I understand the second example (girlfriend taking a phone screen): some people never took statistics past a linear models course, so they use what they know. Understandable, if inefficient. But they don't seem to be claiming linear models are optimal all the time, they're just stuck in a local maximum of trying to speed up the system they have.
But the PCA guy I really don't understand.
An older explanation for PCA is that it's a sort of default factor model prior to rotations that introduce structural assumptions. (Varimax etc. factor analysis is really underrated in exploratory statistics; but then, by now data science training never introduces people to the FWL theorem, identification, etc. With large enough deep learning pretty much anything is possible -- also because using deep learning implies having truckloads of data -- but the median xgboost guy is way out of his element.)
So - a distribution which is linear?
As others have said, it's better for estimation (not for classification).
It is definitely not optimal in a classification sense.