Sure now it is easy, but in 3-10 years AI will get significantly better. It is a lot like the audio quality of an MP3 recording. It is not perfect (lossless audio is better), but for the majority of users it is "good enough".
At a certain point AI generated content, PR's, etc will be good enough for humans to accept it as "human". What happens then, when even the best checks and balances are fooled?
I think a lot of anti-LLM opinions just come from interacting with the lowest effort LLM slop and someone not realizing that it's really a problem with a low value person behind it.
It's why "no AI allowed" is pointless; high value contributors won't follow it because they know how to use it productively and they know there's no way for you to tell, and low value people never cared about wasting your time with low effort output, so the rule is performative.
e.g. If you tell me AI isn't allowed because it writes bad code, then you're clearly not talking to someone who uses AI to plan, specify, and implement high quality code.
"But I ain't likely to write you no poem, if you follow me. Your AI, it just might. But it ain't no way human.”Human society exists because we value humans, full stop. The easiest way to "solve" all of humanity's problems is to simply say that humans aren't valuable. Sometimes it feels like we're conceding a ridiculous amount of ground on that basic principle every year - one more human value gone because it "doesn't matter", so hey, we've obviously made progress!
I disagree that the rule is pointless, and your last point is a strawman. AI is disallowed because it’s the manner in which the would-be contributors are attempting to contribute to these projects. It’s a proxy rule.
Unfortunately for AI maximalists, code is more than just letters on the screen. There needs to be human understanding, and if you’re not a core contributor who’s proven you’re willing to stick around when shit hits the fan, a +3000 PR is a liability, not an asset.
Maybe there needs to be something like the MMORPG concept of “Dragon Kill Points (DKP)”, where you’re not entitled to loot (contribution) until you’ve proven that you give a shit.
And in the context of high-value contributors that GP was mentioning, they are never going to land a +3000 PR because they know there is going to be a human reviewer on the other side.
This isn't necessarily true; I've seen some projects absorb a PR of roughly that size, and after the smoke tests and other standard development stuff, the original PR author basically disappeared.
It added a feature he wanted, he tested and coded it, and got it in.
Like its been years and years now, if all this is true, you'd think there would be more or paradigm shift? Im happy I guess waiting for Godot like everyone else, but the shadows are getting a little long now, people are starting to just repeat the same things over and over.
Like, I am so tired now, it's causing such messes everywhere. Can all the best things about AI be manifest soon? Is there a timeline?
Like what can I take so that I can see the brave new world just out of reach? Where can I go? If I could just even taste the mindset of the true believer for a moment, I feel like it would be a reprieve.
If you believe the outputs of LLMs are derivative products of the materials the LLMs were trained on (which is a position I lean towards myself, but I also understand the viewpoint of those who disagree), then no, that's not a good thing, because it would be a license violation to accept those derived products without following the original material's license terms, such as attribution and copyleft terms. You are now party to violating the original materials' copyright by accepting AI generated code. That's ethically dubious, even if those original authors may have a hard time bringing a court case against you.
In that case a lot of proprietary software is in breach of copyleft licences. Its probably by far the commonest breach.
> You are now party to violating the original materials' copyright by accepting AI generated code. That's ethically dubious
That is arguable. Is it always ethically dubious to breach a law? If not, which is it ethically dubious to breach this law in this particular way?
Sure, but this doesn't really seem relevant to the conversation. Someone else violating software license terms doesn't justify me (or Debian, in the case of TFA) doing so.
> Is it always ethically dubious to breach a law?
I'm not really concerned with the law, here. I think it is ethically dubious to use someone else's work without compensating them in the manner they declared. Copyright law happens to be the method we've used for a couple hundred years to standardize the discussion about that compensation, and sometimes enforce it. Breaching the law doesn't really enter into the conversation, except as a way our society agrees to hold everyone to a minimum ethical standard.
It seems that gun control—though imperfect—in regions that have implemented it has had a good bit of success and the legitimate/non-harmful capabilities lost seem worth it to me in trade for the gains. (Reasonable people can disagree here!)
Whereas it seems to me that if we accept the proposition that the vast majority of code in the future is going to be written by AI (and I do), these valuable projects that are taking hard-line stances against it are going to find themselves either having to retreat from that position or facing insurmountable difficulties in staying relevant while holding to their stance.
This is even true despite the fact that there are bad actors only a few minutes drive away in many cases (Chicago->Indiana border, for example).
It is the conservative position: it will be easier to walk back the policy and start accepting AI produced code some time down the road when its benefits are clearer than it will be to excise AI produced code from years prior if there's a technical or social reason to do that.
Even if the promise of AI is fulfilled and projects that don't use it are comparatively smaller, that doesn't mean there's no value in that, in the same way that people still make furniture in wood with traditional methods today even if a company can make the same widget cheaper in an almost fully automated way.
But the projects aren't drowning under PRs from reputable people. They're drowning in drive-by PRs from people with no reputation to speak of. Even if you outright ban their account, they'll just spin up a new one and try again.
Blocking AI submissions serves as a heuristic to reduce this flood of PRs, because the alternative is to ban submissions from people without reputation, and that'd be very harmful to open source.
And AI cannot be the solution here, because open source projects have no funds. Asking maintainers to fork over $200/month for "AI code reviews" just kills the project.
We need to rethink some UX design and processes here, not pretend low quality people are going to follow your "no low quality pls i'm serious >:(" rules. Rather, design the processes against low quality.
Also, we're in a new world where code-change PRs are trivial, and the hard part isn't writing code anymore but generating the spec. Maybe we don't even allow PRs anymore except for trusted contributors, everyone else can only create an issue and help refine a plan there which the code impl is derived?
You know, even before LLMs, it would have been pretty cool if we had a better process around deliberating and collaborating around a plan before the implementation step of any non-trivial code change. Changing code in a PR with no link to discussion around what the impl should actually look like always did feel like the cart before the horse.
And for the major projects where there was a flood of PRs, it was fairly easy to identify if someone knew what they were talking about by looking at their language; Correct use of jargon, especially domain-specific jargon.
The broader reason why "unknown contributor" PRs were held in high regard is that, outside of some specific incidents (thank you, DigitalOcean and your stupid tshirts), the odds were pretty good of a drive by PR coming from someone who identified a problem in your software by using it. Those are incredibly valuable PRs, especially as the work of diagnosing the problem generally also identifies the solution.
It's very hard to design a UX that impedes clueless fools spamming PRs but not the occasional random person finding sincere issues and having the time to identify (and fix them) but not permanent project contribution.
> and the hard part isn't writing code anymore but generating the spec
My POV: This is a bunch of crap and always has been.
Any sufficiently detailed specification is code. And the cost of writing such a specification is the cost of writing code. Every time "low code" has been tried, it doesn't work for this very reason.
e.g. The work of a ticket "Create a product category for 'Lime'" consists not of adding a database entry and typing in the word 'Lime', it consists of the human work of calling your client and asking whether it should go under Fruit or Cement.
The latter is where you get all known contributors from! So if you close off unknown contributors the project will eventually stagnate and die.
Hmmm, no? That's actually very common in open source. Maybe "banning" isn't the right word, but lots of projects don't accept random drive-by submissions and never have. Debian is a perfect example, you are very unlikely to get a nontrivial patch or package into Debian unless you have some kind of interaction or rapport with a package maintainer, or commit to the process of building trust to become a maintainer yourself.
I have seen high profile GitHub projects that summarily close PRs if you didn't raise the bug/feature as an issue or join their discord first.
> you are very unlikely to get a nontrivial patch or package into Debian unless you have some kind of interaction or rapport with a package maintainer
I did mean the "trivial" patches as well, as often it's a lot of these small little fixes to single issues that improve software quality overall.
But yes, it's true that it's not uncommon for projects to refuse outside PRs.
This already causes massive amounts of friction and contributes (heh) heavily to what makes Open Source such a pain in the ass to use.
Conversely, many popular "good" open source libraries rely extensively on this inflow of small contributions to become comprehensively good.
And so it's a tradeoff. Forcing all open source into refusing drive-by PRs will have costs. What makes sense for major security-sensitive projects with large resources doesn't make sense for others.
It's not that we won't have open source at all. It's that it'll just be worse and encourage further fragmentation. e.g. One doesn't build a good .ZIP library by carefully reading the specification, you get it by collecting a million little examples of weird zip files in the wild breaking your code.
McDonalds cooks ~great~ (edit: fair enough, decent) burgers when measured objectively, but people still go to more niche burger restaurants because they want something different and made with more care.
That's not to say that an human can't use AI with intent, but then AI becomes another tool and not an autonomous code generating agent.
Wait, what? In what world are McDonalds burgers "great"? They're cheap. Maybe even a good value. But that's not the same as great.
Some of the best burgers I've ever had came from fast food.
If everything the maintainer wants can (hypothetically) be one-shotted, then there is no need to accept PR's at all. Just allow forks in case of open source.
Can you reliably tell that the contributor is truly the author of the patch and that they aren't working for a company that asserts copyright on that code? No, but it's probably still a good idea to have a policy that says "you can't do that", and you should be on the lookout for obvious violations.
It's the same story here. If you do nothing, you invite problems. If you do something, you won't stop every instance, but you're on stronger footing if it ever blows up.
Of course, the next question is whether AI-generated code that matches or surpasses human quality is even a problem. But right now, it's academic: most of the AI submissions received by open source projects are low quality. And if it improves, some projects might still have issues with it on legal (copyright) or ideological grounds, and that's their prerogative.
Crystal ball or time machine?
Past performance does not guarantee future results, of course. But acting like AI is now magically going to stagnate is also a really bold bet.
This is the basis of the argument - it doesn't matter if you use AI or not, but it does matter if you know what you're doing or not.
Quixotic, unworkable, pointless. It’s fundamentally impossible (at least without a level of surveillance that would obviously be unavceptable) to prove the “artisanal hand-crafted human code” label.
> contributors should "fully understand" their submissions and would be accountable for the contributions, "including vouching for the technical merit, security, license compliance, and utility of their submissions".
This is in the right direction.
I think the missing link is around formalizing the reputation system; this exists for senior contributors but the on-ramp for new contributors is currently not working.
Perhaps bots should ruthlessly triage in-vouched submissions until the actor has proven a good-faith ability to deliver meaningful results. (Or the principal has staked / donated real money to the foundation to prove they are serious.)
I think the real problem here is the flood of low-effort slop, not AI tooling itself. In the hands of a responsible contributor LLMs are already providing big wins to many. (See antirez’s posts for example, if you are skeptical.)
They can spin up LLM-backed contributors faster than you can ban them.
Difficulty of enforcing is a detail. Since the rule exists, it can be used when detection is done. And importantly it means that ignoring the rule means you’re intentionally defrauding the project.
Something might be required now as some people might think that just asking an LLM is "the most he can done", but it's not about using AI it's about being aware and responsible about using it.
But like the XZ attack, we kind of have to assume that advanced perissitant threats are a reality for FOSS too.
I can envisage a Sybil attack where several seemingly disaparate contributors are actually one actor building a backdoor.
Right now we have a disparity in that many contributors can use LLMs but the recieving projects aren't able to review them as effectively with LLMs.
LLM generated content often (perhaps by definition) seems acceptable to LLMs. This is the critical issue.
If we had means of effectively assessing PRs objectively that would make this moot.
I wonder if those is a whole new class of issue. Is judging a PR harder than making one? It seems so right now
Depends on the assumptions. If you assume good intent of the submitter and you spend time to explain what he should improve, why something is not good, etc, than it's a lot of effort. If you assume bad intent, you can just reject with something like "too large review from unproven user, please contribute something smaller first".
Yes, we might need to take things a bit slower, and build relations to the people you collaborate with in order to have some trust (this can also be attacked, but this was already possible).
I think that's backwards, at least as far as accepting a PR. Better that all code is reviewed as if it is probably a carefully thought out Trojan horse from a dedicated enemy until proven otherwise.
A lot of low quality AI contributions arrive using free tiers of these AI models, the output of which is pretty crap. On the other hand, if you max out the model configs, i.e. get "the best money can buy", then those models are actually quite useful and powerful.
OSS should not miss out on the power LLMs can unleash. Talking about the maxed out versions of the newest models only, i.e. stuff like Claude 4.5+ and Gemini 3, so developments of the last 5 months.
But at the same time, maintainers should not have to review code written by a low quality model (and the high quality models, for now, are all closed, although I heard good things about Minmax 2.5 but I haven't tried it).
Given how hard it is to tell which model made a specific output, without doing an actual review, I think it would make most sense to have a rule restricting AI access to trusted contributors only, i.e. maintainers as a start, and maybe some trusted group of contributors where you know that they use the expensive but useful models, and not the cheap but crap models.
The problem is having an unwritten rule is sometimes worse than a written one, even if it "works".
With the advent of LLMs, AI-autocomplete, and agent-based development workflows, my ability to deliver reliable, high-quality code is restored and (arguably) better. Personally, I love the "hallucinations" as they help me fine-tune my prompts, base instructions, and reinforce intentionality; e.g. is that >really< the right solution/suggestion to accept? It's like peer programming without a battle of ego.
When analyzing problems, I think you have to look at both upsides and downsides. Folks have done well to debate the many, many downsides of AI and this tends to dominate the conversation. Probably thats a good thing.
But, on the flip side, I personally advocate hard for AI from the point-of-view on accessibility. I know (more-or-less) exactly what output I'm aiming for and control that obsessively, but it's AI and my voice at the helm instead of my fingertips.
I also think it incorrect to look at it from a perspective of "does the good outweigh the bad?". Relevant, yes, but utilitarian arguments often lead to counter-intuitive results and end up amplifying the problems they seek to solve.
I'd MUCH rather see a holistic embrace and integration of these tools into our ecosystems. Telling people "no AI!" (even if very well defined on what that means) is toothless against people with little regard for making the world (or just one specific repo) a better place.
I think the ugly unspoken truth whether Mozilla or Debian or someone else, is that there are going to be plausible and valuable use cases and that AI as a paradigm is going to be a hard problem the same way that presiding over, say, a justice system is a hard problem (stay with me). What I mean is it can have a legitimate purpose but be prone to abuse and it's a matter of building in institutional safeguards and winning people's trust while never fully being able to eliminate risk.
It's easy for someone to roll their eyes at the idea that there's utility but accessibility is perfect and clear-eyed use case, that makes it harder to simply default to hedonic skepticism against any and all AI applications. I actually think it could have huge implications for leveling the playing field in the browser wars for my particular pet issue.
Quality should always be the responsibility of the person submitting changes. Whether a person used LLMs should not be a large concern if someone is acting in good-faith. If they submitted bad code, having used AI is also not a valid excuse.
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