NEW DELHI: A January 2026 research briefing by Oxford Economics titled “Evidence of an AI-driven shakeup of job markets is patchy” has put hard numbers to what labour economists had been observing for months. While AI was cited as the reason for nearly 55,000 US 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 totalling 245,000. The gap between the narrative and the numbers is what researchers are beginning to call AI whitewashing.
What the Numbers Actually Show
Oxford Economics applies a straightforward test to evaluate whether AI is genuinely replacing workers at scale. If AI were already replacing labour at scale, productivity growth would be expected to accelerate. Instead productivity growth across major advanced economies has remained weak and volatile indicating that AI adoption is still largely experimental rather than transformational.
Rising unemployment among recent university graduates US graduates hit 5.5% in early 2025 with underemployment around 42.5% is frequently attributed to AI replacing entry-level white-collar roles. Oxford Economics argued this is likely cyclical rather than structural pointing to a supply glut of degree-holders as a more probable culprit. More graduates entering a softening labour market not algorithms eating their jobs.
Oxford Economics concludes that shifts in the labour market are likely to be evolutionary rather than revolutionary with AI playing a modest role alongside broader economic cycle and corporate strategy factors.
However IMF Chief Kristalina Georgieva has argued that AI could displace roughly 40% of global jobs over time, mainly routine and entry-level roles even if large-scale cuts are not yet visible in headline data. And MIT Sloan’s Erik Brynjolfsson found a 13% relative decline in employment for early-career workers in high AI-exposure occupations suggesting AI already has a measurable impact at the entry level, even if it does not yet dominate aggregate layoff counts.
The Corporate Playbook
The pattern is consistent across multiple major companies Meta announced cuts of around 600 AI-focused roles in late 2025 with plans for up to 15,000 further reductions in 2026 framing them as AI-driven restructuring. The financial context tells a different story slowing ad revenue growth, post-pandemic hiring excess and rising AI infrastructure costs. Oracle cut around 30,000 roles globally including approximately 10,000 in India, citing a shift to AI-led investments. Sources told the Economic Times that the company was cutting roles because it was not seeing enough returns on its AI investments which is a financial problem not a technological transition. Snap cut around 1,000 roles citing AI-assisted ad tools while simultaneously facing ad revenue headwinds and competition from TikTok. Block cut roughly 4,000 employees about 40% of its workforce framing it as an AI-first rebuild while its fintech business faced tighter regulation and slower growth.
Oxford Economics argues that companies frequently cite AI as a reason for layoffs when in reality cost-cutting measures and efficiency drives are the dominant forces. This tendency to scapegoat technology risks distracting policymakers and the public from addressing deeper structural challenges in the economy.
As Christine Alemany, a management consultancy CEO, put it directly “Firms aren’t replacing workers with AI on a significant scale. Instead they’re using AI as cover for routine headcount reductions. The numbers expose the issue only 55,000 US job cuts were attributed to AI in 2025, just 4.5% of total layoffs.” Boards demanded cuts and citing AI sounds more visionary to investors than admitting over-hiring mistakes.
The India Picture
India’s corporate landscape shows the same pattern, with an additional layer of opacity. Oracle’s India workforce reduction of approximately 10,000 roles around 20% of its India headcount was framed as AI-led restructuring. Across India’s broader IT sector commentators have flagged approximately 50,000 roles at risk in 2025 through what are being described as silent layoffs that is workforce reductions that do not use the word AI in press releases but are quietly attributed to automation and AI-enabled delivery models in internal communications.
The adoption data does not support the narrative NASSCOM’s 2025 AI Enterprise Adoption Index finds that only about 15% of Indian manufacturers run generative AI proofs of concept and just 4% have generative AI in production implying that AI-scale workforce replacement is still largely aspirational in India even as the rhetoric around it intensifies. The actual pressures Indian IT companies face are more prosaic global clients cutting discretionary tech spending, post-pandemic project pipelines thinning and margin pressure from a stronger rupee.
The Investor Angle
Market commentary consistently notes that AI-framed restructuring announcements tend to see short-term stock price resilience or modest gains whereas pure cost-cutting-due-to-demand-weakness messaging causes more downward pressure even when the absolute number of cuts is similar. This creates a direct financial incentive for companies to reach for AI language when announcing workforce reductions regardless of whether AI is genuinely driving those decisions.
When AI consistently absorbs blame for layoffs driven by economic cycles, post-pandemic corrections and over-hiring, it distorts public understanding of both AI’s actual capabilities and the real structural pressures in the labour market. Policymakers respond to the wrong problem, workers are told to reskill for an AI transition that has not yet arrived at the scale being described. And the genuine economic causes of job losses weak demand, interest rate cycles, valuation corrections go unaddressed.
What Needs to Change
Oxford Economics stresses the importance of reskilling and upskilling programmes to prepare workers for evolving roles in an AI-driven environment and recommends that governments and businesses focus on equipping employees with tools needed to transition into new opportunities rather than resisting innovation. But that recommendation assumes the diagnosis is correct. If the displacement is primarily economic rather than technological, reskilling programmes address the wrong problem.
What is actually needed is more precise corporate disclosure a requirement that companies specifically distinguish between roles eliminated due to automation and roles eliminated due to financial restructuring. Until it is, AI will remain a convenient explanation for decisions that have very little to do with it.
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