The strangeness of this argument is its unnecessary for any one technique to be all in capable because a mixture of techniques is more powerful than one alone.

The strangeness of this argument is its unnecessary for any one technique to be all in capable because a mixture of techniques is more powerful than one alone. A LLM forming an abductive “plan” and synthesizing results from agent functions that do the deductive reasoning, optimization, math, and other functions LLMs are bad at is remarkably powerful and is incredibly flexible in new semantic contexts. Who cares if LLMs can solve partial differential equations if they can establish a partial differential equation to solve given semantic context and a sufficient training set of semantically grounded PDEs ? That can be delegated to the last 70 years of research on numerical methods. Similarly we don’t expect a recursive decent parser to play chess, but you can absolutely use a parser to make a grammar that exposes a chess playing solver. The combination of the solver and the grammar that enables its use is more powerful than the sum of its parts. LLMs neatly solve an enormous number of hitherto unsolved problems and yield untapped solutions to many more. Generative AI in general composes in powerful ways to unlock even more - semantic analysis of visual information expressible directly in structured language alone is powerful, as is exposing generative robotic motion and audio processing, paired with solvers, optimizers, etc - what’s the residual? Synthetic agency? We’ve had multi level goal based agents for a long time. One that’s reprogrammable by the generative model assembly?