I think this is due to Yudkowsky's focus on AI theory; an AI can't use discretion to choose the right method unless we formalize this discretion. Bayes' theorem is applicable to all inference problems, while frequentist methods have domains of applicability. This may seem philosophical to working statisticians - after all, Bayes' theorem is rather inefficient for many problems, so it may still be considered inapplicable in this sense - but programming an AI to use a frequentist method without a complete understanding of its domain of applicability could be disastrous, while that problem just does not exist for Bayesianism. There is the problem of choosing a prior, but that can be dealt with by using objective priors or Solomonoff induction.