The Pytthon standard lib calls out to hand optimized assembly language versions of the crypto algos. It is of no relevance to a JIT-vs-interpreted debate.
And as noted upthread that's a significant part of the uptake of Python in scientific fields, and why pypy despite the heroic work that's gone into it is often a non-entity.
This is a major problem in scientific fields. Currently there are sort of "two tiers" of scientific programmers: ones who write the fast binary libraries and ones that use these from Python (until they encounter e.g. having to loop and they are SOL).
This is known as the two language problem. It arises from Python being slow to run and compiled languages being bad to write. Julia tries to solve this (but fails due to implementation details). Numba etc try to hack around it.
Pypy is sadly vaporware. The failure from the beginning was not supporting most popular (scientific) Python libraries. It nowadays kind of does, but is brittle and often hard to set up. And anyway Pypy is not very fast compared to e.g. V8 or SpiderMonkey.
Reee.
And even these issues are part of the greater problem of late stage capitalism that in general produces god awful stuff with questionable value. E.g. vast majority of industry code is such.
Care to list some of those details ? (I have zero knowledge in Julia)
It's my assesment that the problems listed in there are a cause why Julia will not take off and we're largely stuck with Python for the foreseeable future.
There is something big going on in caching the binaries, so there's a chance the TTFX will get workable.