"On the other hand, Julia’s focus on scientific applications is both blessing and curse. In this day and age, a lot of the progress in computing is driven by applications outside academia (mostly through machine learning)." This seems like a crazy mis-read to me. Julia is probably the language that has the best integration of differential equations and machine learning. Jax closes the gap a little, but is still way behind.
For example https://gist.github.com/ChrisRackauckas/62a063f23cccf3a55a4a... shows a pretty simple case where DifferentialEquations.JL is 6x faster at gradient calculations than Jax.