You are right that the current adoption rate of Python makes it seem like Julia is in a losing position, but on the other hand it took Python a long time to get in to the position it is right now and systemic changes take time.

The two arguments that speak for Julia from a user perspective (there exist much stronger arguments for package developers).

a; Low overhead interoperability with Python, R, C and Fortran (and C++); You don't need to rewrite your code and you can start using Julia and slowly transition.

b; User code as fast as package/base library code. You are not stuck with what already exists.

As someone who's watched the rise of R and Python from its beginnings, who pulled my hair out in frustration over the longevity of MATLAB long ago, and who has tried just about every numerical programming language it seems, I think the threat to Julia probably comes more from other LLVM-based languages like Nim or Crystal, or things like Rust or Kotlin.

Julia's adoption rate is really impressive compared to R or things like numpy, etc. so I'm not worried about that. But I do think it will have to contend with a number of competitors in the same space.