I get a sense that the julia community is more anxious about adoption than, e.g. python or R communities. Its probably natural given it is a relative newcomer in the broader "data science" / "scientific computing" thing and in the past years there was an explosion of interest / hype around some of its subsets (in particular anything that can be labelled machine learning or AI)
But participating in that "hype" is not necessarily what will entrench julia for the long term. Turning its unique characteristics (unique versus these other two open source contestants, not across the entire programming language landscape) into unmissable developer / user experiences seems to me a safer route. E.g what makes R impossible to ignore is the richness of its statistical toolkit. What makes python impossible to ignore is the productivity boost for typical tasks etc.
But participating in that "hype" is not necessarily what will entrench julia for the long term. Turning its unique characteristics (unique versus these other two open source contestants, not across the entire programming language landscape) into unmissable developer / user experiences seems to me a safer route. E.g what makes R impossible to ignore is the richness of its statistical toolkit. What makes python impossible to ignore is the productivity boost for typical tasks etc.