This reminds of Jeff Hawkins' book On Intelligence. He takes a similar stance on the overarching/conceptual solution vs. more specialized solutions that dance around the knowledge gap. He criticizes neural networks as not sufficiently detailed to mirror the brain's behavior, and proposes the Hierarchical Temporal Memory model, or HTM.

The hierarchy is crucial: as in the brain, there are different regions at various levels of cognition, with higher regions responsible for greater levels of abstraction. A moment of real learning/understanding is characterized by a sudden, system-wide switch of signals between layers mostly traveling up the hierarchy (this input is confusing! I don't know what to do with it!) to signals mostly cascading down (AHA! I get what that means! Let's do this in response).

It was written in 2004, so like this article it lacks some of the insight we've gained over the past decade or so. But I think some of its general insights are still relevant. Whether it's NNs or HTMs or some yet-to-be-discovered algorithm that ends up being the driver of general AI, this article definitely resonates. At the most general levels of AI, we are still in the "confused" phase: viewing the state of AI as a whole system, our signals are still largely questions going up the conceptual hierarchy, rather than understanding cascading down.