"AI Slop is DDoSing Open Source": Understanding the Impact of AI-Generated Contributions on Open Source Sustainability

2026-07-04

AI Summary

This study addresses the emerging “AI-DDoS” phenomenon in open-source communities, wherein generative AI fuels a surge of low-quality contributions that exacerbate maintainer burden and threaten project sustainability. Drawing on practitioner narratives from Reddit, mailing lists, and blogs, the authors formulate hypotheses and employ Bayesian structural time-series analysis on over two million pull requests and issues across 294 repositories, supplemented by interviews and surveys for validation. The work introduces the “AI-DDoS” concept, revealing not merely a quantitative influx but a structural sustainability trap: while total PR volume rises through 2025, merge rates decline, with first-time contributors’ PRs showing an 18.18% lower merge rate compared to counterfactual scenarios. The study systematically identifies eleven mitigation strategies, categorizing them into protective, adaptive, and transformative approaches, and cautions that overreliance on short-term defenses may undermine long-term openness.

Abstract

Open source software (OSS) communities are facing increasing pressure from Generative AI (GenAI) tools. We call it AI-DDoS: a denial-of-service effect in which plausible but low-quality AI-generated contributions overwhelm OSS community capacity. Using a phenomenon-based mixed-methods approach, we first analyze practitioner accounts from Reddit, OSS mentor mailing lists, and blogs to identify six recurring themes and derive hypotheses. We then evaluate these hypotheses using Bayesian Structural Time Series analysis across 294 repositories with over 2 million pull requests and issues. Our results show that while PR volume increased in 2025, merge rates declined, with one-time contributors experiencing an 18.18% drop in PR merge rates relative to the counterfactual. Finally, we identify 11 remediation strategies through practitioners' interviews and validate them with a survey of 229 OSS practitioners, grouping them into preservative, adaptive, and transformative orientations. Our findings show that AI-DDoS is not only a contribution-volume problem but a sustainability trap: communities often default to low-effort defensive strategies that protect short-term review capacity while making openness difficult to sustain.

Problem

• AI-DDoS
• open source sustainability
• AI-generated contributions
• Generative AI
• community capacity

Innovation

• AI-DDoS
• Generative AI
• Open Source Sustainability
• Bayesian Structural Time Series
• Mixed-Methods Research