At least a Bayesian posterior tries to describe reality. In a way which is consistent with the prior and the data. But again, GIGO. Including prior information into the inferential process will be beneficial if it's correct but detrimental if it isn't. Hardly surprising.
On the other hand, Frequentist methods do not claim anything concrete about reality. Only about long-run frequencies in hypothetical replications.
You may think that makes them better, it's your choice.
Sure, I agree bad priors will give inaccurate inferences. My point is simply that to make a statement like, "an inaccurate prior generates many inaccurate inferences, and therefore it is garbage," one has to adopt a frequentist criterion for the quality of an estimator (like "gets good results most of the time").
On the other hand, Frequentist methods do not claim anything concrete about reality. Only about long-run frequencies in hypothetical replications.
You may think that makes them better, it's your choice.