I think this is to be expected. The oscillation between first-principles and empirical models falls out of the scientific method: see a few datapoints, develop

I think this is to be expected. The oscillation between first-principles and empirical models falls out of the scientific method: see a few datapoints, develop a predictive theory, try to prove the theory wrong with new datapoints, and reiterate for alternative explanations with fewer assumptions, greater predictive power, etc... This happens even in pure mathematics, just at a more abstract level: start with conjectures seen on finite examples, prove some limited infinite cases, eventually prove or disprove the conjecture entirely. Current DL models are so huge they've outpaced the scale where our existing first-principles tools (like linear algebra) can efficiently predict the phenomena we see when we use them. The space has gotten larger, but human brains haven't, so if we still want humans to be productive, we need to develop a more efficient theory. Empirical models explaining empirical models might work, but not for humans.