The rapid improvement and adoption of AI coding tools should not surprise anyone paying attention. It raises many intellectual property questions. One interesting question: what are the legal and practical impacts on clean room development, a long-standing approach for copying functionality of software while avoiding copyrightable code itself.
Case in point, a developer used AI to rewrite an established Python library named chardet maintained under a copyleft license for twenty years, and he did so from scratch in roughly five days. The new version is reportedly much faster, more accurate, has less than 1.3 percent code similarity with the original, and was released under a permissive license. This raises questions. Did an AI rewrite from a design document in an empty repository effectively sidestep two decades of copyleft protection? Or did the AI rewrite merely create a derivative work, albeit one with limited code similarity and substantial differences?
What clean room development is
For those outside of this corner of the IP world, clean room development (sometimes called clean room reverse engineering) refers to a disciplined process for recreating software without copying the original code. The concept is straightforward. One team, sometimes called the "dirty team," studies the existing software, documents its functional behavior, and produces a specification describing what the software does without revealing how it does it in code. The goal of this step is to capture the not-copyright-protected functionality of the code without capturing any of the code’s creative expression. A second team comprising people who have never seen or accessed the original code, or "clean team," then writes entirely new code based solely on that specification. This way the new code reflects only the clean team’s own creative expression, even if it achieves the same functionality as the original code. And the process should generate contemporaneous documentation proving independent creation to support a defense against any later alleged copyright infringement.
Why use clean rooms
Companies facing end-of-life software licenses, substantial licensing fees, or defunct licensors have used clean rooms to replace proprietary systems without exposing themselves to copyright infringement claims. A bank running legacy COBOL software, for example, might employ this method when the original licensor goes out of business or demands increasing renewal fees. And companies can use it any other time they desire to copy functionality while generating evidence to show that they are not copying code itself.
The catch has always been cost. A rigorous clean room process requires two separate teams and often involves extensive documentation, legal oversight, and months or years of engineering effort. That cost functioned as a natural barrier. If you wanted to use someone else's copyrighted code, it was typically cheaper to accept the license terms than to rewrite everything from scratch.
How AI coding agents change the calculus
This is where the chardet story becomes instructive. The developer’s process followed a modified clean room process. He wrote a design document specifying the architecture and requirements, started in a fresh repository with explicit instructions for the AI not to look at the original licensed source code or use LGPL or GPL code, and let the AI build the entire library from that specification. The result shared less than 1.3 percent similarity with any prior version. There are significant differences from a typical clean room process in this specific case (e.g., Claude already knows chardet), but it shows the incredible capability of AI coding agents to efficiently write code to spec.
The legal principle has not changed. But a process that might have required a team of engineers and many months now might take one developer and AI agents less than a week. The cost of clean room reimplementation has collapsed by orders of magnitude. The consequence: if any motivated developer can rewrite a complex library in days (or hours) rather than months (or years), the economic assumption that reimplementation is preclusively expensive falters.
AI coding tools also introduce questions and complications. Could AI agents be used for both the clean and dirty teams, if given appropriate instructions to partition the clean team from any of the source information? For publicly available code, can AI agents qualify as a clean team if they had (or may have had) prior access to the code in training? If AI ever had access to the source code and then produces a rewrite, is that independent creation or a derivative work? Does the amount of similarity matter? And while AI-generated code is not copyrightable, how much additional human developer involvement is needed to render the new code copyrightable, if the purely AI-generated portions are disclaimed?
Other overlapping legal risks apply to AI-assisted clean room code generation that traditional clean room analysis was never designed to address. For example, trade secret and patent claims operate independently of copyright. A clean room rewrite that avoids copyright liability does not create a defense for a potential trade secret misappropriation claim (e.g., if through the process trade secrets are acquired through improper means) or patent claim (e.g., if the generated code practices any patents). And as AI tools make it easier to reverse-engineer proprietary functionality, companies may need to rely more heavily on trade secret, patent, and contractual protections.
What companies and developers should do now
Companies need practical solutions as the law develops. Know that AI has compressed the time, cost, and effort required for reimplementation. So, think beyond copyright. Trade secret protections, patents, contractual restrictions, and technical safeguards may matter more in a world where copyright's practical enforcement is weakened by AI's ability to produce non-literally-similar rewrites overnight.
For those deploying AI-assisted clean room processes, carefully consider the design and documentation of the process to mitigate copyright infringement risk and assume that AI-assisted clean room processes will receive additional scrutiny as the law around AI and copyright infringement continues to develop. And don’t overlook legal exposure beyond copyright.
The reality is that AI coding agents deliver competitive advantages. But the legal infrastructure surrounding software IP developed in an era when reimplementation was slow and expensive. That era is over. AI-assisted development is not a legal minefield to avoid, but a capability that demands updated governance, including layered IP strategies, and a realistic understanding that some protections developers traditionally relied on for their own code are eroding for everyone else's code too.