This book establishes Applied AI Epistemics as a field of inquiry at the intersection of political economy, epistemology, cognitive psychology, and institutional theory. It advances a single, claim that the vocabulary through which large language models are currently understood — intelligence, understanding, capability — constitutes a governance failure before it constitutes a philosophical error. Against narratives centered on machine autonomy or alignment, the book argues that the most consequential questions raised by generative AI concern who owns the cognitive infrastructure of the future, how it redistributes agency and economic surplus, and what institutional arrangements can restore epistemic sovereignty to those whose cognition these systems extract and capitalise.

The argument develops across three analytical axes. The first is the political economy of epistemic extraction, theorised through cognitive capitalism and epistemic colonialism, which reframes AI not as a tool of intelligence but as a system for enclosing the epistemic commons. The second is the epistemology of naming, addressed through the Synthetic Interlocutor Systems (SIS) framework: a corrective to the misclassification of probabilistic synthesis as autonomous cognition, and an account of how that misclassification reconfigures both human judgment and machine behavior. The third is institutional design, examined through engineered directionality, the Cognitive Growth Index (CGI), and Epistemic Arbitrage, including an analysis of whether financial markets are mispricing the risks of algorithmic opacity.

Moving from political economy through the philosophy of naming, authorship and cognitive identity, institutional governance, the reallocation of human agency, and the systemic exposure of financial markets, the book culminates in an agenda for cognitive sovereignty and a human-in-the-loop economy. Its position is critical without being techno-dystopian, insisting that AI governance is a psychopolitical and economic problem, not merely a technical one. Applied AI Epistemics offers researchers, practitioners, and institutions a rigorous framework for confronting what has actually changed in the epistemic configuration of institutions, and what those changes demand.

Prince Sarpong is the Founder of Epistemica Global, an epistemic engineering firm. He is a former Associate Professor of Finance and a post-disciplinary systems architect whose work spans corporate finance, financial psychology, AI epistemics, and institutional behavior. He serves on the advisory board of AI 2030, a global initiative on responsible AI.

He has developed several pioneering frameworks, including Brittle Financial Health, Antifragile Financial Therapy, Executive Symbolic Risk Analytics (ESRA), and the Cognitive Growth Index (CGI), a framework for measuring epistemic agency in AI-assisted learning environments.

His current work confronts the limits of traditional disciplinary boundaries by designing systems that metabolize pressure into structure across money, identity, capital, and corporate leadership.