When I think more about it, I think there might be another angle to look at it.
Julia’s vision about solving the languages problem was about having both dynamic high productivity language and being fast. It was reasonable to believe that people will embrace the vision and hence will embrace the Julia run time.
Mojo’s take on solving the problem is different.
The assumption is people like their current language (Mainly Python).
The issue is the dynamic range between the coding skills needed to use the host language (The preferred one) and the languages used to generate the accelerated code.
For instance, in Python, it means C / C++ / Cython capabilities.
So Mojo, in my opinion, will also be developed in that direction as the 2nd step. Generate functions / packages to accelerate Python with support to any accelerator under MLIR.
Now we see the 1st step, embracing Python into Mojo. Next step we’ll see Python embracing Mojo with many packages rewritten in Mojo.
Think for instance on NumPy, SciPy, etc… Having built in support for all the accelerators using a single code.
Strategically thinking, this is something Julia can achieve as well. Maybe even faster?