A few weeks ago, New York Times columnist David Leonhardt hosted an online conversation about the impact that AI is already having on employment and how large a transition society may be facing with three prominent economists: MIT’s David Autor, the University of Virginia’s Anton Korinek, and Yale University’s Natasha Sarin. I found their discussion quite interesting at multiple levels. Let me summarize some of their key points.
The Near-Term Impact of AI on Jobs
“Before we look toward the future, let’s talk about the present,” Leonhardt began. “There is debate among economists about whether A.I. has already led to a meaningful amount of job loss. What do you each think?”
“The evidence is inconclusive,” said Professor Autor. Some widely discussed findings suggest that entry-level employment for young workers has declined in AI-exposed occupations such as software development and customer service. However, other recent business-cycle factors — such as tariffs and interest rates — may also be influencing hiring trends. “That said,” he added, “there’s every reason to believe that advancing A.I. will fundamentally change hiring and skill requirements across much of the economy. In many cases, I think we’ll see fewer people doing this work, and those who do it will be more expert, solving the thorny problems that A.I. currently cannot solve on its own.”
Professor Sarin also noted that the evidence is inconclusive. That “despite all the vibes and anecdotes you hear about A.I. labor market displacement, there just isn’t evidence in the data that this has happened in a meaningful way so far. … We don’t find differences in employment in the last few years between the occupations most exposed to A.I. and those least exposed.” That, she noted, should not be surprising. “It’s been only three years since the mass introduction of this technology, and it takes firms — and all of us — time to understand how to deploy it in ways that are truly transformative.”
Professor Korinek offered a different lens. While employment data may be ambiguous, he argued, investment data is not. “The leading A.I. labs aren’t making hundred-billion-dollar bets because they expect A.I. to have minor effects on the labor market. They are betting on achieving artificial general intelligence (A.G.I.), which could substitute for human labor across much of the economy.” He also mentioned that few people work at these labs relative to the scale of investment. “The employment effects we are looking for may simply be lagging indicators of a transformation that’s already locked in by the capital being deployed.”
A.I., Korinek acknowledged, may ultimately deliver enormous benefits by revolutionizing scientific discovery, health care, and human well-being. “But we should be preparing now for the possibility of significant labor market disruption, rather than waiting for it to show up conclusively in the statistics.”
Autor pushed back on the idea that success at the leading AI labs necessarily implies the end of work for most people. Their breakthroughs, he argued, could instead create value throughout the economy — improving health care, transportation, education, legal services, manufacturing, and construction. Silicon Valley itself has never employed many workers, yet its rise over the past three decades coincided with robust employment growth and historically low unemployment. History, he noted, shows that new technologies do not merely replace labor in existing industries; they also create entirely new ones. “Centuries ago, there were no automobiles, airplanes or telecommunications, and those industries all employ people.”
Korinek agreed that the massive bets being placed by AI companies and their investors are far from guaranteed. “Incidentally, the same is true of empirical relationships in economics,” he added. “In the past, new technologies have led to rising employment and wages, but we cannot be sure that this will be true in the future.”
What If Labor Becomes Less Necessary?
Leonhardt pointed out that technological disruption has never before caused humanity to run out of jobs, despite centuries of anxiety to the contrary. He asked Sarin whether she could sketch a relatively optimistic scenario in which A.I. is revolutionary but does not lead to mass unemployment.
“This time could be different,” Sarin replied. “This revolution could reduce the need for labor as a whole.” In that case, she suggested, the world might move toward something like the 15-hour workweek famously predicted by John Maynard Keynes, — one of the most influential economists of the 20th century.
In a 1930 essay, Keynes warned of “technological unemployment” — job loss caused by labor-saving innovations advancing faster than society’s ability to create new uses for labor. He predicted that by 2030, rising productivity would allow most people to work only about 15 hours a week, enough to feel useful while freeing humanity from pressing economic concerns. The deeper challenge, he argued, would be learning how to use that newfound leisure “to live wisely and agreeably and well.”
So, with 2030 only a few years away, will Keynes’s prediction come true?
“More likely,” Sarin said, “new jobs will emerge, as they have in the past, offsetting jobs that are less necessary in a world where we all have laptops and no longer need typists.” There will be winners and losers. The losers, she suggested, may include first-year law firm associates or graduate students in economics who spent years honing skills that A.I. can now perform effortlessly.
“But the gains will be real, too,” she added. Access to legal and other professional services will expand, and new roles will emerge to monitor and supervise A.I. systems. “It is not a foregone conclusion —and not even likely, in my mind — that productivity growth from A.I. will shrink employment overall. If history is any guide, even revolutionary technologies tend to change how we work, not whether we work.”
The Industrial Revolution and the Emerging AI Revolution
Leonhardt then asked whether, — even if A.I. boosts overall economic output, — it might still hurt many more workers than it helps in the medium term, and what society should do to reduce that risk.
“The Industrial Revolution is a useful analogy,” Sarin said. “Real wages for weavers more than halved in the first two decades of the 1800s.”
“In the artisanal era,” Autor added, “most goods were handmade by skilled craftsmen — wagon wheels by wheelwrights, clothing by tailors, shoes by cobblers.” These artisans spent decades mastering their trades, and their expertise was highly valued. But during the Industrial Revolution, much of that expertise was rapidly devalued, and many artisans never recovered.
“Even as innovations drove dramatic productivity gains,” Autor noted, “it took roughly five decades before working-class living standards began to rise.” Early industrialization displaced skilled work while relegating humans to monotonous, grueling labor — feeding what the English poet William Blake famously called “dark satanic mills.”
If A.I. advances only modestly, Korinek suggested, a similar pattern of disruption followed by adaptation may play out. But he worries that we are thinking too small. “If the quest for artificial general intelligence succeeds, we are not looking at another Industrial Revolution.” For centuries, labor has been the economy’s scarcest factor, which is why wages rose so dramatically above preindustrial levels. “If labor itself becomes optional, that would be very different.” The upside, Korinek emphasized, is that A.G.I. could generate extraordinary economic abundance. The challenge will be ensuring that humans share in those gains when their labor is no longer central to production.
On a note of cautious humility, professor Sarin concluded that we are likely to be poor at predicting exactly which policy tools — be it universal basic income, tax reform, or new education models — might be needed. “The A.I. transition may be hugely challenging no matter what, but we should put ourselves in the best position to manage it,” she said.