The Atlantic‘s Josh Tyrangiel warns, “America Isn’t Ready for What AI Will Do to Jobs.” After a long setup, he observes,
This is a technology that can digest a hundred reports before you’ve finished your coffee, draft and analyze documents faster than teams of paralegals, compose music indistinguishable from the genius of a pop star or a Juilliard grad, code—really code, not just copy-paste from Stack Overflow—with the precision of a top engineer. Tasks that once required skill, judgment, and years of training are now being executed, relentlessly and indifferently, by software that learns as it goes.
AI is already so ubiquitous that any resourceful knowledge worker can delegate some of their job’s drudgery to machines. Many companies—Microsoft and PricewaterhouseCoopers among them—have instructed their employees to increase productivity by doing just that. But anyone subcontracting tasks to AI is clever enough to imagine what might come next—a day when augmentation crosses into automation, and cognitive obsolescence compels them to seek work at a food truck, pet spa, or massage table. At least until the humanoid robots arrive.
We’ve worried about this all for a while now, but the technology is improving at a rapid clip. We’re already at the point where people who have figured out how to protect themselves from the known flaws of the LLMs, such as their tendency to “hallucinate” (i.e., make shit up), can do months’ worth of high-level analytical work in minutes. University professors are creating syllabi, lectures, and even entire books at a pace not previously imaginable.
But, of course, this means that these tasks are less valuable than before.
In May 2025, Dario Amodei, the CEO of the AI company Anthropic, said that AI could drive unemployment up 10 to 20 percent in the next one to five years and “wipe out half of all entry-level white-collar jobs.” Jim Farley, the CEO of Ford, estimated that it would eliminate “literally half of all white-collar workers” in a decade. Sam Altman, the CEO of OpenAI, revealed that “my little group chat with my tech-CEO friends” has a bet about the inevitable date when a billion-dollar company is staffed by just one person. (The business side of this magazine, like some other publishers, has a corporate partnership with OpenAI.) Other companies, including Meta, Amazon, UnitedHealth, Walmart, JPMorgan Chase, and UPS, which have recently announced layoffs, have framed them more euphemistically in sunny reports to investors about the rise of “automation” and “head count trending down.”
As detailed in the piece, there is conflicting data as to the impact. There is evidence of a pretty significant decline in the hiring of those entering the white-collar labor force over the past few years. Yet overall employment and productivity are actually up over the same period.
Productivity is the cheat code for a more prosperous society. If each worker can produce more in the same hour—more goods, better services, faster results—then the total economic pie grows, even if the number of workers doesn’t. It’s the rare efficiency gain that expands the pie rather than merely redistributing slices.
America has been on a productivity tear for the past few years. It might be temporary, the result of a onetime boost, such as the COVID-era boom in new small businesses. But with the special joy of someone paid to complicate everything, [Austan] Goolsbee [president of the Federal Reserve Bank of Chicago, the Robert P. Gwinn Professor of Economics at the University of Chicago’s Booth School of Business, and a former chair of the Council of Economic Advisers under Barack Obama] pointed out that general-purpose technologies such as electricity and computing can create lasting productivity gains, the kind that make whole societies wealthier.
Whether AI is one of those technologies will only become clear over time. How long before we’ll know? “Years,” Goolsbee said.
Those hopeful that the impact will be slow enough for society to adapt point to built-in obstacles.
The argument goes like this: Before AI can transform a company, it has to access the company’s data and be woven into existing systems—which sounds easy, provided you’re not a chief technology officer. A trade secret of most Fortune 500 companies is that they still run many critical functions on lumbering, industrial-strength mainframe computers that almost never break down and therefore can never be replaced. Mainframes are like Christopher Walken: They’ve been going nonstop since the 1960s, they’re fantastic at performing peculiar roles (processing payments, safeguarding data), and nobody alive really understands how they work.
Integrating legacy tech with modern AI means navigating hardware, vendors, contracts, ancient coding languages, and humans—every one of whom has a strong opinion about the “right” way to make changes. Months pass, then years; another company holiday party comes and goes; and the CEO still can’t understand why the miracle of AI isn’t solving all of their problems.
Every new general-purpose technology is, for a time, held hostage by the mess of what already exists. The first electric-power stations opened in the 1880s, and no one debated whether they were superior to steam engines. But factories had been built with steam engines in their basements, powering overhead shafts that ran the length of the buildings, with belts and pulleys carrying power to individual machines. To adopt electricity, factory owners didn’t just need to buy motors—they needed to demolish and rebuild their entire operations. Some did. Most just waited for their infrastructure to wear out, which explains why the major economic gains from electrification didn’t show up for 40 years.
Pessimists argue that AI is simply different, since it mostly leverages existing business systems. It’s built into programs like Microsoft Office and is easily accessible through existing web browsers. And the pressure to quickly get on the bandwagon is intense.
Consider consulting firms, which have always charged high fees for having junior associates do research and draft reports—fees clients tolerated because there was no alternative. But if one firm can use AI to deliver the same work faster and cheaper, its competitors face a stark choice: adopt the technology, or explain why they are still charging a premium for human hours. Once a firm plugs in and undercuts its rivals, the rest must either race to follow or be left behind. Competition doesn’t just reward adoption; it makes delay indefensible.
Furthermore, the external demands for ever-increasing efficiency will be impossible to ignore.
“It’s a fever,” Gina Raimondo, the former governor of Rhode Island and commerce secretary under Joe Biden, told me, referring to the rush to cut jobs. “Every CEO and every board feels like they need to go faster. ‘We have 40,000 people doing customer service? Take it down to 10,000. AI can handle the rest.’ If the whole thing is about moving fast with your eye strictly on efficiency, then an awful lot of people are going to get really hurt. And I don’t think this country can handle that, given where we already are.”
This is followed by a long discussion about how the U.S. political system is almost certainly not equipped to deal with this on a public policy level.
