US vs. China in the LLM Race: Capability, Diffusion, Development & the Deterministic Factor of Third Economy Adoption
The prevailing narrative of the AI race assumes that technological competition culminates in a single winner, a malformed rhetoric that rests on an under-specified concept of victory (Siegel, 2026). Unlike historical technological competitions, the AI race has no agreed endpoint, no universally accepted metric of success, and no consensus on whether achievement should be defined by artificial general intelligence (AGI), frontier-model capability, compute capacity, scientific productivity, economic competitiveness, military advantage, or global diffusion (Akula & Guest, 2026). Consequently, assertions that one state will ultimately win the AI race are conceptually incomplete. This working paper therefore shifts attention from identifying a presumed winner to examining an alternative question: whether strategic influence may depend not only on frontier capability, but also on how AI systems are adopted, trusted, and deployed across third-country markets. The following corpus outlines ten working parts.