The conventional discourse on existential risks (x-risks) from AI typically
focuses on abrupt, dire events caused by advanced AI systems, particularly
those that might achieve or surpass human-level intelligence. These events have
severe consequences that either lead to human extinction or irreversibly
cripple human civilization to a point beyond recovery. This discourse, however,
often neglects the serious possibility of AI x-risks manifesting incrementally
through a series of smaller yet interconnected disruptions, gradually crossing
critical thresholds over time. This paper contrasts the conventional "decisive
AI x-risk hypothesis" with an "accumulative AI x-risk hypothesis." While the
former envisions an overt AI takeover pathway, characterized by scenarios like
uncontrollable superintelligence, the latter suggests a different causal
pathway to existential catastrophes. This involves a gradual accumulation of
critical AI-induced threats such as severe vulnerabilities and systemic erosion
of econopolitical structures. The accumulative hypothesis suggests a boiling
frog scenario where incremental AI risks slowly converge, undermining
resilience until a triggering event results in irreversible collapse. Through
systems analysis, this paper examines the distinct assumptions differentiating
these two hypotheses. It is then argued that the accumulative view reconciles
seemingly incompatible perspectives on AI risks. The implications of
differentiating between these causal pathways -- the decisive and the
accumulative -- for the governance of AI risks as well as long-term AI safety
are discussed.

这篇论文对比了传统的 “决定性人工智能风险假设” 和 “累积人工智能风险假设”，指出通过逐渐积累关键的人工智能引发的威胁，如严重漏洞和经济政治结构的系统侵蚀，逐渐形成灾难。通过对系统进行分析，提出了累积观点能够调和看似不相容的对人工智能风险的观点，并讨论了这两种因果路径的不同对人工智能风险治理和长期安全的影响。