Computational Verification (CV)
CV(e) — The process by which AI systems verify claims, trust signals, and preconditions before recommending or acting on assets.
Description
Computational Verification enables AI systems to assess claim validity through structured trust signals, verification primitives, and provenance data. CV measures the extent to which AI systems can autonomously verify representation claims before recommendation or action.
Related Concepts
Related Research
Machine-Readable Trust Infrastructure
The emergence of AI-mediated markets represents not merely a technological transition but a structural reorganization of trust itself. This paper establishes that in markets coordinated by AI systems, trust transitions from human perception and platform reputation toward machine-readable, continuously verifiable, inferential infrastructure. We argue that the Cognitive Web requires a completely new trust architecture—one where trust becomes protocol-native, representation-dependent, and autonomously validated. The transition creates a new trust infrastructure layer centered around machine-readable attestations, inferential verification systems, canonical trust layers, representation integrity infrastructure, and coordination trust stacks. Control over trust infrastructure becomes strategic infrastructure. Inferential trust—the set of machine-readable signals that determine whether AI systems can coordinate with entities—becomes economic infrastructure. Trust portability—ability to carry trust signals across protocols, platforms, and coordination contexts—becomes strategically decisive.
The Zero-Click Economy
The Zero-Click Economy examines how AI-mediated discovery, selection, recommendation, verification, and action alter the transmission of economic signals from policy and demand to firms, assets, households, sectors, and jurisdictions. We introduce the Current Reporting-Period Hypothesis, which states that AI systems construct consideration sets from representations as they exist at inference time, not from the period the policy or demand signal was emitted. This creates Computational Transmission Attrition—policy or demand-induced signals may attenuate, misallocate, or leak before reaching intended economic targets. We formalize Dynamic Computational Risk as the interaction between exposure (dependence on AI-mediated allocation), technological velocity (rate of change in AI-mediated discovery), financial sensitivity (margin of capital, liquidity dependence), and adaptation capacity (speed of organizational response). The paper consolidates the Representation Economy measurement stack: Agent Readiness Index (ARI), Global Agent Readiness Index (GARI), Zero-Click Exposure Index (ZCEI), Platform Dependency Index (PDI), Computational Business Risk Index (CBRI), Dynamic Computational Risk Index (DCRI), Enterprise Adaptation Velocity Index (EAVI), Computable Asset Ratio (CAR), National Computable Economy Index (NCEI), Sovereign Adaptation Velocity Index (SAVI), and sovereign outputs including Compound Regional Adaptation Velocity Index (CRAVI), Global Computable Economy Index (GCEI), Sovereign Adaptation Gap (SAG), and Dynamic Monetary Sovereignty Risk Index (DMSRI).
Related Primitives
Verification Primitive
A machine-readable attestation, certification, or proof that validates the accuracy, authenticity, or status of a representation attribute.
Machine-Readable Trust
Trust signals encoded in structured, machine-readable format that AI systems can autonomously verify and process for confidence assessment.