The General Framework of AI Economics: Computation, Data, and the Limits of Formalized Intelligence

Artificial intelligence (AI) is the first general-purpose technology automating cognitive labor—the core of human productivity—but existing economic frameworks fail to explain its unique cost structure, extreme scale economies, and fundamental physical/logical limits. This paper develops a unified theory of AI economics grounded in computability theory and the ontological distinction between formalized computational systems and human consciousness. We propose a six-factor framework partitioning AI into two incommensurable layers: a formalized layer (compute, algorithms, data, parameters, energy) bound by Turing computability and thermodynamics, and a hyper-Turing layer (human consciousness) as the sole source of paradigm shifts. We make three core contributions: (1) formalize the training-inference cost migration theory, deriving the natural monopoly property of foundation models; (2) reinterpret scaling laws as production functions incorporating consciousness as a non-substitutable factor, explaining the AI productivity paradox; (3) prove three unbreakable "cages" constraining all AI systems, demonstrating consciousness as the irreducible source of value creation. Our framework explains key AI economic phenomena including platform monopolies, limits to AI self-evolution, and the tiered structure of token markets. We further develop a downstream value-augmentation theory of token pricing, demonstrating that token value is anchored by downstream industrial productivity gains rather than production costs, and that quality thresholds create discontinuous price gaps between frontier and commodity tokens. We conclude with testable empirical implications and targeted policy recommendations.