Theoretically you are correct, but taking the skills of the user into account, I think the very opposite is true. In practice, hardly anyone has the skills to actually implement anything with high performance anymore (especially in numerics) and it is thus (in most cases ) more performant to simply stick to stuff like numpy.
In most cases, "working code, fast" is more important than "fast working code".
> "working code, fast" is more important than "fast working code"
That depends on who you ask. If you ask the people who are managing the schedules, whose job reviews and bonuses are tied to meeting a specific date? Yes, absolutely, performance is a "nice to have" as long as the dates don't "slip". Now ask the users, and the number one complaint I hear most often is "why is this thing so damned slow?"
> Now ask the users, and the number one complaint I hear most often is "why is this thing so damned slow?"
Good point. My viewpoint is rather focused on numerical linear algebra, since I worked there and the article is about it. If you skim through the paper mentioned, in the article you can e.g. compare numpy and armadillo. You will see that the speedups from using a (arguably) much more complex C++ framework instead of numpy are marginal and will not be visible for the user. The increased production/maintenance costs due to a more complex code, will be visible for the user.
In most cases, "working code, fast" is more important than "fast working code".