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.

翻译：关于人工智能存在风险（x-risks）的传统论述通常聚焦于高级AI系统（尤其是可能达到或超越人类智能水平的系统）引发的突发性灾难事件。这些事件将导致人类灭绝，或对文明造成不可逆转的毁灭性打击。然而，这类论述往往忽视了另一种严峻可能性：AI存在风险可能通过一系列较小但相互关联的干扰逐步显现，并随时间推移逐渐跨越关键临界点。本文对比了传统的"决断性人工智能存在风险假说"与"累积性人工智能存在风险假说"。前者描绘了以不可控超级智能等情景为特征的显性AI接管路径，而后者则指向导致存在性灾难的不同因果路径——即通过关键威胁（如严重脆弱性和经济政治体系的系统性侵蚀）的逐步累积。累积性假说提出了"温水煮青蛙"式的情景：渐进式AI风险缓慢汇聚，持续削弱系统韧性，直至某个触发事件引发不可逆转的崩溃。本文通过系统分析，深入剖析了区分这两种假说的独特假设，并论证累积性视角能够调和有关AI风险看似矛盾的观点。最后，本文探讨了区分决断性与累积性这两种因果路径对AI风险治理及长期AI安全的意义。