It's necessarily high-level, so you still need to learn about specific approaches to get practical things done.
And based on that clarity, they were able to then go on and make immediately productive contributions?
A third option you may have meant which makes sense to me in context is the authors of this paper going on to write SoTA papers. I actually looked through the published works of the first two authors and didn't find any practical work, all similar very long form math notation heavy theoretic papers, which supports what I initially worried about that there's basically two diverging branches of ML papers, one of which I'm skeptical about the practical value of. I have actually not seen any examples of first authors on theory papers going on to publish SoTA results on applications, seeing such a thing I would also consider pretty convincing evidence of the utility of these papers and the symbiotic unification of the two groups.
I wonder if there's been similar concerns raised historically, like at the advent of electricity, between practical engineers focused on creating groundbreaking applications versus people still focused on theory, and in retrospect what contributions continued to be gained by the theoretical work afterwards in those cases.