Python proves that there is a use case for a tool with imprecise semantics (no meaningful type system) even if that tool has terrible performance compared to other options (GIL). That use case is gluing together C++.
Hottest take? In 5-10 years, ML advances will result in Python being mostly replaced by LLM based “programming glue” systems. Much Python doesn’t need to be “correct” the way statically typed languages are correct, it only needs to be correct for the few things you ask it to do (i.e. train this particular NN). LLMs are similarly imprecise, but more powerful. We will move from a world of Python gluing together C++, to LLMs gluing together some hopefully-better language (I bet $10 on Swift). Main delay will be from getting LLM based solutions to run performantly-enough on developer hardware.
Related to your prediction, I think that's a major part of why python took over - looking English-like, a lot more people saw it and were like "hey, I can do this!" than with (most of) what came before.
Hottest take? In 5-10 years, ML advances will result in Python being mostly replaced by LLM based “programming glue” systems. Much Python doesn’t need to be “correct” the way statically typed languages are correct, it only needs to be correct for the few things you ask it to do (i.e. train this particular NN). LLMs are similarly imprecise, but more powerful. We will move from a world of Python gluing together C++, to LLMs gluing together some hopefully-better language (I bet $10 on Swift). Main delay will be from getting LLM based solutions to run performantly-enough on developer hardware.