It's growing, but certainly not growing exponentially or anything like that. Here's some statistics from January this year: https://info.juliahub.com/julia-annual-growth-statistics-jan...
Regarding your negative experiences, the bad news is that we haven't solved those issues, but the good news is that we're making real progress on them. Version 1.9 released in may of this year and is the first version to cache native code from packages, which makes loading of julia code MUCH faster through more AOT compilation, and there are even more improvements coming in v1.10 later this year. https://julialang.org/blog/2023/04/julia-1.9-highlights/#cac...
Error messages are also receiving a fair amount of attention, but it's a hard problem and there's less agreement on what the best way forward is. However, there's been some good work going into improving the readability and clarity of error messages that I think will help alleviate these struggles.
I've used a bit for various pet projects (mainly some graph search and JuMP stuff) and it was convincing. But now I can see Fortran perform in real production code (where it shines, at the cost of being so antiquated that it's not funny anymore) and my expectations for Julia are now higher.
I'll give it another try 'cos you spend some time answering my question :-) (and because the charts in the 2nd provided link are just really convincing)
Let us know if you're experiencing any new painpoints too, or if things like code loading aren't as fast as you had hoped, there might be things we can do to help.
is it in the us, europe, asia?
I know cba and anz in australia supports julia internally for monte-carlo.