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.
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.
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.
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.
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.
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.
.
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…
Alright, you’ve demonstrated the difficulty posed by reality; let’s hear your proposed solution?