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Zero-Click and Computational Transmission Primitives

Volume XII: Primitives for analyzing the Zero-Click Economy, computational transmission gaps, and value reallocation in AI-mediated markets. These primitives measure how demand, visibility, and value flow through AI systems.

Primitives (44)

emerging

Computational Selection (CS)

CS(e) — The process by which AI systems evaluate and select assets for inclusion in consideration sets, independent of human browsing or clicking.

ai-mediated-selectionselection-readiness
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Computational Recommendation (CR)

CR(e) — The stage where AI systems present selected options to users with explanations and rationale for the recommendation.

ai-selectionexplainability
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Computational Verification (CV)

CV(e) — The process by which AI systems verify claims, trust signals, and preconditions before recommending or acting on assets.

verification-primitivetrust-infrastructuremachine-readable-trust
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Computational Actionability (CAc)

CAc(e) — The extent to which AI systems can initiate or coordinate transactions on behalf of users, subject to authorization and safety constraints.

action-constraintsowner-confirmationtransaction-capability
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Computational Conversion (CC)

CC — The rate at which AI-mediated selection and recommendation convert into actual transactions or economic outcomes.

ai-mediated-actiontransaction-capabilityselection-readiness
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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.

computational-transmission-gapcomputational-exclusionsilent-exclusion
hypothesis

Realised Demand (RD)

RD — Economic demand that successfully completes the AI-mediated funnel from discovery through selection to transaction.

potential-demandcomputational-transmission-gapcomputational-conversion
hypothesis

Transmission Coefficient (TE)

TE = RD / PD — The ratio of realised demand to potential demand, measuring how effectively economic demand transmits through AI-mediated channels.

potential-demandrealised-demandcomputational-transmission-gap
hypothesis

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.

potential-demandrealised-demandtransmission-coefficientsilent-exclusion
hypothesis

Visibility Transmission Gap (VTG)

VTG — The portion of assets that are visible online but not discoverable by AI systems through computational search.

computational-visibilitysilent-exclusionai-mediated-discovery
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Selection Transmission Gap (STG)

STG — The portion of AI-discovered assets that are not selected or recommended by AI systems for user consideration.

computational-selectionselection-readinessai-selection
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Recommendation Transmission Gap (RTG)

RTG — The portion of AI-selected assets that are not recommended to users or where recommendations fail to convert to user consideration.

computational-recommendationai-selectionexplainability
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Verification Transmission Gap (VtG)

VtG — The portion of recommended assets where users or AI systems cannot verify claims or preconditions before taking action.

computational-verificationverification-primitivetrust-infrastructure
hypothesis

Action Transmission Gap (ATG)

ATG — The portion of verified assets where transactions cannot be completed due to action protocol gaps, authorization failures, or missing infrastructure.

computational-actionabilityaction-constraintsowner-confirmation
hypothesis

Revenue Realisation Gap (RRG)

RRG — The portion of economic value that exists but is not captured as revenue due to AI-mediated transmission failures.

computational-transmission-gapcomputational-conversionai-mediated-revenue
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Citation Transmission Branch (CtB)

CtB — The phenomenon where assets are cited or referenced by AI systems but not transmitted through to economic transactions or revenue capture.

computational-transmission-gapai-mediated-selectioncitation-without-monetisation
hypothesis

Computational Visibility Loss (CVL)

CVL — The degradation or complete loss of AI-mediated visibility due to representation decay, canonical drift, or platform dependency.

computational-visibilitycanonical-driftplatform-dependency
hypothesis

Computational Access Gap (CAG)

CAG — The disparity between online presence and AI-mediated access, where assets exist online but cannot be discovered or selected by AI systems.

computational-visibilitysilent-exclusionai-mediated-discovery
hypothesis

Economic Recommendation Loss (ERL)

ERL — The economic value lost when AI systems recommend alternatives over economically optimal choices due to representation quality or algorithmic bias.

ai-selectionrepresentation-qualityselection-readiness
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Inference Burden (IB)

IB — The computational cost and complexity required for AI systems to extract, infer, or reconstruct information from representations.

representation-qualityrepresentation-efficiencymachine-readability
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Token Efficiency (TEf)

TEf — The amount of useful information extracted per unit of computational cost (tokens) in AI-mediated processing.

inference-burdenrepresentation-efficiencycomputational-cost
hypothesis

Computational Liquidity (CL)

CL — The ease with which assets can be discovered, evaluated, compared, and transacted by AI systems in AI-mediated markets.

selection-readinesstransaction-readinesscomputational-eligibility
hypothesis

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.

computational-eligibilitycomputational-admissibilityrepresentation-capital
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AI Allocability Discount (AAD)

AAD — The reduction in economic value or market access for assets with low AI allocability, independent of their fundamental economic quality.

ai-allocabilitycomputational-eligibilityrepresentation-capital
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Platform Dependency (PD)

PD — The extent to which AI-mediated access and allocability depend on specific platforms, infrastructures, or intermediaries.

inferential-monopolycomputational-consideration-infrastructurecanonical-representation
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Zero-Click Exposure (ZCE)

ZCE — The degree to which economic entities are exposed to AI-mediated allocation without human intermediate clicking or browsing.

ai-mediated-selectionai-mediated-discoveryzero-click-economy
hypothesis

Computational Business Risk (CBR)

CBR — The risk to business continuity and revenue from AI-mediated market dynamics, including exclusion, transmission loss, and algorithmic dependence.

computational-transmission-gapplatform-dependencyai-allocability
hypothesis

Dynamic Computational Risk (DCR)

DCR — Time-varying risk to economic actors from AI-mediated market dynamics, including algorithm changes, representation decay, and competitive exclusion.

computational-business-riskcomputational-visibility-lossplatform-dependency
hypothesis

Enterprise Adaptation Velocity (EAV)

EAV — The speed at which organizations can improve representation quality, add verification infrastructure, and increase AI allocability.

adaptation-velocityrepresentation-capitalai-allocability
hypothesis

Technological Velocity (TV)

TV — The rate of change in AI system capabilities, representation standards, and market infrastructure that affects allocability over time.

dynamic-computational-riskadaptation-velocityenterprise-adaptation-velocity
hypothesis

Adaptation Velocity (AV)

AV — The rate at which economic entities can improve their AI allocability through representation enhancement, verification infrastructure, and protocol adoption.

technological-velocitydynamic-computational-riskai-allocability
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Computable Asset (CA)

CA — An economic asset or entity encoded as machine-readable information that AI systems can discover, evaluate, compare, and transact.

verified-property-recordcanonical-representationmachine-readable-entity
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Computable Asset Ratio (CAR)

CAR = CA / TA — The proportion of total assets in an economy or portfolio that are computable (machine-readable, verifiable, transaction-capable for AI systems).

computable-assetnational-computabilityai-allocability
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National Computability (NC)

NC — The degree to which a national economy has machine-readable, verifiable, and transaction-capable representations across its asset base, institutions, and infrastructure.

computable-assetcomputable-asset-ratiosovereign-adaptation-velocity
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Sovereign Adaptation Velocity (SAV)

SAV — The speed at which nations, jurisdictions, or sovereign entities can improve representation infrastructure, verification systems, and AI-readiness across their economic base.

adaptation-velocitynational-computabilitycomputational-sovereignty
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Sovereign Adaptation Gap (SAG)

SAG = TV - SAV — The gap between technological change velocity and sovereign adaptation velocity, creating vulnerability to AI-mediated exclusion.

technological-velocitysovereign-adaptation-velocitydynamic-computational-risk
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Monetary Velocity Gap (MVG)

MVG — The reduction in monetary policy effectiveness due to computational transmission gaps, AI-mediated allocation, and sovereign exposure to external AI systems.

computational-transmission-gapsovereign-adaptation-gapcomputational-monetary-transmission
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Dynamic Monetary Sovereignty Risk (DMSR)

DMSR(j) = f(EAD, CTG, SAG, MPE) — Sovereign risk arising from external AI dependency, computational transmission gaps, adaptation velocity, and monetary policy effectiveness constraints.

monetary-velocity-gapsovereign-adaptation-gapcomputational-transmission-gap
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AI-Mediated Revenue Share (AMRS)

AMRS = R_AI / (R_AI + R_H) — The proportion of total revenue transacted through AI-mediated channels versus human-mediated channels.

computational-conversionzero-click-exposureai-mediated-action
hypothesis

Representation Selection Elasticity (RSE)

RSE — The sensitivity of AI-mediated selection outcomes to changes in representation quality, measured as the change in selection probability per unit change in representation metrics.

ai-selectionrepresentation-qualityselection-readiness
hypothesis

Inference Cost per Successful Action (ICSA)

ICSA = TC / SA — The computational cost incurred per successful action or transaction mediated by AI systems.

token-efficiencycomputational-conversionai-mediated-action
hypothesis

Computational Revenue at Risk (CRAR)

CRAR — The revenue exposed to loss or reduction due to poor AI allocability, representation gaps, or computational transmission failures.

computational-business-riskai-allocabilitycomputational-transmission-gap
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Representation Return on Investment (R-ROI)

R-ROI = (Gains - Costs) / Representation Investment Costs — The economic return on investment in representation quality improvements for AI-mediated markets.

representation-capitalai-allocabilityselection-readiness
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Cross-Border Computational Reallocation (CBCR)

CBCR — The extent to which AI-mediated allocation redirects economic demand across jurisdictional borders, favoring some regions over others.

computational-transmission-gapjurisdictional-legibilitynational-computability