Hi Matt, There are two aspects: can we use AI-generated code in numpy/scipy, and if we can should we ? And to make it more complicated, the type of AI usage affects those questions differently. E.g. I think almost nobody would object to the use I described originally: using chats to research, analyze literature and understand existing codebases under acceptable license. There is no code generated there. Another extreme is all code generated and reviewed by AI.
I will for now continue my original approach (no AI to generate any code unless trivial + disclose its use when PR time comes). David On Sun, Feb 8, 2026 at 2:52 AM Matthew Brett via NumPy-Discussion < [email protected]> wrote: > Hi > > On Sat, Feb 7, 2026 at 4:54 PM Charles R Harris > <[email protected]> wrote: > > > > > > > > On Sat, Feb 7, 2026 at 7:05 AM Matthew Brett via NumPy-Discussion < > [email protected]> wrote: > >> > >> Hi, > >> > >> This is just a plea for some careful thought at this point. > >> > >> There are futures here that we likely don't want. For example, > >> imagine Numpy filling up with large blocks of AI-generated code, and > >> huge PRs that are effectively impossible for humans to review. As > >> Oscar and Stefan have pointed out - consider what effect that is going > >> to have on the social enterprise of open-source coding - and our > >> ability to train new contributors. > >> > >> I believe we are also obliged to think hard about the consequences for > >> copyright. We discussed that a bit here: > >> > >> https://github.com/matthew-brett/sp-ai-post/blob/main/notes.md > >> > >> In particular - there is no good way to ensure that the AI has not > >> sucked in copyrighted code - even if you've asked it to do a simple > >> port of other and clearly licensed code. There is some evidence that > >> AI coding agents are, for whatever reason, particularly reluctant to > >> point to GPL-licensing, when asked for code attribution. > >> > >> I don't think the argument that AI is inevitable is useful - yes, it's > >> clear that AI will be part of coding in some sense, but we have yet to > >> work out what part that will be. > >> > >> For example, there are different models of AI use - some of us are > >> starting to generate large bodies of code with AI - such as Matthew > >> Rocklin : https://matthewrocklin.com/ai-zealotry/ - but his discussion > >> is useful. Here are two key quotes: > >> > >> * "LLMs generate a lot of junk" > >> * "AI creates technical debt, but it can clean some of it up too. (at > >> least at a certain granularity)" > >> * "The code we write with AI probably won't be as good as hand-crafted > >> code, but we'll write 10x more of it" > >> > >> https://matthewrocklin.com/ai-zealotry/ > >> > >> Another experienced engineer reflecting on his use of AI: > >> > >> """ ... LLM coding will split up engineers based on those who > >> primarily liked coding and those who primarily liked building. > >> > >> Atrophy. I've already noticed that I am slowly starting to atrophy my > >> ability to write code manually. Generation (writing code) and > >> discrimination (reading code) are different capabilities in the brain. > >> Largely due to all the little mostly syntactic details involved in > >> programming, you can review code just fine even if you struggle to > >> write it. > >> """ > >> > >> https://x.com/karpathy/status/2015883857489522876 > >> > >> Conversely - Linus Torvalds has a different model of how AI should work: > >> > >> """ > >> Torvalds said he's "much less interested in AI for writing code" and > >> far more excited about "AI as the tool to help maintain code, > >> including automated patch checking and code review before changes ever > >> reach him." > >> """ > >> > >> > https://www.zdnet.com/article/linus-torvalds-ai-tool-maintaining-linux-code/ > >> > >> I guess y'all saw the recent Anthropic research paper comparing groups > >> randomized to AI vs no-AI working on code problems. They found little > >> speedup from AI, but a dramatic drop in the level of understanding of > >> the library they were using (in fact this was Trio). This effect was > >> particularly marked for experienced developers - see their figure 7. > >> > >> https://arxiv.org/pdf/2601.20245 > >> > >> But in general - my argument is that now is a good time to step back > >> and ask where we want AI to fit into the open-source world. We > >> open-source developers tend to care a lot about copyright, and we > >> depend very greatly on the social aspects of coding, including our > >> ability to train the next generation of developers, in the particular > >> and informal way that we have learned. We have much to lose from > >> careless use of AI. > >> > > > > E. S. Raymond is another recent convert. > > > > Programming with AI assistance is very revealing. It turns out I'm not > quite who I thought I was. > > > > There are a lot of programmers out there who have a tremendous amount of > ego and identity invested in the craft of coding. In knowing how to beat > useful and correct behavior out of one language and system environment, or > better yet many. > > > > If you asked me a week ago, I might have said I was one of those people. > But a curious thing has occurred. LLMs are so good now that I can validate > and generate a tremendous amount of code while doing hardly any hand-coding > at all. > > > > And it's dawning on me that I don't miss it. > > > > Things are moving fast. > > Yes - but - it's important to separate how people feel using AI, and > the actual outcome. Many of y'all will I am sure have seen this > study: > > https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/ > > that showed that developers estimated they would get a 25% speedup > from AI, before they did the task; after they did the task, they felt > they they had got a 20% speedup, and in fact (compared to matched > tasks without AI), they suffered from a 20% slowdown. > > Personally - I am not very egotistical about my code, but I am > extremely suspicious. I know my tendency to become sloppy, to make > and miss mistakes - what David Donoho called "the ubiquity of error": > https://blog.nipy.org/ubiquity-of-error.html . So AI makes me > increasingly uncomfortable, as I feel my skill starting to atrophy (in > the words of Andrej Karpathy quoted above). > > So it seems to me we have to take someone like Linus Torvalds > seriously when he says he's "much less interested in AI for writing > code". Perhaps it is possible, at some point, to show that > delegating coding to the AI leads to increased learning and greater > ability to spot error - but so far the evidence seems to go the other > way. And if we "embrace" AI for that use, we run the risk of > deskilling ourselves, filling the code-base with maintenance debt, > effectively voiding copyright, and making it much harder to train the > next generation, > > Cheers, > > Matthew > > > > > -- > This email is fully human-source. Unless I'm quoting AI, I did not > use AI for any text in this email. > _______________________________________________ > NumPy-Discussion mailing list -- [email protected] > To unsubscribe send an email to [email protected] > https://mail.python.org/mailman3//lists/numpy-discussion.python.org > Member address: [email protected] >
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