My argument: the theoretical limitations of NNs (lack of modularity, symbolic reasoning, verifiability) cause no practical problems to usefulness - we can just analyze the code artifacts as we do with human programmers. Do you disagree?
A neural network, conversely, is a big ball of mud. Impossible to reason about and to test except for whole-system, end-to-end testing, which is impossible to do exhaustively because of the size of the state space. It is, by design, unexplainable and untestable, and therefore unreliable. It's why you use globals in C only judiciously. (I am just rephrasing the article here, not saying anything new.)
And the evidence that it causes practical problems to usefulness is already out there; "hallucinations" are simply errors, just that corporate PR likes to pretend that it's a "feature" and not a bug. This is delusional. A society seeking digitalization should run away from this level of stupidity.