Human Knowledge as an Economic Asset in the Age of Artificial Intelligence

Artificial intelligence systems do not create knowledge. They aggregate, compress, and recombine knowledge that was first produced by human beings - through writing, research, code, dialogue, and judgment. Despite this foundational dependency, no economic mechanism currently exists to recognize or compensate the human contributors whose intellectual output makes AI capability possible. This paper introduces the Knowledge Value Flow (KVF) framework - a systematic model describing how human knowledge is created, extracted, processed, and monetized within AI value chains, while redistribution to original contributors remains structurally absent. It argues that human knowledge constitutes a distinct economic asset class with properties — context-richness, non-depletion, and high marginal utility in machine learning systems - that differentiate it from raw data, labor, or capital as traditionally defined. Drawing on recent work in AI economics, data governance, machine learning theory, and intellectual property law, the paper identifies four structural barriers to value redistribution: the attribution problem, scale complexity, legal ambiguity, and economic resistance from incumbent business models. It evaluates six emerging compensation mechanisms — ranging from data licensing agreements and Shapley-based attribution to the proposed KVF Redistribution Layer - assessing their technical feasibility and economic viability. The paper concludes that the current structure of AI value creation represents the largest uncompensated extraction of intellectual output in economic history. Correcting this imbalance requires not merely ethical appeal, but regulatory mandate, platform redesign, and a formal reclassification of human knowledge within economic theory.

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