Sea Change in Software Development: Economic and Productivity Analysis of the AI-Powered Developer Lifecycle

Thomas Dohmke, Marco Iansiti and Greg Richards
Additional contact information
Thomas Dohmke: GitHub
Marco Iansiti: Harvard Business School and Keystone.AI
Greg Richards: Keystone.AI

Papers from arXiv.org

Abstract: This study examines the impact of GitHub Copilot on a large sample of Copilot users (n=934,533). The analysis shows that users on average accept nearly 30% of the suggested code, leading to increased productivity. Furthermore, our research demonstrates that the acceptance rate rises over time and is particularly high among less experienced developers, providing them with substantial benefits. Additionally, our estimations indicate that the adoption of generative AI productivity tools could potentially contribute to a $1.5 trillion increase in global GDP by 2030. Moreover, our investigation sheds light on the diverse contributors in the generative AI landscape, including major technology companies, startups, academia, and individual developers. The findings suggest that the driving force behind generative AI software innovation lies within the open-source ecosystem, particularly in the United States. Remarkably, a majority of repositories on GitHub are led by individual developers. As more developers embrace these tools and acquire proficiency in the art of prompting with generative AI, it becomes evident that this novel approach to software development has forged a unique inextricable link between humans and artificial intelligence. This symbiotic relationship has the potential to shape the construction of the world's software for future generations.

Date: 2023-06
New Economics Papers: this item is included in nep-ain, nep-big and nep-eff
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