Computational Transmission Gap (CTG)
CTG = PD - RD — The portion of potential economic demand that is lost due to exclusion, friction, or gaps in AI-mediated channels. Also referred to as Computational Demand Leakage.
Description
The Computational Transmission Gap measures economic value that fails to transmit through AI-mediated systems. CTG arises from computational exclusion, representation gaps, verification failures, or action constraints. High CTG indicates significant economic loss even in the presence of actual demand. The Balance-Sheet Economics paper links CTG to Distribution Dependency, Acquisition and Distribution Cost, and Contribution-Margin Compression.
Related Concepts
Related Research
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).
The Balance-Sheet Economics of AI-Mediated Demand
The migration of discovery and comparison from human-mediated search to AI-generated answers and agentic interfaces may alter the economics of acquiring and distributing demand in physical-asset markets. This paper examines how AI-mediated demand formation could affect customer acquisition costs, distribution dependency, contribution margins, and asset productivity in real estate and hospitality. We propose that zero-click—initially observed as a traffic problem—may transmit structurally into distribution cost inflation and ultimately appear as margin pressure. We formalize a transmission mechanism in which representation deficits may transmit through demand leakage, distribution dependency, and acquisition-cost inflation to contribution-margin compression, while lower qualified-demand capture may separately affect occupancy, time-to-match, and asset productivity. Contribution margin and asset productivity may subsequently interact through operating and reinvestment feedback effects. The paper introduces a measurement architecture designed for empirical validation: representation quality (VIS), readiness (GARI), market outcomes (ARS, PDD, CDL), financial impact (RAAC, CMP, RROI), and exploratory composite indices. The Verified Property Representation (VPR) is positioned as a proposed persistent representation layer intended to improve computational legibility—a testable intervention through which the paper's hypotheses may be validated.
Related Primitives
Potential Demand (PD)
PD — Economic demand that exists or could exist but is not realized through AI-mediated channels due to exclusion, friction, or transmission gaps.
Realised Demand (RD)
RD — Economic demand that successfully completes the AI-mediated funnel from discovery through selection to transaction.
Transmission Coefficient (TE)
TE = RD / PD — The ratio of realised demand to potential demand, measuring how effectively economic demand transmits through AI-mediated channels.
Distribution Dependency (DD)
The share of demand or revenue dependent on paid, commissioned, portal, OTA, or other intermediated channels.
Contribution-Margin Compression
A reduction in contribution margin caused by increased variable acquisition and distribution costs, holding other relevant factors constant.