The Ninth Circuit’s decision to weigh a narrow but potent question about the Digital Millennium Copyright Act (DMCA) in the GitHub/Copilot litigation has quietly become a landmark moment for how courts will treat training data, copyright management information, and the limits of liability for companies that build commercial AI on top of web‑scale code repositories. In a case that slices directly into the mechanics of model training, the scope of Section 1202 of the DMCA, and whether an “identicality” threshold is required for a viable claim, the appellate court’s handling of the appeal could reshape obligations for platform operators, the contours of open‑source licensing, and the compliance calculus for enterprises that deploy or embed generative coding assistants.
Beyond the legal technicalities, this dispute spotlights a simple yet consequential reality: technology has outpaced many regulatory frameworks, and the law — through litigation and, ultimately, legislation or rulemaking — must decide how to reconcile innovation with fair attribution, compensation, and risk management. For developers, enterprises, and platform operators, the moment calls for concrete operational changes now, not later: better metadata hygiene, more transparent training practices, and preemptive contractual clarity. The Ninth Circuit’s ruling will arrive as a legal waypoint, but the industry’s response will determine whether creators and innovators can coexist under a shared set of reasonable, enforceable norms.
Source: Law360 9th Circ. Mulls DMCA Claim Against Microsoft And OpenAI - Law360
Background and overview
The dispute in brief
The case, styled Doe et al. v. GitHub, Inc. et al., began as a group action brought by software developers who alleged that GitHub Copilot and the underlying Codex model were trained on source code drawn from public Git repositories — including code belonging to the plaintiffs — and that the AI tools sometimes output code that was substantially similar to that protected work. Plaintiffs brought a range of claims, including copyright, contract (license breach), and a claim under Section 1202(b) of the DMCA, which forbids the removal or alteration of copyright management information (CMI). The district court dismissed the Section 1202(b) DMCA claim on the ground that plaintiffs had not plausibly alleged the sort of “identicality” between the outputs and protected works needed to show that CMI was removed in a way that would cause injury. The Ninth Circuit granted interlocutory review of that precise legal question.Why the DMCA question matters
Section 1202(b) targets the removal or alteration of CMI — metadata, author names, copyright notices, license information — when such removal is done intentionally and causes or facilitates infringement. If the Ninth Circuit adopts a rigid “identical copy” requirement for a DMCA claim in the context of generative models, it could narrow the range of actionable harms for authors whose work is used to train AI. Conversely, if the court rejects a strict identicality rule and accepts broader theories of CMI removal in the machine‑learning context, platform operators could face far greater exposure for not preserving or passing through license and attribution metadata when training or serving outputs. The stakes extend well beyond this case: the ruling may influence how courts treat memorization, how licensing metadata must be recorded for mass automated ingestion, and whether filtering/attribution mechanisms are a reasonable safety valve for large‑scale model builders.Legal mechanics: DMCA Section 1202(b), identicality, and “copyright management information”
What Section 1202(b) actually prohibits
Section 1202(b) of the DMCA makes it unlawful to, “intentionally remove or alter” CMI, knowing or having reasonable grounds to know that doing so will induce, enable, facilitate, or conceal infringement. Historically this provision addressed scenarios like the removal of author names or licensing information when digital works were copied or redistributed. In traditional multimedia and publishing contexts, the statute serves as an anti‑tampering rule for attribution and usage controls. The core legal tension today is whether and how that statute applies when the “use” takes the form of model training and when the allegedly infringing acts take the form of downstream generative outputs rather than literal distribution of a human‑readable copy.The “identicality” question and the district court’s view
At the heart of the interlocutory appeal is whether plaintiffs needed to allege that any output from Copilot or Codex was identical to their works in order to claim that CMI was removed or obscured. Judge Jon S. Tigar, in trimming the developers’ complaint, concluded that plaintiffs had not identified a single concrete example where Copilot produced an identical copy of a plaintiff’s work; that failure was fatal to their DMCA theory as pleaded. The court framed the problem as one of legal causation and plausibility: a removed license or missing metadata becomes actionable under Section 1202(b) when it is meaningfully connected to an infringement that resulted from the CMI removal. Without a plausible allegation that the AI outputs would be used in a way that reproduces protected code, the statute’s remedial machinery could not be triggered.Plaintiffs’ counter‑argument and the implications of a broader reading
Plaintiffs have pressed a twofold argument: first, that training on code sets that included licensing and attribution information effectively removes or decouples that information in the training process; and second, that model outputs — even if not byte‑for‑byte identical — can meaningfully reproduce copyrighted expressions, thereby making the absence of CMI relevant and injurious. They further argue that an identicality requirement is unrealistic for modern machine learning, which often blends and regenerates expressions in ways that can still be substantially similar or practically interchangeable. If the Ninth Circuit accepts that view, developers and licensors could assert DMCA claims based on hybrid or derivative outputs that were generated from models trained without a preserved chain of attribution.The technical layer: how Copilot and Codex work — and where the risk lies
Training on public code and the mechanics of “memorization”
Copilot and the Codex family are trained on vast corpora of publicly available source code. Neural language models encode statistical patterns from training data and, under certain circumstances, can reproduce snippets or sequences that reflect memorized content. Empirical work on “memorization” in large language models has shown instances — especially with long sequences, small datasets, or repeated exposures — where the model spits out verbatim text from its training set. Plaintiffs lean on these empirical observations to argue that the risk is real and that the model’s outputs can be practically indistinguishable from original, copyrighted code in consequential sorts of usages. The defense — and to an extent the district court — has pushed back, emphasizing that memorization is rare in benign prompts and that the plaintiffs have failed to identify concrete instances tied to their own code.Practical vectors that increase risk
- Reproducing code verbatim is more likely when the target sequence is short, highly repetitive, or the model has been exposed to the same snippet multiple times.
- Users who seed prompts with verbatim lines from an existing repository can coax the model into continuing or completing that exact code, producing outputs that resemble the original.
- Caches, search engine indexes, and archived pages create a vector for content to linger in training feeds even after authors make content private or attempt to restrict access; this has been shown in other Copilot incidents involving “zombie” repositories.
Cross‑checking the record: what the filings and reporting show
To assess the strongest, most consequential factual claims we can draw on multiple public sources. Law360’s reporting of defendants’ briefs summarizes the companies’ position: Microsoft and OpenAI asked the Ninth Circuit to affirm dismissal, arguing the developers’ injuries are hypothetical and lacking a concrete nexus to any removal of CMI. The district court orders, also reported in legal trade outlets, show the court dismissed the DMCA theory for failure to plead an identicality link, while leaving breach‑of‑contract and licensing claims alive. Independent docket records and appellate filings corroborate that the Ninth Circuit accepted interlocutory review of the DMCA question. The empirical concerns about memorization and lingering data caches are supported by third‑party reporting and security research that documented Copilot’s exposure of private or formerly public repositories. Together, these sources present a consistent factual picture: technical plausibility for memorization exists, but the plaintiffs have struggled to tie specific verifiable outputs to their own works in a way that would meet the pleading standard for Section 1202(b) as the district court construed it.Strengths and weaknesses: a critical legal analysis
Strengths of the plaintiffs’ case
- Practical risk: The combination of empirical research on memorization and real‑world incidents showing that AI systems can reproduce or expose previously public materials supports a plausible harm narrative.
- Policy resonance: Courts and policymakers are increasingly receptive to the argument that web‑scale scraping of creative work imposes economic and reputational harms on originators — an argument that courts have begun to entertain in other publisher suits against model builders.
- Licensing and contract claims survive: Even if the DMCA route is narrowed, the plaintiffs’ license‑based claims (breach of open‑source license terms) remain in play and can produce discovery into what was used, how models were trained, and whether attribution mechanisms were respected.
Weaknesses and legal risks for plaintiffs
- Pleading burden: The district court’s dismissal highlights the challenge of pleading the required causal connection — plaintiffs have so far lacked publicly disclosed, prosaic examples of the model reproducing their exact protected code in ordinary use. That gap is material in federal pleadings.
- Scope of the DMCA: Section 1202(b) was enacted in a different technological era; extending it to cover model training and blended generations raises doctrinal and statutory questions that appellate courts may be reluctant to answer expansively without clear congressional guidance.
- Strategic fragmentation: Plaintiffs face an evidentiary challenge — the discovery they seek from defendants is crucial, but interlocutory appeals that resolve the DMCA question against them could narrow discovery and limit remedies.
Strengths of Microsoft and OpenAI’s position
- Focus on concreteness: By framing plaintiffs’ injuries as speculative and hypothetical — and emphasizing the absence of concrete examples of identical reproduction — defendants place the dispute squarely within federal pleading norms and standing doctrine.
- Technical defenses: Microsoft and OpenAI have available technical arguments and empirical evidence suggesting that verbatim memorization is rare and that Copilot includes duplication‑detection and suppression features intended to reduce risks.
- Commercial reliance: The companies can credibly argue that broad liability would chill development of useful AI tools and create unpredictable exposure for platforms that index public code, with wide impacts on innovation and developer tools.
Weaknesses and risks for Microsoft and OpenAI
- Data hygiene and cache problems: Incidents where Copilot or affiliated systems exposed private or removed data — even if limited — undermine a blanket assertion that the system cannot reproduce copyrighted works, and provide persuasive anecdotal evidence to regulators and juries.
- Policy and public opinion: A ruling that narrows DMCA protections too aggressively could create a political and industry backlash, leading to contract renegotiations, regulatory scrutiny, or legislative fixes that impose obligations on model builders.
- Discovery exposure: If the appellate court allows the plaintiffs to proceed, Microsoft and OpenAI face invasive discovery into training corpora, filtering methods, and commercial reintegration of model outputs — discovery that could reveal trade secrets or competitive details and impose operational burdens.
Practical consequences for developers, enterprises, and platform operators
What developers and open‑source maintainers should consider
- Revise exposure assumptions: Treat any publicly posted code as potentially subject to long‑term indexing and model ingestion; once public, deletion or privatization is not a guaranteed shield.
- Use robust license headers and explicit CMI: While Section 1202 questions are in flux, thorough and consistent metadata practices make attribution easier to trace and strengthen future enforcement claims.
- Key rotation and security hygiene: If private credentials or keys are ever mistakenly published, rotate them immediately and treat the leak as irrevocable; models and caches can retain fragments that persist beyond a quick takedown.
What enterprises and product teams should do
- Audit Copilot and similar tools: Before enabling AI code assistants in production workflows, perform a risk assessment and consider configurations that favor privacy and suppression modes.
- Contractual safeguards: Negotiate vendor terms that clarify training data practices, indemnities, and responsibilities related to third‑party code usage.
- DLP and access control: Implement data‑loss prevention measures and keep secrets out of code repositories to reduce the chance of accidental exposure that AI assistants could resurface.
What platform operators should implement
- Metadata preservation: If platforms allow data scraping or provide training feeds, design pipelines that preserve CMI or at least track provenance to enable attribution or compliance downstream.
- Transparency and tooling: Provide controls that let authors opt out of training uses, and offer clear interfaces for removing content from indexes and caches — with verifiable removal processes.
- Defensive engineering: Strengthen deduplication, filter tuning, and suppression mechanisms to minimize verbatim repetition, and publish engineering notices describing the limits and protections in place.
Scenarios for the Ninth Circuit’s ruling and what they would mean
- Narrow ruling (affirming district court): The court could require a showing of a high degree of similarity — effectively an identicality threshold — before Section 1202(b) claims can proceed. That would reduce DMCA exposure for AI builders but leave open contract and copyright claims under theories tailored to output‑specific harms. Enterprises and platforms would get breathing room but not immunity.
- Broad ruling (rejecting identicality and allowing broader DMCA claims): The court could interpret Section 1202(b) to permit claims where the absence of metadata in training materially contributes to downstream reproductions that need not be literal clones. This would increase exposure for model builders, probably accelerate licensing demands from content owners, and push platform operators to build provenance scaffolding and stricter ingestion rules.
- Middle path (procedural or remand outcome): The Ninth Circuit could avoid a sweeping doctrinal pronouncement and instead remand for fact development, signaling that these issues require fuller discovery into how models were trained and what outputs were produced, while emphasizing case‑specific analysis. That outcome would favor development of the law via evidence rather than categorical rules.
Why this litigation matters beyond the parties
This appeal is not merely a narrow fight over code snippets. It intersects with several broader fault lines:- The evolving fit between older statutory frameworks and newer AI capabilities. The DMCA and other copyright rules were written in an era before neural networks could synthesize large swaths of human work and reproduce them in blended forms.
- The economics of creative labor. If model builders are permitted to train on public works without metadata obligations, creators may press for licensing regimes or other market adjustments to capture value.
- Platform responsibility and engineering governance. How platforms index, preserve, and filter content — and how they handle removal requests — influences both compliance and the social license to operate.
Recommendations for stakeholders (practical checklist)
- For developers and open‑source maintainers:
- Keep robust, machine‑readable license headers and CMI in each repository.
- Avoid embedding secrets in code; treat public posts as irrevocable.
- Maintain local archives that document provenance and publication timestamps.
- For enterprises:
- Audit AI assistants and disable features that increase verbatim outputs where confidentiality or license compliance is required.
- Require vendors to disclose training data policies and offer contractual indemnities tied to provenance failures.
- Include compliance checks in CI/CD pipelines to detect and strip accidental secret exposure.
- For platform operators and model builders:
- Implement provenance tracking for ingestion pipelines; store origin metadata in a manner that survives processing where feasible.
- Provide verifiable takedown and cache‑clearing tools and document them clearly to reduce disputes about lingering content.
- Offer opt‑out avenues and transparent safety reports so creators can understand how their content might be used.
Conclusion
The Ninth Circuit’s willingness to hear an interlocutory appeal on the DMCA’s application to AI training signals that courts acknowledge the legal and policy significance of the questions before them. Whether the panel adopts a narrow identicality standard, a broader reading of Section 1202(b), or an evidentiary remand will have palpable consequences: for creators seeking remedies; for companies that build, offer, or embed generative coding assistants; and for the open‑source ecosystem whose practices and expectations may need recalibration.Beyond the legal technicalities, this dispute spotlights a simple yet consequential reality: technology has outpaced many regulatory frameworks, and the law — through litigation and, ultimately, legislation or rulemaking — must decide how to reconcile innovation with fair attribution, compensation, and risk management. For developers, enterprises, and platform operators, the moment calls for concrete operational changes now, not later: better metadata hygiene, more transparent training practices, and preemptive contractual clarity. The Ninth Circuit’s ruling will arrive as a legal waypoint, but the industry’s response will determine whether creators and innovators can coexist under a shared set of reasonable, enforceable norms.
Source: Law360 9th Circ. Mulls DMCA Claim Against Microsoft And OpenAI - Law360
