[cchianel]

The primary reason, in my opinion, is the vast majority of Python libraries lack type annotations (this includes the standard library). Without type annotations, there is very little for a non-JIT compiler to optimize, since:

- The vast majority of code generation would have to be dynamic dispatches, which would not be too different from CPython's bytecode.

- Types are dynamic; the methods on a type can change at runtime due to monkey patching. As a result, the compiler must be able to "recompile" a type at runtime (and thus, you cannot ship optimized target files).

- There are multiple ways every single operation in Python might be called; for instance `a.b` either does a __dict__ lookup or a descriptor lookup, and you don't know which method is used unless you know the type (and if that type is monkeypatched, then the method that called might change).

A JIT compiler might be able to optimize some of these cases (observing what is the actual type used), but a JIT compiler can use the source file/be included in the CPython interpreter.

[hwpythonner]

You make a great point — type information is definitely a huge part of the challenge.

I'd add that even beyond types, late binding is fundamental to Python’s dynamism: Variables, functions, and classes are often only bound at runtime, and can be reassigned or modified dynamically.

So even if every object had a type annotation, you would still need to deal with names and behaviors changing during execution — which makes traditional static compilation very hard.

That’s why PyXL focuses more on efficient dynamic execution rather than trying to statically "lock down" Python like C++.

[pjmlp]

Solved by Smalltalk, Self, and Lisp JITs, that are in the genesis of JIT technology, some of it landed on Hotspot and V8.

[dragonwriter]

Python starting with 3.13 also has a JIT available.

[jonathaneunice]

"Addressed" or "mitigated" perhaps. Not "solved." Just "made less painful" or "enough less painful that we don't need to run screaming from the room."

[Qem]

> The primary reason, in my opinion, is the vast majority of Python libraries lack type annotations (this includes the standard library).

When type annotations are available, it's already possible to compile Python to improve performance, using Mypyc. See for example https://blog.glyph.im/2022/04/you-should-compile-your-python...