I don't think static type checking is a matter of tooling, getting Julia to perform well amounts to either a lot of trust in the compiler (is static type inference for the Julia type system even possible?) or manual type annotations as far as I can tell. And static types are not there in a large program to make them necesarily faster, but to automatically enforce contracts. For this to work they have to be mandatory, not added on as an after thought. I don't want to be able to run a program only to discover it crashes because a method was not overloaded with this particular combination of parameter types.

Swift has clearly an advantage in that domain, as has C++, C#. In an enterprise setting such as Google no one actually wants speed and dynamism out of a language (C++ programmers are not allowed to use rtti for example), rather consistently boring, predictable, statically guaranteed results with as minimal fuzz as possible (Go, Java, C++) and enough competent programmers in that language.

I agree that it is neat that you can hack the Julia compiler in a library to do compile time automatic differentiation, but my point was that if you build it into the language like the Swift for Tensorflow authors are doing this will result in a more conservative, if inflexible, but potentially more performant solution. In other words precisely because you need a team of people to implement and integrate this feature in C++ you will get a stable "single source of truth" implementation in the language, not several (Flux, Zygote) constantly changing libraries.

Well I've tried DiffEqFlux.jl with Julia 1.1, it works well enough, but the stack traces are ridiculously long if something goes wrong, a ~200 LOC program takes seconds to compile and the library and package management story seems immature compared to rust/swift/python/c++.