I think Julia will gain some adoption replacing Fortran-, C- and C++ based libraries that can then also be used within Python. If you use any numerical packages, the above is already what you’re using under the hood.

I also think that not enough people are aware of the performance limitations of NumPy code and how much more straightforward it can be to drop into a lower level language once you cross a certain optimization barrier. This is where Julia can increasingly serve an important role.

This will not happen. There is so much hype around Julia and false claims such as Julia outperforming Fortran, etc. One can always design an isolated benchmark in which Julia outperforms any other language. But what people care, is performance in practice. And false claims will only hurt Julia in the long run, in the same way VolksWagen got badly hurt for its "US diesel emission fraud".

It's a compiled language with a close-to-baremetal type system and implemented as an LLVM frontend. I don't see what would make it slower than e.g. Fortran except for a couple more years of LLVM backend optimizations required (which I still think will happen given LLVM's adoption), but probably I'm missing something here.

And do you have any evidence that well written (but still high level) Julia isn't faster/as fast as fortran in practice?

Poorly written code in any language will be slow, so don't start with that.

Does Julia play well with Rust code? I see Rust as the future replacement for C++ so that would be important.

tbh. I don't yet see many use cases for that. or are there popular Rust numeric libraries I'm not aware of?

Here's one project idea: `sled` is a transactional KV store written in Rust. Might be fun to build an embedded DB in Julia using sled as the storage engine.

I don’t see anything intrinsic to Julia that makes it more difficult than Python. Personally, I find the language a bit more consistent.

Python is better for library users in the sense that Python has more libraries, but I remember not many years ago when nobody was doing anything serious in Python for statistics because the libraries were so poor and so few compared to R.

Julia has some great libraries being built that leverage some of the properties of Julia that Python can’t easily match.

Will Julia “beat” Python? Maybe in the sense that Python has “beaten” R. Note that R continues to do just fine, thank you. So too will Python do fine. But you may be surprised to find some killer libraries or frameworks that are firmly in Julia’s domain in the future.

As a library user, I far prefer Julia to python. With Julia I don't have to deal with python's ugly math syntax, terrible abstractions, SLOWWW custom types ) and 3-5 different array/collection "type" systems.