Non-Great-Power Conflict and AI Risk
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FIG Fellows: Kristina Kempkey, Seán Boddy, Catherine Ge-Wang
Authors: Kristina Kempkey, Seán Boddy, Catherine Ge-Wang
Abstract:
Research on advanced AI and the risk of war has focused almost exclusively on great power conflict, on the grounds that confrontation between nuclear-armed adversaries poses the greatest risk of catastrophic or existential harm. Considerably less attention has been paid to non-great-power conflict (NGPC): wars between non-great powers, between non-great powers and great powers, civil wars, proxy wars, and conflicts involving nonstate actors. This paper evaluates the null hypothesis that NGPC is much less important than great power conflict (GPC) as a source of catastrophic risk in an era of increasingly capable AI, against the alternative that it is within an order of magnitude of GPC in importance. We assess three sub-hypotheses: that NGPC increases the likelihood of great power conflict; that it increases the expected harm from catastrophic terrorism; and that it increases the expected harm from loss of control over advanced AI systems. For each, we construct a causal model linking NGPC to the risk outcome, decompose that model into a parameterized multiplicative risk model where the evidence permits, and assess the parameters qualitatively using literature review, historical case studies, and the PHIA probability yardstick. We find the null poorly supported for H1 and H2, and identify H3 as a priority for further work rather than a settled finding. We do not estimate GPC risk directly and therefore make no quantitative comparison. Our claim is that the pathways from NGPC to catastrophic risk are numerous, mutually reinforcing, and sufficiently underexamined that the field’s default assumption of a large gap should not be taken for granted, and that further investigation, at a minimum into the cause area’s tractability, is warranted. We also identify five intermediate variables that recur across the pathways—information environment quality, decision-making timeline compression, great power threat perception, capability diffusion, and norm erosion—and argue that these shared nodes are the highest-priority targets for further investigation and intervention.
