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What Git blame actually records
Ordinary git blame attributes each line to the commit metadata Git has: an author, a committer, a timestamp and a commit identifier. It does not inspect the writing process and cannot infer whether a person, an AI assistant, a copied snippet or several people produced a line.
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That means a commit containing generated code will usually look like any other commit. If a developer asks an assistant for a function, reviews the result and commits it, blame will show the developer named in the commit. This is useful because the named person remains the human contact for questions, maintenance and contribution obligations, but it is not a complete record of how the code was produced.
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Why changing the author to an AI identity is risky
Rewriting commit authorship to a model or bot can obscure the accountable human and conflict with repository rules. It also creates a misleading impression that the software agent can accept legal or project obligations. Git metadata is an operational record, not a legal determination of authorship.
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A signed commit does not solve this problem. GitHub documents workflows in which Copilot cloud-agent commits are authored by Copilot, identify the requesting developer as a co-author, are signed and marked Verified, and link to a session log. Those commits still require human review and a human merge decision. A cryptographic signature proves control of a signing key; it does not prove that a human wrote every line.
Three workable attribution models
Teams should choose an attribution model deliberately. The right choice depends on whether the goal is accountability, audit evidence, line-level provenance, or all three.
| Model | Granularity | Accountability | Auditability | Portability and privacy | Workflow friction |
|---|---|---|---|---|---|
| Ordinary Git metadata | Commit-level | Human author and committer remain visible | No automatic record of prompts, sessions or generated lines | Works everywhere; exposes little extra context | Low; uses normal Git workflow |
| Vendor agent metadata | Usually commit-level, with a vendor session link | Requesting developer can remain visible as co-author; human review is still required | Session logs, signed commits and service-specific controls may be available | Records depend on the vendor and may expose prompts or source context | Moderate; depends on provider settings and branch controls |
| Git AI authorship log | Line-level, linked to sessions | Human contributor can remain the responsible submitter while AI-originated lines are recorded | Detailed provenance in Git Notes under refs/notes/ai |
Open Git Notes format is portable; prompt retention creates privacy obligations | Higher; requires tooling, policy and note preservation |
The Git AI Project’s v3.0.0 specification describes authorship logs that identify lines authored by AI agents and link them to conversation sessions. The logs are stored in Git Notes under refs/notes/ai, so the existing commit history is not rewritten. This is a better fit than inventing an AI author when a project needs forensic detail.
Who owns code generated by an AI assistant?
“Ownership” combines several different questions: who may submit the code, whether copyright exists, who receives contractual rights, and whether someone else’s licensed material is embedded in it. A single answer such as “the user owns it” is too broad.
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What the service terms say
GitHub’s terms say that GitHub does not claim ownership of AI Feature input or output. They also warn that output can resemble training-data or open-source code and place review, testing, validation and license-compliance duties on the user. A user therefore cannot treat a provider’s non-ownership statement as a warranty that an output is original, unrestricted or safe to distribute.
What U.S. copyright guidance says
On January 29, 2025, the U.S. Copyright Office published Part 2 of its report on the copyrightability of generative-AI outputs. Its position is fact-specific: AI output can be protected when a human author determines sufficient expressive elements. Prompting alone is not enough. Human creative arrangement or modification can support protection, and using AI assistance inside a larger human-authored work does not automatically defeat copyrightability.
As Register of Copyrights and Director Shira Perlmutter put it, “Where that creativity is expressed through the use of AI systems, it continues to enjoy protection.” The same report cautions that extending protection to expressive elements determined by a machine would undermine, rather than advance, copyright’s constitutional goals.
The practical result is layered. Human-authored expression may be protected and assigned under an employment or contractor agreement. Expression determined entirely by a machine may lack copyright protection under the U.S. analysis. None of that removes rights attached to recognizable third-party code, documentation or assets reproduced in the output. Other countries, contracts and court decisions may apply different tests.
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Licensing: generated does not mean license-free
If an assistant reproduces code from an open-source project, the original license and attribution requirements can still matter. The fact that a model produced the text does not erase copyright notices, copyleft conditions, patent terms or attribution obligations attached to the underlying material.
Preserve notices and identify inherited material
GSA TTS guidance treats AI output as a draft that must be verified before submission. It says inherited material keeps its own rights and that contributors must identify it so it is not mistaken for public-domain material. In practice, retain required headers, notices and license files, and document where a substantial borrowed component came from.
Check compatibility with the repository license
A project must compare generated or reproduced material with its outbound license. Strimzi’s AI policy, for example, requires contributors to avoid introducing content incompatible with Apache 2.0. Your repository may impose different rules, so read its contribution guide and license before opening a pull request.
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Do not assume that a model’s output is automatically public domain. The U.S. Copyright Office’s analysis turns on human creative contribution and third-party rights, not on the mere fact that a model generated the first draft.
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Can you sign off a commit containing AI-generated code?
Usually, yes—but the signature must mean what the project says it means. A human contributor can review generated code, verify its provenance and certify that they have the right to submit it. The AI system cannot make that certification.
Understand DCO and trailers
The Developer Certificate of Origin is a legal certification by the contributor. Strimzi states plainly: “Only humans can legally certify the Developer Certificate of Origin (DCO).” Its policy also prohibits AI tools from being used in Signed-off-by, Co-authored-by, Assisted-by and similar trailers. Under such a policy, the human contributor signs off after review; an AI identity must not be inserted into those legal attestations.
Repository rules differ. GitHub’s cloud-agent workflow uses an agent author and a human co-author, while another project may prohibit AI-related trailers entirely. Treat a co-author line as project metadata, not a universal legal rule. Follow the repository’s documented policy and never use a trailer to imply a certification the named entity cannot make.
A defensible review and audit workflow
The following process keeps human accountability clear while preserving useful evidence about AI assistance.
- Name the human requester and reviewer. Record both in the pull request or equivalent change record. The requester owns the decision to submit; the reviewer provides an independent check where policy requires it.
- Retain prompts and session identifiers when allowed. Keep agent logs, prompts or session links only when your privacy, confidentiality and retention rules permit. Sensitive source code and personal data may need redaction or a shorter retention period.
- Use signed commits and protected branches. Require the normal signing policy, restrict direct pushes and require human approval before merge. Remember that “Verified” indicates signature validation, not proof of human authorship.
- Run the same technical gates as for human-written code. Execute tests, dependency and license checks, secret scanning, static analysis and security analysis. GSA TTS describes AI output as a draft requiring verification; generated code should not receive a lower review standard.
- Investigate unusually specific output. Compare distinctive functions, comments or license headers against known code. If a match is found, preserve the required attribution and license terms rather than treating the text as newly created.
- Attach line-level provenance when needed. Use an authorship log such as the Git AI Standard and preserve its Git Notes alongside the repository. Do not rewrite historical commit authorship merely to make blame display an AI label.
- Document the merge decision. Keep the final human approval, test results and any exceptions with the pull request so a later maintainer can reconstruct why the change was accepted.
How these records affect maintenance and investigations
When ordinary blame is enough
For many projects, the important question is “Which human team should fix this line?” Normal author and committer fields answer that quickly, provided the project has a clear ownership policy and reviewers are recorded in the pull request.
When line-level provenance is worth the cost
Use a separate AI authorship log when a regulated environment, security investigation, license review or internal policy requires knowing which lines came from an agent. Git Notes preserve the original commit identifiers and avoid a disruptive history rewrite, but notes must be fetched, backed up and protected like other audit records. Decide who may read prompts and how long session data is retained before enabling detailed logging.
What blame cannot establish
- It cannot prove that the named author typed the line.
- It cannot show which model, prompt, retrieval source or session produced it.
- It cannot determine whether the expression is copyrightable.
- It cannot prove that an output is free of third-party license obligations.
- It cannot replace code review, testing, security analysis or a DCO certification.
Policy decisions teams should make explicitly
- Attribution: Decide whether AI assistance appears in commit trailers, pull-request fields, a private audit system or a Git AI authorship log.
- Accountability: Require a named human requester and reviewer, and state who signs the DCO.
- Evidence: Define whether prompts, session IDs and model versions are retained, and under what privacy controls.
- Licensing: Require contributors to check dependencies, copied snippets, notices and compatibility with the project’s outbound license.
- Merge gates: Apply protected branches, human approval, signed commits and automated security checks consistently.
- Incident response: Specify how to suspend distribution, investigate a suspected copied fragment and add missing attribution or license text.
These decisions should appear in the contribution guide and be enforced by tooling where possible. A policy that says “AI is allowed” without defining sign-off, review, provenance and license checks leaves the most important questions unanswered.
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- Who should Git blame show? The accountable human author and committer recorded in the commit, unless your project has a documented alternative. Blame alone will not reveal AI involvement.
- Should an AI tool be a co-author? Only if the repository explicitly permits that metadata and it does not masquerade as a legal certification. Some workflows use an AI author plus human co-author; others prohibit AI trailers.
- Can you sign off a commit containing generated code? A human can, after reviewing the code and confirming the right to submit it. An AI system cannot sign a DCO.
- Who owns the output? No single rule covers every case. Contractual allocation, human creative contribution, provider terms and third-party licenses all matter.
Bottom line for maintainers
Keep the human contributor visible, treat generated code as an untrusted draft until it passes normal engineering and licensing checks, and separate legal certification from provenance metadata. Standard Git history remains the accountability layer. Add vendor session records or Git AI authorship notes only when their audit value justifies the privacy and workflow cost. This approach answers maintenance questions without pretending that Git blame, a Verified signature or an AI co-author trailer can decide copyright or licensing by themselves.
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