How exposed are UK jobs to generative AI? Developing and applying a novel task-based index

Henseke, Golo, Davies, Rhys ORCID: https://orcid.org/0000-0002-3479-625X, Felstead, Alan ORCID: https://orcid.org/0000-0002-8851-4289, Gallie, Duncan, Green, Francis and Zhou, Ying 2025. How exposed are UK jobs to generative AI? Developing and applying a novel task-based index. [Online]. arXiv. Available at: https://doi.org/10.48550/arXiv.2507.22748

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Abstract

We introduce the Generative AI Susceptibility Index (GAISI), a task-based measure of UK job exposure to large language models (LLMs), such as ChatGPT. GAISI is derived from probabilistic task ratings by LLMs and linked to worker-reported task data from the Skills and Employment Surveys. It reflects the share of job activities where an LLM or LLM-powered system can reduce task completion time by at least 25 per cent beyond existing productivity tools. The index demonstrates high reliability, strong alignment with AI capabilities, and superior predictive power compared to existing exposure measures. By 2023-24, nearly all UK jobs exhibited some exposure, yet only a minority were heavily affected. Aggregate exposure has risen since 2017, primarily due to occupational shifts rather than changes in task profiles. The price premium for AI-exposed tasks declined relative to 2017, measuring approximately 11 per cent lower in 2023-24. Job postings in high-exposure roles also fell by 6.5 per cent following the release of ChatGPT. GAISI offers a robust framework for assessing generative AI's impact on work, providing early evidence that displacement effects may already outweigh productivity gains.

Item Type:	Website Content
Date Type:	Publication
Status:	Published
Schools:	Schools > Social Sciences (Includes Criminology and Education) Research Institutes & Centres > Wales Institute of Social & Economic Research, Data & Methods (WISERD)
Publisher:	arXiv
Date of Acceptance:	31 July 2025
Last Modified:	05 Aug 2025 15:00
URI:	https://orca.cardiff.ac.uk/id/eprint/180198

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