If AI can find new proofs for well posed math problems, i see no reason why it shouldn't be able to implement a more performant fully featured version of an existing interpreter (eg with JIT and AOT) that emulates python api well and passes all python tests and tests of other projects. It is true that a lot of human effort and thought has been put into squeezing performance out of the existing implementation. It is true that many people have found that getting that last 1% of python test suite to pass turned out to be insurmountably hard. Same is true for math, and yet AI sometimes finds simple solutions that we somehow missed. Maybe there's a simple optimization that was used in an obscure interpreter of a domain specific language that we never heard of. Worth a shot in my mind. If that turns out to be successful, we should ideally find the code that served "as an inspiration" if any.

It might make more practical sense to start from CPython and try to optimize that further though. It even has a "not fully fleshed out" JIT already.

If humans can find (and have been finding for millennia) new proofs for well posted math problems, I see no reason why they shouldn’t be able to implement a more performant fully featured version of an existing interpreter.