That is true, indeed. ML is very good at augmenting algorithms and techniques. DLSS and FSR4 SR are very good examples of that (parameter prediction of an SR algorithm). Algorithm augmentation is much easier to control than having a 100% DNN blackbox.
That algorithm augmentation is missing from many parts of the rendering pipeline, like e.g. standard ReSTIR. But to address that issue, cooperative vectors have been "invented". We will see many more ML augmented algorithms in the future. Not only from GPU vendors like AMD and Nvidia, but also game developers themselves.
I think the most important part will be having good matrix core acceleration together with good support for cooperative vectors. They will not leapfrog Nvidia with that, but at least not fall back as far as with RT when Nvidia introduces more neural rendering stuff.
If AMD really wants to kick Nvidias a$$:
- FSR Redstone SR brings better quality than DLSS 4 SR and runs faster
- And it runs on all GPUs since Turing and RDNA1
- FSR Redstone RR gets on par with DLSS 4 RR
- If PS5 Pro gets RR support as well (I expect that to happen), RR adoption will accelerate (RR can also be used without pathtracing) and AMD can get the merits for that
- FSR Redstone FG brings 4x MFG with better quality and higher performance than Nvidias solution
- Ultimate FG leapfrog: Extrapolation with time-warp -> FG frames feel like real rendered frames. But that seems to be unlikely, it is maybe a thing for DLSS 5 and FSR for RDNA5 / NextGen consoles
- NRC solves the pathtracing weakness of RDNA4 (compared to Lovelace and Blackwell)
- Less rays due to NRC help RDNA4 and RR makes a nice image out of it
An additional factor will be work graphs. That allows for very cool algorithm concepts. Not sure if also useful for cooperative vectors or stuff like FSR. RDNA5 will likely bring very strong support for work graphs.