This article never really addresses SciPy or NumPy despite the title and much of the discussion. Rather, the author is ranting about the change from python 2 to 3.

And even then his only supporting anecdote is that matplotlib made a breaking change to the way it handles legends. Meanwhile he was perfectly capable of reproducing his scientific results 4 years after publication despite the update from python 2.7 to 3.5 and minor updates to the rest of the cited libraries.

In light of that paucity of evidence I find it hard to support the many hyperbolic statements that the situation is a "big mistake", "calamity", or "earthquake" for the scientific community.

I do agree with the more general point that scientific code requires funding consideration for long term maintenance. Many aspects of research have adopted provisions for equipment like reagents and computing hardware. These are considered core infrastructure and are often shared among labs. I could see a future where software development is supported in a similar way.