AI-Centrism, Human Alienation, and the Erosion of Meaning: Anthropomorphism and the Mystification of Artificial Intelligence

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Authors: David (Daoud) Matta
Cited by: 0

Abstract

Contemporary discourse on artificial intelligence increasingly frames human value, intelligence, and future relevance in relation to artificial systems. This paper argues that such framings contribute to human alienation and the erosion of meaning through what is here termed AI-centrism—a background orientation in which artificial intelligence functions as an implicit reference point for interpreting human capacities and purposes. Rather than replacing human abilities or explicitly redefining standards of worth, AI-centrism operates through a gradual reorientation of significance that redirects the human project itself. Drawing on existential philosophy, the phenomenology of technology, and psychological research on social comparison and self-determination, the paper develops a layered conceptual framework to explain how this process unfolds. At the cultural level, normative drift describes a quiet shift in what is implicitly treated as valuable. At the evaluative level, metric displacement captures the migration of tool-specific measures into domains of human self-understanding. At the existential level, these dynamics culminate in project misalignment, whereby human lives are oriented toward system-centered standards indifferent to lived relevance. The paper further examines how the anthropomorphization and mystification of AI intelligence intensifies this reorientation, and how the emergence of AI agents deepens the threat by occupying roles through which humans have historically generated meaning. The central claim is not that artificial intelligence threatens human capacities, but that misplaced centrality threatens human meaning.

Fields of Study & Subjects

Ethics and Social Impacts of AI Cybernetics and Technology in Society Phenomenology and Existential Philosophy

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
6 February 2026
Document type
Preprint