It depends, Mr. Autor said. “Affected could mean made better, made worse, disappeared, doubled.”

One complicating factor is that technology tends to automate tasks, not entire occupations. In 2016, for instance, the artificial intelligence pioneer Geoffrey Hinton considered new “deep learning” technology capable of reading medical images. He concluded that “if you work as a radiologist, you are like the coyote that’s already over the edge of the cliff but hasn’t yet looked down.”

He gave it five years, maybe ten, before algorithms would “do better” than humans. What he probably overlooked was that reading the images is just one of many tasks (30 of them, according to the U.S. government) that radiologists do. They also do things like “confer with medical professionals” and “provide counseling.” Today, some in the field worry about an impending shortage of radiologists. And Mr. Hinton has since become a vocal public critic of the same technology he helped create.

Mr. Frey and Mr. Osborne calculated their 47 percent number in part by asking technology experts to rate how likely entire occupations like “telemarketer” or “accountant” were to be automated. But three years after their paper published, a group of researchers at the ZEW Center for European Economic Research, based in Mannheim, Germany, published a similar study that assessed tasks — like “explain products or services” — and found that just 9 percent of occupations across 21 countries could be automated.

“People like numbers,” said Melanie Arntz, the lead author of the ZEW paper. “People always think that the number must be somehow solid, you know, because it’s a number. But numbers can really be very misleading.”

In some scenarios, A.I. has essentially created a tool, not a full job replacement. You’re now a digger who can use an excavator instead of a shovel. Or a nurse practitioner with access to better information for diagnosing a patient. It’s possible that you should charge more per hour, because you’re going to get a lot more done.