Human Capital 2.0: Ownership, Prosperity and Human Time in the Age of Intelligent Machines

Human Capital 2.0 (HC2): Ownership, Prosperity and Human Time in the Age of Intelligent Machines Abstract Human Capital 2.0 (HC2) proposes an economic and institutional framework for examining how technological productivity gains, particularly those associated with artificial intelligence and automation, might translate into broader capital ownership, reduced economic dependence on labor, and greater human agency. As intelligent machines increasingly perform economically valuable tasks, the relationship between human labor, productive capital, income, and wealth distribution may undergo significant structural change. HC2 addresses a central question: Can the productivity gains of increasingly autonomous systems be converted into measurable economic benefits and meaningful ownership opportunities for individuals, rather than primarily reinforcing existing concentrations of capital? The framework distinguishes two core economic mechanisms: 1. Capital Efficiency (CE): Reducing the full economic cost of accessing equivalent goods, services, or productive capabilities through demonstrably superior arrangements. Savings qualify only when genuine substitution occurs and all relevant costs, risks, and capability requirements are accounted for. 2. Distributed Capital Formation (DCF): Creating pathways through which individuals can acquire legally enforceable, economically meaningful, and potentially portable ownership interests in productive assets. The framework distinguishes genuinely additional capital formation from ordinary saving, investment, or redistribution. These mechanisms operate within a broader principle of Human Sovereignty, emphasizing individual autonomy, accountable governance, freedom of choice, and the preservation of human agency as technological systems become more capable. HC2 also introduces Labour Optionality as a measurable outcome: the extent to which economic improvements can reduce the amount of paid labor required to sustain a given standard of living, potentially allowing individuals greater discretion over how they allocate their time. Version 1.2.1 presents the framework's first quantitative research phase (Level 1), incorporating household-level economic comparisons, capital-efficiency audits, DCF distinctiveness and source-attribution tests, integrated scenario modelling, and stochastic stress testing. Seven accompanying analytical workbooks document the assumptions, calculations, sensitivity analyses, and findings. The results are deliberately conditional. Capital Efficiency can produce measurable benefits under qualifying substitution scenarios, but may be negative where shared arrangements introduce additional costs or underperform conventional alternatives. Distributed Capital Formation remains empirically unresolved: the current analysis identifies no verified HC2-specific source of additional productive capital. Accordingly, the framework does not claim demonstrated economic superiority. Rather than treating these limitations as secondary, HC2 incorporates explicit evidence thresholds, adverse comparisons, and conditions under which its propositions should be revised or rejected. The paper establishes a falsifiable research agenda for examining whether AI-enabled productivity, institutional innovation, and broader capital participation can improve economic resilience, expand meaningful ownership, and increase human freedom over time. Research status: Level 1 — Quantitatively Modelled (Conditional; Not Demonstrated). Further validation requires pre-specified real-world implementation and independent empirical assessment. Keywords: Artificial Intelligence; Automation; Capital Efficiency; Distributed Capital Formation; Capital Ownership; Labour Optionality; Human Sovereignty; Economic Inequality; Productivity; Wealth Distribution; Institutional Economics; AI Governance. Version: 1.2.1 — Public Release Author: Maurice Ghosn, Independent Researcher Publication type: Independent Research Preprint / Working Paper DOI: https://doi.org/10.5281/zenodo.23314570 License: Creative Commons Attribution 4.0 International (CC BY 4.0).