Judge dismisses DMCA copyright claim in GitHub Copilot suit

Original Article ↗ Hacker News Discussion ↗

Case outcome and legal reasoning

  • Judge dismissed the DMCA §1202(b) claim because plaintiffs did not show Copilot outputting their code identically with copyright/attribution stripped, which §1202 requires.
  • Commenters note the ruling is narrow: about DMCA “copyright management information,” not all copyright issues.
  • Some think plaintiffs’ strategy was weak: they alleged verbatim copying but couldn’t produce a single accepted example from their own code.

Evidence and “identicality”

  • People recall public demos of Copilot reproducing famous snippets (e.g., Quake fast inverse sqrt) or NYT text, but note:
    • Those rights-holders weren’t plaintiffs here.
    • Courts require evidence tied to plaintiffs’ works, not “in theory this happens.”
  • GitHub reportedly added a “copyright filter”; debate on whether that’s prudence or “destroying evidence.” Others note old versions still exist and can be subpoenaed.

Training on copyrighted code and fair use

  • One side: training on public code (even GPL, art, prose) is non‑infringing “learning”; function and style aren’t protected, only expression.
  • Other side: training creates a derivative commercial product built on copyrighted works without consent or compensation; fair use was never meant for mass AI training.
  • Dispute over whether model weights are a derivative work and whether paraphrased output can still infringe or violate licenses (e.g., GPL conditions, attribution).

Ethics, scale, and impact on creators

  • Critics see AI training on non‑consenting artists’ and coders’ work as “pure exploitation,” especially when it displaces their income.
  • Defenders argue automation has always displaced labor; the economic problem is distribution, not the tool.
  • Scale and lack of accountability of machine agents are recurring concerns.

Licensing, GitHub, and OSS reactions

  • Debate on whether GitHub’s ToS gives it rights to use code for Copilot; some quote language that seems limited to “providing the service.”
  • Edge cases: code uploaded by non‑authors; GPL projects mirrored on GitHub; authors whose code was uploaded by others.
  • Some propose “no-AI” or anti-training licenses; others note if training is ruled fair use, such clauses may be ineffective and are not FOSS.
  • A few developers say they’ll stop publishing open source or avoid GitHub; others think the OSS ecosystem will largely continue.

Technical behavior of LLMs

  • Discussion of memorization vs abstraction: models usually compress patterns, but can “recite” training data in some prompts.
  • Filters that avoid verbatim output don’t prevent close paraphrases, which may still raise legal and ethical questions.