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.
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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