I would be very hesitant to read DWave's published results as an indictment against classical solvers, many of which are not as hardcoded towards combinatorial optimization as DWave.
In comparisons against other hardware level solvers, DWave's annealers have had significant competition from non-quantum digital annealers, such as Fujitsu's ASIC for annealing as in https://arxiv.org/abs/2103.08464 and https://arxiv.org/abs/2203.02325. Really it seems that DWave's annealers only outperform on very contrived problems.
Previous analyses have discussed that DWave's typical venue of forward adiabatic annealing may not be enough for universal quantum computation, which may pass on the question of a potential quantum advantage.
For a good picture of adibatic quantum computation and optimization, this review is a great starting place. Of note would be the overview concerning the speed limit of quantum annealers found in the inverse of the cubic/square spectral gap of the evolution hamiltonian.
On the more general question of understanding the performance of various different quantum algorithms for optimization, we understand different paradigms that can even take advantage of both annealing and the QAOA, such as in Brady et al. This kind of thought, coupled with the Zhou-Lukin observation that the QAOA takes advantage of diabatic mechanisms in its evolution (by diabatic we mean that instead of how adiabatic quantum annealing tends to stay in the instantaneous ground state of the evolution hamiltonian, diabatic transitions are those in which the state of the system is in a superposition of various energy states of the hamiltonian), may lead us to finding annealing schedules far faster than the standard quantum annealing picture, as in this example where a problem hamiltonian that is very difficult for the DWave (and standard adiabatic anneals) is solved via a diabatic anneal in quick time. But these improved methods have not, to my knowledge, been implemented in hardware.