Realistically, this is as much about not wanting to police constant AI drama as it is about AI coding utility. Like it or not that cat is out of the bag, but as long as you enforce the same development guidelines in terms of code quality, PR rules and such, the impact should be small. Arguably the bigger issue would come with taking a hardline approach where you inevitably spend a bunch of time arguing over AI accusations. We are already quickly approaching a point where it feels half the Internet is AI, and the other half is arguing about whether something is or isn’t AI. That’s just not going to work for FOSS development.
I’m arguing that it’s not a small difference since the cognitive burden is put on maintainers who are already overworked. Now they need to edit through AI PR manure to find decent requests. A lot of OSS projects have stopped taking public PRs for this reason. It shifts the cognitive burden from the programmer to make good code, onto the reviewer to read and understand AI slop contribution.
It’s not worth it to review community PRs anymore
AI slop is DDOSing open source
Agentic code is worse to maintain and breaks more often, especially when refactoring.
Straight up banning ai won’t solve this issue either. People will just commit code either way and simply won’t tell you they used ai.
We’ve had low code tools before that caused similar issues. Nothing like AI though… I do agree with the other commenter here that a reputation system is probably the path forwar. Something like a minimum number of PRs accepted on gold standard projects or something? People will game that too though and post slop regardless… It’s a fine line between making proof so onerous that nobody contributes, or so easy to get people just spin up new accounts to contribute and don’t care if they get banned from projects.
Realistically AI is still heavily subsidized by circular financing and IOUs. The real cost of AI is still orders of magnitude more expensive (if you want to truly break even) so this might end up being a self solving problem once the AI bubble pops and it now costs $15 to shit out a slop PR
Yeah I think those workflows will adapt eventually, with more maintainers and some kind of more formal trust or reputation process to help sort things. It sucks but figuring out how to deal with it is really the only path forward. Otherwise all the cognitive load goes to arguing about AI.
I also think the worst offenders will get bored once the novelty wears off, and if the standards are kept high enough that getting a PR through still requires some amount of human work.
Does any of what you just said sound reasonable or likely?
Edit: looking back through history we’ve had low-code tools cause a boom in software abundance before. AI is following the exact same trajectory as the late 90s. Devs being laid off left and right, slop code everywhere, and then suddenly, inevitably, experienced developers are worth their weight in gold again because the core issue isn’t generating code, it’s understanding and managing complexity in a code base which AI is shit at long term. It adds entropy with every single prompt. It seems like it can’t help it… It just has to delete random lines, add redundant features, spam extra files, and do other stupid shit.
It’s like a gifted but kind of stupid intern. If you just set it free on your project it sets it on fire within a year. The limiting factor has always been and always will be comprehending and synthesizing a code base.
Yes and yes.
Based on what? Hopium? Open source maintainers are burning out and even extremely popular projects struggle to recruit new devs to help. What’s supporting your argument here besides “lol idk they’ll figure it out I guess”
There’s a ton of load bearing stuff that’s going to break once maybe 100 people have enough and stop thanklessly maintaining things. In fact it’s even worse than being thanklessly expected to fix shit since people are outright hostile towards you for maintaining your own passion project that nobody else wants to help with. And now you have people pushing AI slop PRs they shat out of Claude in 15 seconds, and you’re expected to comb through it and suggest changes?
And don’t even get me started on AI’s ability to code. It can work well for small constrained tasks like scripting or unit testing. It breaks down entirely when you aks it to maintain a complex code base for years. It just can’t help but duplicate code, delete features, spam files, and it has no ability to architect a project.
Based on the fact that it is now reality, and there is no alternative. Adapt or die.
Even if, moronically, Linus had followed the popular trend of banning AI tools - the only thing that would change is disclosure. The reality is, the tools will be used either way, and banning just means they won’t report the tools used.
Whether AI tools are used or not, a person is still attaching their identity to the submission. Regardless of the tools they did, or did not use, they are stamping the end result. The only feasible solution in the long term is to scrutinize those submitting PR, rather than attempting to police the tools they used to create them.
I think it’s important to distinguish between descriptive and normative claims here. Nobody is disputing that AI exists, or that people will continue to use AI. That observation, while factually correct, doesn’t meaningfully address the underlying concerns regarding review burden, maintainer workload, long-term software sustainability, or governance challenges. The existence of a technology does not, in and of itself, imply that existing institutions have already adapted to it, nor does it imply that adaptation is trivial, inevitable, or costless.
Similarly, I think there’s a tendency to frame this as a binary choice between “adapt” and “die,” when in reality the situation is substantially more nuanced than that. Adaptation itself is not a single event but rather an ongoing process involving changes to community norms, tooling, contributor expectations, trust mechanisms, and review workflows. The fact that this process is occurring says relatively little about whether it is succeeding, who bears the associated costs, or whether those costs are being distributed equitably across maintainers and contributors.
Regarding disclosure, I broadly agree that attempting to prohibit AI tooling outright is unlikely to be effective over the long term. However, that observation does not eliminate the practical challenges introduced by dramatically increasing the marginal cost asymmetry between producing code and reviewing it. Even if disclosure disappeared entirely tomorrow, maintainers would still need to invest substantial cognitive effort into understanding, validating, testing, and integrating incoming changes. In other words, removing disclosure changes the visibility of AI usage but does not meaningfully reduce the review burden itself.
Likewise, while I agree that individuals ultimately attach their identities to pull requests, identity alone should not necessarily be interpreted as a proxy for trustworthiness or software quality. Trust is not merely an intrinsic property of an individual contributor but rather an emergent property built over repeated interactions, demonstrated competence, consistency, responsiveness to review, architectural understanding, and alignment with project goals. Consequently, shifting emphasis toward contributor reputation may indeed form part of a broader governance strategy, but it should not be viewed as a comprehensive solution to the broader ecosystem-wide challenges associated with increasing contribution volume.
I also think it’s worth recognizing that contributor reputation systems themselves introduce additional complexities. Reputation requires accumulation, maintenance, interpretation, and governance. New contributors necessarily begin without reputation. Existing contributors can experience changes in quality over time. Organizations can rotate personnel while preserving identities. Accounts can change ownership. Even sophisticated trust models therefore require ongoing human oversight rather than eliminating the need for reviewer judgment altogether.
Ultimately, I think this discussion benefits from avoiding false dichotomies. The issue has never been whether AI exists, whether contributors will continue using AI, or whether maintainers should attempt to inspect people’s prompt histories. Rather, the central question is how finite pools of human attention are allocated in an environment where the cost of generating plausible-looking contributions has fallen dramatically while the cost of validating correctness, preserving architectural integrity, and preventing long-term maintenance debt remains comparatively unchanged. That seems to me to be the more interesting systems question, and one that likely cannot be answered solely through increased reliance on identity-based trust mechanisms.
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To demonstrate the core issue, from now on I’ll argue with you using chatgpt. You’ll need to read this wall of AI slop, and all I need to do is copy paste your comment into my current chat and hit “generate”
I think there are actually several different dimensions to this discussion, and I don’t think it’s quite as straightforward as you’re presenting it. It’s important to recognize that technological transitions have historically been disruptive before new equilibria emerge, and while the current situation certainly creates challenges for maintainers, I don’t think that necessarily implies a long-term negative trajectory for the open-source ecosystem as a whole. From a systems perspective, what we’re really observing is a temporary mismatch between contribution velocity and review capacity. Historically, software engineering has repeatedly experienced periods where productivity increased faster than existing workflows could absorb those gains. While AI-generated pull requests undoubtedly increase the volume of contributions, that doesn’t automatically mean the ecosystem is fundamentally unsustainable. Instead, it suggests that governance models, review methodologies, contributor onboarding, and trust mechanisms will likely evolve over time. Another point worth considering is that AI-assisted development should not necessarily be evaluated solely in terms of code quality. There are also accessibility benefits, educational benefits, and opportunities for new contributors who otherwise might never have engaged with open source. While some of these contributions may indeed be lower quality, the broader increase in participation could, over a sufficiently long time horizon, create a larger pool of experienced contributors than currently exists. This is, admittedly, speculative, but it is also consistent with historical patterns observed during previous shifts in software tooling. Additionally, I think it’s useful to separate concerns regarding code generation from concerns regarding software architecture. Current language models certainly have limitations with maintaining long-lived systems, preserving architectural consistency, and minimizing technical debt. However, these limitations should not necessarily be interpreted as permanent characteristics rather than temporary engineering constraints. Future iterations may demonstrate substantially improved long-context reasoning, architectural awareness, and repository-scale understanding. Finally, I would caution against assuming that current social dynamics necessarily represent the eventual steady state. Communities have historically developed moderation strategies, reputation systems, automated quality gates, and contribution standards in response to changing incentives. While the present situation may be frustrating, it seems plausible that open-source governance will adapt in ways that reduce reviewer burden while maintaining quality. Ultimately, I think the long-term outcome remains uncertain. There are certainly valid concerns about maintainer burnout, review overload, and declining signal-to-noise ratios. At the same time, there are also reasons to believe that new institutional norms, improved tooling, and changing contributor behavior could partially or substantially mitigate those issues over time. As such, I don’t think it is possible to confidently conclude either that open source is doomed or that everything will automatically work itself out. The reality is likely to be considerably more nuanced than either extreme.
At which point, I’d reply: eliminate the verbosity. Be succinct, or I’m closing this PR and line of communication.
I appreciate the feedback regarding verbosity, and I think that concern is both understandable and illustrative of the broader discussion we’re having. However, I would suggest that your response actually reinforces the systemic dynamics that I’m attempting to highlight. Specifically, while you are entirely correct that an individual response can simply be closed or ignored if it fails to meet project standards, that observation does not necessarily scale in environments where the overall volume of incoming communication increases by one or two orders of magnitude.
To elaborate, the issue isn’t that any individual AI-generated pull request, issue report, or discussion comment is impossible to dismiss. On the contrary, as you’ve demonstrated, a maintainer can absolutely review a single submission, determine that it lacks sufficient value, and reject it accordingly. The challenge emerges not at the level of individual interactions but at the aggregate systems level, where review capacity becomes the scarce resource rather than code generation capacity.
For example, if one contributor submits an unnecessarily verbose pull request, the cost of evaluating that submission may be relatively modest. If ten contributors do the same, the cost increases proportionally. If one hundred contributors begin submitting plausible-looking but low-value AI-assisted changes every week, each of which requires even a few minutes of human evaluation before being confidently rejected, the cumulative impact becomes substantially more significant. This isn’t because any individual submission is uniquely problematic but because the total review burden scales with submission volume while maintainer attention does not.
From a queueing theory perspective, this creates an interesting imbalance. Human review throughput remains relatively fixed, whereas AI-assisted content generation dramatically increases the arrival rate of new work. Once the arrival rate consistently exceeds the processing rate, backlog accumulates regardless of how efficient reviewers become at rejecting individual items. Consequently, “just reject it” is a locally optimal strategy that may nevertheless fail to address the global characteristics of the system.
Furthermore, I think it’s useful to distinguish between identifying low-quality contributions and doing so at scale. The cognitive effort required to conclude “this is not worth merging” is still non-zero. Every submission requires context switching, repository loading, architectural reasoning, verification that nothing subtle has been overlooked, and ultimately a decision. While each of these activities may appear trivial in isolation, their cumulative effect across hundreds or thousands of submissions represents a meaningful opportunity cost for maintainers who might otherwise be spending that same time reviewing genuinely valuable work or implementing new features.
For that reason, I don’t think the existence of a rejection mechanism meaningfully addresses the broader concern being discussed. Rather, it demonstrates that the cost of filtering remains attached to human reviewers even as the marginal cost of generating candidate contributions approaches zero.
In other words, I agree that you can close this thread.
Now imagine doing that another 699 times today…
It’s a perfectly reasonable position: fork the kernel with a purist no-ai approach and see where it’s going. It’s literally what open-source is about. No need for a screaming contest, just do the thing you are convinced of and compete for the best solution.
+1
Proof is in the puddin’.
If people think these specific guidelines will poison the kernel, well, let’s see if they do.
If it happens, go back to or fork it from an earlier point.
It honestly isn’t any different than previous kernel controversies.
Arent the issues with ai in the linux kernel ethical, not about code quality?
It’s not linux’s job to be an ethical gatekeeper, within reason.
Let me ask you this; if you’re from a certain country, from a certain background or whatever, should you not be allowed to contribute the kernel? Or submit a bug report?
Devs of open source projects often find out others doing things they don’t like with their code. Or find major contributors have backgrounds they don’t like.
Happens all the time; drama ensues. But at the end of the day a permissively licensed project is out there, the work on it is done by whoever’s already contributed. And the Linux project, in particular, is not in the business of trying to personally judge every entity behind the code contributions.
I’d assert LLM agent usage is the same. Maintainers/reviewers don’t have to like LLMs, but it’s not really Linux’s job to judge who (or what) wrote the contribution; just if the code is good, or not.
I’d argue a different policy is a bad precedent. It opens the door to judging people, too.
And it’s also impractical. Contributors are going to use agents, so mind as well encourage them to attribute them rather than encouraging them to be unethical, and hiding it.
Now, should LLMs be maintainers? Hell no.
Should they blindly contribute code? Hell. No. Hence the policy requires a human to be responsible for it: https://docs.kernel.org/process/coding-assistants.html
It’s not linux’s job to be an ethical gatekeeper
Are people not allowed to complain when someone supports something that is unethical when they could easily not do that?
if you’re from a certain country, from a certain background or whatever, should you not be allowed to contribute the kernel? Or submit a bug report?
The people ruling the country are doing the unethical things, not the people that just happened to be born in the country, maybe fell victim to propaganda / Is it not normal to not allow someone to contribute to a project if they are currently doing problematic things?
And the Linux project, in particular, is not in the business of trying to personally judge every entity behind the code contributions.
Judging a technology is not the same as judging a person. There arent an unreasonable amount of different technologies that people complain about to not be able to judge each one
Maintainers/reviewers don’t have to like LLMs, but it’s not really Linux’s job to judge who (or what) wrote the contribution
People can feel alienated from contributing to a project if they consider it unethical
And it’s also impractical. Contributors are going to use agents, so mind as well encourage them to attribute them rather than encouraging them to be unethical, and hiding it.
Stealing is illegal, but people still do it. Its impractical to catch every person that has ever stolen something so why bother trying at all?
There’s also arguments to be made that there is nothing unethical about the tools in general, only with specific LLM models due to the practices of their maintainers, and the closed status of specific models.
“hi I’m Linus. I’m 23, and I like computers.”
Wtf is with using old pictures of tech guys.
maybe the license was cheaper? idk how getty images works.
Glorious leader does not age. His youth and virility are timeless.
Theres a forming deeply uninformed / misinformed loud group of people who desperately want to hate on AI being used for anything, even in scenarios where its actually perfectly good at its job.
Even when extremely seasoned professionals with waaaay more experience are stating “its a perfectly fine tool” these folks somehow cannot wrap their head around the possibility someone like fucking Linus Torvalds might know better than them.
Imagine having the utter balls to assert you know better than Torvalds about whether a technology is good or not 😐
Meanwhile those of us actually in the field have known for over a year now that the tools are pretty good now and very useful.
better than Torvalds
Have you forgotten about Bitkeeper?
“the only conceivable reason anyone could object is that they’re ignorant” is cultish logic. Adding “my hero likes it so it can’t be bad” does not help.
Some of us have been “in the field” for decades and don’t like these tools for many reasons, including “the output is unacceptable bad” and “the tools are built in unethical and morally deficient ways”.
It’s not just Linus though. This is the exact position of many devs all over the world, ones who are still doing a good job and have clearly not fallen into AI psychosis.
I’m with OP here. There’s tons to hate about AI, for sure. But there’s also an extreme, people angry over things they have zero personal experience with.
You don’t need to experience suffering to empathize with it.
Listening to the stories of people who are personally dealing with the pushing of data centers into urban areas is heartbreaking and should be a deal killer by itself. Honestly if that doesn’t make you angry in the extreme that is very concerning to me.
Watching people lose their livelihoods in a massive wealth transfer should be infuriating. The class warfare is out and plain sight and should feel intolerable.
Watching scores testing decision making skills plummet at alarming speeds, education benchmarks erode, and young people get talked into suicide; all of these things do not require personal experience to understand.
The writing is on the wall, I honestly can not understand reading it and not getting angry.
including “the output is unacceptable bad”
Anyone with this view instantly goes in the “doesnt know wtf they are talking about, skill issue” bin.
“the tools are built in unethical and morally deficient ways”.
For coding, not really tbh. People are also deeply misinformed on this as well.
People are still operating on assumptions of how these tools work like its 2023 and not 2026.
The luddites haven’t been keeping up with the modern standards and probably couldnt even describe the nuance of when to choose a tool vs a skill for an optimization… let alone can speak as to what tools or skills to be using.
If you dont have opinions on how you’ve found stuff like caveman and headroom to work for you, I just straight up dont consider your opinion to be up to date enough to matter.
A big part of my job now is interacting with companies who have only just started to dip their toes into using these tools and so many developers are getting the sudden realization that keeping their head buried in the sand for the past 3 years has ruined their resume.
The difference between devs that at least tried to stay on top of things vs the luddites is palpable.
And on the total other end of the section you have fake devs cosplaying as real ones vis AI-everything but have 0 clue how to actually code.
Only the senior devs who both know how to code and how to use AI efficiently are aggressively outpacing everyone else.
Try reading your own post out loud, get out the rubber duck if you need to, and see if you can understand why you sound like a deranged zealot rather than simply a passionate user.
I’ll help you get started: note that every argument you’ve made starts with a base assumption about your interlocutors pulled from nothing, and note that every assumption conveniently draws a stark dichotomy between you as the informed rational party and the other as a bumbling uninformed moron.
Also notice how you draw lines in the sand that let you completely dismiss any thought you don’t like.
People truly are just exceptionally out of touch.
These convos constantly have people pulling out the tired old remarks.
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Quoting 1 shitty study from years ago that is both heavily outdated and doesn’t support their argument the way they think it does, and their renarks indicate they didnt actually read the study
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“But its theft” which is more of an ethical Grey discussion that Ill leave it up to courts to decide, and so far courts have not ruled AI usage for coding as illegal. Which means I do not give a shit. The entire concept is such obtuse ethical showboating. Only people grasping at straws try and pull this one out.
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“But what about water usage” bitch we are software devs, data centers using water has been an issue for centuries do not pretend this is magically a new issue. If you have been deploying basically anything in the cloud, especially AWS, then picking AI as special makes you look like an incredibly uninformed idiot. Did you seriously think deploying entire docker containers constantly with integration tested pipelines on every PR wasnt also using water…?
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“Its output is bad” skill issue. Literally skill issue. As in, “go learn how to use skill files”. In 2026 if your llm is still producing bad output thats 100% user error. Get good.
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“But what about AIs deleting prod databases” yeah thats an even bigger user error. Why the hell did the LLM have access to be able to do that? Learn to properly use MCP tools and hiw to properly control what commands and operations you grant an LLM access to. Its not even that hard but people are just so lazy.
Every single goddamn thread people crawl out of the woodwork and hammer on repeating these same things. Its all they have.
If you actually wanna have a serious discussion maybe bring up more interesting dilemmas like Clean Room problems…
I like you, a lot. Thanks for existing, intelligent, rational and pragmatic stranger.
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Anyone with this view instantly goes in the “doesnt know wtf they are talking about, skill issue” bin.
Ah, I see painting everyone with one brush. Never mind the numerous studies stating otherwise. Oh the irony when you claim others are spreading misinformation.
“the tools are built in unethical and morally deficient ways”.
For coding, not really tbh. People are also deeply misinformed on this as well.
People are still operating on assumptions of how these tools work like its 2023 and not 2026.
So tell me, what has changed? Are we suddenly not gobbling Up the entire internet without permission to train AI models anymore? I must have missed something… (/s)
Honestly, personally, I tend to agree with Linus on his position, but holy fuck you have your hypocritical nose way up in the air.
Never mind the numerous studies stating otherwise
20 bucks says this person is basing their stance off that 1 study from a couple years ago where like a dozen developers with 0 experience with AI were tested using it and didnt show an increase in productivity.
Which is, in fact, precisely also what I was referring to when I said “People are still operating on assumptions of how these tools work like its 2023 and not 2026”
People use that study like its some big gotcha when in reality it just proves the simple fact that people need to be trained in how to use a tool in order for the tool to be useful.
So tell me, what has changed?
Dawg is indeed in the "doesnt know wtf they are talking about, skill issue” bin. Imagine unironically asking this question.
If you have to ask, you arent ready to participate in these convos and are just spreading a bunch of uninformed misinfo.
I dont want to talk to someone about this who hasnt put in the work to be informed.
Go get informed first, then come back in a few months and we can chat once you know the answer to that question.
numerous studies
that 1 study
Spot the difference. If you’re so knowledgeable you should be able to figure it out. Mind you, you should have been able to figure it out without my help, but I digress.
Imagine unironically asking this question.
Yeah, imagine how that person (oh wait, that’s me! Hint: you can address me directly) can unironically ask about how the process of training LLMs and the continued use of them potentially violates literally thousands of agreements. The problem is the answer hasn’t changed, as much as you would like to ignore it. If you don’t know that, you have absolutely no business accusing others of being misinformed.
If you dont know, you arent prepared to engage in this convo.
Im not interested in discussing this topic with someone who even has to ask, lol.
The fact that some people don’t want to see AI being used to generate code does not make them dumb or misinformed. They may be informed, and simply reached a different conclusion than you.
Also, you have to consider that some of the most popular tools are controlled by a select few and that concentration can be used to control markets and users, reinforce biases, and shape what gets encouraged.
Finally, the use of AI can easily feed into a maximalist view of “produce more and faster” that has direct consequences like layoffs, increased cost of hardware, increased resource usage, increased environmental impact.
The majority of naysayers are touting deeply misinformed falsifities.
Its not a matter of guessing their intent, theyre quite outspoken on why they think its bad.
And the majority of them tout talking points that are years out of date at best.
Some valid points get brought up, but most of the points tend to be in the “these armchair devs have zero goddamn clue about how things actually are, they just are bandwagoning into an angry mob”
falsifities
Did AI also tell you this was a word?
All major operating systems and software has been around before AI. Sure, AI might have some uses, even good ones but not enough to warrant all the negative effects it and all those datacenters running the models have.
Some people just don’t care about the negative effects and Torvalds is obviously one of them.
To be fair, many have had their workplace enshittified by AI, or managers with AI psychosis.
Or fired over it.
Maybe they have family that’s gone down the ChatGPT rabbit hole. I do.
…I think it’s understandable for people to be pissed, and take an absolutionist position, as the leads who see it as a tool seem to be few and far between.
Blaming AI for this, instead of the shitty people who made the decisions, is still very dumb.
I agree that aforementioned companies/managers/etc are shitty.
Thats not AIs fault lol, and isn’t a reflection of it.
Blaming AI as tge issue is just kneejerk mob mentality.
All true.
I’m just saying I can understand how people got in that mindset. Everything feels shitty, and it all seems like it’s because of “AI.”
And I think the hate should redirected to the actual perpetrators: the Tech Bros.
I can agree with that.
I hate AI as in, I hate Microsoft, Google, OpenAI, etc. for pushing it everywhere, being an oligopoly, and using it for mass surveillance. But AI as a tool is fantastic for some tasks
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Nobody really argues about that here though. The context of Linus statement is whether or not experienced kernel developers should be forbidden from using AI tools or not.
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You may be surprised to learn that the maintainers of linux are not “untrained monkeys”
Every single generative tool is built on theft. No one has a training database that only contains code they were given legal permission to use for that purpose. Moreover, if these tools were trained on any GNU GPL code, then the tool and all of its output should also be GNU GPL:
if you distribute a derivative work or modification, you must provide the source code to those recipients under the same or equivalent license terms
https://en.wikipedia.org/wiki/GNU_General_Public_License
None of the companies developing these tools are obeying the terms of the licenses. The trained models are the product of theft.
You cannot be a moral person and approve of the use of these tools, they are diametrically opposed.
Copying is not theft.
Copying GNU GPL code without licensing your derivative code as GNU GPL and publishing it where it’s publicly accessible, and then using your derivative code to generate profit for a corporation, is definitely theft. Just because it’s open source doesn’t mean you can just do whatever you want with it, the original programmer still has rights over the code they wrote.
It literally isn’t.
Given that you’ve presumably looked at open source code in the past, and got more proficient (however marginally) as a result, does all your future code now belong to the GPL?
This comparison is invalid. Training a neural network algorithm is not equivalent to human learning. We are talking about data stored in machine learning models owned and controlled by multi-billion dollar corporations.
It has already been demonstrated multiple times that original training data can be reproduced completely from models, so yes, they are data storage systems. When they reproduce code which they have previously stored, even only in part, that is a derivative work. Adding extra steps to the transcribing process doesn’t make it any less a copy of the original.
Only when the data in question is so obscure to the point that the original source is the only place in which the data exists; which is neither common, nor useful to regurgitate, in practice.
It’s like if someone asked an artist to draw Trump, and then they did. It’s not that they studied what he looks like, but they’re familiar with that walking pusbag and there’s only the one, so it’s going to be a recognizable drawing.
Training a neural network algorithm is not equivalent to human learning
That’s a stretch, since we don’t know exactly how human learning works. Yes we have more than just that one mechanism at work obviously, but that’s not to say we’re not using the same or similar method as part of our learning process.
original training data can be reproduced completely from models
So can a few savants, more if you take into account those with special training, etc. The ability to do so is obviously latent to our brain.
When they reproduce code which they have previously stored, even only in part, that is a derivative work.
I don’t know about you, but I for one have not produced anything ‘original’ my entire coding career by that metric. I feel confident in saying the vast majority of programmers have not either.
Don’t mistake me, I’m well aware LLMs are not intelligent. But I disagree with the idea that their method of learning and their actions is inherently different from what the average person does.
That’s a stretch, since we don’t know exactly how human learning works.
It is not a stretch. We may not know exactly how human learning works, but we do know exactly how machine learning works, and we know that it is not like how human learning works. It is absolutely possible to differentiate things even without complete knowledge.
For instance, I am not a biologist. I do not have complete knowledge of the workings of a horse or a snake. However, I do know that a horse and a snake are different.
I don’t know about you, but I for one have not produced anything ‘original’ my entire coding career by that metric. I feel confident in saying the vast majority of programmers have not either.
This is a bad argument. The output of a generative model is a copy-and-paste function from a library of ingested code samples with a fairly competent keyword search attached to it. Code writing bots are just script kiddie crutches.
If all you did was copy and paste from GNU GPL code, then your output would also be bound by the same license.
But I disagree with the idea that their method of learning and their actions is inherently different from what the average person does.
Then you don’t understand even the basics of the mathematics that makes them actually work. It’s a purely algorithmic process. It’s an outgrowth of multidimensional analysis and optimization, that’s all.
Here is a better explanation than I could give, by someone who knows better than me:
Large Language Models explained briefly by 3Blue1Brown
Yes we have more than just that one mechanism at work obviously, but that’s not to say we’re not using the same or similar method as part of our learning process.
You seem to have entirely ignored the gist of my first point. We may be using a more well rounded method which utilises the same technique LLMs use as a part of our overall learning ability.
If all you did was copy and paste from GNU GPL code, then your output would also be bound by the same license.
We’re back to the same argument that’s been around since the start of the current AI boom, about whether or not people produce art and everything else the same way. At this point it’s very obviously a philosophical argument in general, and from your other comments in this post you’ve offered nothing but semantics as to how they’re different. We’re going to have to agree to disagree there.
Then you don’t understand even the basics of the mathematics that makes them actually work.
I understand how LLMs work. We DON’T understand how humans work, and unless you’ve got a human theory of mind in your back pocket, insisting that they’re inherently different instead of possibly being part of our mental toolkit is premature.
Hard disagree.
This is like saying anyone who has watched a Disney movie now owes Disney a check any time they illustrate anything, even entirely unrelated, because they’ve learned from their commercial products in the past. If they don’t pay Disney, then their creations are based on theft.
Fundamentally that’s a purely ethical decision about whether a machine doing something should be treated with the same understanding the same as a human would. But we don’t consider remembering something with a neurochemical storage the samd as taking a picture of something even if they’re basically the same.
That’s because taking a picture is an idetic copy. Machine learning is NOT copying, it’s learning - hence the name.
This is a misunderstanding based on confusion between technical and colloquial terminology.
A machine learning model “learns” information in the same way that a curve fitting algorithm “learns” the shape of a data set.
This is not the same as the colloquial meaning of human learning. It is a mathematical process.
That’s splitting hairs on definitions, with no change in meaning. It’s still not copying the data, and is far closer to a person learning than to a picture taken by a camera.
It is actually not like a person learning at all. The only way you could believe this is if you have no grasp of the mathematics that are the basis of the multi-dimensional statistical analysis which is neural network training, and haven’t bothered to do any reading on it.
There’s a reason I referenced curve fitting.
Here is a better explanation than I could give, by someone who knows better than me:
Large Language Models explained briefly by 3Blue1Brown
Digital reproduction is digital reproduction no matter how many extra steps are added to the reproduction process. It’s just an algorithm that sorts through a collection of stored data to find specific pieces of data which best fit the keywords supplied by the user, then regurgitates the results that are the best match based on correlation.
In spite of common meaning overlap and popular metaphors, the human brain is not a computer. Ask any neurologist.
Thank you for the opinion. If there’s a trial, and if you’re called as an expert witness, I’m sure they’ll consider your opinion, and have a fun debate on whether a human brain being similar to a computer is relevant in any way.
In the meantime, the technology exists, is useful and the results are no more theft than creating piñatas from newspaper clippings is theft from the newspaper.
Social media is a much bigger and dangerous threat to mankind than AI will ever be.
I’d say they’re in the same tier. It will take time to determine which is worse.
+1
Especially in regards to the nonsense that transformers LLMs will somehow lead to AGI. That’s fiction, sold by con artists like Altman. That bubble is going to pop.
Meanwhile, algorithmic engagement optimization is basically the root of the world’s problems right now.
This is an extremely dangerous misconception.
I’m interested in reading your thoughts about it. Here are mine, in the best english I can muster.
I’m not saying AI is harmless. I’m saying social media has already demonstrated a more immediate and widespread ability to destabilize society. It has transformed how billions of people consume information, created echo chambers, rewarded outrage, accelerated misinformation, increase polarization, and eroded trust in shared facts. Again, AI may eventually become more dangerous, but social media has already created the enviroment in which those dangers can spread. If you think as AI as a weapon, then social media is the distribution system, and we as a society have already given that distribution system to billions of people.
Linus isn’t gonna let you hit little bro
Fuck AI
What a super useful addition to the convo.
Maybe think a little bit harder before hitting the reply button in the future, troll.
As opposed to your comment here?
Maybe ask ChatGPT to think harder next time.
Bravo! 💯
Programmers are hilariously dumb for how genuinely smart they are, the whole concept of AI “being the future” is a perfect example of it.
“Year of the BSD desktop.” ?
I have found no indication that FreeBSD won’t accept LLM-assisted contributions… On their wiki there is a small section about rules for a summer of code event, where they forbid it being used for coding, but just because that event is intended to be about learning… That doesn’t sound like a clear “no” to all LLM contributions.
Yup
What about the third option? Stay, but don’t pay to use an “AI” service.
whos paying for the linux kernel?
Sorry, I don’t follow the money.
so yeah you have a third option for folks you are unaware of since none of us pay to use the kernel?
Money has spoken. Tech giants are main source of funding for Linux project. Torvalds won’t bite the hand that feeds him.
Or, maybe, it’s just his genuine opinion? Not everything is a conspiracy or secret agenda.
How does linus get paid? Money motivates the worst parts of humanity.
Oh yeah, he gets paid a salary by a council made up of tech corporations at the linux foundation. He’s at the financial mercy of Microsoft, Alphabet (Google), Meta, and other tech corp execs and suits.
No conspiracy here, just dialectical materialism.
And yet he publicly speaks against and attacks these companies all the time. But just this once he’s in their pocket?
There is no conspiracy —everything Linus says is able to be scrutinized because of the financial ties and his voluntary choice of material conditions. His bosses are these people: https://www.linuxfoundation.org/about/leadership He chooses to work under them. No one is holding a gun to his head: only his salary and healthcare since he is in the US (he can fly to other places and get cheaper healthcare than you or I).
How many of them own AI stock and have financial incentive to not ban it from linux? Even if this is purely Linus’ opinion (it very well may be), if he instead had the opinion of banning ai completely from linux, you are saying no one above him would be incentivized to change his mind? Zero? Be realistic, don’t assume everyone only acts in good faith. Linus can be the only one acting in good faith here and still could be influenced via his direct financial ties. Not mutually exclusive things.
People are allowed to scrutinize Linus and his actions and his choices. Linus shouldn’t be trusted just because.
I’m not taking away your right to scrutinize it any way you want, I’m just offering a simpler explanation that you are free to disagree with. While I can disagree with his opinion, I don’t believe it’s anything but his actual opinion, as he has historically always held on to his technical opinions even when they burned bridges and were unpopular.
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