All that extra is clear as day compared to the mystery of how neural network training decides to divide and balance the weights in even small neutral networks.

We can, at best, approach a good set of weights, even in tiny neural networks.

Imagine if we found a way to calculate the exact optimal weights for a given loss function. I mean, there is an exact optimal solution, it exists, but we can't find it exactly, even for a neural network with just 50 parameters.

There is no point in that because the loss function itself is already an approximation. No one knows what is the exact loss function for any given non-trivial real-world task.