This question implicitly presumes that language design doesn't matter—that you can just hook a JIT up to any language syntax and semantics and presto... it’s as fast as C. But it doesn't work like that. You can't just take an arbitrary language and "rub some LLVM on it" and make if fast. The dozens of failed efforts to speed up Python and R with LLVM and other JITs over the decades proves that the source language matters. (PyPy is the only one with any real success, and they had to give up on CPython ecosystem compatibility.) It's Python and R semantics that are the problem and this can't be fixed with a JIT. Java is much better in terms of performance, of course, but still has issues. (The lack of value types is a big one, for example; being forced to go through a VM runtime is another problem at the highest levels of performance demands.) Julia can be seen as an experiment which proves that if you design a high-level dynamic language from the start to be compiled and run efficiently, then you can do much better than you can by bolting a JIT onto an existing slow dynamic language after the fact.
