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How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index

2 Pith papers cite this work. Polarity classification is still indexing.

2 Pith papers citing it
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 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.

fields

econ.GN 2

years

2026 2

verdicts

UNVERDICTED 2

representative citing papers

What Jobs Can AI Learn? Measuring Exposure by Reinforcement Learning

econ.GN · 2026-05-04 · unverdicted · novelty 7.0

A new RL Feasibility Index based on task learnability via reinforcement learning diverges from prior AI exposure measures, rating operational jobs like power plant operators as highly feasible while rating creative and interpersonal roles as less so.

From Exposure to Adoption: Generative AI in European Workplaces

econ.GN · 2026-04-20 · unverdicted · novelty 5.0

Generative AI adoption in Europe ranges from under 3% to 25%, is steeper for skilled workers in abstract-task jobs and in digitally advanced countries with training, shows a gender gap in exposed roles, and has produced no detectable shift in reported task content so far.

citing papers explorer

Showing 2 of 2 citing papers.

  • What Jobs Can AI Learn? Measuring Exposure by Reinforcement Learning econ.GN · 2026-05-04 · unverdicted · none · ref 45 · internal anchor

    A new RL Feasibility Index based on task learnability via reinforcement learning diverges from prior AI exposure measures, rating operational jobs like power plant operators as highly feasible while rating creative and interpersonal roles as less so.

  • From Exposure to Adoption: Generative AI in European Workplaces econ.GN · 2026-04-20 · unverdicted · none · ref 16 · internal anchor

    Generative AI adoption in Europe ranges from under 3% to 25%, is steeper for skilled workers in abstract-task jobs and in digitally advanced countries with training, shows a gender gap in exposed roles, and has produced no detectable shift in reported task content so far.