# A Knowledge Theory of Capital:The Value of Natural and Artificial Intelligence, Volume 1

arXiv: https://arxiv.org/abs/2606.18288 · HTML: https://mlsanity.com/paper/2606.18288/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 131 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Self-Consuming Generative Models Go MAD](https://mlsanity.com/paper/2307.01850/) (2023)
- [RIP Twitter API: A eulogy to its vast research contributions](https://mlsanity.com/paper/2404.07340/) (2024)

## Abstract (arXiv)

This volume develops a knowledge theory of capital for economies in which productive capacity increasingly resides in software, data, models, routines, expertise, platforms, organizations, commons, and public epistemic infrastructure. Beginning from Adam Smith's theory of labour, stock, specialization, and market extent, it asks what changes when knowledge becomes stock-like, mobile across forms, scalable, governable, recombinable, and imperfectly visible in accounting. The book introduces knowledge-bearing stock as the central object and analyses how it is generated, converted into governable form, deployed, improved through feedback, enclosed or shared, measured, impaired, and used as input to future production. It distinguishes embodied, disembodied, institutionalized, commons, and public knowledge forms and develops concepts such as first conversion, cognitive enclosure, feedback capture, dark capital, and expected knowledge loss. The argument is conditional and testable: modern wealth depends not only on capital accumulation, but on how productive knowledge is governed.