Perhaps more relevant to the quoted sentence is that it takes advantage of a very cleverly non-flat prior.
Any prior over an unbounded space needs to be non-flat in this way, or you’d never be able to learn anything. To put it more precisely: if you are assigning nonzero probability to every hypothesis in the space, there will exist some description length L() below which 1- of the probability mass resides for arbitrarily small . Granted that you could have priors that are flat below some large value of L, though I think this perspective shows that they would be a bit strange/unnatural.
Any prior over an unbounded space needs to be non-flat in this way, or you’d never be able to learn anything. To put it more precisely: if you are assigning nonzero probability to every hypothesis in the space, there will exist some description length L(