Flow Acceleration and the IHACC Model: Human-AI Co-Creation in Epistemology

This paper introduces the Iterative Human-AI Co-Creation (IHACC) model as a conceptual framework for knowledge production in the age of generative AI. IHACC describes a recursive process in which human agents, AI systems and feedback loops jointly generate, test and refine ideas. At its core is “flow acceleration”: the AI-mediated compression of the classical data-information-knowledge-wisdom progression into rapid human-AI interaction cycles, while leaving humans in charge of epistemic goals, standards and responsibility.

Drawing on actor-network perspectives and the notion of the infosphere, IHACC treats knowledge not as a static product but as an emergent property of socio-technical processes, even as it acknowledges Heideggerian concerns about reduction of the world to mere resource. The model synthesizes empirical findings on productivity gains and error risks from large language models, interpreting them as evidence of reconfigured epistemic agency and temporality rather than simple efficiency effects.

The paper argues that IHACC is a candidate paradigm shift in epistemology because it relocates the locus of “knowing” into governed human-AI systems and opens new possibilities for epistemic justice, while also posing normative questions about dependence, opacity and control in accelerated knowledge infrastructures. We position IHACC as a base model for accelerated human-AI inquiry and specify propositions that invite direct testing.

Keywords: epistemology; human-AI collaboration; flow acceleration; DIKW; actor-network theory; infosphere.