IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2507.22748.html
   My bibliography  Save this paper

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

Author

Listed:
  • Golo Henseke
  • Rhys Davies
  • Alan Felstead
  • Duncan Gallie
  • Francis Green
  • Ying Zhou

Abstract

We draw on Eloundou et al. (2024) to develop 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% 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 12% lower in 2023-24. Job postings fell following the release of ChatGPT, with job postings 5.5% lower in 2025-Q2 than if pre-GPT hiring patterns had persisted. GAISI offers a robust framework for assessing AI's impact on work, providing early evidence that displacement effects may already outweigh productivity gains.

Suggested Citation

  • Golo Henseke & Rhys Davies & Alan Felstead & Duncan Gallie & Francis Green & Ying Zhou, 2025. "How Exposed Are UK Jobs to Generative AI? Developing and Applying a Novel Task-Based Index," Papers 2507.22748, arXiv.org, revised Aug 2025.
    • Handle: RePEc:arx:papers:2507.22748
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2507.22748
    File Function: Latest version
    Download Restriction: no
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2507.22748. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.