My experience with Apple has been more like

I credit Apple in many ways for their choice to design their business in a way that their profit motive often aligns with their users’ interests.

Their app store model for iOS is one of the strongest examples of them not doing that though.

The 747 doesn’t have that bad a safety record. I’m more interested in what random radio transmissions occur from it.

They’re already getting that illegally so that wouldn’t change.

This is not how probability works.

Yeah, Steam is pretty much a monopoly. But I haven’t seen what I’d call monopolistic practices from them. It’s just that everyone else appears to fall flat on their faces when trying to make a competing product.

I’m less mad at Steam and Google because there are clear, simple ways to avoid their cuts.

I have no basis to say whether they’re providing a service worth the 30% charge. I’m also less mad at Steam than at Google because they’re being less shady about trying to push people into their store too.

It’s all about trade-offs. Here are a few reasons why one might care about performance in their Python code:

1. 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.
2. Just because this particular Python code isn’t particularly performance constrained doesn’t mean you’re okay with it taking twice as long.
3. 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).