GoKawiilIllustration: Laura Wächter
Over the past few months, several surveys and media reports have highlighted how artificial-intelligence technologies will increasingly displace workers. And a growing number of companies mention AI as a factor in planned or actual lay-offs. For instance, data from Challenger, a recruitment firm in Chicago, Illinois, that tracks companies’ public announcements, suggests that, in 2025, AI might have been responsible for seven times as many lay-offs in the United States as were the international tariffs the US government have imposed, which are currently a major source of economic disruption globally.
Such warnings have fuelled widespread expectations that the labour market might be on the verge of upheaval. The most drastic forecasts compare the current moment with the First Industrial Revolution (1760–1830), when mechanization ultimately improved living standards but caused widespread disruption to workers’ livelihoods over the short term.
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Knowledge workers and even researchers like me are wondering whether our jobs might be on the line. Will AI take over teaching, research tasks and data collection? Will the workforce need fewer humans in the future?
These fears are heightened because of speculation that the AI revolution could unfold much more quickly than did previous technology-fuelled transformations. The invention of electricity, for instance, did little to disrupt labour markets on its own. The upheaval came later, through the applications it enabled: indoor lighting, elevators, microphones and other tools reshaped work. Although there were winners and losers, large segments of the workforce had years — sometimes decades — to adapt. Will AI challenge this precedent?
Available data suggest that we shouldn’t panic about the downsides of rapid AI adoption just yet. Research by my team at the Budget Lab at Yale University in New Haven, Connecticut, and the Brookings Institution in Washington DC has found no evidence so far that employment patterns have begun to shift meaningfully since the launch of the ChatGPT chatbot in 2022 (see go.nature.com/47aeexk). The upside is that academics and policymakers still have time to work this problem out — and to identify who might need support during the transition. At the same time, the findings underscore the urgent need for better data-collection systems to study the current transformation.
Poor adoption
Despite a flurry of public announcements by chief executives about imminent AI-driven transformations, not all companies are using machine-learning tools in their day-to-day operations. Survey data collected by the US Census from mid-2023 to February 2026 to track short-term technology use show that about 18% of businesses currently report using AI in the preceding two weeks, and just 22% expected to do so in the next six months (see go.nature.com/3prvpjg).
These figures might overstate effective adoption. Executives are often under pressure from shareholders to articulate an ‘AI strategy’ and to seem forward-leaning. However, AI models might be irrelevant to a firm’s operations, they might lack a compelling application or the necessary tools haven’t matured yet.
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