To clarify exactly where I'm coming from, I'm going to expand on my thoughts here.
What is Julia's central conceit? It aims to solve "the two language" problem, i.e. the problem where prototyping or rapid development is done in a dynamic and interactive language like Python or MATLAB, and then moved for production to a faster and less flexible language like Rust or C++.
This is exactly what the speaker in the talk addresses. They are still using Julia for prototyping, but their production use of Julia was replaced with Rust. I've heard several more anecdotal stories of the exact same thing occurring. Here's another high profile instance of Julia not making it to production:
https://discourse.julialang.org/t/julia-used-to-prototype-wh...
Julia is failing at its core conceit.
Julia as a community have to start thinking about what makes a language successful in production.
Quote from the talk:
> "(developers) really love writing Rust ... and I get where they are coming from, especially around the tooling."
Julia's tooling is ... just not good. Try working several hundred thousand line project in Julia and it is painful for so many reasons.
If you don't have a REPL open all the time with the state of your program loaded in the REPL and in your head, Julia becomes painful to work in. The language server crashes all the time, completion is slow, linting has so many false positives, TDD is barebones etc. It's far too easy to write type unstable code. And the worst part is you can write code that you think is type stable, but with a minor refactor your performance can just completely tank. Optimizing for maintaining Julia code over a long period of time with a team just feels futile.
That said, is Python perfect? Absolutely not. There's so many things I wish were different.
But Python was designed (or at the very least evolved) to be a glue language. Being able to write user friendly interfaces to performant C or C++ code was the reason the language took off the way it did.
And the Python language keeps evolving to make it easier to write correct Python code. Type hinting is awesome and Python has much better error messages (static and runtime). I'm far more productive prototyping in Python, even if executing code is slower. When I want to make it fast, it is almost trivial to use PyO3 with Rust to make what I want to run fast. Rust is starting to build up packages used for scientific computing. There's also Numba and Cython, which are pretty awesome and have saved me in a pickle.
As a glue language Python is amazing. And jumping into a million line project still feels practical (Julia's `include` feature alone would prevent this from being tenable). The community is growing still, and projects like `uv` and `ty` are only going to make Python proliferate more.
I do think Julia is ideal for an individual researcher, where one person can keep every line of code in their head and for code that is written to be thrown away. But I'm certainly not betting the near future on this language.