The part about C++ is true, when you need to use a combination of SIMD/CUDA/MPI there is no game in town but C++. The ecosystem is so vast, we rely on too many scientific/numerical libraries that took tiny armies of grad students to create.
But for new projects in the future, I am looking towards Mojo and Chapel.
I feel the opposite, they really are focused on being a language that's great for AI and heterogeneous compute, since that's what Modular is focused on. But the features are attractive for other use cases, it has access to MLIR, compile time metaprogramming, easy and ergonomic SIMD, and soon GPU support.
I am really enjoying the language, the level of improvement over the last few months has been amazing. At work all our heterogeneous compute needs are implemented using C++/CUDA. I've been exploring new languages for the type of work we do on and off over the last few years: Julia, Rust, D, Chapel, and Mojo.
It seems the only new languages serious about native heterogeneous compute are: Julia, Chapel, and Mojo.
Of those languages I can say Mojo and Chapel were the most impressive. But Mojo is just so fun to write, I've ported a bunch of my "hobby numerical code" to Mojo. I am practically all in on Mojo since it'll give me access to MLIR.
I have always wondered though, why does Chapel get no love online???
The language is far from stable, but I have had a LOT of fun writing Mojo code. I was surprised by that! The only promising new languages for low-level numerical coding that can dislodge C/C++/Fortran somewhat, in my opinion, have been Julia/Rust. I feel like I can update that last list to be Julia/Rust/Mojo now.
But, for my work, C++/Fortran reign supreme. I really wish Julia had easy AOT compilation and no GC, that would be perfect, but beggars can't be choosers. I am just glad that there are alternatives to C++/Fortran now.
Rust has been great, but I have noticed something: there isn't much of a community of numerical/scientific/ML library writers in Rust. That's not a big problem, BUT, the new libraries being written by the communities in Julia/C++ have made me question the free time I have spent, writing Rust code for my domain. When it comes time to get serious about heterogeneous compute, you have to drop Rust and go back to C++/CUDA, when you try to replicate some of the C++/CUDA infrastructure for your own needs in Rust: you really feel alone! I don't like that feeling ... of constantly being "one of the few" interested in scientific/numerical code in Rust community discussions ...
Mojo seems to be betting heavy on a world where deep heterogeneous compute abilities are table stakes, it seems the language is really a frontend for MLIR, that is very exciting to me, as someone who works at the intersection of systems programming and numerical programming.
I don't feel like Mojo will cause any issues for Julia, I think that Mojo provides an alternative that complements Julia. After toiling away for years with C/C++/Fortran, I feel great about a future where I have the option of using Julia, Mojo, or Rust for my projects.
But for new projects in the future, I am looking towards Mojo and Chapel.