The only ML that I ever did was a single undergrad NN class around ~2001. That was a long time ago, but I vaguely remember being taught at that time that adding more nodes rarely helped, that you were just going to overfit to your dataset and have worse results on items outside the dataset, or worse end up with a completely degenerate NN - eg that best practice was to use the minimum number of nodes that would do the job.
On the contrary, there was a mathematical proof that one-hidden-layer neural network with nonlinearity is enough to represent any function. Using more than 1 hidden seemed a waste.
