This paper models AI companies as cybernetic systems using category theory, homotopy type theory, and neural tangent kernels to formalize feedback loops between observation, learning, deployment, and governance. The work addresses how firms observe and intervene in their operations while preserving specifications, drawing on organizational cybernetics and categorical learning theory without claiming to explain economic concentration or universal surveillance.
This academic paper proposes a mathematical framework for modeling AI companies as cybernetic systems using category theory, homotopy type theory, and neural tangent kernels. It examines how observations, learning procedures, governance rules, and feedback loops interact within AI firms, while explicitly disclaiming that these mathematical models establish empirical claims about actual corporate behavior or inevitable consolidation.