I think your second point is especially important, as the semantics of Python are dynamic down to the core. And it's not just hypothetical stuff — Python libraries pretty systematically take advantage of this dynamism (anyone who's tried using type stubs for popular libraries will know what I mean).

The examples in the article appear to mainly revolve around numerical calculations, so I suspect the target audience is people doing scientific computing who need to "break out" into a compiled mode for heavy CPU calculations (similar to numba or even Julia) from time to time when their calculations aren't vectorisable.

I've noticed a split between the needs of software engineers on the one hand, who need expressive abstractions to manage systems of extensive rather than intensive complexity, and scientific programmers or model-builders on the other hand, who are much more likely to just use the primitives offered by their language or library as their needs revolve around implementing complicated algorithms.