Ask it to solve sudoku and report back. Not generate code but actually solve a puzzle in the prompt.
My reply to you was predicated on a compilation of centuries of scientific study on the subject of creativity. Your knee-jerk reply is to proclaim it's bad at sudoku while going out of your way to place artificial constraints on it.
Touting its inability to solve sudoku in-context feels like a slightly hamfisted way of saying it's a probability based model operating on tokens but like I said before, there are plenty of us who already understand that.
We also realize that you can find arbitrary gaps in any sufficiently complex system. You didn't even need to rely on such a specific example, you could have touted any number of variations on common logic puzzles that they just fall completely on their faces for.
Gaps aren't damning until you tie them to what you want out of the system. The LLM can be bad at Sudoku and capable of creativity in some domain. It's more useful to explore unexpected properties of a complex system than it is to parade things that the system is already expected to be bad at.
To me the existential risks are pretty boring and current LLMs are already capable of them: train on some biased data, people embed LLMs in a ton of places, the result is spreading bias in a black box where introspection is significantly harder.
In some ways it mirrors the original stochastic parrot warning, except "parrot" is a much significantly less loaded term in this context.
A good friend swore that ML was 50 years away from being able to beat a Go grandmaster. To his credit, he stopped making such sweeping predictions after that happened. He didn't fall back to, "Well, I don't know, let's see how it does at Risk."