UK AI institute chair warns companies are using AI to replace white-collar workers

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AI impact on UK white-collar jobs

Highlights

Simon Johnson, newly appointed chair of the UK AI Economics Institute, warns companies are using AI mainly to cut staff, raising risks of workforce obsolescence.
Johnson highlights AI is likely to accelerate labour market polarization in the UK, especially impacting those in the lower middle income bracket and younger career entrants.
He urges policy changes and education reform to ensure AI-driven productivity gains are distributed equitably, rather than deepening wage and wealth inequalities.

UK policy push on AI and work

As reported by Financial Times, Johnson says AI is now at the center of economic debate, with too many companies treating the technology as a way to cut staff rather than support innovation and new forms of work. He argues that the main risk is not mass unemployment, but another deep shock to workers whose skills may become obsolete as higher earners with AI-related expertise become more productive.

Johnson, a professor at the MIT Sloan School of Management and a Nobel laureate who previously served as chief economist at the IMF, has accepted an invitation from the UK government to chair the new AI Economics Institute. He says the institute should help policymakers understand which evidence is most useful and how to handle large amounts of data properly and sensitively as AI adoption spreads through the economy.

The economist argues that the UK has the kind of workforce needed to work out how AI can generate new tasks and fresh demand for human expertise, even if the country no longer leads global technological change as it once did. He says the challenge is to guide the transition so that productivity gains support wages rather than widen existing inequalities.

Labour market pressure and education challenges

Johnson says AI is likely to intensify labour market polarization, especially for people in the lower middle of the income distribution and for younger workers choosing careers. In his view, people can still build durable careers if they select specialisms carefully or develop broader technical and analytical skills suited to a general purpose technology.

He also says universities need to rethink how they prepare students for the labour market, as AI changes the value of different kinds of knowledge and work. Beyond education, he points to the need for policy debate on how the gains from AI are shared, including the balance between taxes on labour and capital and the broader question of ensuring that wealth created by technological advances is distributed more fairly.

Johnson adds that similar AI economics institutes around the world could help governments shape adoption in ways that create opportunities rather than simply displace workers. That approach, he suggests, is critical if AI is to deliver new products, services and jobs instead of repeating the uneven effects of automation and globalization seen in the 1990s and 2000s.

Our earlier article on the decline of UK returnship programmes explained that listings for structured “return to work” schemes have fallen sharply since their post-pandemic peak, even as long-term unemployment and economic inactivity remain elevated. We noted that these programmes can be a crucial route back into skilled roles for parents, carers and people with health conditions—and that some employers still see returners’ experience as an asset for AI adoption, with many participants interested in retraining for AI-related jobs.

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