This is the fundamental issue and "elephant in the room" that everyone is seems to be overlooking, and putting under the carpet.
The extreme compiled type language guys going gung-ho with very slow to compile and complicated Rust (moreso than C++), while the rest of the world gladly hacking their shiny ML/AI codes in scripting language aka Python "the glue duct tapes language" with most if not all the fast engine libraries (e.g PyTorch) written in unsafe C/C++.
The problem is that Python was meant for scripting not properly designed software system engineering. After all it's based on ABC language for beginners with an asterisk attached "intended for teaching or prototyping, but not as a systems-programming language" [1].
In ten years time people will most probably look in horror at their python software stacks tech debt that they have to maintain for the business continuity. Or for their own sanity, they will rewrite the entire things in much more stable with fast development and compiled modern language eco-system like D language with native engine libraries, and seamless integration C, and C++ (to some extend) if necessary.
[1] ABC (programming language)
I regret to inform you that there are _loads_ of multi-decades-old Python stacks at this point.
On the micro level I'll be like "ugh wish I wasn't paying the costs of Python" decently enough. But on the macro level I don't regret Python stacks. At least not when looking at the alternatives.
Tho I will admit I'm a bit mystified at data science stuff in particular persisting in Python. Lots of CPU churn even if the underlying libs are all C extensions.
What something was meant to do has never, ever stopped people. People find creative ways to use tools in unintended ways all the time. It's what we do.
We can call this dumb or get misanthropic about it, or we can try to understand why people all over the world choose to use Python in "weird" ways, and what this tells us about the way people relate to computing.