Gardiner's Knowledge Theory of Capital: Value of AI, 458pp

Jeffrey Gardiner submitted A Knowledge Theory of Capital: The Value of Natural and Artificial Intelligence to arXiv on 12 June 2026, a 458-page theory-building monograph (arXiv:2606.18288) that reframes knowledge as a form of capital. The work positions "knowledge-bearing stock" as its central object and sets out formal concepts, mechanisms, a measurement apparatus, and falsification conditions across eight figures.

What is Gardiner’s Knowledge Theory of Capital?

Gardiner defines knowledge-bearing stock as the central object and analyzes how it is generated, converted into governable form, deployed, improved through feedback, enclosed or shared, measured, impaired, and used as input to future production. The monograph starts from Adam Smith's theory of labour, stock, specialization, and market extent, then asks what changes when knowledge becomes stock-like, mobile across forms, scalable, governable, recombinable, and imperfectly visible in accounting.

Gardiner distinguishes multiple forms of knowledge-bearing stock: embodied, disembodied, institutionalized, commons, and public knowledge. He introduces new terms and mechanisms for the analysis, including first conversion, cognitive enclosure, feedback capture, dark capital, and expected knowledge loss, and illustrates these concepts across eight figures in the text.

How does the monograph make its argument testable?

The book presents a conditional, testable argument and includes a measurement apparatus and falsification conditions as part of its theoretical apparatus. Gardiner frames the claim about knowledge-bearing stock not as pure philosophy but as an empirical programme: the work develops formal concepts and mechanisms and specifies how those ideas could be measured and falsified.

Concrete publication details underline the scope: the submission is catalogued on arXiv as arXiv:2606.18288, includes 458 pages and eight figures, and carries an arXiv-issued DOI via DataCite that is noted as pending registration. The subject tags list General Economics, Artificial Intelligence, and Theoretical Economics, and classification metadata includes MSC classes Primary 91B08 and secondary classes 91B32, 91B38, 91A80, 90B50, 94A15, plus ACM classes J.4, H.4, I.2.

Why it matters

Gardiner's central assessment is succinct: "modern wealth depends not only on capital accumulation, but on how productive knowledge is governed." If knowledge functions like a stock that is mobile, recombinable, and partially hidden in accounting, then ownership, measurement, and governance regimes will shape where and how value accrues. That shifts the economic question from simply accumulating inputs toward institutions, governance mechanisms, and the capacity to capture feedback and prevent expected knowledge loss.

The monograph lays theoretical groundwork that could change empirical priorities: measuring knowledge-bearing stock, detecting "dark capital," and testing the effects of cognitive enclosure or feedback capture would reorder policy debates about intellectual property, corporate governance, and public epistemic infrastructure.

What to watch

Watch for the DataCite DOI registration for arXiv:2606.18288 and for early empirical work that uses Gardiner's measurement apparatus or attempts his falsification conditions. The uptake of specific terms he coins, such as cognitive enclosure, feedback capture, dark capital, and expected knowledge loss, in follow-up research will signal whether the framework moves from theory-building to operational use.