AI layoffs increasingly look like a corporate fiction that masks a darker reality

In a Jan. 7 report, the research firm argued that while there is anecdotal evidence of job losses, macroeconomic data does not support the idea of ​​a structural change in employment driven by automation. Instead, he highlights a more cynical corporate strategy: “We suspect that some companies are trying to present layoffs as good news rather than bad news, like past excessive hiring. »

Spin the story

The main motivation for this new image of job cuts appears to be investor relations. The report notes that attributing workforce reductions to AI adoption “sends a more positive message to investors” than admitting to traditional business failures, such as weak consumer demand or “past excessive hiring.” By viewing layoffs as a technology pivot, companies can present themselves as forward-thinking innovators rather than companies struggling with cyclical downturns.

In a recent interview, Wharton management professor Pierre Capelli said Fortune that he’s seen research on how companies announce, because markets typically celebrate news of job cuts. “phantom layoffs” this never actually happens. Companies arbitrage the stock market’s positive reaction to news of a potential layoff, but “a few decades ago the market stopped rising because [investors] I started to realize that companies weren’t even making the layoffs they said they were going to make.

Asked about the supposed link between AI and layoffs, Cappelli urged people to look closely at the announcements. “The headline is, ‘It’s because of AI,’ but if you read what they actually say, they say, ‘We hope AI will cover this job.’ I hadn’t done it. They just hope. And they say it because they think it’s what investors want to hear.

The data behind the hype

The Oxford report highlighted data from Challenger, Gray & Christmas, the recruitment firm that is a leading provider of layoff data, to illustrate the disparity between perception and reality. While AI has been cited as the reason for nearly 55,000 job cuts in the United States in the first 11 months of 2025 – accounting for more than 75% of all AI-related cuts reported since 2023 – that figure only represents 4.5% of total reported job cuts.

In comparison, job losses attributed to standard “market and economic conditions” were four times larger, totaling 245,000. Looking at the broader context of the U.S. labor market, where 1.5 million to 1.8 million workers lose their jobs each month, “AI-related job losses remain relatively small.”

The productivity conundrum

Oxford offers a simple economic litmus test for the AI ​​revolution: If machines truly replaced humans on a large scale, output per remaining worker would have to skyrocket. “If AI were already replacing work at scale, productivity growth should accelerate. This is generally not the case.”

The report observes that recent productivity growth has actually slowed, a trend that aligns with cyclical economic behaviors rather than an AI-driven boom. Although the company acknowledges that productivity gains from new technologies often take years to materialize, current data suggests that the use of AI remains “experimental in nature and does not yet replace workers on a large scale.”

At the same time, recent data from the Bureau of Labor Statistics confirm that the “low hiring, low firing” labor market is transforming into “unemployment expansion.” KPMG Chief Economist Diane Swonk previously said Fortuneby Eva Roytburg.

This corresponds to what Bank of America Savita Subramanian, head of U.S. equities and quantitative strategy at Research, said Fortune in August on how businesses learned in the 2020s to generally replace people with processes. At the same time, she acknowledged that productivity measures “have not really improved since 2001”, recalling the famous “productivity paradox” identified by the Nobel Prize-winning economist. Robert Solow: “You can see the computer age everywhere, except in productivity statistics.”

The briefing also addresses concerns that AI is eroding entry-level white-collar jobs. As US graduate unemployment peaked at 5.5% in March 2025, Oxford Economics argued that this phenomenon was likely “cyclical rather than structural”, pointing to an “oversupply” of degree holders as the most likely cause. The share of college-educated 22-27 year-olds in the United States increased to 35% in 2019, with even greater increases seen in the Eurozone.

Ultimately, Oxford Economics concludes that changes in the labor market are likely to be “evolutionary rather than revolutionary.”