Description This preprint introduces the concept of generative AI as an epistemic coprocessor. Rather than framing artificial systems as tools or autonomous agents, the model defines a structured interaction in which human reasoning and probabilistic pattern generation are functionally coupled. The core assumption is a stable asymmetry: the human provides intention, context, and epistemic evaluation, while the system contributes structural clarification, recursive reformulation, and pattern condensation. Within this configuration, a hybrid cognitive space emerges in which reasoning is not delegated but reorganized through iterative interaction. The paper outlines the cognitive effects of this coupling, including increased clarity, structural compression of complex problem spaces, and accelerated hypothesis generation. At the same time, it identifies inherent risks such as dependency formation, bias amplification, and the illusion of epistemic authority. The epistemic coprocessor is presented as a conceptual framework for analyzing and designing human–AI interaction. The model is not tied to a specific system, but describes a general interaction architecture that can be examined empirically and applied across domains such as research, writing, and decision-making. This document is a preprint. Further development and extended versions are planned.
Thomas A. Blüm (Fri,) studied this question.