It isn't inevitable, but some people promoting Julia like to pretend that there is some sort of competition for mindshare happening. There are three basic markets for numerical computing: hobbyist, academic, and commercial. The hobbyists will always remain in Python because it is good enough and there is little benefit to learning a new language for casual numerical computing (this also applies to undergrad-level academics, which is closer to hobbyists than deep academics.) In academics it is possible that Julia will replace R for a lot of use cases, but it is unlikely to make much progress in displacing Python outside of math-heavy fields: a biologist or chemist will stay with Python because of the ecosystem and its applications outside of pure numerical processing code. In the commercial world the race is already over and Python won, it will continue to grow in this role due to simple inertia and because for cases where numerical computation speed actually matters a company can hire people to write the code in something even faster and more efficient than Julia.
I am not sure I agree with your hobbyist point. I do programming as a hobby, and I'd prefer doing it in Julia over Python anytime. I can understand that commercially you wouldn't risk adopting a new programming language like Julia when Python has a bigger mainstream ecosystem with solutions for most problems people have ran into, but for hobby I do not have these constraints; I have much more fun programming in a cleaner and more modern language like Julia withstanding its other benefits.
> Is possible that Julia will replace R for a lot of use cases
R is a interactive statistical programming language that acts as a frontend for more performance languages. AFAIK interactivity is not the strongest points of Julia at the moment.
>interactivity is not the strongest points of Julia at the moment
Not sure what you mean. Julia has the exact same notebook environment (Jupyter) as R and Python. Fun fact: the “Ju” in Jupyter stands for “Julia” (the “pyt” and “r” stand for what you think).
Few people in the R world use Jupyter environment, they are generally inferior to use the Rstudio environment. This might change in the future though, VSCode and DataSpell are becoming really good. Jupyter Labs are slowly improving too.
I think Julia may be the Rust of the Number Crunching world these days and you'll get similar advocacy while the old crowd keeps trucking along with c++/python
