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.
Well it’s what maintainers are already doing. Closing community contributions or burning out. A reputation system can be gamed unless the requirements for contributing are so restrictive it kills contributions anyways since most people don’t have the time and energy to get 100 PRs reviewed and accepted in a gold-standard group of projects to prove they won’t suggest slop.
Honestly I think the solution is the true price of tokens being charged to end users. If it costs you $15 instead of $0.10 to generate a slop PR now you actually give a shit about how it works. I think we’re heading there soon… Anthropic already hiked their prices 9-27X, and it looks like they need at least 2 more of those hikes in a row to begin to break even.
I think the eventual mature path for AI is as a coding assistant instead of autopilot. An expensive tool you use like an intern for making unit tests or finding a persistent bug in your code.
Take me, for example - I get $19 of tokens per month through one of my employers. That used to be more than enough to do nearly everything I wanted every month. Now, that’s one minor feature per month. So I don’t bother even using them. Instead, I use the GPU on my leisure/gaming laptop, and it processes all my LLM requests locally. So I have unlimited tokens.
I think the actual solution is patience. As the tools improved in quality, so too do the quality of the PR. Soon, I believe, they will be better quality than they have been ever before. If maintainers can just hold out that long … 🤞
I don’t know about that. The constraint seems to be context since AI struggles to hold all of the pieces for a project mentally while making changes like an experienced maintainer can. I find AI is a lot like using a credit card. It seems like free money at first until you start paying back the tech debt of a junior programmer who had no idea how to architect a project. You end up making your own legacy code base, except you were the one who made it… And because your context has long since been wiped clean from using your AI it can’t remember why it did what it did.
“Only Claude and God knew what this did when Claude made this code. Now that it’s context has expired, only God knows.”
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?
Well it’s what maintainers are already doing. Closing community contributions or burning out. A reputation system can be gamed unless the requirements for contributing are so restrictive it kills contributions anyways since most people don’t have the time and energy to get 100 PRs reviewed and accepted in a gold-standard group of projects to prove they won’t suggest slop.
Honestly I think the solution is the true price of tokens being charged to end users. If it costs you $15 instead of $0.10 to generate a slop PR now you actually give a shit about how it works. I think we’re heading there soon… Anthropic already hiked their prices 9-27X, and it looks like they need at least 2 more of those hikes in a row to begin to break even.
I think the eventual mature path for AI is as a coding assistant instead of autopilot. An expensive tool you use like an intern for making unit tests or finding a persistent bug in your code.
Eh…I don’t know that that’s a viable solution.
Take me, for example - I get $19 of tokens per month through one of my employers. That used to be more than enough to do nearly everything I wanted every month. Now, that’s one minor feature per month. So I don’t bother even using them. Instead, I use the GPU on my leisure/gaming laptop, and it processes all my LLM requests locally. So I have unlimited tokens.
I think the actual solution is patience. As the tools improved in quality, so too do the quality of the PR. Soon, I believe, they will be better quality than they have been ever before. If maintainers can just hold out that long … 🤞
I don’t know about that. The constraint seems to be context since AI struggles to hold all of the pieces for a project mentally while making changes like an experienced maintainer can. I find AI is a lot like using a credit card. It seems like free money at first until you start paying back the tech debt of a junior programmer who had no idea how to architect a project. You end up making your own legacy code base, except you were the one who made it… And because your context has long since been wiped clean from using your AI it can’t remember why it did what it did.
“Only Claude and God knew what this did when Claude made this code. Now that it’s context has expired, only God knows.”