They argue that if you have knowledge of when it has to extrapolate from the dataset (and therefore has high uncertainty for, under reversion to the prior), you can prevent it from outputting a definitive statement. This is why many researchers (that I know, anyway) argue that uncertainty quantification or "out-of-distribution detection" is likely to be important moving forward.