
Linux kernel networking maintainers are overwhelmed by a flood of AI-generated patches submitted for Linux 7.3.
One-third to one-half of the patches appear AI-driven and low-priority.
The maintainers now plan to use AI themselves—powered by Meta's LLM budget—to automate review and administrative work.
What happened
Linux 7.3 networking subsystem merged 632 net patches and 648 net-next patches; maintainer Jakub Kicinski stated that one-third to one-half of net-next patches appear to be AI-driven low-priority fixes and cleanups, leaving maintainers 'completely overwhelmed' by the volume.
Why it matters
The surge of AI-generated submissions is forcing maintainers to drop old networking code and drivers to manage patch noise, disrupting the normal review workflow. However, the body shows maintainers are adapting by securing LLM budget and access from Meta to run multiple frontier models on each patch to eliminate hallucinations and catch issues.
What to watch
The Linux networking team plans to shift focus in the next release toward having AI handle administrative work—managing patch management, automating process tasks, editing commit messages, and potentially applying pre-reviewed patches—rather than simply reviewing AI-generated code.
Ask the AI about this article →
The Linux kernel networking subsystem is experiencing a sharp increase in patch submissions generated by AI language models. Maintainer Jakub Kicinski quantified the problem in the Linux 7.3 merge window pull request: while the team merged comparable raw numbers of patches across two subsystems (632 and 648), the quality and intent behind submissions differs markedly. The body indicates that a substantial fraction—one-third to one-half of net-next patches—are low-priority fixes and cleanups likely produced by AI agents rather than human developers with domain expertise.
Rather than reject or ignore this trend, the maintainers are adapting by leveraging AI themselves. Meta has provided LLM budget and access that allows the team to run reviews using multiple frontier models on each incoming patch. This approach aims to catch hallucinations and false positives that plague automated submissions. The strategy signals a pragmatic acceptance that AI-generated patches are a permanent feature of Linux development; the maintainers' response is to shift from manual review of every detail toward automated triage and to delegate routine administrative tasks—patch management, process automation, commit message editing—to AI systems they control.
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