In other words, universal prior assigns probability to points, not events. Probability of an event is sum over its elements, which is not related to the complexity of the event specification (and hypotheses are usually events, not points, especially in the context of Bayesian updating, which is why high-complexity hypotheses can have high probability according to universal prior).

For an explicit example of a high-complexity event with high prior, let one-element event S be one of very high Kolmogorov complexity, and therefore contain a single program that is assigned very low universal prior. Then, not-S is an event with about the same K-complexity as S, but which is assigned near-certain probability by universal prior (because it includes all possible programs, except for the one high-complexity program included in S).