Ilya Levin

Generative artificial intelligence represents a technology marked by a growing asymmetry between its capabilities and its conceptual understanding. While such systems exhibit performance comparable to human intelligence, they are still interpreted through frameworks derived from tools, collaboration, or statistical recombination. This paper argues that these frameworks share a key limitation: they treat the medium of generative AI as epistemologically neutral. In contrast, the paper identifies this medium as a high-dimensional geometric space whose properties differ fundamentally from the low-dimensional intuitions shaping traditional epistemology. The analysis develops a framework of navigational epistemology. It begins with threshold logic and the perceptron as the boundary where symbolic computation transitions into geometric processing. It then shows how properties of high-dimensional spaces, including concentration of measure, near-orthogonality, and manifold structure, function as active conditions shaping knowledge. The paper argues that generative AI operates through an indexical mode of signification, where meaning arises from positional relations within a learned manifold. This leads to the concept of navigational knowledge, a form of knowledge that is positional and enactive. This interaction gives rise to an emergent cognitive formation, the Third Entity, defined by the automation of tacit knowledge through geometric traversal. The paper concludes by proposing navigational thinking as an epistemic paradigm in which navigation precedes computation.

The encounter between human reasoning and generative artificial intelligence (GenAI) cannot be adequately described by inherited metaphors of tool-use, augmentation, or collaborative partnership. This article argues that such interactions produce a qualitatively distinct cognitive-epistemic formation, designated here as the Third Entity: an emergent, transient structure that arises from the transductive coupling of two ontologically incommensurable modes of cognition. Drawing on Peirce’s semiotics, Polanyi’s theory of tacit knowledge, Simondon’s philosophy of individuation, Ihde’s postphenomenology, and Morin’s complexity theory, we develop a multi-layered theoretical account of this formation. We introduce the concept of Vibe-Creation to designate the pre-reflective cognitive mode through which the Third Entity navigates high-dimensional semantic space, and argue that this mode constitutes the automation of tacit knowledge—a development with far-reaching consequences for epistemology, the philosophy of mind, and educational theory. We further propose the notion of asymmetric emergence to characterize the agency of the Third Entity: genuinely novel and irreducible, yet anchored in human intentional responsibility. The article concludes by examining the implications of this theoretical framework for the transformation of educational institutions and the redefinition of intellectual competence in the age of GenAI.

Generative AI presents an unprecedented challenge to our understanding ofknowledge and its production. Unlike previous technological transformations, where engineering understanding preceded or accompanied deployment, generative AI operates through mechanisms whose epistemic character remains obscure—and without such understanding, its responsible integration into science, education, and institutional life cannot proceed on a principled basis. This paper argues that the missing account must begin with a paradigmatic break that has not yet received adequate philosophical attention. In the Turing– Shannon–von Neumann tradition, information enters the machine as encoded binary vectors, and semantics remains external to the process. Neural network architectures rupture this regime: symbolic input is instantly projected into a high-dimensional space where coordinates correspond to semantic parameters, transforming binary code into a position in a geometric space of meanings. It is this space that constitutes the active epistemic condition shaping generative production. Draw ingonfourstructuralpropertiesofhigh dimensionalgeometry—concentration of measure, near-orthogonality, exponential directional capacity, and manifold regularity—the paper develops an Indexical Epistemology of High-Dimensional Spaces. Building on Peirce’s semiotics and Papert’s constructionism, it reconceptualizes generative models as navigators of learned manifolds and proposes navigational knowledge—a third mode of knowledge production, distinct from both symbolic reasoning and statistical recombination.