Now, 8 years later, I'd say essentially everything there has been achieved. The most important difference between Julia now and Julia then is the gigantic, vibrant and interacting ecosystem that's sprung up (though share are other important technical differences too!)
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Here's the pitch I'd give:
Julia's value proposition is mostly geared towards people trying to do things that are just too difficult, awkward or slow in other languages. It's not just about speed, dynamism and friendly syntax. It's also about solving the expression problem and providing unprecedented composability.
So while it may not necessarily have great appeal to 'end users' (who are people just calling numpy/scipy/pandas/whatever functions the way they were meant to be called by the library writers), it does appeal to people who make the sort of things end users want so the community is currently swelling with very talented people making state of the art research libraries. The language is designed for 'composability', so that it's very easy to combine packages together in ways the authors didn't necessarily intend or forsee.
This approach is really starting to bear fruit. Our package ecosystem has some real, state of the art stuff not easily available in other languages, and it's often built in less time and with less resources. For 'end users', whether or not Julia makes sense for them really just depends on if the things they need are already available.
Another huge advantage is that even if you're not some super researcher developer, Julia substantially lowers the bar for mere mortals such as myself to do package development. The composability I mentioned above means that I can stand on the shoulders of giants as I implement my own stuff, plus the fact that the majority of libraries are written in pure julia means that I can easily "peek under the hood" to figure out how they work and learn from them. This is really special and can't be replicated in python, because 'peeking under the hood' invariably means reading very specialized C code, rather than Python code.
I think Julia will someday eat Python for scientific computing, but it may be a while before that becomes obvious to Python users. In the meantime, we have an excellent package for calling and running Python code from withing Julia, called PyCall.jl [1], so that can make the burden of switching even easier.