While that's true and what I'm about to say could be legend rather than reality, I grew up being told that giraffes also tend to go to the top of a hill during storms, making them more likely to be struck by lightning.
My ex: what charging cables do you have? They last forever, mine break after a year!
Also my ex: so I got a bunch of the same charging cables you have and they all broke after a year
The only reason my last machine didn't get more than 10 years worth of in-place upgrades was because I decommissioned it as a desktop and turned it into a server, so I wiped it at that point.
Because despite all the people telling me I'm wrong, Kubuntu is still by far the best distro I've ever used. Rock solid, super fast, and continues to improve.
I think a better analogy would be that you're tuning your bike for better performance because the trade-offs of switching to a car are worse than keeping the bike.
It's all about trade-offs. Here are a few reasons why one might care about performance in their Python code:
Performance is often more tied to the code than to the interpreter - an O(n³) algorithm in blazing fast C won't necessarily perform any better than an O(nlogn) algorithm in Python.
Just because this particular Python code isn't particularly performance constrained doesn't mean you're okay with it taking twice as long.
Rewriting a large code base can be very expensive and error-prone. Converting small, very performance-sensitive parts of the code to a compiled language while keeping the bulk of the business logic in Python is often a much better value proposition.
These are also performance benefits one can get essentially for free with linter rules.
Anecdotally: in my final year of university I took a computational physics class. Many of my classmates wrote their simulations in C or C++. I would rotate between Matlab, Octave and Python. During one of our labs where we wrote particle simulations, I wrote and ran Octave and Python simulations in the time it took my classmates to write their C/C++ versions, and the two fastest simulations in the class were my Octave and Python ones, respectively. (The professor's own sim came in third place). The overhead my classmates had dealing with poorly optimised code that caused constant cache misses was far greater than the interpreter overhead in my code (though at the time I don't think I could have explained why their code was so slow compared to mine).
The Reminder bot