Template-Type: ReDIF-Paper 1.0
Author-Name: Leonardo Gambacorta
Author-X-Name-First: Leonardo
Author-X-Name-Last: Gambacorta
Author-Name: Han Qiu
Author-X-Name-First: Han
Author-X-Name-Last: Qiu
Author-Name: Shuo Shan
Author-X-Name-First: Shuo
Author-X-Name-Last: Shan
Author-Name: Daniel M Rees
Author-X-Name-First: Daniel M
Author-X-Name-Last: Rees
Title: Generative AI and labour productivity: a field experiment on coding
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. 
Creation-Date: 2024-09
File-URL: https://www.bis.org/publ/work1208.pdf
File-Format: Application/pdf
File-Function: Full PDF document
File-URL: https://www.bis.org/publ/work1208.htm
File-Format: text/html
Number: 1208
Keywords: artificial intelligence, productivity, field experiment, big tech
Classification-JEL: D22, G31, R30
Handle: RePEc:bis:biswps:1208