Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence
Open accessAbstract
We examine the productivity effects of a generative artificial intelligence technology—the assistive chatbot ChatGPT—in the context of mid-level professional writing tasks. In a preregistered online experiment, we assign occupation-specific, incentivized writing tasks to 444 college-educated professionals, and randomly expose half of them to ChatGPT. Our results show that ChatGPT substantially raises average productivity: time taken decreases by 0.8 SDs and output quality rises by 0.4 SDs. Inequality between workers decreases, as ChatGPT compresses the productivity distribution by benefiting low-ability workers more. ChatGPT mostly substitutes for worker effort rather than complementing worker skills, and restructures tasks towards idea-generation and editing and away from rough-drafting. Exposure to ChatGPT increases job satisfaction and self-efficacy and heightens both concern and excitement about automation technologies.
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Evidence from existing laboratory experiments generally suggests significant productivity effects of generative AI tools for software developers.
“Evidence from existing (laboratory) experiments generally suggests significant productivity effects of generative AI tools for software developers.”
From The Productivity Effects of Generative AI: Evidence from a Field Experiment with GitHub Copilot (2024) · cites this paper as Noy and Zhang 2023
Randomized trials show large productivity gains for users.
“randomized controlled trials (RCTs) demonstrate substantial productivity gains for users”
From Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (2025) · cites this paper as Noy and Zhang, 2023
Randomized trials find productivity gains above 15% in the occupations studied here.
“significant productivity gains-often exceeding 15%-documented by RCTs in our study occupations”
From Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (2025) · cites this paper as Noy and Zhang, 2023
Reported time savings are highest in occupations covered by RCTs, including customer and IT support, marketing and human resources, and software development.
“First, our reported time savings are highest precisely in the occupations covered by RCTs, such as customer and IT support (Brynjolfsson, Li and Raymond, 2025), marketing and human resources (Noy and Zhang, 2023), and software development (Peng et al., 2023).”
From Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (2025) · cites this paper as Noy and Zhang, 2023
Generative AI is heralded as the engine of a new industrial revolution, yet evidence on its economic impacts outside laboratory settings and case studies is lacking.
“Generative AI is heralded as the engine of a new industrial revolution (World Economic Forum, 2024), yet we lack evidence on its economic impacts outside laboratory settings and case studies (Brynjolfsson, Li and Raymond, 2025; Noy and Zhang, 2023).”
From Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (2025) · cites this paper as Noy and Zhang, 2023
A common finding from RCTs is that AI chatbots tend to yield greater benefits for less experienced and typically younger workers.
“A common finding from RCTs is that AI chatbots tend to yield greater benefits for less experienced (and thus typically younger) workers (Brynjolfsson, Li and Raymond, 2025; Dell'Acqua et al., 2023; Noy and Zhang, 2023).”
From Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (2025) · cites this paper as Noy and Zhang, 2023
In the writing task studied by Noy and Zhang (2023), treated workers spent about 3 minutes before pasting in a large block of text, then an additional 14 minutes editing and refining the output.
“For example, in the writing task studied byNoy and Zhang (2023), treated workers spent on average 3 minutes before pasting in a large block of text-presumably generated by ChatGPT.The workers then spend an additional 14 minutes manually editing and refining the output.”
From Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (2025) · cites this paper as Noy and Zhang (2023)
Noy and Zhang (2023) report 37% time savings from ChatGPT in text writing tasks representative of marketing and HR professionals.
“Noy and Zhang (2023) report time savings of 37% from ChatGPT in text writing tasks representative of marketing and HR professionals.”
From Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI (2025) · cites this paper as Noy and Zhang (2023)
ChatGPT allowed the most capable writers to work faster and enabled the less capable to write faster and improve output quality, narrowing the productivity gap.
“Moreover, ChatGPT allowed the most capable writers to work faster and enabled the less capable to not only write faster but also to improve the quality of their output. This narrowing of the productivity gap underscores the potential of LLMs to democratize expertise.”
From Large language models at work in China’s labor market (2025) · cites this paper as Noy and Zhang (2023)
A study by Noy and Zhang (2023) found that access to ChatGPT improved the speed and quality of writing tasks among professionals, reducing time spent on tasks by 40%, and the least skilled writers in the ChatGPT group performed as well as the median writer in the control group.
“Similarly, a study by Noy and Zhang (2023) found that access to ChatGPT improved the speed and quality of writing tasks among professionals such as marketers, grant writers, consultants, and managers. The ChatGPT group reduced the time spent on tasks by 40%, and even the least skilled writers in this group performed as well as the median writer in the control group-highlighting a significant boost”
From Large language models at work in China’s labor market (2025) · cites this paper as Noy and Zhang (2023)
When people are placed in the role of an editor, AI determines the initial direction of the creative process because it provides a default creative product that the editor must evaluate.
“When people are placed in the role of an editor, as is often the case by default in human-AI collaboration2,23, AI determines the initial direction of the creative process because it provides a default creative product that the editor must evaluate.”
From Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence (2024) · cites this paper as [23]
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Seok, J., Lee, B. H., Kim, D., Bak, S., Kim, S., Kim, S.-T., & Park, S.-M. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2443263 · Cited by 15 · see citing works
The Unintended Consequences of Censoring Digital Technology - Evidence from Italy's ChatGPT Ban
Kreitmeir, D., & Raschky, P. A. (2023) · doi:10.31235/osf.io/v3cgs · Cited by 15 · see citing works
Exploring the Impacts of ChatGPT on Future Scientific WorkPreprint
Irons, J. L., Mason, C., Cooper, P. S., Sidra, S., Reeson, A. F., & Paris, C. L. (2023) · doi:10.31235/osf.io/j2u9x · Cited by 16 · see citing works
Generative AI and Copyright: A Dynamic PerspectivePreprint
Yang, S. A., & Zhang, A. H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4716233 · Cited by 17 · see citing works
When Are Combinations of Humans and AI Useful?Preprint
Vaccaro, M. A., Almaatouq, A., & Malone, T. W. (2024) · doi:10.31234/osf.io/mk46p · Cited by 16 · see citing works
A task-based approach to inequality
Acemoğlu, D., & Restrepo, P. (2024) · Oxford Open Economics · doi:10.1093/ooec/odad082 · Cited by 18 · see citing works
Birkbeck, A., & Rowe, L. (2023) · Journal of Family Business Management · doi:10.1108/jfbm-09-2023-0157 · Cited by 14 · see citing works
- Authors
- Noy, S., & Zhang, W.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Noy, Shakked
- Venue
- Science
- Publisher
- Elsevier BV
- Published
- 2023
- Volume
- 381
- Issue
- 6654
- Pages
- 187-192
- ISSN
- 0036-8075 (ISSN-L)
- OpenAlex
- W4384154918
- Cited by
- 2,157
- References
- 0
- Citations by year
- 2026: 991 · 2025: 639 · 2024: 406 · 2023: 116 · 2022: 1 · 2021: 1 · 2020: 1 · 2018: 1
- FWCI
- 437.26
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Science (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- generative AI, ChatGPT, productivity, professional writing, artificial intelligence adoption, output quality, task completion time, attitudes toward AI, workplace technology use
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