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

2026-06-24

AI Summary

This study investigates the causal impact of adopting AI programming agents on the quantity, composition, and behavior of human contributors in open-source software projects. Leveraging a dataset of 11,097 GitHub repositories and employing a staggered difference-in-differences design with Sun–Abraham estimators, the analysis uniquely treats the human contributor ecosystem as the primary outcome. The findings reveal that while AI adoption does not significantly alter the absolute number of contributors, it reduces contributor density by 1.9%, decreases the share of first-time contributors by 3.7 percentage points, and increases code review depth by 5.3%. These effects are moderated by project size, programming language, and maturity, collectively indicating an “augmentation-with-dilution” pattern: AI tools do not displace human contributors but instead reconfigure participation structures within the ecosystem.

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.

Problem

• AI coding agents
• human contributor ecosystem
• open-source software
• contributor dilution
• participation structure

Innovation

• AI coding agents
• human contributor ecosystem
• augmentation with dilution
• staggered difference-in-differences
• open-source software