AI Is Making Economists Rethink the Story of Automation

Is artificial intelligence about to put vast numbers of people out of a job? Most economists would argue the answer is no:

If technology permanently puts people out of work then why, after centuries of new technologies, are there still so many jobs left? New technologies, they claim, make the economy more productive and allow people to enter new fields — like the shift from agriculture to manufacturing. For that reason, economists have historically shared a general view that whatever upheaval might be caused by technological change, it is “somewhere between benign and benevolent.”

Economists understand the world by building models. Those models attempt to capture the messy, sprawling reality of modern economies but they are intentionally simplified.

The aim is to illustrate key choices and tradeoffs that shape the economy. In the process, these models often help to shape what policymakers pay attention to.

As economists update their models of automation, they are both changing the field’s understanding of what technology does to workers and shifting the debate about how politicians and regulators should respond.

Labor economists later complicated that story, by distinguishing between “high-skilled” and “low-skilled” workers — usually approximated using data on education levels.

This allowed them to model how technologies could increase inequality. Computers made many knowledge workers much more productive — thanks to innovations like spreadsheets and email — and therefore raised their wages.

But it did less for less-educated workers, which gave rise to what Harvard economists Claudia Goldin and Lawrence Katz called a “race between education and technology.”

Economists’ more recent models of automation also provide crucial lessons for the coming tech wave.

If AI is going to usher in an era of widely-shared prosperity, two things will need to be true. First, it needs to create new kinds of work that humans can excel at — new tasks that didn’t exist before.

Second, decision-making at all levels, from firms to governments, needs to include workers’ voices.

That doesn’t necessarily mean giving workers a veto over every potential AI use case or insisting that no jobs be lost. But it does mean ensuring workers have the power to make their perspective heard.

Economists as a group remain less pessimistic about AI than many; few predict a jobless future.

They recognize that, like many of the great “general purpose” technologies of past eras,

AI has the potential to dramatically improve our lives. The key, as Autor says, is to make it work for us.

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