Specification Readiness: Measuring an Architectural Antecedent of Functional Friction and AI Returns

This paper introduces specification readiness – the degree to which a firm’s commitments are codified in versioned, machine-readable, queryable form – as a new strategic construct and supplies a scalable archival operationalization. Drawing on an information-theoretic model of multi-interface architecture (Zharnikov, 2026am), the construct is argued to substitute query-based alignment for costly interface-maintenance functions, reducing a structural friction tax, accelerating brand-capital accumulation, lowering cross-stakeholder valuation dispersion, and raising returns to artificial-intelligence deployment. The primary measure is a continuous Specification Coherence Index built from year-over-year cosine similarity of 10-K narrative embeddings; a four-event sharp index supplies robustness. Five hypotheses (H1–H5) link the construct to these outcomes for US public firms 2010–2025, identified by within-firm continuous treatment, staggered difference-in-differences (Callaway & Sant’Anna, 2021; Goodman-Bacon, 2021), regulatory-compliance instruments, and advertising-cessation event studies, with Oster (2019) bounds. Pre-registered Monte Carlo mechanism tests (12.96 million trials; \(\alpha^* = .91\); Cohen’s d = 88.4) confirm the friction-tax phase shift. Three contributions follow: a first scalable archival measure of specification readiness, a reusable identification template pairing continuous textual treatment with sharp codification events, and formal evidence that specification readiness moderates the AI augmentation paradox. Includes zharnikov-2026an-specification-readiness-empirical.yaml (Paper Spec v0.1.0) – a machine-readable specification of the paper's claims, assumptions, and dependencies. The paper's full machine-first bundle (the SPINE claim/dependency graph and the ONTOLOGY term module) lives in the public repository; see https://github.com/spectralbranding/paper-spec for the standard. This PDF is generated programmatically from that machine-first source under a research-as-repository model.