It makes no sense at all to do that. The only thing that matters is whether the code is good.
If I contributed code to an Open Source project behind my old employer’s back, that would have been bad, because that code was owned by them and not me, even if I wrote it on my own time using my own equipment, because of the contract I signed with them.
If I copied code out of an AGPLv3-licensed codebase and contributed it to a BSD-licensed codebase without telling anyone, that would have been bad, because I did not have the right to change the license on that code to BSD (or change the license on the codebase to which I was contributing to AGPLv3).
If you use an LLM to produce code, you may well be doing the latter since an LLM is actually just regurgitating portions of its inputs. This is not a hypothetical scenario; I’ve personally encountered a case of someone using an LLM attempt to contribute code I recognized from a specific Open Source project under one license to another project under a different license, while claiming they “wrote it themselves.”
Any project that accepts contributions needs to take liability seriously and manage their risk appropriately.
Unfortunately, a large number of people are being told—and here, you can see many who believe it—that the output of an LLM either carries no copyright or is copyright by the one prompting it. In other words, even right here on Hacker News it’s widely believed that LLMs “launder” copyright.
You say you "recognized code". Does it mean that you weren't able to find the exact match?
> an LLM is actually just regurgitating portions of its inputs
You seem to be talking about the inputs to the autoregressive pretraining stage. Correct? Then it's not how LLMs work, unless we use a definition of portions as a "few letters blocks."
The LLM the person used was trained on a very large corpus of Open Source code, and reproduced that code exactly. Just like LLMs have reproduced chapters of books and articles from the New York Times exactly.
Were those functions trivial? With, say, 1% probability of someone who have not seen them writing them like that?
> Just like LLMs have reproduced chapters of books and articles from the New York Times exactly.
Have you read the articles? As far as I remember they fed large chunks of an article multiple times to an LLM to sometimes get a not-so-long exact match. It can mean that LLMs can infer a style and humans are predictable.
The possibility of SUV clearly shows that a model does more than "just regurgitating."
So they had to prompt? An LLM? I got this argument before and still don’t get what it’s trying to say. These models do not output anything unless prompted, that’s not any kind of gotcha.
On the code outputting front there is a lot of relevant evidence beyond the NYC lawsuit [0].
If I slightly modify GPL code, that doesn’t give me the right to relicense.
[0] https://arxiv.org/html/2601.02671?amp=&= and https://arxiv.org/abs/2506.12286 and https://ai.stanford.edu/blog/verbatim-memorization/