I highly doubt it for one core feature: performance. For a little while it looked like Python 4 might fix this (by using type hints to JIT) , but it was quickly given up. IMO a big shame as it is the one thing holding python back.
I think Julia actually does a pretty good job of showing that the problem isn't types. The problem is semantics. Julia's restriction is eval to the global scope is a perfect example of this. It has a pretty minor on code, but a massive effect on performance. Python has a ton of things like this where a slightly different (and totally breaking) semantic change prevents optimization.
My poinr wasn’t about type system per se but the ability to compile code down to natively execute without going through an interpreter. Whether you solve that through type inference or type hints I honestly don’t care, but afaik Julia has an easier job by allowing you to make types explicit on the data structure side while being flexible for the algorithms. Semantics are certainly also a big part, but it would be nice to have first party support for what Numba does, JITing a reduced set of semantics but keep everything else compatible.
Julia runs fast enough to catch Python, because Python will never improve the main reason why Julia was created in first place, the community embracing a JIT compiler instead of re-writing working code into C.