The Economics of Digital Intelligence Capital: Endogenous Depreciation and the Structural Jevons Paradox

2026-01-18

AI Summary

This study investigates the economic dynamics of large language models as “digital intelligent capital” in the artificial intelligence industry, with a focus on their impact on market competition, demand structure, and value creation. By developing a microeconomic model integrated with agent-based simulation and calibration—incorporating mechanisms of data decay and user feedback learning—the work uncovers three core phenomena: the “Red Queen effect,” a “structural Jevons paradox,” and a “data-flywheel-driven winner-takes-all” dynamic. It further identifies the conditions under which upstream capability expansion undermines downstream value, termed the “encapsulation trap.” The analysis confirms endogenous depreciation of digital intelligent capital, super-elastic growth in computational demand, and spontaneous market polarization, offering a unified theoretical framework for strategic and policy design in the AI sector.

Technology Category

• Natural Language Processing: (Large) Language Models
• Game Theory and Economic Paradigms: Mechanism Design
• Machine Learning: Deep Neural Architectures and Foundation Models

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

This paper develops a micro-founded economic theory of the AI industry by modeling large language models as a distinct asset class-Digital Intelligence Capital-characterized by data-compute complementarities, increasing returns to scale, and relative (rather than absolute) valuation. We show that these features fundamentally reshape industry dynamics along three dimensions. First, because downstream demand depends on relative capability, innovation by one firm endogenously depreciates the economic value of rivals'existing capital, generating a persistent innovation pressure we term the Red Queen Effect. Second, falling inference prices induce downstream firms to adopt more compute-intensive agent architectures, rendering aggregate demand for compute super-elastic and producing a structural Jevons paradox. Third, learning from user feedback creates a data flywheel that can destabilize symmetric competition: when data accumulation outpaces data decay, the market bifurcates endogenously toward a winner-takes-all equilibrium. We further characterize conditions under which expanding upstream capabilities erode downstream application value (the Wrapper Trap). A calibrated agent-based model confirms these mechanisms and their quantitative implications. Together, the results provide a unified framework linking intelligence production upstream with agentic demand downstream, offering new insights into competition, scalability, and regulation in the AI economy.

Problem

• Digital Intelligence Capital
• Endogenous Depreciation
• Structural Jevons Paradox
• Red Queen Effect
• Data Flywheel

Innovation

• Digital Intelligence Capital
• Red Queen Effect
• Structural Jevons Paradox
• Data Flywheel
• Wrapper Trap