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Generative AI and labour productivity: a field experiment on coding

Author

Listed:
  • Leonardo Gambacorta
  • Han Qiu
  • Shuo Shan
  • Daniel M Rees

Abstract

In this paper we examine the effects of generative artificial intelligence (gen AI) on labour productivity. In September 2023, Ant Group introduced CodeFuse, a large language model (LLM) designed to assist programmer teams with coding. While one group of programmers used it, other programmer teams were not informed about this LLM. Leveraging this event, we conducted a field experiment on these two groups of programmers. We identified employees who used CodeFuse as the treatment group and paired them with comparable employees in the control group, to assess the impact of AI on their productivity. Our findings indicate that the use of gen AI increased code output by more than 50%. However, productivity gains are statistically significant only among entry-level or junior staff, while the impact on more senior employees is less pronounced.

Suggested Citation

  • Leonardo Gambacorta & Han Qiu & Shuo Shan & Daniel M Rees, 2024. "Generative AI and labour productivity: a field experiment on coding," BIS Working Papers 1208, Bank for International Settlements.
    • Handle: RePEc:bis:biswps:1208
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    References listed on IDEAS

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    1. Erik Brynjolfsson & Danielle Li & Lindsey Raymond, 2025. "Generative AI at Work," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 140(2), pages 889-942.
    2. Zied Bahroun & Chiraz Anane & Vian Ahmed & Andrew Zacca, 2023. "Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis," Sustainability, MDPI, vol. 15(17), pages 1-40, August.
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    Cited by:

    1. Lukas Althoff & Hugo Reichardt, 2026. "Task-Specific Technical Change and Comparative Advantage," CESifo Working Paper Series 12403, CESifo.
    2. Douglas Araujo & Rafael Schmidt & Olivier Sirello & Bruno Tissot & Ricardo Villarreal, 2025. "Governance and implementation of artificial intelligence in central banks," IFC Reports 18, Bank for International Settlements.
    3. Iñaki Aldasoro & Leonardo Gambacorta & Rozalia Pal & Debora Revoltella & Christoph Weiss & Marcin Wolski, 2026. "AI adoption, productivity and employment: evidence from European firms," BIS Working Papers 1325, Bank for International Settlements.
    4. Herbert Dawid & Philipp Harting & Hankui Wang & Zhongli Wang & Jiachen Yi, 2025. "Agentic Workflows for Economic Research: Design and Implementation," Papers 2504.09736, arXiv.org.
    5. Leonardo Gambacorta & Enisse Kharroubi & Aaron Mehrotra & Livia Pancotto, 2026. "Economic impact of AI in emerging market economies," BIS Bulletins 121, Bank for International Settlements.
    6. Gambacorta, Leonardo & Jappelli, Tullio & Oliviero, Tommaso, 2025. "Exploring Household Adoption and Usage of Generative AI: New Evidence from Italy," CEPR Discussion Papers 20762, Centre for Economic Policy Research.

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    Keywords

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    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • G31 - Financial Economics - - Corporate Finance and Governance - - - Capital Budgeting; Fixed Investment and Inventory Studies
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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