Modern DL uses stochastic gradient descent, which Domingos's paper doesn't deal with, in fact he leaves the generalisation as an open problem.

Be that as it may, universality in computing models is a well known issue. I could also say "Every Model Learned by Gradient Descent Is an x86 processor + a lot of RAM", or ""Every Model Learned by Gradient Descent Is a 2 Layer Neural Net" (as 2 layer nets are universal approximators), but this is not terribly illuminating.