The uniform distribution ("flat") prior lets you interpret a maximum likelihood result as a maximum-a-posteriori (MAP) Bayesian point-estimate (implying a 0-or-1 loss function). One could argue that if you refrain from doing this and just stick to a literal application of the likelihood principle, you're not really depending on a flat prior.

For that matter, what is a "flat" prior over the parameters also depends on what parameterization you're using. Results that are 'intuitive' under one parameterization may not be under a different one.