A federal appeals court handed GitHub, Microsoft, and OpenAI an important win in the first major appellate ruling over how AI coding tools can use open-source code.

As we all know, all the AI code-generating programs learned their lessons largely from open-source code. So a group of anonymous open-source plaintiffs argued that GitHub Copilot and OpenAI Codex were built using code from public GitHub repositories, including open-source licensed code. They argued the AI tools had generated code without the author attribution, copyright notices, and license terms that came with the original work. Now, the Ninth Circuit has returned a verdict in Doe vs. GitHub siding with Big AI.

Ouch!

But it may not be as troublesome as it first appears. The Ninth Circuit’s September 16 decision turns on a technical but consequential distinction: AI-generated code that lacks author names, copyright notices, and license information is not necessarily the same as code from which that information has been unlawfully removed under Section 1202 of the Digital Millennium Copyright Act (DMCA).

Writing for the court, Judge Eric Miller rejected the idea that a tool’s failure to include copyright information in a newly generated work automatically amounts to removal of that information. “One who creates a new work and fails to include CMI [copyright management information] cannot be said to have ‘removed’ or ‘altered’ anything,” the court said. It declined, in the court’s words, to transform “run-of-the-mill copyright-infringement claims into DMCA claims.”

For open-source developers, that means one potentially potent legal pathway has narrowed. For AI companies, it means reduced litigation risk, but they’re not getting a blank check to do what they want with open-source code.

Still, as Duane O’Brien, the Open Source Initiative (OSI)new executive director, told me, “The Ninth Circuit answered a narrow question about one provision of the DMCA. It did not decide whether the developers’ open source licenses were honored, and that claim is still before the district court. Nearly every open source license, from MIT to the GPL, is unambiguous about the obligation to keep the copyright notice and attribution intact. Developers offered their work to the world on those terms, and anyone who builds on that work, including companies building AI tools, should honor them.”

Yes, yes, they should. But as writers can attest, AI companies can be cavalier, at best, about their use of copyrighted materials.

However, Karen Sadler, attorney and executive director of the Software Freedom Conservancy (SFC), noted in an interview, “It’s very easy to overread this decision.” Most of the claims (like whether Microsoft removed copyright information before using the code for training) were dismissed on procedural grounds. The plaintiffs hinted at the theory but didn’t actually assert it, so the court did not rule on it but instead said that it considered them forfeited. Or, as the court put it, the “complaint is not about training. It just isn’t.”

That’s not to say another, better-argued case, might come to a different decision. Sandler continued, “The decision notes that the generation of code may indeed be infringement. So the whole decision comes back to whether copyright information was actually removed, not about whether the use was appropriate. Ultimately, this case is really about the way these very narrow claims were made, and it doesn’t read to me like any sweeping conclusion about the use of FOSS by LLMs.”

It is worrisome, though. The accepted rule is that code’s open-source licensing travels with it. For example, a developer reusing an Apache-licensed file is expected to preserve relevant notices. A developer incorporating GPL-licensed code can take on broader reciprocal obligations. Even permissive licenses such as MIT and BSD commonly require preservation of copyright and license notices. AI-generated code deletes the provenance chain.

GitHub and Microsoft have argued from the start that Copilot doesn’t simply copy and paste code. The ruling doesn’t endorse every aspect of that position. But it does make clear that lack of attribution, standing alone, will not convert an AI-code output into a DMCA metadata-removal violation.

That matters not only to Copilot. The same reasoning could apply to providers of AI code assistants, general-purpose language models, and agentic development systems. In short, all AI programs that can generate code. Or, in other words, it’s business as usual for AI coding systems.

That said, the court did not decide whether copying open-source code to train a model is copyright infringement. It does not decide whether such training is fair use. It doesn’t determine whether output that closely matches protected source code infringes copyright. And it does not decide whether an AI vendor, a model user, or both may be liable when generated code triggers an open-source license obligation.

The court did recognize the difference between a generic output and a tool returning an existing work with information stripped out. It indicated that a different case could arise if a system operated more like a search engine, returning identical code without accompanying CMI. It also didn’t foreclose conventional copyright claims where output substantially reproduces protected work.

It’s an important caveat. The practical risk in AI coding is likely to be highly fact-dependent. A commonplace loop, sort routine, or API call won’t be copyrightable. A substantial, distinctive code block from a specific repository is a different kettle of fish.

What does all that mean? Stay tuned; there’s a lot of litigation and court decisions to come. Still, as Bruce Perens, one of open-source’s co-founders, told me, “We are indeed facing the issue that this is effectively the end of copyright for both proprietary and Open Source software and many other products. In explaining this to Richard Stallman recently, I noted that Copyleft was a casualty, but the copyright-free world he once dreamed of- the actual motivation behind Free Software- was being achieved, with various unforeseen circumstances.