A reality check on the AI jobs hysteria

Haven’t you heard? White-collar jobs are going away, decimated by AI. Waves of layoffs in the tech sector (most recently at Coinbase and Meta and Cisco) are said to presage what will soon come for all of us knowledge workers. But before you quit your job as a software developer or financial analyst—or tech journalist—and look to join the plumbers’ union, it’s worth considering today’s economic research on whether artificial intelligence has actually begun to devour white-collar work. The short answer is: No. Despite the warning by some of an imminent jobs apocalypse that will destroy much of if not most such work, or the rumblings about a “permanent underclass,” there’s scant evidence that AI has yet had any large-scale impact on the US labor market.  Analysis of the data gathered for the US Bureau of Labor Statistics (BLS) shows that the unemployment rate for the jobs potentially most affected by AI is actually lower than that for occupations less exposed to the technology. And, critically in the mind of economists, there are no signs that large numbers of people are shifting from jobs threatened by AI to supposedly safer ones, such as those involving mostly manual labor. While the current labor statistics don’t preclude a sudden job upheaval in the coming years, they do throw doubt on the inevitability of the doomsday scenarios and the pace at which they’d unfold. Everyone in the AI community, it seems, is predicting that the technology will soon wipe out jobs, and everyone, it also seems, knows some young wannabe workers who can’t find one. Perhaps we haven’t seen any major disruption in the labor market statistics yet, people often say, but just wait.  But maybe we should pay attention to what the data is showing us. And right now, the numbers paint a picture of a relatively stable labor market in which AI disruptions remain largely speculative. “It could be disruptive, but the data is telling us right now that disruption is not yet here, and we have time to plan.” “All of the available evidence to date suggests that AI’s impact on current labor market conditions is likely small right now,” says Erika McEntarfer, a labor economist who headed the BLS until President Trump fired her last fall after a jobs report that displeased the administration. (

Not surprisingly, BLS reports of sluggish job growth have continued since her dismissal.) McEntarfer, who is now a fellow at the Stanford Institute for Economic Policy Research, says the relatively small impact that AI is having so far on today’s labor market “surprises many people, but it shouldn’t. What we know from history is that it takes time for innovations to work their way through changes in industries and changes in occupations. AI is unlikely to transform labor markets until it first transforms businesses.” McEntarfer points to US Census data showing that only one in five companies are using AI in any business function. “The data are a great reality check on the fear that AI will be enormously disruptive,” she says. “It could be. It likely will be disruptive, but the data is telling us right now that disruption is not yet here, and that we have time to plan.” Things ain’t great—but the question is why The US job market, to be sure, sucks for many, especially younger would-be workers. Unemployment rates for recent college graduates stand at around 5.6%, well above the level for all workers. It’s a rate not seen since the pandemic and the years immediately after the 2008 recession. Even more troubling is that hiring rates have been particularly dismal during the post-covid economy, a trend that hits hard at young people trying to enter the workforce. If you’re a recent college graduate and looking for a tech job, no one, it can seem, is hiring. There are signs that AI is contributing to the pain for the 22-to-25-year-olds seeking jobs in software development and other occupations that are feeling a big impact from AI. But these professions represent just a sliver of the overall labor market. What’s more, it’s uncertain how much blame AI should get for the job woes. Similarly unknown is whether the loss of entry-level jobs in AI-exposed occupations is a harbinger of what’s coming for others or simply an isolated symptom of what economists refer to as a “low-fire, low-hire” labor market caused by a variety of macroeconomic forces. Insights into these uncertainties will tell us much about our working fates in the transition to an AI economy.

There are no shortage of confident assertions and predictions about what is about to happen; while some people forecast the end of work, others say economic history teaches us that technology advances always lead to more and better jobs eventually.  The honest answer is that no one knows for sure what AI will bring and whether this time will be different. To help figure it out, we need better and far more comprehensive data. The statistics gleaned from the federal government’s monthly survey of 60,000 households for the BLS provide a broad overview of the changes to the labor market, while academics and even some AI companies have begun trying to gain a more granular view of specific jobs that are being affected. But the existing data-gathering tools don’t adequately explain how AI is affecting the huge and diverse US labor market. There’s a long list of questions that we don’t have the data to fully answer. How is AI being used in the workplace? Does the increased use of AI mean the technology will replace workers, or will it make them more productive and valuable? Which occupations and skills are most affected? Who is in most peril from the changes? As David Deming, a professor of economics at Harvard University, puts it: “We’re sort of flying blind.” To gather more insight into some of these questions, Deming and his colleagues have been surveying several thousand people every three months since 2024, asking them basic questions: Do you use generative AI, and how often? Does it save you time at work? Tracking the answers over time gives the economists important clues (it’s used by a little over 40% of workers but adoption varies by sectors) and allows them to estimate productivity gains (they’ve found some, but nothing economy-shaking). It has also helps document how quickly AI has been adopted in the workplace and how it compares with earlier technologies such as the PC and the internet (the pace has been faster but roughly in the same ballpark).

It’s far from a complete picture of how AI is changing work. But it provides some intriguing results; for example, a fair number of workers in manufacturing and other industrial sectors have tried AI. Deming’s results show that while businesses in general might be relatively slow to formally adopt the technology, lots of their employees are using it.

Getting a picture of these early adopters and how they’re using AI provides a “crystal ball for the future of the labor market,” Deming says. “It gives you important clues about how it’s going to be used tomorrow, and who’s going to be affected, and who’s going to be harmed and how do we need to get ready for it. It’s a diagnostic of what’s coming down the road.” But what it doesn’t tell you is the fate of various jobs. The young are most vulnerable Analysis of how AI will affect jobs typically begins with identifying so-called exposure of various occupations to the technology. This approach is based on the idea that any given job is a collection of tasks. By evaluating which tasks can be performed by, say, the latest large language model, researchers gauge an occupation’s overall exposure. A small army of economists have created a slew of such studies, meticulously ranking hundreds of jobs and scrambling to update the results as the capabilities of generative AI keep exploding.  The results have often triggered a panic, with graphics showing the growing vulnerability of different jobs to AI. But by themselves the exposure results are not a true predictor of which jobs will be lost to AI. That depends on the kinds of tasks done by the technology, the extent to which the AI is adopted, various business calculations about the value of workers, and even the costs of deploying AI. But the exposure findings are a valuable starting point.  In a working paper called “Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence,” researchers at the Stanford Digital Economy Lab looked at 950 jobs, placing the occupations into five categories from least exposed to most. Then they used a vast data set from ADP, the world’s largest payroll provider, to look at employment growth in each of the categories. Their exclusive access to the ADP data set, which is far larger than the one available through the BLS, allows the researchers to better spot impacts by demographic. When they examined what was happening to different age groups, says Erik Brynjolfsson, the director of the lab who led the effort, “it was extremely striking.”

They spotted the drop in head count for 22-to-25-year-olds in the most exposed occupations, such as software development and customer service, beginning in late 2022, when ChatGPT was first publicly released. Other researchers reported evidence that the decline in these jobs began well before ChatGPT and questioned whether the labor market could react so quickly to the introduction of AI technology.  But while the Stanford researchers acknowledge that other factors in addition to AI probably contributed to the early declines, they say that after controlling for those factors, they saw convincing evidence of a significant effect from AI after 2024 and growing in 2025 to a 16% decline in entry-level jobs in AI-exposed occupations. In contrast, head count grew for older workers in the same occupations, as did the number of jobs in the less exposed occupations. Digging deeper into the data, the researchers found another important clue, though one that wasn’t totally unexpected. The impact on head counts depended on how AI was being used. It was specifically the jobs where tasks could be automated (that is, AI could do them “with minimal human involvement”) that accounted for the decrease in employment—jobs for people like software developers. In jobs where AI was mainly used but to augment human work, head counts grew faster than the average for entry-level workers. That’s consistent with one explanation for the woes of many young would-be workers. It could be, according to the Stanford paper, that entry-level jobs depend more on the types of knowledge that people acquire through education but that can readily be mimicked by AI; the authors call this codified knowledge. It might be particularly easy to automate such tasks as entry-level coding. In contrast, older workers have more so-called tacit knowledge, the type based on their experience. That type of wisdom is harder for AI to replace. Despite the findings about AI’s impact on young workers, Bharat Chandar, an economist at Stanford and one of the authors (along with Brynjolfsson and Ruyu Chen), stresses that it’s still early when it comes to understanding how the technology will affect jobs in the future. It could be that the job loss will spread to older workers and to less AI-exposed occupations, he says. But Chandar says it is also possible that firms and workers will adjust to shifting labor demands, and the effects will level off or even disappear.