My guess (based on my experiences) is that companies wind up in this position from having inexperienced people building early versions of products instead of hiring experienced engineers (who are usually more expensive).
I would categorize it as a subset of dynamic typing, and that's what Wikipedia says too.
Dynamic typing means that types can be changed arbitrarily at runtime, compared to statically typed languages which define all types at compile time.
Strong/weak means that type coercions rarely/never happen automatically. For instance JS has some interesting behavior enabled by weak typing `[] + [] -> ""`. Whereas Python rarely coerces things for you. The division operator in Python 2 was strongly typed, while they changed it to weak typing in Python 3 (inline with the practicality vs. purity convention).
In Python everything has a type, and you can't use a float as a list, for instance. It's correct to call it both strongly typed and dynamic, those are not antonyms.
For me, it's not "status anxiety". It's simply not worth the effort.
The last couple static analysis tools I ran on my programs, I spent a while getting the tool to not-crash (because even though the authors obviously had a static analysis tool themselves, they either didn't bother to run it on their own code, or it wasn't good enough to find actual issues). These tools flagged only a couple issues, and almost all of them were places where it couldn't really cause any problems, but the type system was not strong enough for me to prove why it couldn't go bad. So I spent a while sorting through false-positives.
I'm not going to spend hours with a tool to find only a couple (real) bugs, which no user has ever reported seeing, and which I've gotten no automated crash reports about. I have much better uses for my time.