What they completely missed was that LLMs are a golden opportunity for the corpo-ratfuckers to completely work around the issue of those pesky OS licenses. Who cares if it's GPL/AGPL, just run it through the slop machine and everything is fine.
Open-source doesn't exist in a vacuum, they rely on inherently restrictive licenses to force corporations into contributing back instead of just sucking up everything.
In 2001 I had a guy at Oracle tell me it was a suckers game to write open source code.
I've had M$ heads tell me ruby was a joke that would never amount to much because you couldn't use it in a "real business".
I've had bad phil bros tell me in 2004 that open source was "anti american communism bullshit".
It's ok to be a humanist and not a corporatist.
It's ok to show generosity.
It's ok to reject the crab bucket framing.
Some of us are mature enough to be ok living in a better world even if still imperfect.
The issue is that all attributions get stripped away by the LLM (even in cases where it is trivial to avoid). For example, as a test, I asked DeepSeek to tell me how one can do an exact substring-boolean-test in Common Lisp, and it gave me code that was, character for character, _identical_ to the code that appears in Seibel's Common Lisp Book. It clearly memorized the code. It stands to reason, that it could have also memorized where it saw it, and been trained to tell the user that the code was from that book (which is NOT in the public domain, despite being free to read on the internet)[0].
To give you an analogy, nobody has a problem with someone downloading and distributing the public domain works of Leo Tolstoy or Charles Dickens or Jane Austen or Karl Marx, etc. But to remove their names from those works, and placing your (or someone else's) name on them instead, is plainly malicious behavior. Such behavior needs to shunned.
LLMs (I believe) _can_ be trained to not plagiarize. They do not have to be original, nor to use public domain training-data, they just have to give attribution (which, as far as I can tell, requires little more than a corpus and a stack of similarity-measures).
I suspect that they are not trained this way, because fully automated plagiarism creates an ambiguous situation, where a copyleft license (like GPL or EUPL etc) can be unknowingly violated, and there is hardly any trace of the violation. Plagiarism is the product.
[0]: Such attribution can only make LLMs _more_ useful and _more_ widely and confidently integrated into workflows. The lack of such attributions, risks diluting the value of open source projects, as (1) a high-signal indicator of skill and achievement, and (2) a way to ensure the provenance and quality of the code (both very important, if you are considering to build a business on top of one or more such projects). Just look at all those repos that claim to have "made" Minecraft-like game or Unix-like OS, when all they did was have an LLM plagiarize the code for them. This is a kind of, at best, spam and, at worst, fraud.
And something like attributions can be determined even if the LLM is paraphrasing, if I'm understanding Cosma Shalizi correctly[1]. He gives Grosse et al.[2] as a reference for doing so in a scalable manner.
At least with the old school web search engines, you got a hyperlink that you could use to find out more. Now the only way to learn more is to keep asking the LLM more questions. There is no way to get from the LLM to a source outside the LLM.
This code could appear many times in training data; but I wonder what decoding params did you actually used, because that effect appears likely with close-to-greedy way. Also, if it is a coding assistant model, chances are this skill was increased by additional sampling from that domain during training? Or maybe RL has teached model to provide verbatim answers for certain questions? So it was rewarded if that kind of answers is really helpful (I guess it was?).