Comment by D-Machine

It is especially glaring in this case because, when queried, it is clear that far too many of the most zealous proponents don't even understand the simplest basics of how these models actually work (e.g. tokenization, positional or other encoding schemes, linear algebra, pre-training, basic input/output shaping/dimensions, recursive application, training data sources, etc).

There are simple limitations that follow from these basic facts (or which follow with e.g. extreme but not 100% certainty), such that many experts openly state that e.g. LLMs have serious limitations, but, still, despite all this, you get some very extreme claims about capabilities, from supporters, that are extremely hard to reconcile with these basic and indisputable facts.

That, and the massive investment and financial incentives means that the counter-reaction is really quite rational (but still potentially unwarranted, in some/many practical cases).