I thought I had a pretty good grasp on this, but the idea that an infinite sum of higher order moments uniquely defines a distribution in a way analogous to a Taylor series, was new and super interesting! It gives credence to the shorthand that the lower order moments (mean, variance, etc) are the most important properties of a distribution to capture, and is how you should approximate an unknown distribution given limited parameters.