I am not sure if you are not trading "high human efficiency" against increased risk of blowing up at some point. Good luck doing forecasting without thorough understanding of priors and statistics in general.
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.
I don’t say this naively. At least in industries, the weight of imposter data scientists I think is getting to a level that may cause the profession to implode due to customer disillusionment within the next 10 years precisely because fit(X,y) is so accessible.
1. Deep learning doesn't require thorough understanding of priors or statistical techniques. This opens the door to more programmers in the same way high level languages empower far more people than pure assembly. The tradeoffs are analogous - high human efficiency, loss of compute efficiency.
2. Near-CPU deep learning accelerators are making certain classes of models far easier to run efficiently. For example, an M1 chip can run matrix multiplies (DL primitive composed of floating point operations) 1000x faster than individual instructions (2TFlops vs 2GHz). This really changes the game, since we're now able to compare 1000 floating point multiplications with a single if statement.