Haven\u2019t 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\u2014or tech journalist\u2014and look to join the plumbers\u2019 union, it\u2019s worth considering today\u2019s economic research on whether artificial intelligence has actually begun to devour white-collar work.<\/p>\n
The short answer is: No.<\/p>\n
Despite the warning by some of an imminent jobs apocalypse<\/a> that will destroy much of if not most such work, or the rumblings about a \u201cpermanent underclass<\/a>,\u201d there\u2019s scant evidence that AI has yet had any large-scale impact on the US labor market.\u00a0<\/p>\n<\/div>\n Analysis of the data<\/a> 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<\/a> from jobs threatened by AI to supposedly safer ones, such as those involving mostly manual labor.<\/p>\n While the current labor statistics don\u2019t 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\u2019d 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\u2019t find one. Perhaps we haven\u2019t seen any major disruption in the labor market statistics yet<\/em>, people often say, but just wait.\u00a0<\/p>\n But maybe we should <\/em>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.<\/p>\n \u201cIt could be disruptive, but the data is telling us right now that disruption is not yet here, and we have time to plan.\u201d<\/strong><\/p>\n<\/blockquote>\n \u201cAll of the available evidence to date suggests that AI\u2019s impact on current labor market conditions is likely small right now,\u201d 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.)<\/p>\n 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\u2019s labor market \u201csurprises many people, but it shouldn\u2019t. 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.\u201d<\/p>\n McEntarfer points to US Census data showing that only one in five companies<\/a> are using AI in any business function. \u201cThe data are a great reality check on the fear that AI will be enormously disruptive,\u201d she says. \u201cIt 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.\u201d<\/p>\n The US job market, to be sure, sucks for many, especially younger would-be workers<\/a>. Unemployment rates for recent college graduates stand at around 5.6%<\/a>, well above the level for all workers. It\u2019s 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<\/a> during the post-covid economy, a trend that hits hard at young people trying to enter the workforce. If you\u2019re a recent college graduate and looking for a tech job, no one, it can seem, is hiring.<\/p>\n 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\u2019s more, it\u2019s 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\u2019s coming for others or simply an isolated\u00a0symptom of what economists refer to as a \u201clow-fire, low-hire\u201d labor market<\/a> caused by a variety of macroeconomic forces.<\/p>\n 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.\u00a0<\/p>\n 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<\/a>.<\/p>\n The statistics gleaned from the federal government\u2019s monthly survey of 60,000 households for the BLS<\/a> 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<\/a>. But the existing data-gathering tools don\u2019t adequately explain how AI is affecting the huge and diverse US labor market.<\/p>\n There\u2019s a long list of questions that we don\u2019t 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: \u201cWe\u2019re sort of flying blind.\u201d<\/p>\n 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\u2019s used by a little over 40% of workers but adoption varies by sectors) and allows them to estimate productivity gains (they\u2019ve 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).<\/p>\n It\u2019s 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\u2019s results show that while businesses in general might be relatively slow to formally adopt the technology, lots of their employees are using it.<\/p>\n Getting a picture of these early adopters and how they\u2019re using AI provides a \u201ccrystal ball for the future of the labor market,\u201d Deming says. \u201cIt gives you important clues about how it\u2019s going to be used tomorrow, and who\u2019s going to be affected, and who\u2019s going to be harmed and how do we need to get ready for it. It\u2019s a diagnostic of what\u2019s coming down the road.\u201d<\/p>\n But what it doesn\u2019t tell you is the fate of various jobs.<\/p>\n<\/div>\n 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\u2019s 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.\u00a0<\/p>\n The results have often triggered a panic, with graphics showing the growing vulnerability of different jobs to AI<\/a>.<\/p>\n<\/div>\n 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.\u00a0<\/p>\n In a working paper called \u201cCanaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence<\/a>,\u201d 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\u2019s 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, \u201cit was extremely striking.\u201d<\/p>\n 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<\/a> and questioned whether the labor market could react so quickly to the introduction of AI technology.\u00a0<\/p>\n 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<\/a> 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.<\/p>\n Digging deeper into the data, the researchers found another important clue, though one that wasn\u2019t 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 \u201cwith minimal human involvement\u201d) that accounted for the decrease in employment\u2014jobs 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.<\/p>\n That\u2019s 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.<\/p>\n Despite the findings about AI\u2019s impact on young workers, Bharat Chandar, an economist at Stanford and one of the authors (along with Brynjolfsson and Ruyu Chen), stresses that it\u2019s 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.<\/p>\n To track how it plays out, the Stanford Digital Economy Lab is about to launch a regularly updated project providing data on how AI is transforming the economy<\/a>.<\/p>\n The Stanford research and other work has put a particular spotlight on coding, a task at which AI is getting extremely adept.\u00a0<\/p>\n<\/div>\n A recent paper by economists at the Federal Reserve Board<\/a> found, not surprisingly, that annual employment growth for coders has slowed significantly\u2014by about 3%\u2014since the introduction of ChatGPT. But here\u2019s a critical detail: Overall employment for coders continues to grow<\/em>. Employment in coding jobs is still rising, they noted, just more slowly than before 2022.\u00a0<\/p>\n In short, coding jobs are not going away, at least not anytime soon. But it\u2019s an occupation that is clearly being transformed by AI. <\/p>\n One of the somewhat surprising wrinkles uncovered by recent research is that wages in sectors highly exposed to AI have risen relatively fast since the introduction of ChatGPT<\/a>. One explanation is that employers are still willing to pay for the kinds of knowledge and experience that are, at least for now, hard to replace with AI. If true, this suggests not the end of work in AI-exposed jobs but, more specifically, the demise of the typical career model in which young graduates are hired to do software tasks that can<\/em> be automated and are slowly trained to gain that valuable tacit experience.<\/a> The earn-while-you-learn model might finally be broken\u2014at least for some occupations.<\/p>\n The simple truth could be that coding skills are <\/a>no longer a guarantee of a job. That may help to explain the drop-off of computer science majors <\/a>at schools around the country. Future canaries in the cubicles are sniffing out the dangers of looking for a job when their skills can be matched by AI.<\/p>\n But a closer look at the data<\/a> shows that students are not necessarily turning away from AI-related careers. Rather, they appear to be tailoring their skills to the changes they see underway as AI becomes increasingly important for various disciplines. Interest is rising in AI-adjacent fields like data science and cybersecurity. One fast-growing major: artificial intelligence<\/a> itself (a recent addition to many college offerings).<\/p>\n Anxiety over the potential of AI to replace workers is nothing new. I wrote \u201cHow Technology Is Destroying Jobs<\/a>\u201d in 2013, describing how a slew of new digital technologies, including AI, were beginning to threaten white-collar work. I wasn\u2019t alone. It was a popular theme at a time when the labor market was sluggish and jobs were scarce.\u00a0<\/p>\n In one of his last days in office in late 2016, President Obama issued a report written by his top economic and science advisors warning that AI was threatening workers. Among the findings was that automated vehicles\u2014especially driverless trucks\u2014could eliminate 2.2 million to 3.1 million existing US jobs.<\/a>\u00a0 Around the same time, one of the pioneers of AI, Geoffrey Hinton, <\/a>said that \u201cpeople should stop training radiologists\u201d because it was \u201ccompletely obvious\u201d the occupation was soon to be replaced by AI.<\/p>\n None of these predictions came true, of course (nor did so-called technological unemployment occur during several earlier tech-related job panics<\/a>). The forecasts were often wrong about the pace of the technological advances\u2014we\u2019re still waiting for fleets of driverless trucks on the highways\u2014and failed to understand the complex portfolio of tasks that make up many jobs. AI has indeed become a tool for screening radiology images, but there are more radiologists than ever.<\/a> It turns out that human radiologists perform a multitude of valuable tasks, including interpreting results and interacting with patients, that can\u2019t be accomplished with AI (yet).<\/p>\n<\/div>\n Perhaps this time is different, and we can put aside the lessons of economic history. Certainly, AI has gained unimaginable powers to do humanlike tasks. Perhaps it will devour jobs in ways that we\u2019ve never seen before. And perhaps that will happen abruptly, without a warning buried in the labor statistics. But the previous bouts of AI job anxiety still hold a prescient lesson: Our real focus needs to be less on the dystopian fears and more on the very real transitions in the workplace that will likely affect millions of people.<\/p>\n \u201cEven if there is not mass or even increased unemployment, the transition could still be very difficult,\u201d says Jed Kolko, senior fellow at the Peterson Institute for International Economics and former undersecretary of commerce in the Biden administration. \u201cAnd what does a difficult transition period mean? It means people losing jobs, or people\u2019s jobs being redefined in ways that make those jobs pay worse or be less meaningful. And some people whose jobs are threatened may not be able to adapt.\u201d<\/p>\n The more we understand this transition, the better prepared we\u2019ll be to deal with it.\u00a0 And for that we\u2019ll need better and more complete data.<\/p>\n For McEntarfer, the former commissioner of the BLS, the real question is the speed of any disruption. \u201cIf it happens at the normal pace of technological change, labor markets will have time to adapt. If there is a sudden and severe disruption, then that will be a big challenge for policymakers,\u201d she says. \u201cThat\u2019s really the most important question facing us right now: how rapid this transformation is going to be.\u201d And, she adds, \u201cwe\u2019ll know by watching the data.\u201d<\/p>\n Two decades ago, the country was caught flat-footed by the so-called China shock as free-trade policies led to an influx of imports and the devastation of manufacturing jobs in many parts of the country. It took years for researchers to understand the data showing how the trade policies, generally welcomed by economists, were destroying communities. Today the threat of an economic transformation brought on by AI is far larger and points to potentially far more damage for huge groups of workers.<\/p>\n To head off another devastating labor transition, we will need well-timed government and business policies, especially programs to train and reskill workers. If McEntarfer and other labor economists are correct, we probably have time to design deliberate and effective strategies to manage the transition. But first we need to better understand what is going on\u2014and how fast.<\/p>\n It\u2019s hard to find an economist who is more enthusiastic about AI\u2019s future than Stanford\u2019s Brynjolfsson, who believes that we\u2019re likely on the brink of a huge boost that will transform the economy. \u201cPerhaps the best productivity growth of my lifetime is coming up,\u201d he says.<\/p>\n But Brynjolfsson also warns that a lack of data is severely limiting our visibility into the economic and societal impacts that are coming. At a time when hundreds of billions are being spent on rolling out the technology, he says, \u201cwe\u2019re not investing even 1% of that on understanding the transition.\u201d<\/p>","protected":false},"excerpt":{"rendered":" Haven\u2019t 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\u2014or tech journalist\u2014and look to join the plumbers\u2019 union, it\u2019s worth considering today\u2019s 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 \u201cpermanent underclass,\u201d there\u2019s scant evidence that AI has yet had any large-scale impact on the US labor market.\u00a0 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\u2019t 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\u2019d 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\u2019t find one. Perhaps we haven\u2019t seen any major disruption in the labor market statistics yet, people often say, but just wait.\u00a0 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. \u201cIt could be disruptive, but the data is telling us right now that disruption is not yet here, and we have time to plan.\u201d \u201cAll of the available evidence to date suggests that AI\u2019s impact on current labor market conditions is likely small right now,\u201d 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\u2019s labor market \u201csurprises many people, but it shouldn\u2019t. 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.\u201d McEntarfer points to US Census data showing that only one in five companies are using AI in any business function. \u201cThe data are a great reality check on the fear that AI will be enormously disruptive,\u201d she says. \u201cIt 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.\u201d Things ain\u2019t great\u2014but 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\u2019s 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\u2019re 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\u2019s more, it\u2019s 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\u2019s coming for others or simply an isolated\u00a0symptom of what economists refer to as a \u201clow-fire, low-hire\u201d 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.\u00a0 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\u2019s 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\u2019t adequately explain how AI is affecting the huge and diverse US labor market. There\u2019s a long list of questions that we don\u2019t 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: \u201cWe\u2019re sort of flying blind.\u201d 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:<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-93040","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\n\n
Things ain\u2019t great<\/strong>\u2014but the question is why
<\/h3>\nThe young are most vulnerable<\/strong><\/h3>\n
Is this time different?<\/strong><\/h3>\n