To my understanding NFL states that, we cannot have an hypothesis (let's assume it is an approximator like NN in this case) class that can't achieve certain accuracy parameters $\leq \epsilon$ with probability greater than a certain $p$ given the number of points from which we can sample is upper bounded to $m$.
Whereas, the UAC states that an approximator like a NN given enough hidden units can approximate any function (to my knowledge the function must be bounded).
The point where these 2 clashes (as per my knowledge) is that if we increase the paramters in a NN, the UAC will start to hold good, but the VC dimension will increase (or hypothesis class becomes richer) and for the same $m$ our $\epsilon$ increases or $p$ decreases (not sure which one is affected).
So what are the gaps in my knowledge here? How do we make these 2 consistent with each other?