Related papers: Augmentation with Dilution: A Large-Scale Empirical Study of Human Contributor Ecosystems After AI Coding Agent Adoption

Augmentation with Dilution: A Large-Scale Empirical Study of Human Contributor Ecosystems After AI Coding Agent Adoption

URL: http://arxiv.org/abs/2606.26289v1
Date: Wed, 24 Jun 2026 18:36:01 GMT
Title: Augmentation with Dilution: A Large-Scale Empirical Study of Human Contributor Ecosystems After AI Coding Agent Adoption
Abstract summary: We study how the number, composition, and behavior of human participants change following AI coding agent adoption in open-source projects.<n>Our results show that AI agent adoption does not significantly change the absolute number of human contributors.<n>We find that AI agents are not displacing human contributors, but are systematically reshaping the structure of open-source ecosystems.
Score: 5.3319140556634315
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: AI coding agents are penetrating open-source software development at an unprecedented pace, yet existing research predominantly treats human contributors as a static backdrop rather than as the subject of inquiry. This paper presents the first large-scale empirical study that takes the human contributor ecosystem as its dependent variable, examining how the number, composition, and behavior of human participants change following AI coding agent adoption in open-source projects. Using a staggered difference-in-differences design on a dataset of 11,097 GitHub repositories spanning January 2023 to May 2026, we provide causal evidence via the Sun and Abraham estimator. Our results show that AI agent adoption does not significantly change the absolute number of human contributors (ATT = 0.014, p = 0.224), but significantly reduces human contributor density (ATT = -0.019, p = 0.002), indicating that the relative share of human participation declines as AI-generated pull requests accumulate. The relative participation share of newcomers declines significantly by 3.7 percentage points (ATT = -0.037, p < 0.001), with the effect emerging immediately after adoption and remaining stable throughout the observation window. Review depth increases significantly by 5.3% (ATT = +0.0168, p < 0.001), indicating that AI agents shift burden from the code production stage to the review stage. Moderator analysis reveals that these effects vary systematically with project size, programming language, and project maturity. Together, these findings present a pattern of augmentation with dilution: AI agents are not displacing human contributors, but are systematically reshaping the participation structure of open-source ecosystems.

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