How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index
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| Format: | Preprint |
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2025
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| _version_ | 1866910147683549184 |
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| author | Henseke, Golo Davies, Rhys Felstead, Alan Gallie, Duncan Green, Francis Zhou, Ying |
| author_facet | Henseke, Golo Davies, Rhys Felstead, Alan Gallie, Duncan Green, Francis Zhou, Ying |
| contents | 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 shifts rather than within-occupation task changes. The wage premium for AI-exposed tasks declined 12% between 2017 and 2023/24, and the period since ChatGPT's release has coincided with a relative contraction of job postings in more AI-exposed occupations. These findings are consistent with generative AI beginning to affect hiring and pay in exposed occupations, though causal attribution requires further research. GAISI offers policymakers and researchers a validated, replicable tool for monitoring AI exposure at the job level as this technology diffuses. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_22748 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index Henseke, Golo Davies, Rhys Felstead, Alan Gallie, Duncan Green, Francis Zhou, Ying General Economics Economics 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 shifts rather than within-occupation task changes. The wage premium for AI-exposed tasks declined 12% between 2017 and 2023/24, and the period since ChatGPT's release has coincided with a relative contraction of job postings in more AI-exposed occupations. These findings are consistent with generative AI beginning to affect hiring and pay in exposed occupations, though causal attribution requires further research. GAISI offers policymakers and researchers a validated, replicable tool for monitoring AI exposure at the job level as this technology diffuses. |
| title | How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2507.22748 |