I think a lot of it is the massive amount of compute we've got in the last decade. While inference may have been possible on the hardware the training would have taken lifetimes.
Compute was just too expensive to have neural networks big enough for this not to be true.
Overfitting is the problem of having too little training data for your network size.
I'm sure that's what the text book meant, rather than any point about the expense of computing power.