No right to relicense this project
An open source Python library for character encoding detection was recently “rewritten from scratch” with an AI assistant and relicensed from the copyleft LGPL to permissive MIT, while keeping the same name and package identity. Commenters debate whether this constitutes an illegal derivative work given the maintainers’ prior exposure to the original code and the likelihood that the model was trained on it, raising broader questions about clean-room reimplementations, AI-generated code, and the enforceability of software licenses. Many also worry about the practical risks: unreviewed AI-generated code silently replacing a widely used dependency, and the precedent it might set for “license washing” both open source and proprietary software.
Project rewrite and relicensing
- Library’s v7.0.0 is a near-total rewrite produced in a few days with an LLM and relicensed from LGPL to MIT while keeping the same name, repo, and version history.
- Many see this as “license-washing”: trying to escape copyleft obligations while retaining accumulated reputation and ecosystem position.
- Others argue a full rewrite with a different internal architecture and similar API can be a new work, and thus legitimately MIT-licensed.
Derivative work vs. clean-room implementation
- One side claims any rewrite by people heavily exposed to the original LGPL code (and using an LLM trained on it) is presumptively a derivative work, so must remain under LGPL.
- Counterpoint: copyright law does not require a “clean room”; exposure alone doesn’t prove infringement. What matters is whether protectable expression was copied.
- There’s disagreement over burden of proof: some say accusers must show substantial similarity; others argue the maintainers effectively admitted derivation by keeping the name, API, and version lineage.
AI-generated code and copyright status
- Several commenters note recent rulings that purely AI-generated works are not copyrightable (at least in the US), raising questions whether v7 code can be licensed at all or is effectively public domain.
- Others push back that humans guiding AI may still be authors and, separately, that AI output can still be a derivative work of training data.
- There is concern that if courts accepted LLM rewrites as “original,” this would effectively gut copyright and copyleft for software.
Ethics, governance, and open source norms
- Many see the move as ethically wrong even if it were legal: a maintainer treated as a trustee for a community project is perceived as unilaterally changing the social contract.
- Suggested “proper” approach: create a new project and name, or obtain explicit relicensing consent from all prior contributors.
- Debate over GPL/LGPL: some call them “problematic” licenses; others argue they work as intended to keep improvements free and defend end-user rights.
Security, quality, and ecosystem risk
- Huge one-shot AI rewrite (hundreds of thousands of lines deleted and replaced) is viewed as a potential supply-chain hazard: impossible to properly review, test coverage changed, CI initially broken.
- Claims of “drop-in” compatibility are disputed: tests from v6 show behavior and encoding labels differ in practice.
- Broader concern: core dependencies in ecosystems like Python being silently replaced with unvetted AI-generated code.