Julia is a dynamic language that seems to do better because it was designed for this purpose.
But it doesn't seem to have panned out in practice in Python, or PHP as far as I know. Those languages have huge piles of C, and whenever you call into C, the JIT gets confused. People don't seem to rewrite their huge piles of C in Python or PHP. In Python, it's more likely Cython.
I'd like to see pointers to counterexamples -- where people actually wrote some C-like code in Python or PHP and let the JIT do its work. I haven't seen it, aside from the PyPy project itself, and maybe a few other examples. I think you would still take a significant performance hit.
The issue is that C compilers in 2020 are even better at compiling the example I showed. They do amazing things with that kind of code that state-of-the-art JITs don't in practice.
However a call to a shared library that isn’t linked against your language API is not very expensive as you have a much better handle on the values that are escaping and can make much better optimization choices.
In the Truffle project we are using an LLVM bitcode interpreter that allows us to JIT right through that language boundary and still link to native shared libraries. This means people shouldn’t have to rewrite their C extensions and we can hopefully still run the combination of high level language and C extension faster.
JSC and LuaJIT have simpler ways to deal with calling native code which might do weird stuff.
Well, C hardly is, either...
So if a Python JIT does 10-50x better than CPython on a numeric workload, that sounds impressive, but it's still slow compared to C.
And again they don't get 10-50x on string/hash/method call workloads. I think they're lucky to get 2x in some of those cases.
You usually don't care how your matrix multiplication/regex matching/unicode normalization/JSON parsing is implemented, but people had to make those, and they are users of the language too.
Even though it might not change the bottom-line for your high-level app.