Will A.I. Still Take Our Jobs? - The New Yorker

Suppose that your company wants to launch a new product. A substantial budget is available to fund a prototype. You think you have a good idea; so do Frank and Jill, your workplace frenemies. Your boss asks the three of you to confer with your respective teams and draft proposals, which will be circulated to those in charge.

In the past, you and your subordinates might have embarked on a lengthy research effort. You would’ve calculated the size of your potential market, surveyed competitors, evaluated barriers to entry, estimated manufacturing costs, and worked up branding ideas. This would have taken weeks—and it would only have been after gathering these materials that you’d have sat down to write your draft. But your team happens to include Claudette, a recent college graduate who is great at using artificial intelligence. She organizes a few brainstorming sessions, records them, then runs the transcripts through a large language model, which creates a research plan. She tweaks it, then delegates various parts of it to A.I. agents, assigning yet more agents as fact checkers. She takes all their work and synthesizes it. A first draft of the proposal, complete with visuals, is done in a week; after everyone gives Claudette feedback, the revised draft is done a few days later. As far as you can tell, the proposal is fantastic. You click “Send.”

Soon afterward, you get a look at Frank’s and Jill’s proposals. Apparently, their teams have recent college grads, too. Their proposals, like yours, are shockingly well researched, with lots of data alongside polished writing and images. It seems that the efficiency and quality of everyone’s output has increased. This creates two sets of problems: one for you and one for your bosses. It used to be that the people in charge could evaluate proposals not just for their content but as indirect measures of intelligence, creativity, and ambition; they believed that only the best, most committed workers could produce high-quality results. Now that all proposals are good, though, the underlying realities are harder to discern. You, meanwhile, struggle to prove your own value compared with your competitors’. Also, you used to believe in the work you presented: honing it over long periods, you became sure of its worth. By helping you rush to the finish line, A.I. has deprived you of the time you need to know that you’re right. What if your idea isn’t so good, after all?

“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.

Garicano, Li, and Wu call this the “90/10 production function.” A.I. makes it easy to build something that’s ninety-per-cent great. But if you want to get to a hundred per cent—that is, if you want to achieve true excellence—it will often leave you stranded. The challenge, therefore, is to distinguish between “commodity tasks,” for which good enough is actually good enough, and “star tasks,” for which excellence is required. (In some fields, of course, excellence is required nearly all the time.) Workers have to make this distinction in advance, because these two kinds of tasks might demand entirely different approaches: you can’t decide, halfway through cooking your stew, to turn it into steak frites. And bosses have to figure out how to identify and reward excellence in star tasks. “How do you distinguish workers who pushed to 98 percent from those who stopped at 91 percent?” the authors ask. If you fail to look closely, “the surface looks identical.”

Companies don’t only make products. They also foster workers—ideally, experienced ones, who have grown in knowledge, wisdom, and trustworthiness over time. The 90/10 production function has profound implications for the experienced-worker production function. How will less-experienced workers gain skills when the creation of good-enough work is automated? Junior associates at law firms learn, in part, by producing the simpler documents that A.I. can now create; trainee physicians learn to diagnose difficult cases by figuring out easier ones. In doing such tasks, younger employees acquire work histories and networks of relationships that allow their superiors to see how trustworthy they can be. In order to occupy a position of authority—to decide “who gets their way”—you have to understand the big picture; you have to know that Geoff in accounting is always slow, that Linda in the marketing department is always worth consulting, and that the worst time to pitch an idea to the C.F.O. is on Thursday mornings, before she meets with her boss. You have to owe people favors, so that they owe you favors. How will you gain all this if all you do is prompt?

If work is this messy, then does A.I. even have a place in it? The authors of “Messy Jobs” argue that the technology actually has substantial value for many companies and workers. (It’s because of this value, presumably, that people are adopting the tools on their own, outside of their bosses’ supervision.) In their view, there are some jobs that will certainly be out-and-out eliminated by A.I.—jobs centered on discrete, predictable tasks that tend to be accomplished by individuals in isolation. And yet, they argue, lots of jobs are actually bundles of tasks, only some of which can be automated. Whether such jobs will be reshaped by A.I. depends on whether their tasks form a “weak bundle,” which can be broken up without penalty, or a “strong bundle,” in which “separation destroys value.”

Lots of jobs are strong bundles. When we go to the doctor, we want the person who prescribes our medicine to be the same one who diagnoses us; we also want that person to be legally liable if things go wrong. When we enter into a contract with a vender, we want the person we negotiate with today to be the same one we’ll negotiate with two years from now, and we want them to understand what they’re selling and to track its implementation. Both the physician and the salesperson have strong bundles. Are you called upon to do different kinds of tasks at different times? Are you better at some parts of your job because you do other parts? If your answer to such questions is yes, then your bundle is probably strong.

Garicano, Li, and Wu think that people with strong bundles have a lot to gain from A.I. In one scenario, which they call “robots above,” workers get access to expertise they’d otherwise lack; they need to escalate to their bosses less frequently, and can accomplish more on their own initiative. In another, which they call “robots below,” A.I. “takes over the routine problems of the job while the human handles the exceptions.” In both cases, the value of a worker—especially an experienced one—goes up. In some industries, workers who are “upskilled” by A.I. will still be in trouble: if all workers can do more, but demand doesn’t increase, then fewer workers will be necessary. But in many industries demand can grow. There is an insatiable need for cheaper, better health care; if A.I. helps clinicians do more, there will be more for them to do.

Entrepreneurs are perfectly positioned to take advantage of A.I.; there’s no org chart to hem them in. But employees inside big companies need managers to notice what’s possible, so that workflows can change. During this transitional period, the authors argue, the scarcest workers are the people who can help organizations adapt to A.I. The people best positioned to help aren’t chief information officers or consultants, they suggest, but rank-and-file workers who are intimately familiar with how work is actually done. They cite, as a positive example, the Spanish bank BBVA—a large company with eighty million customers and more than a hundred and twenty thousand employees. Instead of equipping all of them with A.I., BBVA’s leaders offered it only to the most enthusiastic workers—people across the company who were excited about the technology. They then rewarded those who came up with good ways to use it. The transformation was led from within, instead of from the top down, by individuals who were simultaneously technically literate, familiar with the details of day-to-day work, and cognizant of the workplace politics that resist change. There aren’t many people with those three competencies—and their scarcity, the authors argue, is what is keeping A.I. from transforming work at the speed some predicted. Smart bosses will start hunting for candidates.

Is “Messy Jobs” right? In many respects, it’s a reassuring book. According to its analysis, A.I. isn’t a bubble but a complex and valuable new technology that takes time to figure out; instead of causing a generalized jobs apocalypse, it will hurt some workers while helping others to do more, both for themselves and for their companies. Two alternative views loom on either side, both less encouraging. It could be that bosses will simply be bad at implementing A.I.—that, instead of comprehending its potential to increase the value of workers, they will see it mainly as a cost-cutting tool, hollowing out their companies as a result. And it could also be that the progress of artificial intelligence will accelerate further, making some kinds of work so cheap and easy that many strong bundles become weak. No one can predict the future; only time will tell. Our best bet, right now, is to make sure that we’re asking the right questions. ♦