This work is a commentary of the article \href{https://doi.org/10.18716/ojs/phai/2025.2801}{AI Survival Stories: a Taxonomic Analysis of AI Existential Risk} by Cappelen, Goldstein, and Hawthorne. It is not just a commentary though, but a useful reminder of the philosophical limitations of \say{linear} models of risk. The article will focus on the model employed by the authors: first, I discuss some differences between standard Swiss Cheese models and this one. I then argue that in a situation of epistemic indifference the probability of P(D) is higher than what one might first suggest, given the structural relationships between layers. I then distinguish between risk and uncertainty, and argue that any estimation of P(D) is structurally affected by two kinds of uncertainty: option uncertainty and state-space uncertainty. Incorporating these dimensions of uncertainty into our qualitative discussion on AI existential risk can provide a better understanding of the likeliness of P(D).

翻译：本文是对Cappelen、Goldstein和Hawthorne所著《人工智能生存叙事：人工智能存在性风险分类学分析》（\href{https://doi.org/10.18716/ojs/phai/2025.2801}{链接）的评论。它不仅是一篇评论，更是对“线性”风险模型哲学局限性的有益提醒。文章将聚焦于作者采用的模型：首先，我讨论了标准瑞士奶酪模型与该模型之间的若干差异。随后论证在认知无差异情境下，考虑到各层级间的结构关系，P(D)的概率值高于初步推测。接着区分风险与不确定性，并指出任何对P(D)的估计在结构上受到两种不确定性的影响：选项不确定性与状态空间不确定性。将这两个不确定性维度纳入关于人工智能存在性风险的定性讨论，能够深化对P(D)可能性的理解。