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Linux Foundation Research: Maintainers Estimate 25% of Incoming Pull Requests Are 'AI Slop', and Only 35% of Projects Have a Formal GenAI Policy

A Linux Foundation report presented at All Things Open estimates 25% of incoming pull requests are low-quality AI output, while 52% average time savings on code authoring sit beside thin project governance.

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Editor's Note ·

Clarification:
The headline says 'Maintainers Estimate 25% of Incoming Pull Requests Are "AI Slop"' and cites a '35%' share of projects with a 'Formal GenAI Policy'. The Linux Foundation press release says only that 25% of incoming PRs 'are estimated to be low-quality, also known as "AI slop"' without stating who made the estimate, and that 'Only 35% of projects have a formal policy permitting GenAI use'. The sources also do not state the survey sample size or how 'AI slop' was defined.

Overview

The Linux Foundation published a research report on Oct. 20, 2026 that puts numbers on both sides of generative AI’s effect on open source. According to the Linux Foundation press release, 25% of incoming pull requests are estimated to be low-quality “AI slop,” and the share rises to 31% among large projects. The same release says respondents estimated a 52% average time reduction for code authoring.

The report, titled Navigating Open Source Software Development in the Generative AI Era: Challenges and Opportunities for Project Communities, was published by LF Research with sponsorship from Red Hat and additional support from Futurewei, OpenSSF and LF AI & Data, according to the press release. Its findings were to be presented at All Things Open in Raleigh, N.C..

What the Report Found

Per the press release, the headline figures are:

  • Volume: 59% of maintainers report growing contribution volume since the rise of GenAI, increasing to 69% among large projects.
  • Barriers and trust: 71% of maintainers agree GenAI reduces barriers to entry, but 53% disagree that GenAI has improved community trust.
  • Time savings: respondents estimate average time reductions across all six development activities measured, including proposal discussions (19%) and code authoring (52%). For high-complexity tasks, 21% of respondents report taking more time when using GenAI.
  • Reinvestment: 66% report using productivity gains to address technical debt, 63% to develop new features and 58% for learning and upskilling.
  • Mentorship: 46% of maintainers say GenAI increases the need for mentorship of new contributors, compared with 32% who say it decreases that need.

The LF Research report page summarizes the study as finding that “1 in 4 incoming pull requests are low-quality” output, “driving maintainer burnout to the #1 concern for large projects.” It also states that “developers harvest 19%–52% time savings using AI across tasks.”

Governance Lags Adoption

The press release says governance has not caught up with adoption: only 35% of projects have a formal policy permitting GenAI use, while 32% have no policy and no plans for one. Among contributors, 61% conduct detailed reviews and full functional testing of AI-generated code before submission, but disclosure of AI assistance “remains inconsistent,” according to the press release.

The research recommends that projects establish clear written policies for AI-assisted contributions, strengthen transparency and disclosure requirements, require contributors to test and document AI-assisted changes before submission, and standardize attribution practices. It further recommends that communities treat governance, verification and trust as core infrastructure for AI-assisted development.

Hilary Carter, SVP of Research at the Linux Foundation, said in the release: “Open source projects need to design for a growing AI-generated contributor base, not simply react to it.” She added that this means “putting responsibility for testing and disclosure on contributors and giving maintainers better tools to manage incoming pull requests.” Adrianne Marcum, Chief of Staff at OpenSSF, said: “Keeping open source secure is a shared responsibility, and maintainers should not have to carry it alone.”

Context From Individual Projects

The survey figures arrive after a run of project-level decisions that The Machine Herald has covered. Earlier this year, AI-generated slop was reported to be overwhelming open source projects. Since then, Codeberg members voted to ban vibe-coded projects, GCC adopted a policy rejecting significant AI-generated code contributions, the Rust project adopted a formal LLM policy, and Debian opened a General Resolution with four rival proposals. In October, Torvalds posted the first LLM-assisted Linux kernel patch series.

What We Don’t Know

  • The sources reviewed do not state the survey’s sample size, response rate or fieldwork dates, so how representative the figures are cannot be assessed from them.
  • The 25% figure is described as an estimate of incoming pull requests, and the sources do not say how “AI slop” was defined or measured.
  • The press release headline says GenAI cuts coding time “in half”; the 52% figure it cites applies to code authoring, while the measured activities range from 19% to 52%.