Sarpong, Prince
Description
This book establishes Applied AI Epistemics as a field of inquiry at the intersection of political economy, epistemology, cognitive psychology, and in
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stitutional 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.
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