Artificial Intelligence (AI) has permeated nearly every domain of human activity, presenting a paradox: while it augments efficiency, it simultaneously erodes the cognitive faculties that make humans irreplaceable. This paper argues that human cognitive offloading to AI constitutes a compounding, multi-domain vulnerability—not merely a productivity concern. We introduce Cognitive Offloading as an Attack Surface (COAS) and formally model the Cognitive Doom Loop (CDL), a six-stage, self-reinforcing cycle of AI dependency and cognitive atrophy. Unlike prior theoretical treatments of AI risk, this paper grounds its argument in peer-reviewed empirical evidence from 2024–2026: Neurological measurements from MIT Media Lab (Kosmyna et al., 2025) demonstrating up to 55% reduced brain connectivity in AI-assisted tasks and an 83% memory recall deficit. A CHI 2025 study by Microsoft and Carnegie Mellon University (Lee et al., 2025) showing that higher confidence in AI is associated with less critical thinking across 319 knowledge workers. The peer-reviewed Nature publication (Shumailov et al., 2024) mathematically proving Model Collapse—the degradation of AI output distributions when trained recursively on synthetic data. Together, these findings validate three interconnected collapses: a Security Collapse, driven by Automation Bias and the Capability-Comprehension Gap; an Innovation Collapse, driven by the mathematically proven interpolation boundary and Model Collapse dynamics; and a Civilizational Collapse, characterized by Cognitive Foreclosure in younger demographics and a crisis of credential without competence. We propose the Human-First AI Augmentation (HFAA) framework—updated to incorporate Scaffolding Cognitive Friction and alignment with the World Economic Forum's 2026 Cognitive Resilience Policy—as a structural remedy. This paper contends that the most dangerous vulnerability in the age of intelligent machines is not in the code, but in the operator.
Harsh Patel (2026) studied this question.