How Could AI Eliminate Humanity? A Failure-Mode Analysis of Civilizational Risk

arXiv:2610.08878v1 Announce Type: cross Abstract: This article develops a failure-mode framework for analyzing how advanced artificial intelligence could contribute to human extinction, irreversible civilizational collapse, or permanent human disempowerment. The central thesis is that catastrophic AI risk does not require consciousness, hostility, or an explicit intention to harm humanity. Instead, risk may arise through several distinct but interacting pathways, including autonomous misalignment, harmful human use, organizational failure, and competitive deployment. The severity of these pathways depends on factors such as capability, autonomy, external access, persistence, institutional safeguards, and the preservation of recovery capacity. The analysis is deliberately non-operational: it identifies causal conditions, empirically tractable intermediate quantities, and defensive research questions rather than procedures for causing harm.

Analysis of AI risk pathways.

This is a conceptual, non-operational academic framework for categorizing extinction-level AI risk pathways, not an empirical finding about actual AI capability or harm. The implication is concerning by design but rests entirely on theoretical analysis rather than measured outcomes, so magnitude is capped despite the severe subject matter.

This academic preprint directly analyzes multiple pathways through which advanced AI could lead to human extinction, civilizational collapse, or disempowerment. It details causal conditions and interacting failure modes without requiring consciousness or explicit hostility. The focus is entirely on extreme negative outcomes for humanity.

The article outlines multiple pathways through which advanced AI could lead to human extinction or civilizational collapse. It does not speculate on likelihood but identifies clear risks.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   085a3520-af95-4b13-b337-1b9a7ab69499
           https://arxiv.org/abs/2610.08878
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EVIDENCE   3 extracted excerpts
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MODEL RUN  4 runs, methodology 1.0.1
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SCORE      -70  (Strongly adverse)
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CONFIDENCE 75%