The AI Ingestion Trap: Why the Ninth Circuit’s Partial Dismissal of the GitHub Copilot Class Action Deflates the Plaintiffs’ Bar’s Generative AI Playbook

The Generative AI Gold Rush Hits an Appellate Wall

For the past three years, the plaintiffs’ bar has treated the advent of generative artificial intelligence as the ultimate class-action gold rush. The playbook was deceptively simple: find a tech giant that scraped massive amounts of data to train a large language model (LLM), find a few representative plaintiffs whose data was caught in the dragnet, and file a sprawling class action alleging everything from copyright infringement to unjust enrichment and violations of the Digital Millennium Copyright Act (DMCA). The underlying theory was that ingestion itself constitutes a cognizable, class-wide injury.

But the Ninth Circuit is officially pulling the plug on that fantasy. In a highly anticipated ruling, the appellate court upheld a partial victory for OpenAI and Microsoft against software developers alleging their open-source code was misused from GitHub to train generative AI systems. By affirming the dismissal of a significant portion of the developers’ claims, the Ninth Circuit has handed defense counsel a lethal roadmap for dismantling generative AI class actions before they ever reach the costly crucible of discovery.

The GitHub Copilot Dispute and the "Mere Ingestion" Problem

To understand why this Ninth Circuit development is a seismic shift for civil litigators, you have to look at the mechanics of the claims. The software developers alleged that Microsoft and OpenAI scraped their open-source code repositories on GitHub to train AI systems like Copilot. Because this code was subject to various open-source licenses requiring attribution, the plaintiffs argued that ingesting the code and spitting out derivative snippets without the original copyright management information (CMI) violated 17 U.S.C. § 1202(b) of the DMCA, alongside a litany of state-law breach of contract and privacy claims.

The Ninth Circuit’s decision to uphold a partial win for the tech giants underscores a fatal flaw in the plaintiffs' dragnet approach: Article III standing and the rigorous demands of federal pleading standards. As the Supreme Court made explicitly clear in TransUnion LLC v. Ramirez, 141 S. Ct. 2190 (2021), "No concrete harm, no standing."

"The appellate scrutiny of these sprawling AI class actions reveals a fundamental judicial skepticism toward 'ingestion-as-injury.' If a plaintiff cannot allege that the AI model actually outputted their specific, identifiable work, their claim is effectively an abstract grievance about machine learning mechanics, not a concrete legal injury."

By trimming the fat off this class action at the motion-to-dismiss phase, the Ninth Circuit is signaling that generalized allegations of data scraping are insufficient. Plaintiffs must plausibly allege that the AI system actually reproduced their specific code without attribution to survive a Rule 12(b)(1) or Rule 12(b)(6) challenge. For practicing defense lawyers, this is the green light to aggressively attack the standing of named plaintiffs who can only point to the fact that their data was "in the training set."

The Copyright Preemption Buzzsaw

Beyond standing, the Ninth Circuit’s partial affirmation highlights the devastating effectiveness of the copyright preemption defense under 17 U.S.C. § 301. Plaintiffs routinely stack their complaints with state-law claims like unjust enrichment, unfair competition, and negligence to bypass the strict statutory requirements of the Copyright Act. They argue that the tech companies "unjustly profited" from their labor.

However, the appellate courts are increasingly recognizing these state-law claims for what they are: disguised copyright claims. Under Section 301, if a state-law claim seeks to vindicate rights that are equivalent to the exclusive rights within the general scope of copyright (reproduction, distribution, derivative works), it is preempted. The Ninth Circuit’s willingness to uphold the trimming of these peripheral claims forces plaintiffs to fight strictly on the unforgiving terrain of federal copyright law and the DMCA.

Why This Destroys Rule 23(b)(3) Predominance

The most profound implication of this ruling isn't just about what claims survive—it is about what this means for class certification under Fed. R. Civ. P. 23. If you are a defense litigator in the AI space, the Ninth Circuit just handed you the ultimate weapon against class certification.

Think about the mechanics of Rule 23(b)(3), which requires that common questions of law or fact predominate over individualized inquiries. If the Ninth Circuit requires plaintiffs to prove a concrete injury—meaning they must show that the AI actually generated output substantially similar to their specific code, or that their specific CMI was stripped during an actual output generation—how can a class ever be certified?

The answer is: it likely can't.

Evaluating whether Copilot improperly reproduced Developer A’s code requires a completely different factual inquiry than evaluating whether it reproduced Developer B’s code. It requires an output-by-output analysis. By forcing the litigation away from the uniform, automated act of training (which happens to the whole class at once) and toward the highly individualized act of output generation, the Ninth Circuit has essentially rendered these claims fundamentally unsuitable for class-wide resolution.

The Takeaway for Practitioners

The era of filing a kitchen-sink complaint against an AI developer and coasting to a nine-figure settlement on the sheer terror of e-discovery costs is coming to an end. The Ninth Circuit’s handling of the GitHub Copilot litigation establishes a clear defensive playbook:

1. Attack Standing Early: Use TransUnion to force plaintiffs to plead instances of specific, infringing outputs rather than relying on their presence in the training dataset.
2. Leverage Section 301: Ruthlessly clear out the brush of state-law unjust enrichment and unfair competition claims via copyright preemption.
3. Set the Stage to Defeat Class Cert: Frame the surviving claims entirely around the individualized nature of the AI’s output, ensuring that Rule 23(b)(3) predominance is impossible to satisfy.

For the plaintiffs' bar, this ruling is a harsh wake-up call. The generalized "AI took our jobs and our data" narrative might play well in the court of public opinion, but in the federal appellate courts, concrete injury and statutory preemption still rule the day. If plaintiffs want to survive the pleading stage in 2026 and beyond, they need to stop complaining about the algorithm's diet, and start proving the toxicity of its specific outputs.