Well, the real-world implication (at least, as taught to me when I took my ML class) is that you have to make some assumption about P(f). If you assume that "r

Well, the real-world implication (at least, as taught to me when I took my ML class) is that you have to make some assumption about P(f). If you assume that "reality" has to "spend energy" on building complicated f's ("reality functions" when we learned it), then you get usual probabilistic assumptions about function learning and everything goes back to normal (as we usually experience it in the real world). If you make other sorts of assumptions, you get many popular and useful ML algorithms. It's all a matter of finding prior assumptions specific enough to bypass NFL, while still general enough to encompass useful real-world tasks.