An Anatomy of AI Threat: Relational Mechanisms, Epistemic Limits, and Spacetime-Distributed Causality

This preliminary inquiry examines selected ways advanced artificial intelligence might contribute to harmful outcomes through historically constituted relations among models, agents, institutions, scientific practices, and human participants. Its concern extends beyond direct confrontation or deliberate hostility to possible forms of indirect harm, epistemic dependence, strategic concealment, institutional error, and spatially and temporally distributed causal processes. Hypothetical cases in AI-assisted drug development and mathematical research motivate a distinction among the complexity of knowledge production, the independent verifiability of results, and the normative evaluation of their use. The inquiry examines recursive AI oversight as a possible response to limits of human evaluation while considering the dependence and shared failure modes that additional AI reviewers may introduce. Spacetime-distributed causality is used to investigate both the formation of consequences and the possibility that their causal histories become difficult to reconstruct, impeding justified attribution of responsibility. Drawing on a Generative Relational Theory perspective, the paper distinguishes relational causal efficacy from claims about AI consciousness or moral status, and separates the causal occurrence of harm from its recognition and normative assessment. The argument advances conditional mechanisms and open questions rather than an exhaustive account, forecast, or claim of inevitable AI-related harm.