On Mon, Feb 9, 2026, at 13:58, Ralf Gommers via NumPy-Discussion wrote: > > > On Mon, Feb 9, 2026 at 6:23 PM Matthew Brett via NumPy-Discussion > <[email protected]> wrote: >> I think it's correct that it's not sensible for policies to reflect >> things like dislike of AI's use of energy or the effects on the >> environment of AI data centers. However, it seems obvious to me that >> it is sensible for policies to take into account the effect of AI on >> learning. > > Why would that be obvious? It seems incredibly presumptuous to decide for > other people what methods or tools they are or aren't allowed to use for > learning. We're not running a high school or university here.
The way I read Matthew's comment is not that we should prescribe how people use their tools, but that we should be aware of the risks we are facing, and also communicate those risks to contributors who want to use AI tools to do NumPy development. > This also presumes that you, or we, are able to determine what usage of AI > tools helps or hinders learning. That is not possible at the level of > individuals: people can learn in very different ways, plus it will strongly > depend on how the tools are used. And even in the aggregate it's not > practically possible: most of the studies that have been referenced in this > and linked thread (a) are one-offs, and often inconsistent with each other, > and (b) already outdated, given how fast the field is developing. It is true that things are moving fast, and while the original METR study (which has been informally replicated in other settings) is perhaps outdated, Anthropic's just-released paper shows a broadly similar trend. Specifically, they show that time-to-solution is faster for junior developers, but not so much for senior developers. They also show that knowledge about the library is worse, having done a task with AI vs without. I'm sure, over time, we will figure out the best patterns for using AI and how to avoid the worst traps. Best regards, Stéfan
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