Explanation on allowing Linux kernel AI code: Why we are moving towards disclosure and responsibility rather than a ban, and what should the development team change?

One-line problem definition

With AI coding tools already in the development field, the real question is not “Can they be used?” but “Who will review and disclose them with what responsibility?”

The Linux kernel chose an operating rule over a ban on this question. This is because completely blocking AI-generated code distances it from reality, and allowing it indefinitely increases license and quality issues.

This article does not summarize kernel policy as simple news, but rather breaks it down into review standards that open source contributors and in-house development teams can immediately apply. Rather than covering entire individual toy projects, we focus on team development environments where code ownership and review responsibilities are important.

Conclusion first

To conclude, this policy is not “I believe in AI,” but closer to “Do not hide AI and let humans take responsibility until the end.”

So this criterion is a good fit for teams that already use tools like Copilot, Claude, Gemini, and Codex. Conversely, it is still too much for teams that do not have reviewers, do not have a license source review system, or have a strong culture of quickly pasting generated code.

Decomposition of core structure

The structure of the Linux kernel document is easy to understand in three layers.

1. Development process layer: Follows existing kernel development procedures, coding style, and patch submission rules.
2. Legal responsibility layer: Signed-off-by can only be added by humans, and DCO and licensing responsibility are also held by humans.
3. Transparency layer: If AI is involved, leave tool and model version with Assisted-by tag.

In other words, AI is not a separate exception area, but is incorporated into the existing contribution system. This is important. Instead of viewing tool use as a new privilege, it is treated as subject to stricter tracking.

Explanation of design intent

Why did you choose this structure? There are three reasons:

First, it is realistic. Many developers are already using AI tools, but simply establishing a ban will increase private use. In fact, community conflict exploded more over ‘hidden and submitted patches’ than over ‘AI use itself’.

Second, there are DCO and licensing issues. AI cannot sign-off-by. This is because it is impossible to legally prove where and how the code was derived. Therefore, the final submitter must review the creation path and licensing risks and assume responsibility in his/her own name.

Third, maintenance costs. What Linux feared was not so much AI itself as it was so-called AI slop, or low-quality patches that seemed plausible but only increased review costs. It may seem like a permissive policy, but in reality, it puts review costs back on humans and sets a lower limit on quality.

Evidence and comparison

There are three main options to deal with the same problem.

The Linux kernel chose the third. The Linux Foundation's Generative AI Policy also provides direction that “AI artifacts may be contributed, but contributors must review the inclusion of third-party works and the terms of use.” In other words, a prerequisite for acceptance is review.

Actual operation flow, step-by-step execution method

If the in-house development team creates internal rules by referring to this policy, the flow below is the most practical.

1. Record of creation steps: Leave which model and tool used in the PR body.
2. Manual rewrite or review: Instead of pasting the code generated by AI, have a human reread and modify the core logic and exception handling.
3. License check: Checks for similarities to external code, comments, and test data sources.
4. Declaration of review responsibility: The final author or reviewer leaves a check saying “I understand and am responsible for this code.”
5. Check failure conditions before merge: Do not merge if tests pass, security check, exception handling, no rollback path.

PR Template Example
- Whether to use AI: Yes
- Tool/Model: Claude Code / claude-3-opus
- Scope of use: error handling draft, test case draft
- Items reviewed again by humans: exception handling, licensing, performance impact
- Merger Manager: Hong Gil-dong

The kernel's Assisted-by tag is open source patch metadata, but in-house, it is sufficient to replace it with a PR template, merge checklist, or commit trailer.

Mistakes and Pitfalls

• Plot 1, hide the fact of AI use
  Prevention: Enforce AI use items in PR templates.
  Recovery: Re-examine and supplement metadata when discovered later, merge rights when repeated. Limit.
• Pit 2, adopt generated code without license review
  Prevention: Long or unique code fragments perform searches and comparisons
  Recovery: Remove suspect code, reimplement, copyright notice Summary.
• Pitfall 3, skip testing because it is a small patch
  Prevention: Even simple exception handling or error message correction requires minimal testing.
  Recovery: When regression occurs, review the scope of AI usage and the review process together. Looking back.
• Pit 4, Approval without the reviewer understanding the code
  Prevention: Insert “can it be explained” as an approval criterion.
  Recovery: Strengthening the approver checklist, responsible reviewer system Introduction.

Strengths and limitations

The strengths are clear. First, acknowledge reality. Second, responsibility is clear. Third, organizations can move from emotional debates around AI adoption to operating rules.

But it also has great limitations. Notations such as Assisted-by only increase transparency but do not guarantee quality. Also, large communities or mature teams can handle this system because they have review staff, but for small projects, the review burden may actually increase.

Therefore, this policy is not a “victory for AI acceptance,” but is closer to a declaration that “only organizations with review capabilities can safely absorb AI.”

Points to study more deeply

If you are a novice developer, you should look at three things first.

1. What is a DCO: Signed-off-by is not a simple signature but a declaration of legal responsibility
2. License compatibility: Why code entering a GPL project requires source tracking
3. Boundary between review automation and human review: AI is strong at drafting and detection, but merging responsibility still needs to be borne by humans

Practitioners should read the AI Coding Assistants document at docs.kernel.org, the Linux Foundation Generated AI Policy, and the actual kernel patch submission document together to see the overall structure.

Execution Checklist + Author's Perspective

• Has AI usage been left in PR or commit metadata
• Have you recorded the model name and scope of use?
• Has a person understood and modified the generated code line by line?
• Have you run the tests and static analysis again
• Have you reviewed external code similarities or licensing risks
• Did the approver know and approve the scope of responsibility?
• Is there a rollback path in case of regression or security issue

Definition of Done: Even code involving AI is done as long as the author and approver can explain the code meaning, license, and test results.

Reference material

• AI Times, article on Linux AI generated code acceptance policy (2026-04-13)
• Linux Kernel Documentation, AI Coding Assistants (Confirmation date 2026-04-13)
• Linux Foundation, Generative AI Policy (Confirmation date 2026-04-13)
• The Register, Greg Kroah-Hartman on AI-generated reports and review load (2026-03-26)
• Tom's Hardware, Linux lays down the law on AI-generated code (2026-04-12)