It has a lot of potential. You can often write code in Julia that is really performant by just following a few simple rules. Most notably, keeping type stability. Libraries are now catching up. Some are great, some are unique, others are still missing.

I have generated SIMD code that outperforms C and C++, even after some hand optimizations. As a matter of fact, some people are re-writing a BLAS drop-in replacement in pure Julia, and the performance is not going to be too far off from BLAS [1]. If you work in scientific computing, you can probably appreciate how amazing that is.

The combination of multiple dispatch, a simple type system and an LLVM backend is wonderful. In the long run, Julia will probably grab lots of Python's and R's marketshare because having a single language instead of two languages (Python + Cython/Pythran/... or R + C++) is a huge advantage.

[1] https://discourse.julialang.org/t/we-can-write-an-optimized-...