Introduction

On September 16, 2026, the Ninth Circuit held in Doe v. GitHub, Inc. that outputs produced by a generative AI coding tool are newly generated works, not copies of existing works, and therefore fall outside the Digital Millennium Copyright Act’s (DMCA) prohibition on removing or altering copyright management information (CMI).¹ The court limited that characterization to the § 1202(b) removal and alteration analysis, and declined to reach whether AI outputs that reproduce copyrighted material could constitute copyright infringement.² The ruling turned on the generative architecture of the accused AI systems, distinguishing generative AI tools that generate outputs by predicting sequences from statistical patterns from retrieval-based systems that retrieve and disseminate existing copyrighted material, with the court suggesting the latter could face different legal treatment. The Ninth Circuit also declined to reach, on procedural grounds, the question of whether training on copyright-protected materials from which CMI has been stripped violates § 1202(b).

Background

Section 1202(b) of the DMCA protects CMI from deliberate removal or alteration. CMI includes data identifying a work, its author or copyright owner, and the terms and conditions for use. The statute prohibits (1) intentionally removing or altering CMI, (2) distributing CMI knowing it was removed or altered without authority, and (3) distributing works or copies of works knowing that CMI was removed or altered without authority.³ Each prohibition also requires a showing that the defendant knew, or had reasonable grounds to know, that the challenged conduct would “induce, enable, facilitate, or conceal” copyright infringement.⁴ The DMCA does not require works to carry CMI in the first place; but where CMI is present, § 1202(b) shields it.

Although § 1202(b) has been applied for decades in traditional media contexts, such as cropping a photographer’s credit from a reprinted image, courts have only recently confronted how the statute applies to generative AI systems that learn from copyrighted material and produce new outputs. As plaintiffs have increasingly turned to § 1202(b) as a vehicle for challenging AI-generated content, Doe v. GitHub marks the first appellate-level decision to squarely address the question.

GitHub Copilot, which uses a modified version of OpenAI’s Codex model, was trained on billions of lines of publicly available code, including repositories carrying author names, copyright notices, and license terms as CMI.⁵ In this dispute, plaintiffs are anonymous programmers who alleged that Copilot violated § 1202(b) by generating code without such information. They advanced two theories of liability: (1) the “output theory,” that Copilot’s outputs are reproductions of copyrighted code from which CMI has been removed; and (2) the “input theory,” that ingesting copyrighted code into the training pipeline, while stripping CMI in the process, independently violates § 1202(b).⁶

The district court dismissed the output theory claims, holding that the complaint’s examples did not meet § 1202(b)’s requirement that copies be “identical.” Further, the district court understood plaintiffs not to have asserted the input theory, and did not address it.⁷ The Ninth Circuit took up the case on interlocutory appeal.

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