> > The first part is true of all statistical models: To improve performance by a factor of k, at least k^2 more data points must be used to train the model. The second part of the computational cost comes explicitly from overparameterization. Once accounted for, this yields a total computational cost for improvement of at least k4.

Those claims are entirely new to me, and I've been a researcher in the field for almost 10 years. Where do they come from/what theorems are they based on? It's unfortunate this article doesn't have any citations.