The Zero-Click Economy
AI Intermediation, Computational Transmission Failure, and Dynamic Enterprise Risk
Abstract
This working paper introduces the Zero-Click Economy framework, examining 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 across ten primary indices: ARI, GARI, ZCEI, PDI, CBRI, DCRI, EAVI, CAR, NCEI, SAVI, plus sovereign outputs including CRAVI, GCEI, SAG, and DMSRI.
Published: July 12, 2026 (Working Paper v1.0)
This working paper has been published on Zenodo with DOI 10.5281/zenodo.21321629. The proposed metrics and measurement frameworks are theoretical and require empirical calibration. This research presents a framework and does not endorse specific implementations.
Core Thesis
The fundamental problem the Zero-Click Economy addresses
The Zero-Click Problem
In markets where AI systems construct consideration sets and recommend actions before any user action occurs, the economics of transmission change fundamentally. Policy signals, demand changes, and capital flows may attenuate, misallocate, or leak before reaching intended targets. The bottleneck shifts from click-based traffic to computational allocability.
From Click-Based to Zero-Click Allocation
Traditional digital economics operated on click-based assumptions: users initiate queries, browse results, click through, and convert. Traffic flowed through observable channels. Zero-click allocation means AI systems construct consideration sets and recommend actions before any user action occurs. The user may never click—the AI system presents a bounded choice set and executes on user approval.
Click-Based Allocation
Users initiate queries, browse results, click through. Traffic flows through observable channels. Discovery precedes consideration.
Zero-Click Allocation
AI constructs consideration sets before user action. Recommendations happen within AI inference. Consideration precedes discovery.
The Core Shift
Economic signals transmit not through traffic, but through computational representation at inference time.
When AI systems construct consideration sets from representations as they exist at inference time—not from the period policy or demand signals were emitted—temporal disconnects emerge. Signals may arrive, representations may change, and by the time inference occurs, the signal has attenuated or misallocated.
Key Contributions
Primary frameworks and concepts introduced in this paper
Current Reporting-Period Hypothesis
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 temporal disconnect between signal emission and allocative outcome.
Law of Computational Transmission Attrition
Policy and demand signals attenuate across AI-mediated allocation stages. Each stage (citation, traffic, recommendation, action, computational demand) introduces attrition, creating cumulative signal loss.
Computational Transmission Gap
Signals may be financially operative (reaching financial channels) while computationally incomplete (failing to reach allocative consideration sets). Policy effectiveness requires measuring both channels.
Financial Transmission Pathway
Monetary policy and demand signals transmit through financial channels but may fail to reach computationally-mediated consideration sets. Financial transmission does not guarantee computational transmission.
Dynamic Computational Risk
Risk emerges from interaction between exposure (AI-mediated dependence), technological velocity (rate of change), financial sensitivity (capital structure), and adaptation capacity (response speed).
Computable Assets
Assets that are registered, identified, structured, verified, fresh, interoperable, discoverable, and actionable. The Computable Asset Ratio measures asset-level readiness for AI-mediated allocation.
Sovereign Adaptation Velocity
Effective speed at which jurisdictions recognize, implement, diffuse, evaluate, and update responses to AI-driven economic change. High SAVI correlates with preserved policy effectiveness.
Representation Economy Measurement Stack
Consolidated framework across ten primary indices (ARI, GARI, ZCEI, PDI, CBRI, DCRI, EAVI, CAR, NCEI, SAVI) plus sovereign outputs (CRAVI, GCEI, SAG, DMSRI).
Representation Economy Measurement Stack
Consolidated framework across five measurement levels
Level 1 — Representation Primitives
Representation Completeness Score
Measures the completeness of asset representation attributes required for AI-mediated consideration.
Machine Readability Index
Assesses the degree to which asset information is structured, machine-readable, and interoperable.
Representation Entropy Score
Quantifies the uncertainty and ambiguity in asset representation across conflicting sources.
Inference Burden Score
Measures the computational cost required to process fragmented, unstructured, or heterogeneous asset information.
Token Efficiency Ratio
Measures information density per token in asset representation for inference-efficient processing.
Level 2 — Readiness and Allocability
Agent Readiness Index
Firm- or asset-level readiness for AI-mediated allocation across representation, verification, and actionability dimensions.
Global Agent Readiness Index
Jurisdictional readiness for AI-mediated allocation across institutional, infrastructural, and interoperability dimensions.
Semantic Readiness Score
Assesses semantic portability and cross-system interpretability of asset representations.
AI Allocability Score
Probability that an asset is admitted, processed, compared, and recommended by AI systems under bounded inference.
AI Allocability Discount
Potential valuation, liquidity, or demand-access penalty from poor computational representation.
Verified Property Record Score
Completeness and quality of verified property record representation for AI-mediated discovery.
Computational Liquidity
Degree to which an asset can be discovered, interpreted, verified, compared, and acted upon by computational agents.
Level 3 — Computational Transmission
Citation Transmission Rate
Rate at which citations or references successfully transmit through AI-mediated consideration sets.
Traffic Transmission Rate
Rate at which traffic-based discovery successfully converts to AI-mediated consideration.
Recommendation Transmission Rate
Rate at which recommendations successfully transmit to user consideration and action.
Action Transmission Rate
Rate at which actionable recommendations successfully convert to completed transactions.
Visibility Transmission Gap
Difference between visibility-based exposure and AI-mediated consideration inclusion.
Level 4 — Risk, Adaptation, and Value
Zero-Click Exposure Index
Dependence on AI-mediated discovery, selection, and recommendation without human-initiated clicks.
Platform Dependency Index
Concentration of allocative access across external platforms and AI intermediaries.
Computational Business Risk Index
Composite static risk integrating exposure, readiness, dependency, and financial sensitivity.
Dynamic Computational Risk Index
Dynamic risk combining exposure, technological velocity, financial sensitivity, readiness, and adaptation capacity.
Enterprise Adaptation Velocity Index
Speed of enterprise adaptation across product, data, infrastructure, organizational, and commercial dimensions.
Level 5 — Asset, National, Regional, Sovereign Capacity
Computable Asset Ratio
Share of assets that are registered, identified, structured, verified, fresh, interoperable, discoverable, and actionable.
National Computable Economy Index
National-level measure of asset and economic-object computability.
Sovereign Adaptation Velocity Index
Effective speed at which jurisdictions recognize, implement, diffuse, evaluate, and update responses to AI-driven change.
Compound Regional Adaptation Velocity Index
Regional aggregation of SAVI adjusted for coordination and integration.
Sovereign Adaptation Gap
Difference or ratio between technological velocity and sovereign adaptation velocity.
Explore All Indices
View detailed definitions, formulas, and interpretations for each metric.
Applications
How the Zero-Click Economy framework applies across stakeholders
For Investors
- Allocability risk assessment for portfolio construction
- Dynamic risk modeling incorporating technological velocity
- Sector-level transition timing and relative performance
- Jurisdictional SAVI for sovereign risk evaluation
- CAR and NCEI for asset and country screening
For Firms
- ARI diagnostic for firm-level AI readiness
- EAVI measurement of organizational adaptation velocity
- ZCEI quantification of AI-mediated discovery dependence
- PDI assessment of platform and intermediation concentration
- DCRI for dynamic computational risk exposure
For Boards
- Computational risk oversight and governance
- Adaptation velocity as a board-level KPI
- Allocability risk in enterprise valuation
- Dynamic risk monitoring and reporting
For Policymakers
- Computational transmission measurement for policy design
- Adaptation policy for representation infrastructure
- SAVI as a sovereign competitiveness indicator
- Cross-border computational reallocation assessment
For Central Banks
- Computational channel measurement for monetary policy
- DMSRI for computational sovereignty risk assessment
- Traditional vs computational transmission effectiveness
- Policy calibration for computational transmission gaps
HomeSelf as Illustrative Infrastructure
Agent-ready infrastructure as implementation reference
Agent-Ready Infrastructure Layer
The Zero-Click Economy paper positions the HomeSelf Protocol as an illustrative implementation of agent-ready infrastructure. Verified Property Records (VPR) provide the canonical factual layer. AnswerPacks provide the inference-efficient interface layer. The protocol demonstrates how computable assets reduce inference burden, improve allocability, and mitigate computational transmission attrition.
Verified Property Records
Canonical, machine-readable property records with verified attributes and trust signals.
AnswerPacks
Inference-efficient interfaces for AI-to-AI property data exchange.
Action Protocols
Transaction-ready economic objects with verified action mandates.
Independence statement: The Zero-Click Economy framework validity does not depend on HomeSelf implementation. The framework is generalizable across agent-ready infrastructure implementations. HomeSelf serves as an illustrative example, not as a required or endorsed solution.
Relationship to Representation Economy Papers
How Layer 21 connects to prior layers
The Computational Transmission Gap
Monetary policy, inflation persistence, domestic recovery, external leakage, and computational sovereignty.
The AI Allocability Discount
Computational liquidity, AI allocability risk, GARI, Inference Burden Score, and VPR Readiness.
Agent Action Infrastructure
Permissioned action, verified mandates, Action Boundary Objects, and transaction-ready economic objects.
Agent-Ready Market Infrastructure
Computational Eligibility, GARI, and cross-jurisdictional market access.
Computational Sovereignty
Structural economic risks, Representation Capital, and computational market infrastructure.
Theoretical and Empirical Status
This working paper presents a theoretical framework. The Current Reporting-Period Hypothesis, Computational Transmission Attrition, Dynamic Computational Risk, and the consolidated Representation Economy measurement stack require empirical validation. Metric weights, thresholds, and interaction effects are illustrative and require calibration against observed outcomes.
The paper does not present HomeSelf as a monetary-policy tool, sovereign risk evaluator, or enterprise risk management system. The frameworks are generalizable and do not depend on specific implementation.
Version 1.0. Published July 12, 2026. DOI: 10.5281/zenodo.21321629.