I think it is easier to write fast, complicated code in Julia than in C, C++, or Fortran. Not just the syntax, but because of great support for generic code by default and metaprogramming making it relatively simple to write code to generate the code you actually want for any given scenario. Interactive benchmarking and profiling are a boon too.

An example of the value of generic code is that forward mode AD is extremely easy, and almost always just works on whatever code you run it on.

Then, once that's done, multiple dispatch (and possible macros for a DSL) allows for a much cleaner user interface than Python offers for numeric code.

I have a lot more experience with R than Python, but seeing more of the scientist/mathematician/researchers side of things, I have to strongly disagree with the view that they should write slow code and contact a CS guy to write fast code in another language when they need it. Do you honestly think that's practical for grad student's projects? Recently, one of my friends wrote a simulation in R. Most of the work was done by hcubature -- a package written in C -- integrating a function written in R. Could just have easily been written in Python. That function was slow, and the simulation ran for days. Before an error caused it to crash, losing days of compute time. I -- a statistics grad student -- helped him rewrite it in Julia, and it finished in 2 hours.

That C/C++ code will still run slowly if they have to call your R/Python code is a problem. They also can't apply things like AD easily. A common solution, used by Stan for example, is to create a whole new modeling language and have users interface through that. Learning a new language -- albeit relatively simple/domain specific -- which they then cannot debug interactively, is another pain point. All this can be avoided by simply using Julia.