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（AI）引发存在风险（x-risks）的传统论述通常聚焦于先进AI系统（尤其是可能达到或超越人类智力水平的系统）所导致的突发性、灾难性事件。这些事件会引发严重后果，要么导致人类灭绝，要么对人类文明造成不可逆的摧毁，使其无法恢复。然而，这种论述往往忽视了另一类严重的可能性：AI存在风险通过一系列较小但相互关联的扰动逐步显现，并随时间推移逐渐跨越关键阈值。本文对比了传统的"突发性AI存在风险假说"与"累积性AI存在风险假说"。前者设想了一种显性的AI接管路径，典型场景包括不可控的超级智能；后者则揭示了导致存在灾难的不同因果路径，即关键性的AI诱发威胁（如严重脆弱性与经济政治结构的系统性侵蚀）逐步累积。累积性假说描绘了一种"温水煮青蛙"式的情景：递增的AI风险缓慢汇聚，不断削弱系统韧性，直至某个触发事件导致不可逆的崩溃。通过系统分析，本文检验了区分这两种假说的独特假设，进而论证累积性视角能够调和关于AI风险的看似矛盾的观点。文章最后探讨了区分这两种因果路径（突发性与累积性）对AI风险治理及长期AI安全性研究的启示。