The advantage of this flavor of engine is that it might make parallel position evaluation extremely efficient. Calculate 1024 leaf positions and batch them to the model, take the top 10% and explore their sub-trees either via further GPU batching or minimax eval.
NNUE already tries to distill a subtree eval into a neural net, but it’s optimized for CPU rather than GPU.
As a game player I want to play an opponent that behaves like a human. Otherwise I’m always looking for the flaw in the design that I can exploit, which wins me the game but is less fun.
What you’re discussing sounds like intuition with checking, which is pretty close to how humans with a moderate degree of skill behave. I haven’t known enough Chess or Go masters to have any claim on how they think. But most of us don’t want an opponent at that level and if we did, we would certainly find a human, or just play against ourselves.
The issue is that humans and computers don't evaluate board positions in the same way. A computer will analyze every possible move, and then every possible response to each of those moves, etc. Human grandmasters will typically only analyze a handful of candidate moves, and a few possible replies to those moves. This means human search is much narrower and shallower.
If you want a computer that plays like a human, you will probably need to imitate the way that a human thinks about the game. This means for example thinking about the interactions between pieces and the flow of the game rather than stateless evaluations.
Grandparent was suggesting the hybrid approach where you select a handful of good candidate positions and then explore them (DFS) as far as possible. Which is pretty much how humans work.