The issue is that this investigation is so biased by what is slow in specifically the Python VM that I'm not sure what the effects would be in a case where perf

The issue is that this investigation is so biased by what is slow in specifically the Python VM that I'm not sure what the effects would be in a case where performance is actually something you are trying to optimize for. The moment you translate this into C, C++, Java etc you suddenly have a smart compiler that can deal with things like function inlining, to where things like cache sizes and memory streaming speed will present as bottlenecks instead of object to object pointer jumping and bytecode interpreter overhead. Languages don't get slower on a single dimension, and computers have multiple dimensions of performance. Hitting a performance bottleneck in one dimension on one language doesn't mean you'll hit the same bottleneck on a different language. Different scales might have different bottlenecks as well, think exceeding L2 cache or finally using all your GPU cores on problems that parallelize with scale. If you're using Python you'll never hit those other bottlenecks because the VM is always 40x slower than a naive native implementation. Unless, of course, your bottleneck is in native code, which is where the easy prototyping of Python really shines.