The Economics of Human-Gated Intelligence: Empirical Validation of Professional AI Service Models

This paper provides simulation-based and real-world empirical validation of professional AI service models. Using a hybrid approach, we combine economic modeling with case study analysis of 2026 autonomous agent failures at Meta and Amazon to validate the architectural advantages of Non-Agentic General Intelligence (NAGI).Controlled simulations ($N=90$) utilizing verified February 2026 pricing demonstrate that human-gated NAGI achieves a 90.7% cost advantage over commodity APIs ($p < 0.000001$). The model further enables negative customer acquisition costs (mean CAC = −$10,736). We defend these findings through an epistemological framework grounded in the philosophy of science, likening simulation-based inquiry to the study of virtual particles in quantum field theory.NAGI’s cost eliminations stem from its formal mathematical theory, which mitigates the structural risks of agency. Results align with broader human-AI collaboration research and transaction cost economics. Notably, cost advantages strengthen as task complexity increases (89.7% simple to 92.0% complex), contradicting "human-in-the-loop" bottleneck assumptions. Finally, we present a commoditization–professionalization spectrum to guide enterprise AI deployment strategies.