This statement doesn’t really make sense to me. A frequentist approach assumes a random (or noisy) underlying process and characterizes its probability. A Bayesian approach assumes a fixed but unknown process and updates the probability a given model matches that reality.

It is ultimately the same math, if you are comparing apples to apples. Just different ways of looking at a problem. Sometimes one is better suited than the other.

It’s like as if physicists were arguing over whether Cartesian or Polar coordinates were better. It’s the same damn physics, just expressed differently. In some problems one approach is easier to work with than the other, and can even make seemingly intractable problems solvable. But that doesn’t mean the other approach was “wrong.”