What You Need To Know
- The U.S. Court of Appeals for the Ninth Circuit affirmed dismissal of claims under the Digital Millennium Copyright Act (DMCA) against GitHub, Microsoft, and OpenAI.
- The court found that generative AI tools such as GitHub Copilot and Codex, which learn from existing works but create new outputs, do not “remove” or “alter” copyright management information (CMI), such as author names, copyright notices, and license terms.
- The court recasts the “identicality” requirement as an evidentiary inference rather than a required element of the claim. Near-verbatim copying without CMI may still support the inference that the CMI was removed, while material differences suggest a new work that would never have had CMI attached in the first place.
- Plaintiffs cleared standing but lost the claim. The court rejected the “output” theory because Copilot, as plaintiffs described it, generates new works rather than copying existing protected works.
The Ninth Circuit issued its opinion in Doe v. GitHub, Inc., No. 24-7700, on September 16, 2026, affirming the district court’s dismissal of claims that Copilot and OpenAI’s Codex violated § 1202(b) by reproducing plaintiffs’ code without its author attributions, copyright notices, and license terms. The Ninth Circuit held that plaintiffs’ own description of how GitHub Copilot works defeated their claim. A tool “that does not look up and reproduce stored work but rather creates new work” cannot strip or remove CMI from a copy that never existed. The decision is among the first federal appellate rulings applying the CMI rules of § 1202(b) to generative AI output.
Commit History: How the Case Got Here #
Plaintiffs are anonymous programmers who published code in public GitHub repositories under open-source licenses that require attribution. Copilot and OpenAI’s Codex are large language models trained on billions of lines of publicly available code, including public GitHub repositories. Plaintiffs alleged that Copilot sometimes emits “essentially verbatim” copies of their code stripped of attribution and license terms, in violation of the DMCA (17 U.S.C. § 1202(b)(1) and (b)(3)).
After two rounds of dismissals and amendments, the district court dismissed the § 1202(b) claims with prejudice, reasoning that § 1202(b) requires “identical” copies and that plaintiffs’ own examples described a “modified format,” “variation,” or “functional equivalent” of the licensed code.
Close Enough? Remove, Alter, and the Demotion of ‘Identicality’ #
On appeal, the Ninth Circuit started with ordinary dictionary meanings. To “remove” is to get rid of something; to “alter” is to change it. Both require doing something to credit information that is already attached to an existing work. One who creates a new work and fails to include CMI has not removed anything.
The statute is worded similarly, protecting author information attached to “copies” of a work, not to excerpts or works merely inspired by it. That distinction matters for generative AI, whose output is a new work rather than a copy.
The panel then declared “identicality” “something of a misnomer” and narrowed its role:
- It is a gloss on the statutory terms, not an independent element.
- Its work is evidentiary. Where two works are otherwise identical and only the CMI is missing, a fact finder may infer removal. Substantial reproduction without CMI can be strong circumstantial evidence of removal, though not always dispositive.
- The inference cuts both ways, and material differences suggest a new derivative work to which CMI was never attached.
Fetch vs. Predict: Why Generation Is Not Removal #
Plaintiffs’ own allegations sank the output theory. The complaint did not allege facts showing that CMI was removed from, or altered in, copies of plaintiffs’ code. It described a complex probabilistic process that infers statistical patterns and predicts the most likely completion, which generates new works, rather than removing CMI attached to an existing one.
The panel also drew an express line at search engines, which retrieve and display stored copies. Had Copilot worked like a search engine and produced CMI-free identical output, the claim may have been stronger.
The closing passage of the court’s decision is one future defendants may quote. Many copyright cases involve creating a substantially similar work without attribution. If that alone violated § 1202(b), the DMCA “would supplant traditional copyright protections and subject defendants to potentially ruinous liability” — up to $25,000 per violation under § 1203(c)(3)(B), compared with $30,000 per work under § 504(c)(1). The court expressed no view on whether Copilot’s output infringes copyright but declined to convert run-of-the-mill infringement claims into DMCA claims.
Pull Requests: What This Means Going Forward #
- For AI developers and platforms , the per-violation statutory damages exposure that gave these § 1202(b) claims settlement leverage is reduced for generative output. However, retrieval-augmented systems, caching layers, and the surfacing of stored copies may fall outside this decision’s reasoning. Consider reviewing how your system is described in marketing, documentation, and discovery.
- For creators and copyright owners , the decision narrows the output theory, though the training-stage question remains open. Consider pleading facts showing when and how CMI was removed from copies of the work, not merely that they are missing from the AI tool’s output.
- For open-source and licensing programs , the contract claims are still alive in the trial court. As statutory attribution theories narrow, enforcement is migrating to breach of the license. Careful license drafting and having clarity about attribution obligations on derivative output may offer protection.