Why the Copilot Lawsuit Is a Wake-Up Call for GenAI Users

Imagine you had a complex idea of building something new for a good cause like an app to track environmental pollution in your local community. You pour hours into coding it, showcase it in your college projects, hackathons, and competitions, and proudly upload it to GitHub for the world to see and collaborate on. Boom — it's out there, gaining stars and forks. But then, without your explicit permission or credit, your code gets scooped up as training data for a massive AI like GitHub Copilot.

Suddenly, your innovative snippets are being regurgitated in someone else's project, stripped of attribution. Sounds like a nightmare?

Well, that's at the heart of the Github Copilot Litigation
and as students, it's hitting closer to home than you might think.
In this post, I'll break down the lawsuit, its current status, and how it could reshape academia for us budding devs. Whether you're grinding through CS assignments or building side projects, this case raises big questions about ethics, and the future of AI in coding.

What is the Github Copilot Litigation ?

I would like to provide a gist on this

• A group of developers filed a lawsuit against Microsoft, GitHub, and OpenAI (who built the AI).
• They argue that Copilot breaks copyright law and open-source licenses or as they say "unprecedented open-source software piracy".
• The companies argue that Copilot is more like learning from examples, similar to how a child learns a language by listening to conversations around him and dosen't speak entire paragraphs at one go.

Also found a pretty cool video on this, do check it out here

Status of the Case

• May 2023 - A judge dismissed some claims but let breach of license and DMCA ones proceed. But wait, what is DMCA?

DMCA Violations: Under the Digital Millennium Copyright Act, it's claimed that Copilot removes or alters copyright management info (like author names) when outputting code. ( And hence not giving credit to it's creator )

• June 2024 - The DMCA claims got tossed because Copilot's outputs aren't "identical" enough to the originals. A major setback for the devs.

• April 2025 - Plaintiffs appealed that decision to the Ninth Circuit arguing the law shouldn't require exact copies for liability.
  Their main argument was the judge was too strict in requiring “identical copies” for liability. They believe the law should also cover substantially similar outputs (not just word-for-word copies), which I strong agree too!
  For example say if Copilot outputs 90% of your poem but changes a few words, it still feels copied — they argue the law should recognize that.

As of August 2025, this case remains on appeal as the Ninth Circuit has not yet issued a decision.

Also found an interesting article. Give it a read here

How This Affects Us as Students in Academia ?

Here are some points which are crucial to be discussed upon

1. Plagiarism and Academic Integrity Risks:If Copilot suggests code that's derived from someone else's licensed work without credit, your submission could flag as plagiarized. Imagine turning in a project only to have it questioned because the AI "borrowed" unethically. Schools like MIT and UC Berkeley already mandate disclosing AI assistance, and this case might push for stricter rules on verifying code origins.

2. Ethical Learning Challenges: Over-relying on Copilot might stunt your skills, which is commonly seen in upcoming devs.The litigation emphasizes that open-source is about community and credit, not free-for-all data grabs. In academia, this could mean more emphasis on original coding in curricula, or even courses on AI ethics.
   Plus, if you're contributing to open-source for resumes or portfolios, like me and lots of other CS undergrads knowing your code might fuel commercial AIs without payoff could discourage sharing.

Lastly I would conclude by saying that as students, we should use these tools wisely so that it dosen't undermine the creators who make it possible. Also take up GenAI courses to learn the ethics, safe practices, and best ways to collaborate with these tools.
(PS: I’m currently taking a GenAI course online myself, which is why this case felt especially relevant.)