Substrate Economics: The Token-Cost Asymmetry Between Deterministic and Inference-Based Governance

Runtime governance validation costs differ by orders of magnitude depending on
validation type. Deterministic CPU-bound validation and inference-based LLM-
bound validation occupy entirely different cost regimes. This paper quantifies the
per-decision cost gap. It pairs a directly measured SDC CPU benchmark with
protocol-based token-consumption estimates from the 183,924-evaluation MTCP
corpus. The substrate side is therefore an empirical measurement and the
inference side an economic model, and the paper is framed as an economic
modeling study in which that asymmetry is treated explicitly. The gap is
structural, not incremental. It widens at scale. A second structural difference is
verdict variance. Deterministic substrate validation produces zero verdict
variance by construction. It automatically meets MDR and NMI repeatable verdict
requirements. Inference-based evaluation has an average re-run multiplier of
1.36x across the evaluated models. Worst-case multipliers reach 3-5x for high-
variance models. The combined cost-and-variance argument constitutes an
economic case for substrate-first governance. This case is independent of
architectural and intellectual arguments.