Twenty Watts Versus a Factory: Empirical Proof-of-Concept for the Grimm-Tuttle Framework via Thermodynamic Efficiency and Autonomous Theorem Proving

Recent institutional achievements in automated theorem proving and combinatorial problem-solving—exemplified by Google DeepMind's AlphaProof Nexus framework—demonstrate that conversational multi-agent reasoning loops can independently resolve long-standing mathematical conjectures. In this monograph, we examine these industrial-scale achievements through the lens of the Grimm-Tuttle Framework. By comparing the multi-megawatt infrastructure ("the factory") required by centralized artificial intelligence laboratories with the biological-conversational baseline of an independent researcher operating at approximately 20 watts, we establish that DeepMind's architectural methodology serves as an empirical proof-of-concept for Grimm's Law of Networking. We demonstrate that dialogue-driven invariant refinement, when governed by balanced ternary logic and discrete taxicab metrics, achieves identical theoretical velocity at zero industrial overhead, validating decentralized, low-wattage substrate routing.