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

arXiv: https://arxiv.org/abs/2606.26289 · HTML: https://mlsanity.com/paper/2606.26289/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 13 of 13)

- [On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub](https://mlsanity.com/paper/2509.14745/) (2025)
- [Agentic Much? Adoption of Coding Agents on GitHub](https://mlsanity.com/paper/2601.18341/) (2026)
- [Speed at the Cost of Quality: How Cursor AI Increases Short-Term Velocity and Long-Term Complexity in Open-Source Projects](https://mlsanity.com/paper/2511.04427/) (2025)
- [Where Do AI Coding Agents Fail? An Empirical Study of Failed Agentic Pull Requests in GitHub](https://mlsanity.com/paper/2601.15195/) (2026)
- [AI IDEs or Autonomous Agents? Measuring the Impact of Coding Agents on Software Development](https://mlsanity.com/paper/2601.13597/) (2026)
- [Debt Behind the AI Boom: A Large-Scale Empirical Study of AI-Generated Code in the Wild](https://mlsanity.com/paper/2603.28592/) (2026)
- [How AI Coding Agents Modify Code: A Large-Scale Study of GitHub Pull Requests](https://mlsanity.com/paper/2601.17581/) (2026)
- [On Autopilot? An Empirical Study of Human-AI Teaming and Review Practices in Open Source](https://mlsanity.com/paper/2601.13754/) (2026)
- [Towards Causal Analysis of Empirical Software Engineering Data: The Impact of Programming Languages on Coding Competitions](https://mlsanity.com/paper/2301.07524/) (2023)
- [From Industry Claims to Empirical Reality: An Empirical Study of Code Review Agents in Pull Requests](https://mlsanity.com/paper/2604.03196/) (2026)
- [Early-Stage Prediction of Review Effort in AI-Generated Pull Requests](https://mlsanity.com/paper/2601.00753/) (2026)
- [Will It Survive? Deciphering the Fate of AI-Generated Code in Open Source](https://mlsanity.com/paper/2601.16809/) (2026)
- [The End of Code Review: Coding Agents Supersede Human Inspection](https://mlsanity.com/paper/2606.13175/) (2026)

## Abstract (arXiv)

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.