In the last 2 weeks, it started to feel like the dust is settling around AI in the open-source community. Major open-source projects released their policies or guidelines around AI usage. LLMs are having an existential impact on open-source: plundering its commons, putting a stress on its resources, threatening the trust that is the fabric of this community. In this post, I cover the spectrum of AI policies adopted across popular OSS projects.
Human Emacs#
The GNU project has not yet shared a statement on AI but the GNU Compiler Collection and the GUIX project did. Similarly, several members of the Emacs project decided to take a stance on AI and published it on Human Emacs:
Therefore we are here to declare our intent to use and develop a version of Emacs that is free from LLM-generated contributions regardless of whatever policy GNU decides upon.
Human Emacs stands firmly against any AI usage. It is also the only document on that list that included moral and ethical ground in the policy itself. It garnered a fair bit of interest (and lots of praise) on r/emacs and Lobster. This one hit close to my heart. Emacs has been gaining in popularity since Covid, its community has been thriving since. I would hate for the momentum to die because of AI and I hope the community leaders will arrive at a consensus soon.
Codeberg#
The maintainers of Codeberg have put up a blog post explaining the changes in their terms of use:
- You must not share projects that mostly consist of code written by “generative AI”-tools (including services such as Claude, OpenAI Codex). Such projects having an unclear copyright status […] and furthermore have little safeguards to ensure that they do not include harmful code […].
After reading their blog post, I expected Codeberg to ban any AI usage, but the updated terms of use remain accepting of LLMs so long as LLMs do not generate the code. The original blog post feels lot more combative against AI and is worth reading because it covers the impact of LLM on the FLOSS community in more detail than the other entries on this list. The maintainers also pledge that Codeberg won’t allow LLMs to train on their hosted data.
GNU Compiler Collection#
The GCC project also recently published their AI policy. It’s a clear policy that declines only legally-significant contributions except for test cases:
For the time being, the GNU Compiler Collection (GCC) policy is to decline any legally significant contributions which include LLM-generated content or are derived from LLM-generated content. […] As an exception, the GCC maintainers are free to accept legally significant contributions to test cases, generated in whole or in part by an LLM.
This policy is an acknowledgement that the project is benefiting from LLM use. It is worth noting their clarity on what constitutes a legally significant change:
A change of just a few lines (less than 15 or so) is not legally significant for copyright.
Their rather accepting stance on AI should not be surprising given that GCC’s development is supported by companies selling LLM products. However, the project requires that commit messages include an “Assisted-by:” tag for any LLM-generated contributions.
Kubernetes#
It’s by far the most AI-friendly project on that list. The 2 most important contributing organizations to the project also compete to produce frontier models. Kubernetes has an AI Conformance SIG.
Recently, I listened to the Kubernetes podcast, episode 269 on AI usage featuring Kat Cosgrove who is a K8s Steering Committee member and Tech Lead in 2 SIGs (And who obviously likes Evangelion). The whole episode was a fun listen. She mentioned that they just updated the project’s Pull Request Process with their AI guidance:
Using AI tools to help write your PR is acceptable, but as the author, you are responsible for understanding every change. If you used AI tools in preparing your PR, you must disclose this in the description of your PR. For example, including “This PR was written in part with the assistance of generative AI,” in the PR description is sufficient.
As expected, there are very few restrictions around AI usage, besides “understand every change” which is definitely the minimum bar. During the podcast, Kat did emphasize however that the project members really wanted to talk to humans, not AI: indeed, the guidelines ban LLM use for commits, PR descriptions and comments.
What caught my attention is that the guidelines also ban the use of “Assisted-by:” statements in the commit messages while enforcing that authors disclose LLM use — presumably, manually and without vendor reference, though it is not explicitly stated. Kat commented on the podcast that AI vendors have used the commits of merged PRs in K8s to legitimize their tools. A neutral choice, for a project that is backed by the fiercest competitors in the AI landscape. I believe the concern is genuine though, and GCC could probably take a page from their book.
Linux#
I think the projects above embody the full spectrum of reactions in the open-source community. But I couldn’t finish this post without the take from the most important person in the largest of all open-source projects (which you have likely already read):
Linux is not one of those anti-AI projects, and if somebody has issues with that, they can do the open-source thing and fork it.
Or just walk away.
AI is a tool, just like other tools we use. And it’s clearly a useful one.
What’s great about this take is that you instantly know it was not LLM-generated. Linus, please don’t ever change.
The Kernel does have a contributing policy with regards to AI coding assistants. I’m not going to requote it — it’s very similar to the GCC policy, which makes sense since both projects share so much in common.
Where do I stand?#
My opinions on AI have not shifted much, but my usage varied a lot. It peaked around May 2026; that month, I spent well over US$1,200 in API-token pricing on Claude alone. That was not something sustainable — we don’t have so many credits — but it did produce useful results. Since then, I’ve been spending much less on LLMs and cut some of the lesser used subscriptions.
On the one hand, I can’t stand the terrible impact AI is having on the open-source community, the environment and our culture. On the other hand, I still want to experience this incredible technology. So, let’s be honest. I’m torn. And, I’m not alone.
I deal with my indecision by categorizing the issues in 2 parts: those that I think will resolve themselves, and those that I can act on now. For example: I think the impact on the open-source community will resolve itself — I believe the community will eventually build the tools and culture to thrive in the age of LLMs. However, I don’t think the pollution generated by the wave of newly built AI data centers will resolve itself. So, as a small step in the right direction, I aim to stop using cloud-based inference. I’ve started to use Pi with a distilled Qwen3.5-27B-Q4_K_M model on my modest Radeon RX 7900 XTX (24GB VRAM) and the results are not what I anticipated: it’s much slower, but significantly more capable than I expected. It is already accomplishing tasks that I thought only a full-fledged product like Claude Code could accomplish. I’ll be writing about it soon.