that in some cases, it could decrease income inequality. If AI automates a task that requires a specialized skill, for example, it could boost the productivity of less-skilled workers and create new employment opportunities for them.

We spoke with Gans, a professor of strategic management at the University of Toronto’s Rotman School of Management, about the automation versus augmentation debate and how AI could decrease income inequality. Here are excerpts from our conversation, lightly edited for length and clarity:

You’ve co-authored articles that challenge the idea that there’s a dichotomy between automation and augmentation. Can you explain?

When you think about how something like ChatGPT works, for instance, it makes it very easy to write a letter. What is it doing when it does that? It is automating the writing task for that letter. That means that if you are a busy person, and writing a letter is part of your job, that’s saving you time. To the extent that it’s allowing you to write an even better letter than you would as well, that’s increasing your productivity. So there we have something that is technically automation, but is actually augmenting people in the plain meaning of the word augment.

You’ve also argued that some tasks, like writing, are barriers to entry for certain professions for people who don’t have those skills. So if you automate that task, you open up this job to more people….

That’s right. AI is very good at imitating what a skillful person could do in a task. Your job may be a multitude of tasks, some of which require some skills that you do not have. If AI has been able to provide that, it enhances your overall productivity. If you’ve already got those skills, of course it’s not going to do very much.

One historical example you’ve written about when it comes to the impact of task automation on an occupation is London taxi drivers, who have had to pass an exam demonstrating their knowledge of the city’s thousands of streets and landmarks since the 19th century. Can you talk about that example?

You have to learn the whole street map of London. And you have to not only do that, but you have to learn the shortest distance between any two points. You have to memorize it. It’s something that takes you two to four years depending on how good you are. But now basically every single person who has a smartphone has that skill and has it for free. So this thing that was a unique capability is now very widespread. Effectively that means that anyone can be as good as a London taxi driver, even if you’ve never been to London before—or it’s very close to that. So you have to say, ‘Why are we forcing these people to still learn all this stuff?’ That doesn’t seem like a good idea, creating a four-year entry barrier to be a taxi driver. But more importantly, it’s a dramatic democratization of that skillset.

One of your arguments is that AI could decrease inequality by opening up more professions for more people. A concern someone could have with that argument is that the technology wouldn’t be decreasing inequality by bringing everyone up, but rather bringing everyone down to low-wage work by devaluing expertise. How do you respond to that?

It’s very hard to work out all of these things, but it’s not doing this to every single skill that people have. Moreover, there’s actually a limited amount of workers out there. What tends to happen in this situation is yes, if you are earning a premium for a particular skill—and the [London] taxi driver is a great case of that—it wipes that out. But in most of these other situations, it’s a barrier to entry to the job, and it unlocks other skills.

For instance, if you have a landscape gardening business, but you are an immigrant and you don’t speak English particularly well, when you’re communicating over email with clients, now AI allows you to do so in a more fluent manner. That allows you to now earn money in your actual skill, which is gardening. Yes, that’s going to mean the native English speaking gardeners aren’t going to earn quite as much as they were previously, but there’s a whole lot of others who earn a lot more.

I’m sure we’ll be able to point to jobs like the taxi drivers, where yes, it has completely transformed this thing. But for the vast majority of jobs, I don’t think that’ll be the case. Even for the very high-skilled people, chances are they’ve got other skills going on as well, other than the ones replaced by AI. It’s not like they’re going to lose those advantages altogether. That’s why it’s kind of hard to predict what’s going to happen with inequality. But that’s our point: It’s not obvious that it’s going to be creating massive amounts of new inequality.

Read a full transcript of our conversation, including what generative AI means for writing professions.

Read more from Charter

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