Generative AI and labour productivity: a field experiment on coding

Leonardo Gambacorta, Han Qiu, Shuo Shan and Daniel Rees

No 1208, BIS Working Papers from Bank for International Settlements

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.

Keywords: artificial intelligence; productivity; field experiment; big tech (search for similar items in EconPapers)
JEL-codes: D22 G31 R30 (search for similar items in EconPapers)
Date: 2024-09
New Economics Papers: this item is included in nep-ain, nep-cmp, nep-eff, nep-exp, nep-ipr and nep-tid
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