Synopsis
*Volume 1: The Theory* develops the main argument of *A Knowledge Theory of Capital: The Value of Natural and Artificial Intelligence*. It asks what changes in capitalism when productive knowledge becomes a central form of capital-like stock: embodied in people, encoded in software and data, institutionalized in organizations, sustained in commons, governed by platforms, or maintained as public epistemic infrastructure.
The theory starts with Adam Smith because *The Wealth of Nations* supplied the classical operating model of capitalist wealth: labour, stock, specialization, exchange, market extent, wages, profit, rent, and national wealth. Smith’s framework remains powerful, but it was built around capital that was more visibly material, separable, vendible, and countable than much of the productive stock driving modern firms and economies. Volume 1 reconstructs that baseline, then identifies where its assumptions strain when value resides in software, datasets, AI models, user feedback, organizational routines, technical standards, expertise, trust, open-source systems, and institutional capability.
The book’s central object is knowledge-bearing stock: accumulated artefacts, routines, rights, systems, capabilities, records, models, commons, or infrastructures that carry productive knowledge without being identical to knowledge itself. Such stock becomes capital-like only when it yields, or is credibly positioned to yield, productive services under conditions of access, capability, governance, maintenance, demand, interoperability, and recombination. Volume 1 distinguishes knowledge as such from knowledge-bearing capital, and treats natural and artificial intelligence as different carriers and operators of productive knowledge.
The theory’s core mechanism is knowledge circulation: productive knowledge is generated, converted into governable stock, deployed, improved through feedback, enclosed or shared through institutions, measured imperfectly, and returned as an input to future generation. Volume 1 separates the Knowledge Generation Model from the Knowledge Conversion Matrix. Generation explains how new productive knowledge arises through recombination, experimentation, discovery, invention, interpretation, and feedback. Conversion explains what happens after knowledge exists: how it becomes codified, institutionalized, accessed, restricted, priced, appropriated, shared, or lost.
The volume develops linked concepts: the Operative Knowledge Unit, conditional separability, first conversion, residence–governance pairing, knowledge potential, knowledge impedance, knowledge yield, cognitive enclosure, feedback capture, strategic enclosure, governance-position risk, dark capital, and the Smithian inversion. The Smithian inversion is the book’s central warning: private accumulation of knowledge-bearing capital can strengthen present production while narrowing the recombination fields and learning loops from which future productive knowledge would otherwise emerge.
Volume 1 is conditional and falsifiable. It does not claim that all knowledge is capital, that all enclosure is harmful, or that accounting systems should recognize every knowledge stock as an asset. Instead, it asks how knowledge becomes capital-like, under what governance conditions it appreciates or depreciates, who captures its yield, who bears its loss, and what existing measures fail to see. Its cases include AI systems, platform APIs, software dependencies, cybersecurity failures, patent thickets, model weights, open-source maintenance, public standards, and accounting shadows.
The last claim is that wealth in a knowledge-bearing economy cannot be understood only by counting output, labour, physical capital, recognized intangible assets, or market exchange. It also depends on how societies generate, preserve, convert, govern, recombine, measure, and damage the knowledge-bearing stocks that make future production possible.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
