The Short-Term Effects of Generative Artificial Intelligence on Employment: Evidence from an Online Labor Market
Xiang Hui, Oren Reshef, Luofeng Zhou · Organization Science · 24 Sep 2024
Where this sits in the debate
- Will AI destroy more jobs than it creates? · its finding supports the claim (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Abstract
Generative artificial intelligence (AI) holds the potential to either complement workers by enhancing their productivity or substitute them. We examine the short-term effects of the recently released generative AI models (ChatGPT, DALL-E 2, and Midjourney) on the employment outcomes of freelancers on a large online platform. We find that freelancers in highly affected occupations suffer from the introduction of generative AI, experiencing reductions in both employment and earnings. We find similar effects studying the release of other image-based generative AI models. Exploring the heterogeneity by freelancers’ employment history, we do not find evidence that high-quality service, measured by their past performance and employment, moderates the adverse effects on employment. In fact, we find suggestive evidence that top freelancers are disproportionately affected by AI. These results suggest that generative AI may transform the role of human capital in the organization and reduce overall demand for workers. Supplemental Material: The online appendices are available at https://doi.org/10.1287/orsc.2023.18441 .
About this publication
Leading field journal. A leading journal in economics or a neighbouring field (on the Tinbergen Institute’s A list or near the top of the RePEc ranking), or a top-rated computer-science venue.
Placed by: Leading field journal. How sources are ranked.
- Published
- 2024 (25 months ago)
- Citations
- 202 (OpenAlex)
Citations in context
202 citations — 100th percentile among the 23 indexed papers from 2024 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.
author affiliation matches seed institution new york university — part of why this paper is in the collection.
Related by theme, not necessarily by method or conclusion.
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SSRN · 2024 - GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
arXiv · 2023 - Extending “GPTs Are GPTs” to Firms
AEA Papers and Proceedings · 2025 - New Frontiers: The Origins and Content of New Work, 1940–2018
The Quarterly Journal of Economics · 2024
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