A Quinnipiac poll in March found that 70 percent of Americans think that artificial intelligence will lead to fewer job opportunities for human beings, up from 56 percent a year ago.
Thirty percent say they’re worried for their own jobs. And why not? Warnings of a coming labor market apocalypse feature prominently in the remarks of A.I. leaders.
Dario Amodei, the chief executive of Anthropic, says that up to half of all entry-level white-collar jobs will dissolve within the next five years. Mustafa Suleyman, the chief executive of Microsoft AI, believes that most white-collar work will “be fully automated by an A.I. within the next 12 to 18 months.”
OpenAI released a policy paper calling for a 32-hour workweek so that A.I. creates mass leisure rather than mass joblessness. Outside my window at The New York Times, there is a large billboard for an A.I. company I’ve never heard of that reads: “Stop Hiring Humans.”
If you believe the story the A.I. labs are telling, it’s hard to see what stands between us and mass unemployment. A.I. has been designed to cheaply mimic what human beings can do on a computer, but never needs to sleep, never tries to form a union, and often outperforms real people on real tasks; of course, companies will want to replace human beings with this human-being-replacement machine.
Maybe they already are. Tech companies like Block, Meta, Oracle, and Microsoft are laying off or buying out workers and naming A.I. as the reason.
But it’s worth being cautious. These tech companies might be unwinding a hiring binge and telling the stock market the tale likeliest to excite or appease investors. The A.I. leaders might understand neural nets better than they understand labor markets — or they might have bought too deeply into their own marketing materials.
For one thing, the macrodata doesn’t match the anecdata: The unemployment rate was 4.3 percent in March 2026; in March 2020, it was 4.4 percent. Average hourly earnings are stable. Claude Code is a marvel, yet the demand for software engineers is booming. Maybe mass layoffs are coming. But maybe not.
Economists, I’ve found, are quite skeptical that mass joblessness is on the horizon. In “What Will Be Scarce?,” Alex Imas, an economist at the University of Chicago, tries to clarify the mistake most A.I. discourse, in his view, makes. “The answer to any question about the future economics of advanced A.I. begins with identifying what becomes scarce,” Imas writes.
For most of human history, calories were scarce. Our energy went into finding or growing food. Agriculture steadily made food more plentiful, and goods became scarce. Then goods were scarce; hand-me-down clothes were common, and tools were expensive.
Technological and manufacturing innovations made goods cheaper. Then, technical knowledge became scarce: doctors, lawyers, and software engineers are paid high salaries because of the rarity of their knowledge.
The fear is that A.I. will make knowledge plentiful; that it will turn the fruits of learning into a commodity as surely as manufacturing turned clothing into a commodity and industrial agriculture made strawberries commonplace.
But something is always scarce. People are looking at the economy as it exists and asking which tasks A.I. can do; they should be asking which jobs people won’t want A.I. doing, or which services A.I. will make us want more of.
Here is a poetic finding from econometrics: As the rich get richer, they want more from other humans, not less. They “shift their spending toward goods and services where the human element, the experience, or the social meaning matters more,” Imas writes.
They seek out clothing with a story, food with a provenance, doctors who make house calls, therapists who make them feel seen, tutors who know their children, and personal trainers who work around their injuries.
This, Imas says, is “the relational sector” of the economy, and it will explode. Instead of so many people working with computers, they will work with other people.
The more automation there is, the more people value a human touch. Take coffee. It was once laborious to make espresso at home. Now, Nespresso machines are everywhere. Has that led to Starbucks closing and neighborhood coffee spots dropping their prices? Of course not. There are more baristas than ever. There are more coffee shops than ever. Coffee as a commodity led to more demand for coffee as an experience. “The fact that the good is scarce is exactly what gives it meaning,” Imas writes.
Imas’s story suggests a place where human labor might move amid mass automation: toward more human roles. But it’s also possible that human labor won’t need to move that much at all.
In 1979, VisiCalc, the first electronic spreadsheet, was released for the Apple II. It could do in minutes what previously took teams of accountants’ days. There were predictions of mass unemployment for bookkeepers. Instead, the number of accountants quadrupled over the next 40 years.
“The spreadsheet didn’t replace the accountant,” Eldar Maksymov writes. “It unleashed latent demand for financial intelligence that had been there all along, waiting for costs to fall far enough to be satisfied.”
Maksymov, an accounting professor at Arizona State University, is describing the “Jevons Paradox,” so named for William Stanley Jevons, a British economist. Jevons, writing in 1865, was interested in Britain’s use of coal.
Not long before, James Watt had invented a new-and-improved steam engine, which generated more than twice as much power from a given amount of coal. But instead of cutting Britain’s use of coal, demand for coal tripled. Cheap coal didn’t lead to less coal being used; it led to coal being used for more things than anyone had previously thought possible.
This, Maksymov thinks, is what A.I. is likely to do even in the industries most exposed to its disruption. He thinks that, in part, because it happened before. “In every major occupational group that adopted computers heavily, employment grew faster than in groups that did not,” he writes.
“Computers eliminated specific tasks within jobs — but the resulting cost reductions created so much new demand that the occupations expanded overall.” Computers can do much that humans once did, but they didn’t put humans out of work. The ability to do more made people realize there was more to do.
This is fairly common. When I started my podcast, 10 years ago, I was its only researcher; now I have an extraordinary team of people who help me prepare episodes. Has that made my job easier? Not in the least.
I spend far more time researching and prepping because they bring me so much more to absorb and consider, and I choose to do more challenging episodes because I am confident that I can do them.
Every enthusiastic A.I. adopter I know is working harder than ever because there is more they can do. Whether they are working smarter is arguable. Studies differ on whether A.I. is making people more productive or simply giving them (and their bosses) the illusion of productivity.
Slowly reading a difficult book is far better than rapidly absorbing summaries of 12 books; struggling through a first draft will lead you to more new ideas than editing five A.I.-written drafts. It’s my belief that the feeling of efficiency should be mistrusted.
By Ezra Klein
Opinion Columnist
