This is only correct for certain problems, like everything it has best use cases. When you only have a hammer everything looks like a nail.

In medicine the backbone of clinical trial results that change the field relies often on 2000-3000 patients (datapoints) and often groundbreaking achievements in medical practice are made by simple statistics and simple methods. Go to the New England journal of medicine and pick any trial and the weight of their conclusions are based off of survival functions, hazard ratios, and chi squared statistics. Then go look at the funding section - these projects are funded by millions. The only disciplines in medicine with ML datapoints are epidemiology and claims level data which strays way into econometrics.

I myself study rare diseases as well as AI/ML applications in medicine and for some projects I’d be stoked to get 80 patients because there just simply aren’t that many around.