Linux drivers and staging maintainers will enforce stricter disclosure and human-review requirements for LLM-generated contributions.
state: resolvedheat: lowuncertainty: mediumknownscott: lowcoding-agents open-source-maintenance ai-policyGreg Kroah-HartmanLinux kernel maintainers
What is this?
The supplied results establish that GCC adopted a policy declining legally significant contributions containing or derived from LLM-generated material, using a threshold of roughly 15 lines, while allowing limited exceptions such as test cases and permitting AI-assisted research, analysis, bug discovery, and review. They also mention broader open-source policy discussions, including Debian proposals, but provide no direct evidence that Greg Kroah-Hartman or Linux drivers/staging maintainers issued the titled policy or will impose stricter disclosure and human-review requirements. The case’s Linux-specific hypothesis therefore remains ungrounded by these snippets.
Why it matters to Scott
Scott already argues that AI-generated work requires retained provenance, human-owned acceptance, and independent verification, especially in established codebases. A Linux maintainer policy could materially validate those positions, but the supplied evidence does not establish that the policy was issued, so this case currently adds no actionable development.
ip:concept.authorship-without-typingip:framework.provenance-coupled-workip:source.fde-delivery-looks-like-waterfall-per-increment-ebookip:concept.human-judgmentradar:concept.coding-agents
queries asked of Scott's wikis
- AI-generated code disclosure in open-source contributions
- human accountability for coding-agent patches
- coding agents versus deliberate-practice work
- maintainer review burden from AI-generated patches
- authorship and provenance in software contributions
- policies for agent-generated pull requests
Measured heat
no measured readings yet — the hourly heat pass fills this in
How the heat travelled
no chain yet — the hourly chain pass fills this in
Evidence (3) — ⭐ canonical anchor
Interpretation history
2026-08-05T19:33:42Z
The case has moved from an ungrounded forecast to a corroborated, already-issued Linux staging policy: LLM-generated patches are generally rejected rather than merely subjected to stricter disclosure and review. This is a completed policy event with little continuing momentum, although the primary source remains available here only through reconstructed testimony.
2026-08-05T19:21:59Z
evidence attached: hn.story.49187445 — This independently corroborates the Linux staging maintainers' move toward stricter disclosure and human review of LLM-generated patches.
2026-08-03T15:27:12Z
grounded: known/low — Scott already argues that AI-generated work requires retained provenance, human-owned acceptance, and independent verification, especially in established codeba
2026-08-03T15:24:53Z
origin walked (codex/luna, conf 0.95): anchor hn.story.49156513 -> echo.blog.9ed649b72f by Daniel Miessler
2026-08-03T15:22:38Z
case created — A first-party kernel-maintainer policy announcement could materially change how AI-generated code is accepted into influential open-source projects.
Decision trace
- 08-06 05:33resolveThe case has moved from an ungrounded forecast to a corroborated, already-issued Linux staging policy: LLM-generated patches are generally rejected rather than merely subjected to stricter disclosure
- 08-06 05:21attachThis independently corroborates the Linux staging maintainers' move toward stricter disclosure and human review of LLM-generated patches.
- 08-06 05:21propose_attachThis independently corroborates the Linux staging maintainers' move toward stricter disclosure and human review of LLM-generated patches.
- 08-04 01:27groundScott already argues that AI-generated work requires retained provenance, human-owned acceptance, and independent verification, especially in established codebases. A Linux maintainer policy could mat
- 08-04 01:24promote_anchororigin walk conf 0.95
- 08-04 01:22createA first-party kernel-maintainer policy announcement could materially change how AI-generated code is accepted into influential open-source projects.