Right, Terrance was hoping it would have something new to think about it, some new perspective, right or wrong. GPTs have the ability to process insane amounts of information across all branches of math, science and art. This ability eclipses that of the most motivated intellectuals such as Terrance. It is thus a little disappointing that it was unable to find anything in its vast knowledgebase to apply a new lens to the problem.
I wonder how much of this is an intrinsic limitation of LLMs, and how much that interdisciplinary thinking and mashing together of problem domains is missing in the training data. It's a pretty rare thing, and the only times these analogies and linkages become noted is when they happen to work out (and they don't seem so far out of left field anymore once this happens).
