Linux Kernel Eyes AI Code: New Guidelines & “Co-developed-by” Tag Proposed for AI-Assisted Patches

NVIDIA engineer Sasha Levin, an active contributor to kernel development, has recently introduced a new patch aimed at regulating the use of AI tools within the Linux kernel project, ensuring transparency and consistency throughout the software development lifecycle.

As portions of the Linux kernel are now developed with the assistance of artificial intelligence—and with AI-generated code expected to become increasingly prevalent over time—Levin argues that it is imperative to establish clear guidelines to govern the responsible use of such tools in kernel development.

This initiative comprises two distinct patches:

Patch 1: Unified Configuration Files
This patch introduces standardized configuration files for various AI-assisted coding tools, including Anthropic Claude Code, GitHub Copilot, and Cursor AI. These configurations are symbolically linked and consolidated into a central documentation repository to promote uniformity across different tools.

Patch 2: Guidelines and Documentation
This patch delineates the standards for employing AI assistants in kernel development. It outlines best practices for adhering to coding conventions, integrating AI-assisted contributions into the development process, properly annotating AI-generated code, and observing licensing requirements.

Requirement to Annotate Every Line of AI-Generated Code
The update further mandates precise labeling of AI-generated code, employing tags such as Co-developed-by to indicate collaboration with AI tools. Examples even include specific model versions, such as Claude claude-opus-4-20250514, to enhance traceability.

Subject: [PATCH] Documentation: power: opp: Fix typo dont -> don't
Fix a typo in the OPP documentation where "dont" was missing an apostrophe.
Co-developed-by: Claude claude-opus-4-20250514

This revision is poised to deliver a significant positive impact on Linux kernel development. While AI tools undoubtedly enhance productivity, they also introduce potential for error. By meticulously marking every line of AI-generated code, human developers will be better equipped to audit and debug, avoiding the arduous task of sifting through massive codebases in search of elusive mistakes.

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• Google Gemini to Support Anthropic’s Model Context Protocol (MCP)

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Do Son

Do Son (aka Ddos) is a seasoned news reporter, bringing over a decade of expertise to the forefront of cyber security and technology reporting. My work provides timely and insightful analysis of emerging trends and critical developments in these rapidly evolving sectors.