Transfer of Cognitive Authority: A Measurement Framework for the Closure of Artificial Intelligence Production

This manuscript proposes a measurement framework for studying the transfer of cognitive authority from human operators to increasingly autonomous artificial intelligence systems. The framework distinguishes capability from cognitive authority and models the transfer of cognitive functions through four operational components: problem selection (P), solution generation (S), evaluation (E), and recursive improvement (R). It introduces measures of human intervention, recursive contribution, evaluator independence, substitution recovery, generalization, and validated capability production. The central research question is whether increasingly autonomous AI systems can assume progressively more of the cognitive functions required not merely to perform tasks, but to produce, evaluate, and improve their own future capabilities. The manuscript develops falsifiable experimental designs, controlled ablation and substitution procedures, difficulty controls, evaluator-independence requirements, synthetic analyses, power considerations, and reproducibility procedures. Published empirical work concerning AI-assisted machine-learning research and autonomous AI research systems is discussed separately from the manuscript's synthetic analyses and proposed measurements. This work was developed with substantial assistance from generative artificial intelligence, including ChatGPT. AI assistance was used during conceptual exploration, literature-oriented research, mathematical formulation, experimental-design development, computational analysis, drafting, editing, and adversarial critique. The author retains responsibility for the final content, interpretation, limitations, citations, and conclusions.