“Generate” the Future of Work through AI: Empirical Evidence from Online Labor Markets
Jin Liu, Xingchen Xu, Xi Nan, Yongjun Li, Yong Tan · Information Systems Research · 23 Oct 2026
Where this sits in the debate
- Will AI destroy more jobs than it creates? · its finding: it depends (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Abstract
Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape labor market dynamics. However, predicting their precise impact a priori is challenging, given AI’s simultaneous effects on both demand and supply, as well as the strategic responses of market participants. Leveraging an extensive dataset from a leading online labor platform, we document a pronounced displacement effect and an overall contraction in submarkets where required skills closely align with core LLM functionalities. Although demand and supply both decline, the reduction in supply is comparatively smaller, thereby intensifying competition among freelancers. Notably, further analysis shows that this heightened competition is especially pronounced in programming-intensive submarkets. This pattern is attributed to skill-transition effects: by lowering the human-capital barrier to programming, ChatGPT enables incumbent freelancers to enter programming tasks. Moreover, these transitions are not homogeneous, with high-skilled freelancers contributing disproportionately to the shift. Our findings illuminate the multifaceted impacts of general-purpose AI on labor markets, highlighting not only the displacement of certain occupations but also the inducement of skill transitions within the labor supply. These insights offer practical implications for policymakers, platform operators, and workers.
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
- 2026 (recently)
- Citations
- 0 (OpenAlex)
0 citations recorded by OpenAlex — too few 2026 papers in this collection for a fair comparison.
author affiliation matches seed institution university of washington — part of why this paper is in the collection.
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 - 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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