Will A.I. Still Take Our Jobs?

“If every team can generate better AI-based analysis to support their arguments, then the demand for conflict resolution and authority-based decisions will increase dramatically,” the economists Luis Garicano, Jin Li, and Yanhui Wu predict, in “Messy Jobs: The Work That AI Cannot Reach.” They note that many workplace decisions aren’t made only on the merits; they also involve deciding “who gets their way.” Who’s ready to take a big swing, or too inexperienced for heavy responsibilities? What kinds of ideas always sound good but never work? What does the C.E.O. really think, but never say? Such information isn’t explicit, but tacit—it’s known, but not written down—and so it isn’t available to an A.I. system. Moreover, the proliferation of A.I.-generated work can make it harder for decision-makers to collect the tacit information they need. If every cover letter is nicely written, and every memo thorough and well structured, how can a boss know whom to trust? If everyone uses A.I. to generate ideas, how do you know who’s actually creative?

ChatGPT first appeared in 2022; Claude, in 2023. Almost immediately, an imminent jobs apocalypse was predicted. There’s no question that people find A.I. useful: studies and surveys show that an increasing number of office workers are now employing it on a daily basis. Certain fields—coding, recruiting, scientific research, the law—really do seem to be getting transformed. And yet A.I.’s effect, in general, is turning out to be hard to measure. Many workers appear to be using it semi-secretly, on their own devices, perhaps saving themselves time or improving their work in ways that aren’t reflected on the bottom line. Recent college grads are finding it harder to get hired, and customer-service jobs may be disappearing, but job openings for software engineers, which decreased substantially in 2025, increased in 2026. Does this mean that A.I. is creating software jobs? Or is the industry merely rebounding after post-pandemic downsizing? Nobody knows.

“Early evidence is hardly the last word on the future of work in an AI world,” a group of Stanford researchers cautioned, in July. Part of the difficulty is that, with A.I. in the mix, we’re realizing that we don’t necessarily know how work works. Why are the jobs we have set up the way they are, and how much could they change? What is distinctly human in what we do, and what is amenable to automation? What makes working with someone valuable, beyond the work they produce? As more people use A.I., the blunt idea of an A.I.-driven jobs apocalypse is getting replaced with a growing number of challenging questions, with which managers and workers are just beginning to grapple.

Economists have a term—the production function—for describing how things are made. Imagine you’re having a dinner party for ten. If you decide to make steak frites, then you’ll have to cook the steaks and the frites in the minutes just before your guests sit down to eat. If you only have four burners on your stove, then you’ll need to sear the steaks in batches; if an extra guest arrives, you must cook an extra steak. Alternatively, you could make a giant pot of stew. In that case, you could do almost all the work a day or two beforehand, then put the pot on the stove when your guests arrive. If an extra guest presents himself, there’s probably enough to go around. Steak frites and stew have completely different production functions. If you graphed them, with effort on one axis and results on the other, you’d get totally different curves.

“Messy Jobs” deals, among other subjects, with the precise ways in which A.I. changes production functions at work. A.I., the authors argue, creates a “new shape of progress” for what we do—and the shape isn’t simply up and to the right. They describe a study in which artists were given A.I. tools that helped them quickly deliver a finished product—an illustration of a scene from a novel. The artists reached, in half an hour, “a quality level that would have taken two hours by hand”—and yet, at that point, progress slowed. Because the artists had used A.I. to “get a polished image before they had thought enough about the composition,” they struggled to improve it; “further gains were barely noticeable, even as artists kept tweaking prompts and patching details.” Ultimately, the artists split into two groups: those who simply suspended their work after about an hour, and those who kept working fruitlessly.