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.