Platform Dependency (PD)
PD — The extent to which AI-mediated access and allocability depend on specific platforms, infrastructures, or intermediaries.
Description
Platform Dependency measures reliance on external platforms for AI-mediated access. High PD means allocability requires platform-specific infrastructure, creating lock-in and vulnerability to platform changes. Low PD indicates canonical, portable representation that works across platforms.
Related Concepts
Related Research
Inferential Monopoly Theory
This working paper introduces inferential monopoly theory as a distinct analytical category for market concentration in AI-mediated markets. Classical monopoly theory examines market power through control over production, distribution, pricing, or market share. This paper argues that AI-mediated markets introduce a prior layer of concentration: control over computational consideration infrastructure. Inferential monopoly describes concentration over the systems that determine which economic entities become admissible to consideration before human choice, price formation, or competitive interaction occurs. The paper defines inferential power, computational consideration sets, computational admissibility, and inferential infrastructure; distinguishes inferential monopoly from platform, data, search, and industrial monopoly; analyzes failure modes including representation exclusion, inferential lock-in, allocative opacity, and protocol capture; and examines theoretical implications for competition policy.
Canonical Entity Infrastructure
The transition from platform-mediated to AI-mediated markets represents not merely a technological shift but a fundamental restructuring of market coordination infrastructure. As AI systems become the primary intermediaries of discovery, comparison, reasoning, and transaction coordination, the representation of market entities transforms from a content concern into an infrastructure concern. This paper introduces Canonical Entity Infrastructure (CEI) as a foundational infrastructure layer for AI-mediated markets, analogous to DNS for navigation, payment rails for settlement, identity systems for authentication, or financial clearing infrastructure for settlement coordination. We argue that when AI systems mediate economic discovery through machine reasoning, entity identity becomes infrastructure. The form, portability, verification, and governance of canonical representations determine whether entities participate in AI-mediated consideration sets. Fragmented representations create coordination failure. Representation portability becomes market power. Verification becomes a trust primitive. Canonical resolution becomes a governance issue. AI systems require authoritative machine-readable entity layers. Representation ownership becomes economically strategic.
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
Canonical Representation
A single, authoritative machine-readable representation of an entity that serves as the source of truth for AI-mediated discovery and coordination.
Computational Consideration Infrastructure (CCI)
CCI — Control over systems that determine which options enter consideration sets in AI-mediated markets.
Inferential Monopoly (IM)
IM — Concentration over computational consideration infrastructure as allocative monopoly power.