MindWar evolved: Artificial intelligence as the operational medium of population-level cognitive warfare

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Authors: Ivor Bukovac
Cited by: 0

Abstract

MindWar Evolved: Artificial Intelligence as the Operational Medium of Population-Level Cognitive Warfare examines large language model AI systems as population-scale cognitive infrastructure and evaluates their implications for cognitive security governance. The paper argues that contemporary AI systems operate not primarily at the level of discrete belief manipulation, but at the level of the cognitive substrate itself—shaping attentional patterns, interpretive habits, and developmental conditions across populations. It introduces the concept of cognitive formatting to describe this environmental mode of influence. Situating this analysis within a broader intellectual lineage, the paper synthesizes the 1980 MindWar doctrine articulated by Michael Aquino with the strategic power framework associated with Peter Thiel, integrating both into the independently developed WSMD (Weapon of Slow Mass Destruction) analytical model. It contends that AI-enabled cognitive formatting operates: - Below conventional attribution thresholds- Through authentic utility rather than overt deception- Across generational timescales that exceed existing governance cycles Positioned within the WSMD trilogy, this work contributes to scholarship in cognitive security, information warfare theory, and AI governance by reframing AI systems as environmental influence infrastructure with long-horizon civilizational implications. The paper is intended for researchers, policymakers, and practitioners concerned with democratic resilience, attribution gaps, and the governance of population-level cognitive systems.

Fields of Study & Subjects

Ethics and Social Impacts of AI Misinformation and Its Impacts European and Russian Geopolitical Military Strategies

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
4 March 2026
Language
EN
Document type
Preprint