See, the reason for the Bitter Lesson being a lesson is that Neural Nets, that Sutton is mainly writing about, are pretty crap at representing background knowledge. The only way you can store expert knowledge in a neural net is to modify its structure and its weights. The weights you can only modify by some kind of learning procedure like backprop, in practice. Very limited forms of background knowledge, like convolutions can be encoded in a neural net's structure, but imagine trying to represent, I don't know, the last ten lines of code you wrote today as a bunch of neural net connections. Continuous functions is just not the right kind of notation for that sort of thing.
If neural nets were any better at encoding background knowledge, they would use it, but they can't so they have to rely on data. And that's why they need so much of it. Background knowledge functions as a strong inductive bias- it directs the search for a hypothesis to hypotheses that we know make sense (again, think of convolutions). Without background knowledge, or with only a little background knowledge, you need tons of examples to learn anything useful.
So the Bitter Lesson is basically making a virtue out of necessity. In any case, it's not a prescriptive thing, only descriptive.