Technology

Ah, the solution that recognizes there's no way to eliminate AI from the supply chain after it's already been introduced.

Ecological, social, economic issues and the answer is on the legal site. FOSS as usual I guess

I don't understand the full picture here, but the person who is submitting AI slop will be held accountable. Never a company.

So if a company is pushing staff to us AI to complete projects faster and their code ends up being AI slop when submitted, only the person working for the company will be held responsible.

I'm not sure what the repercussions are here but hopefully it's not a large fine. Those fines could add up quick if the person is submitting code all the time and doesn't know they are messing up.

I'd still be highly sceptical about pull requests with code created by llms. Personally what I noticed is that the author of such pr doesn't even read the code, and i have to go through all the slop

The title of the article is extraordinary wrong that makes it click bait.

There is no "yes to copilot"

It is only a formalization of what Linux said before: All AI is fine but a human is ultimately responsible.

" AI agents cannot use the legally binding "Signed-off-by" tag, requiring instead a new "Assisted-by" tag for transparency"

The only mention of copilot was this:

"developers using Copilot or ChatGPT can't genuinely guarantee the provenance of what they are submitting"

This remains a problem that the new guidelines don't resolve. Because even using AI as a tool and having a human review it still means the code the LLM output could have come from non GPL sources.

Seems like a reasonable approach. Make people be accountable for the code they submit, no matter the tools used.

This is a bad move. The GPL license cannot be enforced on AI generated code.

“Yes to Copilot, no to AI slop”

Pick One

Ultimately, the policy legally anchors every single line of AI-generated code

How would that even be possible? Given the state of things:

https://dl.acm.org/doi/10.1145/3543507.3583199

Our results suggest that [...] three types of plagiarism widely exist in LMs beyond memorization, [...] Given that a majority of LMs’ training data is scraped from the Web without informing content owners, their reiteration of words, phrases, and even core ideas from training sets into generated texts has ethical implications. Their patterns are likely to exacerbate as both the size of LMs and their training data increase, [...] Plagiarized content can also contain individuals’ personal and sensitive information.

https://www.theatlantic.com/technology/2026/01/ai-memorization-research/685552/

Four popular large language models—OpenAI’s GPT, Anthropic’s Claude, Google’s Gemini, and xAI’s Grok—have stored large portions of some of the books they’ve been trained on, and can reproduce long excerpts from those books. [...] This phenomenon has been called “memorization,” and AI companies have long denied that it happens on a large scale. [...]The Stanford study proves that there are such copies in AI models, and it is just the latest of several studies to do so.

https://www.twobirds.com/en/insights/2025/landmark-ruling-of-the-munich-regional-court-(gema-v-openai)-on-copyright-and-ai-training

The court confirmed that training large language models will generally fall within the scope of application of the text and data mining barriers, [...] the court found that the reproduction of the disputed song lyrics in the models does not constitute text and data mining, as text and data mining aims at the evaluation of information such as abstract syntactic regulations, common terms and semantic relationships, whereas the memorisation of the song lyrics at issue exceeds such an evaluation and is therefore not mere text and data mining

https://www.sciencedirect.com/science/article/pii/S2949719123000213#b7

In this work we explored the relationship between discourse quality and memorization for LLMs. We found that the models that consistently output the highest-quality text are also the ones that have the highest memorization rate.

https://arxiv.org/abs/2601.02671

recent work shows that substantial amounts of copyrighted text can be extracted from open-weight models. However, it remains an open question if similar extraction is feasible for production LLMs, given the safety measures [...]. We investigate this question [...] our work highlights that, even with model- and system-level safeguards, extraction of (in-copyright) training data remains a risk for production LLMs.

How does merely tagging the apparently stolen content make it less problematic, given I'm guessing it still won't have any attribution of the actual source (which for all we know, might often even be GPL incompatible)?

But I'm not a lawyer, so I guess what do I know. But even from a non-legal angle, what is this road the Linux Foundation seems to embrace of just ignoring the license of projects? Why even have the kernel be GPL then, rather than CC0?

I don't get it. And the article calling this "pragmatism" seems absurd to me.

I hate ai in my kernel....

How is this all supposed to be, when AI code can not be copyrighted and thus those submissions to the Linux kernel can not be eg.: GPLv{number}?

This seems like an ill-thought-out decision, especially in a landscape where Linux should be differentiating itself from, and not following Windows.

The titular "slop" just means "bad AI generated code is banned" but the definition of "bad" is as vague as Google's "don't be evil." Good luck enforcing it, especially in an open-source project where people's incentives aren't tied to a paycheck.

Title is also inaccurate regarding CoPilot (the Microsoft brand AI tool), as a comment there mentions

says yes to Copilot

Where in the article does it say that?? The only mention of CoPilot is where it talks about LLM-generated code having unverifiable provenance. Reply

No point getting upset about this, it’s inevitable. So many FOSS programmers work thanklessly for hours and now there’s some tool to take loads of that work away, of course they’re going to use it. I know loads of people complain about it but used responsibly it can take care of so much of the mundane work. I used to spend 10% of my time writing code then 90% debugging it. If I do that 10% then give it to Claude to go over I find it just works.

I'm curious how this is going to play out legally for copyright. If you accept AI code, you can't copyright it, so aren't you essentially forfeiting the copyleft license?