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I feel like this fails on the premise that the models can be improved to the point where they are reliable. I don't know that holds true. It is extremely uncommon that making a system more complex makes it more reliable.

In the rare cases where more complexity produces a more reliable system, that complexity is always incremental, not sudden.

With our current approach to deep neural networks and LLMs, we missed the incremental step and jumped to rodent brain levels of complexity. Now, we are hoping that we can improve our way to stability.

I don't know of any examples where that has happened - so I am not optimistic about the chances here.