that's a good point. I guess as an addendum it's not just compute efficiency but also "statistical efficiency" (if that has any meaning?)

I think that term already has usage as a proxy for "lowest sampling variance"; for example the Gauss Markov theorem shows that OLS is the most efficient unbiased linear estimator.

I guess this is echoing your point 2, but I would have generally said that "principled" statistical models are less efficient these days than DL (see: HMC being much slower than variational Bayes). Priors are usually overrated but I think the risk is more that basic mistakes are made because people don't understand what assumptions go into "basic" machine learning ideas like train/test splits or model selection. I'm not sure it warrants a lot of panic though.

Agreed, I see the "lower barrier to entry" in this particular case as coming with potentially huge risks. IMO, statistics is vastly, vastly, vastly under-appreciated and under-estimated.