AI Allocability (AA)
AA — The extent to which economic entities, assets, or services can be allocated by AI systems in consideration sets and decision processes.
Description
AI Allocability measures how suitable entities are for AI-mediated allocation. High AA means the entity can be discovered, evaluated, compared, and selected by AI systems. Low AA indicates exclusion from AI-mediated consideration regardless of economic merit.
Related Concepts
Related Research
Computational Market Access
Economic participation in markets increasingly mediated by artificial intelligence is undergoing a structural transition whose scope extends beyond technological change to the institutional foundations of market organization itself. The primary constraint is shifting from visibility—whether an actor can be found—to computational market access—whether an actor can be computationally admitted into machine-constructed consideration sets. This document establishes computational market access as the institutional framing layer for understanding AI-mediated markets, explaining why ranking presupposes inclusion, why exclusion now precedes competition, why representation is evolving from communication into allocative infrastructure, and why participation itself becomes infrastructure-dependent. The framework positions three complementary layers: Computational Market Access (institutional framing), Computational Market Economics (mathematical foundation), and Network-Dependent Allocation (formal proof layer).
The AI Allocability Discount
The AI Allocability Discount (AAD) captures the potential reduction in an asset's value, liquidity, or demand access that arises not from weak fundamentals, but from expensive computational representation that reduces its allocability in AI-mediated markets. This paper introduces AAD as a theoretical framework for measuring how poor computational representation may affect Italian real estate and hospitality assets. We develop the Inference Burden Score (IBS) as a metric for computational cost, Computational Liquidity (CL) as machine-processability under bounded inference, and GARI as a jurisdictional measure of allocability risk.
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).