How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index
Golo Henseke, Rhys Davies, Alan Felstead, Duncan Gallie, Francis Green, Ying Zhou

TL;DR
This paper introduces the GAISI, a novel index measuring UK jobs' exposure to large language models, revealing widespread but varied AI impact on employment and wages since 2017.
Contribution
The paper develops and validates a new task-based index, GAISI, to quantify job exposure to generative AI at the UK job level, enabling better monitoring of AI's labor market effects.
Findings
94% of UK jobs have some LLM exposure by 2023/24
Only 13% of jobs are heavily exposed (GAISI > 0.5)
AI exposure increased by 16% since 2017, driven by occupational shifts
Abstract
Building on the task-based approach to labour markets, we develop the Generative AI Susceptibility Index (GAISI), a job-level measure of UK exposure to large language models (LLMs). Drawing on Eloundou et al. (2024), we use LLMs as probabilistic raters to classify task exposure, linking ratings to worker-reported task data from the British Skills and Employment Surveys. GAISI measures the share of job activities where LLMs can reduce task completion time by at least 25% beyond existing tools. Systematic validations demonstrate high reliability, strong validity, and predictive power over existing exposure measures. By 2023/24, nearly all UK jobs (94%) exhibited some LLM exposure, yet only 13% were heavily exposed (GAISI > 0.5), with the highest concentration in scientific and technical professions. Aggregate exposure rose 16% of one standard deviation since 2017, driven by occupational…
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