The Intelligence Capital Manifesto: how enterprises can win in the intelligence economy
Author(s)
Shrier, David
Type
Report
Abstract
This paper develops a unified theoretical and empirical framework for understanding the rise of Intelligence Capital as a new dominant factor of production: institutionalized, process-owning human+AI systems that learn, retain memory, and compound in economic value.
We argue that recent macroeconomic anomalies, including sustained output growth with weak employment creation, rising productivity without proportional hiring, and increased capital concentration, reflect a structural transition rather than cyclical fluctuations. Using cross-national labor and productivity data, we document a “labor inversion” in which output increasingly flows through embedded intelligence systems prior to labor-market absorption, generating persistent “phantom jobs.”
At the microeconomic level, we reinterpret the firm as a learning-and-compounding engine rather than a transaction-cost minimizer, modeling enterprises as portfolios of Intelligence Capital Generators that internalize knowledge, capture feedback, and exhibit power-law return dynamics.
We introduce the Intelligence Capital Yield Function to formalize how exploration, failure, and institutional learning are converted into scalable economic advantage. The analysis shows that high experimental failure rates and valuation asymmetries are endogenous features of Intelligence Capital formation rather than indicators of misallocation.
Finally, we identify organizational and behavioral constraints that limit compounding and propose governance mechanisms that align managerial incentives with long-run learning yield. The findings contribute to theories of the firm, endogenous growth, and technological change by demonstrating how intelligence-based capital reshapes productivity, employment, and competitive dynamics in advanced economies.
We argue that recent macroeconomic anomalies, including sustained output growth with weak employment creation, rising productivity without proportional hiring, and increased capital concentration, reflect a structural transition rather than cyclical fluctuations. Using cross-national labor and productivity data, we document a “labor inversion” in which output increasingly flows through embedded intelligence systems prior to labor-market absorption, generating persistent “phantom jobs.”
At the microeconomic level, we reinterpret the firm as a learning-and-compounding engine rather than a transaction-cost minimizer, modeling enterprises as portfolios of Intelligence Capital Generators that internalize knowledge, capture feedback, and exhibit power-law return dynamics.
We introduce the Intelligence Capital Yield Function to formalize how exploration, failure, and institutional learning are converted into scalable economic advantage. The analysis shows that high experimental failure rates and valuation asymmetries are endogenous features of Intelligence Capital formation rather than indicators of misallocation.
Finally, we identify organizational and behavioral constraints that limit compounding and propose governance mechanisms that align managerial incentives with long-run learning yield. The findings contribute to theories of the firm, endogenous growth, and technological change by demonstrating how intelligence-based capital reshapes productivity, employment, and competitive dynamics in advanced economies.
Date Issued
2026-02-08
Citation
2026
Publisher
Visionary Future LLC.
Copyright Statement
© 2026 Visionary Future LLC. All rights reserved.
Identifier
https://profiles.imperial.ac.uk/david.shrier
Publication Status
Published