Simpler and, I'd wager, wrong. It seems to me that this stems from fundamental limitations of current machine learning tech. As far as I'm aware, not a single neural-net based AI has displayed the ability to do "System 2" style thinking well, regardless of their training set.

We have AIs that are good at System 2 thinking, that is, symbolic AI. But they don't do System 1 thinking at all. We haven't managed to integrate the two meaningfully and I don't think anyone has a clue how to do it. It's not enough to just have the ML-based system make calls into a symbolic AI when it needs to do reasoning, that's like you have a child who doesn't know how to multiply, so you give them a calculator and declare "this child now knows how to multiply".