Extending “GPTs Are GPTs” to Firms
Benjamin Labaschin, Tyna Eloundou, Sam Manning, Pamela Mishkin, Daniel Rock · AEA Papers and Proceedings · 1 May 2025
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Abstract
We extend Eloundou et al. (2024) to build firm-level measures of exposure to large language models (LLMs) with data from two sources: Eloundou et al. (2024) for occupation-level measures of LLM exposure and Revelio Labs for firm-level employee counts by occupation. The results indicate that companies with more technology workers and AI-skilled employees tend to have higher levels of LLM exposure. We also find that differences in LLM exposure are greater between exposure categories than within them, suggesting that integrating LLMs into corporate systems may lead to significant productivity gains.
About this publication
Ranked journal. A peer-reviewed journal in the RePEc ranking of economics journals, below the leading ones.
Placed by: RePEc journal ranking. How sources are ranked.
- Published
- 2025 (18 months ago)
- Citations
- 3 (OpenAlex)
Citations in context
3 citations — 30th percentile among the 33 indexed papers from 2025 in this collection. Citation counts favor older papers; this compares only same-year papers here, not the whole literature.
□ added to this collection in 2026.
Daniel Rock, Pamela Mishkin, Sam Manning, Tyna Eloundou signed the wemustactnow.ai letter · author affiliation matches seed institution university of pennsylvania — part of why this paper is in the collection.
Cited elsewhere in this guide
Related by theme, not necessarily by method or conclusion.
- The Effects of Generative AI on High Skilled Work: Evidence from Three Field Experiments with Software Developers
SSRN · 2024 - GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
arXiv · 2023 - New Frontiers: The Origins and Content of New Work, 1940–2018
The Quarterly Journal of Economics · 2024 - What Makes New Work Different from More Work?
Annual Review of Economics · 2026
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