AI is just as likely to improve human capabilities as it is to displace them.

In theory, new technologies can have several different effects on human workers. They can:

reduce demand for human workers by replacing their labor,

increase wages by making human workers more productive at their jobs,

create new demand for new kinds of jobs, and

increase overall labor demand through economic growth.

In addition, new technology can affect inequality by favoring low-skilled workers or high-skilled workers.

It’s understandable that we might want to steer or shape the development of AI technology so that it maximizes the benefits for workers, and avoids the “replacement” part. But there is a big problem with the idea — namely that no one, including Daron Acemoglu or any economist, has any idea how to predict which technologies will augment humans and which will simply replace their labor.

Imagine yourself in 1870 — on the eve of what we call the Second Industrial Revolution. And suppose that you had foreknowledge of all the marvelous technologies that would be either introduced or greatly expanded over the next half century — mass production, railroads, electricity, automobiles, telegraphs, telephones, water supply, airplanes, and so on. Suppose someone gave you the chance to decide which of those technologies you thought would displace human workers, kill jobs, and decrease wages, and which you thought would augment human workers, create jobs, and raise wages? Which would you put in each category?

In fact, this would have been an incredibly hard prediction to make, even if you had all of the data and tools available to a modern economist. For any one of those technologies, you could name specific people whose jobs might be disrupted. Cars would disrupt the jobs of everyone involved in taking care of horses. Mass production would disrupt the jobs of artisan manufacturers. Electric lighting would disrupt the jobs of people who produced whale oil for lighting, and so on.

But that would tell you little, because there would also be a bunch of jobs created by each of these technologies — car factory workers, machinists, power station workers, and so on. In 1870 you could sort of imagine what those new jobs might be, but you wouldn’t know for sure, nor could you know how many of them there would be or how they might pay. You could write out a mathematical model like Acemoglu’s, and yet without knowing the parameters of that model, you wouldn’t be able to use the model to predict whether any particular industrial technology would displace or augment human labor on net. And because every technology is different, you couldn’t calibrate the parameters for the model on historical data for old technologies.

You would have no choice but to rely on judgement and gut instinct.

Anyway, looking back with the benefit of a century and a half of hindsight, we know that the technologies of the Second Industrial Revolution, overall, turned out well for workers. In 1970, pretty much every human being was still working, and at much higher wages and living standards than in 1870.

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Labor’s share of income fluctuated a bit, but stayed pretty high:

Income inequality also fluctuated somewhat, but overall it stayed the same in North America and fell in Europe:

Overall, looking at this historical record, you might reasonably conclude that trying to intentionally slow down the progress of industrial technology would have been a bad idea.

But even knowing that this was the (happy) outcome of the Second Industrial Revolution in the aggregate, it would be hard to know, looking back from 1970, whether any specific industrial technologies ended up being bad for workers. We can certainly list lots of jobs that were created and destroyed — by 1970 we had a lot more office workers and a lot fewer farm workers, for instance. But isolating the impact of each specific technology on the whole zoo of jobs and wage levels in the economy would be a daunting task for any empirical economist, even with a full historical dataset.

And if it would be nigh impossible in hindsight, it would be utterly hopeless looking forward in 1870. Yet this impossible task is exactly what would be required of any economist, historian, science fiction author, government worker, or economics blogger standing in 2023 and trying to predict the impact of specific AI technologies on jobs and wages over the next 50 or 100 years.

Models don’t help — as I mentioned, their predictions would be totally dependent on parameter values that can’t be estimated from past data. Experiments won’t tell us much either — so far, studies by Brynjolffson et al. (2023), Noy and Zhang (2023), and Peng et al. (2023) have found that generative AI improved human workers’ capabilities in a variety of fields, but this doesn’t tell us much about whether that would lead to employers hiring more of those workers or fewer, or what new jobs the AI tools might enable, or what they’d do to economy-wide demand.