Vibe coding seems to be so simple. You give your AI a prompt, and it creates a new piece of software for you. Yet, most never wonder where the code actually comes from. While AI does some recombination, much of the code it creates simply regurgitates training data. Much of the training data is code licensed under Open Source licenses.
This leaves us with the question: who actually owns the copyright? AI proponents love to argue that AI creates something new and thus a new copyright should apply. The law says that any new code would be machine-created and thus not copyrightable. Yet, what about the code that is a straight one-to-one copy?
In the Papermark and Corgi dispute, the central issue is not only whether source files were lifted, but whether a competitor used AI-assisted development to recreate another product’s protected expression, wording, and structure. That distinction matters because modern copying can hide behind fresh code while still producing a near-identical user experience.
Please note: This content is informational only and does not constitute legal or investment advice.
AI Code Is Not The Whole Story
For years, software disputes were relatively straightforward: compare repositories, inspect diffs, and look for reused functions or comments. AI coding complicates that because a team can generate new code that looks clean on paper while still reproducing the same interface, workflow, and product logic from screenshots or prompts. The Corgi reporting shows exactly that tension, with Corgi denying code theft while Papermark pointed to matching language and comparable settings pages.
That is why “we didn’t copy the code” is no longer a complete defense. Copyright law protects expression, not just the literal source text, and product behavior can cross the line when the copied elements are sufficiently distinctive. AI tools make it easier to rebuild a product’s look and feel without leaving an obvious file-by-file trail.
Why Papermark Has A Case
Papermark should pursue its claim because public allegations concern more than abstract competition. They concern trust, attribution, and the boundaries of Open Source reuse. Papermark’s complaint, as reported, is anchored in screenshots and interface similarity, which can support a claim that Corgi copied protected product expression even if it did not directly import repository code. In Open Source, permission to read or fork code does not mean permission to strip attribution and replicate the product surface as a competing clone.
There is also a broader policy reason to push the issue. If a startup can avoid accountability simply by regenerating similar code with AI, then Open Source creators bear the downside of disclosure without getting the expected norms of credit and fair reuse. Papermark’s case could help draw a clearer line between legitimate inspiration and disguised appropriation.
AI Weakens The Defense Against Open Source Licenses
While AI coding can produce plausible deniability, it does not erase liability. The reporting on this dispute suggests a familiar modern pattern: teams describe a competitor’s product to an assistant, receive a rapid approximation, and then end up with matching layout, microcopy, and control structure. That may reduce the likelihood of a literal code match, but it does not automatically eliminate copyright or unfair competition concerns. Especially when trying to copyright the results yourself, given the human influence in the design process.
The U.S. Copyright Office has been actively examining copyright issues raised by AI, including the copyrightability of AI-generated outputs and the use of copyrighted materials in training. That is a sign that the legal system is still catching up to the reality of AI-assisted copying. Until the law is clearer, the safest assumption for founders is that “generated with AI” is not the same thing as “cleared of infringement”.
The AI Startup Lesson
This dispute is really a warning for product teams. AI coding can accelerate shipping, but it also reduces friction in copying, which means founders need stronger review habits, not weaker ones. If a team uses a live competitor as a reference point, someone has to check not only the code but also the phrasing, screen structure, default states, onboarding flow, and visual hierarchy. In short, you have to start comparing the products instead of simply comparing the code.
That matters even more for startups that move fast and market themselves as builders rather than imitators. A lower-cost alternative is not a legal defense if the product was assembled by tracing another company’s original expression too closely. When companies ignore that boundary, they risk litigation, reputational damage, and the loss of trust that makes early markets fragile.
The Critical AI – Open Source fight
Papermark versus Corgi is more than just a startup argument. It is a test case for whether AI-assisted development will be treated as a productivity tool or as a shortcut around ownership, attribution, and originality. If courts and the market accept “the code is different” as a blanket excuse for copying Open Source software, then product cloning gets easier to rationalize and harder to police.
Papermark should press the claim because the dispute can help define that boundary in public. Even if the final answer is nuanced, forcing the issue may clarify that AI-generated code does not wash away copied product design, copied wording, or copied intent. In the age of vibe coding, that clarification is not just useful; it is necessary for the survival of Open Source software and human innovation.
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