I recently had the need to build an internal system that distributed workloads across many workers via a client/server model. I did the proof-of-concept using druby [1] and it turned out to be so simple and stable that we just ran with it. It'd been years since I had used that library and instinctively I assumed we'd get the prototype out and then rebuild it using some sort of web service and utilize a high concurrency web server but druby just worked!
My guess is some kind of corporate sponsorship. Someone with deep pockets to maintain it, encourage new apis keeping up with the latest papers, and make sure it works out of the box with the accelerator people want to use this month.
I think it’s more than that, Julia exists and adoption is still slow. Lua and torch were plenty fast and they were still replaced by pytorch. I think to compete with python you need at least a fraction of the de-facto corporate sponsorship for python in the ML space.
As a primarily Ruby dev I'd prefer the AI/ML ecosystem not be split-brained between two languages that are semantically 90% the same thing. Just learn Python and integrate the models into your Rails (or whatever) apps.
It's more of a cultural thing. People tend to write Ruby in a literate fashion and think critically about their APIs. Scala devs get a little over their skis sometimes playing with language features.
[1] https://github.com/ruby/drb