I really like Julia. I wouldn't say I come off like a cultist, but I do think they spend an annoying amount of time on PR for themselves.
I think Julia is stuck in a spot where they are better than say R/Python for scientific computing on a fundamental language structure level and package manager level, but that it's not good enough to make up for the fact that those languages have a more robust ecosystem.
I think many people underestimate the inertia present in a large community, particularly one like scientific computing. I think Julia is much-much better than R/Python at basically everything important (as a language and platform). I certainly wouldn't call the python ecosystem robust. It's decades upon decades of ugly hacks and hacks upon hacks. The pandas internals are a Kafkaesque nightmare. But R/Python is where millions of people work daily and produce a huge amount of mindshare and libraries. You'd need an incredible amount of resources to replicate that in any other space.
I'd argue though that this mindshare is not there due to Python/R as a language (and platform) being better, but simply because there were no better alternatives 10-15 years ago and by now the sheer inertia makes it impossible to stop.
You'd need to convince a sufficient portion of people to move to Julia at more or less the same time. Few people want to be first movers. These tend to be the ones who actually care about the qualities of the platform, not just "get the work done and clock out".
If you are a data scientist, you won't be paid for moving to Julia. You'll be paid for coming up with working models. And you take a serious risk by moving to a new platform with little adoption. The platform might die, taking your tools and processes with it. You won't be able to rely on your colleagues advice about technical issues. You can run into bugs more frequently simply because fewer eyes have looked at the ecosystem.
The end result is that few people take the plunge.
I would outright disagree with this - data science and ML is a subfield of software where you have a much higher ratio of greenfield projects over the past decade, and you also have many people who are moving to the field or are starting out in it. It also attracts the kinds of people who will be willing to use cool tech just for the sake of it. If that's not conducive to picking a new programming language with lots of promises, I don't know what is. And they still aren't picking Julia.
What do you disagree with if I may ask? Most of the people I work with really like Julia, we have dabbled with it and would like to see it succeed python/matlab/r.
Why aren't we moving, you ask? Because moving to another ecosystem does not put bread on the table. I cannot go to clients and say: this past 6 months we made no improvements to our strategies, but look, we migrated to a new programming language that is used by a fraction of a percent of our peer group.
The field attracts clever people and there are many greenfield projects. But just because we start a new project, it doesn't mean that doing it in a tiny ecosystem is sensible. Some firms can do it. Jane Street has the means to basically be "OCAML The Systematic Trading Language". This is not a luxury afforded to most.
And thus we get a chicken and egg problem, where nobody wants to be the sole first mover as there's little advantage to it. At the same time, we all see that everyone would be much better off if we moved.
I've been using R since beta and Julia is the first thing that I've seen since then in numerical computing that reminds me of that time.
Julia still has a lot of empty library space compared to R or Python, and isn't perfect, but my guess is it will catch up. R did when it was being compared to SAS, fortran, C/C++, lisp, and so forth.
I'll be honest and say that I wish something else more general-purpose (to the point of say, having a bootstrapped compiler) would be in its spot but I can't really complain right now. Maybe something else will catch up.
I think for me personally is that R and python is a bit in denial about its performance limitations when it comes to hard problems. You pretty much have to drop down into C/C++ to address them, and for certain things, julia really does do many times better time-wise, without the weeds of C/C++. I think there's some people (myself probably included) that are tired of being forced to choose between the C/C++ and R/python worlds. I think there is a bit of overhyping and/or cult-like nature of julia but I also think some of it is trying to convince people that you don't have to choose between expressiveness and performance.
I think Julia is stuck in a spot where they are better than say R/Python for scientific computing on a fundamental language structure level and package manager level, but that it's not good enough to make up for the fact that those languages have a more robust ecosystem.