Artificial Intelligence

AI Risks 8M UK Jobs: Government Action Needed

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May 7, 2024 by Ivy Delaney

The Institute for Public Policy Research (IPPR) warns that up to 8 million UK jobs could be at risk from artificial intelligence (AI) unless the government intervenes. The report suggests that back office, entry level, and part-time jobs are most vulnerable to automation, with women and young people disproportionately affected. The IPPR proposes three potential scenarios: worst case, with 7.9 million job losses; a central scenario, with 4.4 million job losses but economic gains of £144bn per year; and a best case scenario, with no job losses and an economic boost of £306bn per year. The IPPR urges the government to develop a job-centric industrial strategy for AI.

The Impact of AI on the UK Labour Market

A recent report by the Institute for Public Policy Research (IPPR) has revealed the potential impact of generative artificial intelligence (AI) on the UK labour market. The study, which is the first of its kind, suggests that the UK is at a critical juncture, with the potential for significant job disruption or substantial GDP gains, depending on government policy.

The report identifies two key stages of generative AI adoption: the first wave, which is currently underway, and a second wave in which companies will integrate existing AI technologies more deeply into their processes. The IPPR’s analysis of 22,000 tasks in the UK economy, covering every type of job, finds that 11 per cent of tasks done by workers are already exposed to AI in the first wave. This could increase to 59 per cent of tasks in the second wave, affecting increasingly higher earning jobs.

The Jobs Most at Risk

The study suggests that back-office, entry-level, and part-time jobs are at the highest risk of being disrupted during the first wave. These include secretarial, customer service, and administrative roles. Women are more likely to be in such jobs, which means they will be among the most affected. Young people are also at high risk as firms hire fewer people for entry-level jobs and introduce AI technologies instead. In addition, those on medium and low wages are most exposed to being replaced by AI.

Potential Scenarios

The IPPR has modelled three illustrative scenarios for the potential impact of the second wave of AI adoption on the labour market, depending on policy choices. The worst-case scenario involves full displacement, with all jobs at risk being replaced by AI, leading to 7.9 million job losses and no GDP gains. The central scenario predicts 4.4 million jobs disappearing, but with economic gains of 6.3 per cent of GDP (£144bn per year). The best-case scenario involves full augmentation, with all jobs at risk being adapted to AI, leading to no job losses and an economic boost of 13 per cent to GDP (£306bn per year).

The Role of Government and Policy Makers

The report urges government intervention to ensure that the economic gains from AI are widely spread, rather than accruing to only a few. Without government action and with companies left to their own devices, the worst-case scenario is a real possibility, the IPPR warns. The institute recommends the government develops a job-centric industrial strategy for AI that encourages job transitions and ensures that the benefits of automation are shared widely across the economy. This should include supporting green jobs, fiscal policy measures, and regulatory changes.

The Future of Work in the Age of AI

The report concludes that the deployment of AI could free up labour to fill gaps related to unaddressed social needs, such as social care and mental health services, which are currently under-resourced. However, the modelling shows that there is no single predetermined path for how AI implementation will play out in the labour market. The question now is less whether AI can be useful, but rather how fast and in what manner employers will use it. The report emphasizes that technology isn’t destiny and a jobs apocalypse is not inevitable – government, employers and unions have the opportunity to make crucial design decisions now that ensure we manage this new technology well.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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