Despite breathless headlines warning of a robot takeover in the workforce, a new research briefing from Oxford Economics casts doubt on the narrative that artificial intelligence is currently causing mass unemployment. According to the firm’s analysis, “firms don’t appear to be replacing workers with AI on a significant scale,” suggesting instead that companies may be using the technology as a cover for routine headcount reductions.

In a January 7 report, the research firm argued that, while anecdotal evidence of job displacement exists, the macroeconomic data does not support the idea of a structural shift in employment caused by automation. Instead, it points to a more cynical corporate strategy: “We suspect some firms are trying to dress up layoffs as a good news story rather than bad news, such as past over-hiring.”

Spinning the narrative

The primary motivation for this rebranding of job cuts appears to be investor relations. The report notes that attributing staff reductions to AI adoption “conveys a more positive message to investors” than admitting to traditional business failures, such as weak consumer demand or “excessive hiring in the past.” By framing layoffs as a technological pivot, companies can present themselves as forward-thinking innovators rather than businesses struggling with cyclical downturns.

In a recent interview, Wharton management professor Peter Cappelli told Fortune that he’s seen research about how, because markets typically celebrate news of job cuts, firms announce “phantom layoffs” that never actually occur. Companies were arbitraging the positive stock-market reaction to the news of a potential layoff, but “a few decades ago, the market stopped going up because [investors] started to realize that companies were not actually even doing the layoffs that they said they were going to do.”

When asked about the supposed link between AI and layoffs, Cappelli urged people to look closely at announcements. “The headline is, ‘It’s because of AI,’ but if you read what they actually say, they say, ‘We expect that AI will cover this work.’ Hadn’t done it. They’re just hoping. And they’re saying it because that’s what they think investors want to hear.”

Data behind the hype

The Oxford report highlighted data from Challenger, Gray & Christmas, the recruiting firm that is one of the leading providers of layoff data, to illustrate the disparity between perception and reality. While AI was cited as the reason for nearly 55,000 U.S. job cuts in the first 11 months of 2025—accounting for over 75% of all AI-related cuts reported since 2023—this figure represents a mere 4.5% of total reported job losses.

By comparison, job losses attributed to standard “market and economic conditions” were four times larger, totaling 245,000. When viewed against the broader backdrop of the U.S. labor market, where 1.5 million to 1.8 million workers lose their jobs in any given month, “AI-related job losses are still relatively limited.”

The productivity puzzle

Oxford posits a simple economic litmus test for the AI revolution: if machines were truly replacing humans at scale, output per remaining worker should skyrocket. “If AI were already replacing labour at scale, productivity growth should be accelerating. Generally, it isn’t.”

The report observes that recent productivity growth has actually decelerated, a trend that aligns with cyclical economic behaviors rather than an AI-driven boom. While the firm acknowledges that productivity gains from new technologies often take years to materialize, the current data suggests that AI use remains “experimental in nature and isn’t yet replacing workers on a major scale.”

At the same time, recent data from the Bureau of Labor Statistics confirms that the “low-hire, low-fire” labor market is morphing into a “jobless expansion,” KPMG chief economist Diane Swonk previously told Fortune‘s Eva Roytburg.

This tallies with what Bank of America Research’s Head of US Equity & Quantitative Strategy, Savita Subramanian, told Fortune in August about how companies have learned in the 2020s to generally replace people with process. At the same time, she agreed that productivity measures “haven’t really improved all that much since 2001,” recalling the famous “productivity paradox” identified by Nobel prize-winning economist Robert Solow: “You can see the computer age everywhere but in the productivity statistics.”

The briefing also addresses fears that AI is eroding entry-level white-collar jobs. While U.S. graduate unemployment rose to a peak of 5.5% in March 2025, Oxford Economics argued this is likely “cyclical rather than structural,” pointing to a “supply glut” of degree-holders as a more probable culprit. The share of 22-to-27-year-olds with university education in the U.S. rose to 35% by 2019, with even sharper increases observed in the Eurozone.

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