The frequentist/Bayesian dispute is of real import, because ad-hoc frequentist statistical methods often break down in extreme cases, throw away useful data, only work well with Gaussian sampling distributions etc.

I think you have this backwards. Frequentist techniques typically come with adversarial guarantees (i.e., "as long as the underlying distribution has bounded variance, this method will work"), whereas Bayesian techniques, by choosing a specific prior (such as a Gaussian prior), are making an assumption that will hurt them in an extreme cases or when the data is not drawn from the prior. The tradeoff is that frequentist methods tend to be much more conservative as a result (requiring more data to come to the same conclusion).

If you have a reasonable Bayesian generative model, then using it will probably give you better results with less data. But if you really can't even build the model (i.e. specify a prior that you trust) then frequentist techniques might actually be appropriate. Note that the distinction I'm drawing is between Bayesian and frequentist techniques, as opposed to Bayesian and frequentist interpretations of probability. In the former case, there are actual reasons to use both. In the latter case, I agree with you that the Bayesian interpretation is obviously correct.