The fact that neural networks are highly nonconvex has encouraged a lot of research, but it's more of the kind aimed at resolving tension: these methods are probably good for convex functions, why do they continue to work for nonconvex problems, and are there tweaks we can make to improve them in that setting? It's not a lot of de novo theory; more standing on the shoulders of giants, etc etc.
The reasons why optimizers work well for neural networks in their highly nonconvex landscapes has absolutely nothing to do with their performance in convex landscapes. If that were true, everyone would be using Newton-CG. These optimizers were born in the convex optimization literature as a consequence of the genetic optimization nature of incremental publication (and because that was all we had), but their modern study is through the lens of implicit regularization (their preferences for certain minima) and their stepwise vs. continuous rates for feature learning in multilayer models.
This is completely new theory by the way, and requires painful reinvention of the field. It does not stand on the shoulders of convex optimization. The nonconvex setting is assuredly not a perturbation of the convex setting, and those that do continue to work on deep learning optimization from the convex optimization perspective are well behind the times.
[1] https://parameterfree.com/2020/12/06/neural-network-maybe-evolved-to-make-adam-the-best-optimizer/
[2] https://arxiv.org/pdf/1905.09997
I think that Nesterov's first order method is the most efficient general first order algorithm on convex problems, so anything else is in some sense worse. (Edit: removed incorrect ADAM comment.)
I don't think this changes the point, which is that most optimization methods used in AI owe a substantial intellectual debt to convex optimization theory.