The development of AGI threatens to erode government tax bases, lower living standards, and disempower citizens -- risks that make the 40-year stagnation of wages during the first industrial revolution look mild in comparison. While AI safety research has focused primarily on capability risks, comparatively little work has studied how to mitigate the economic risks of AGI. In this paper, we argue that the economic risks posed by a post-AGI world can be effectively mitigated by token taxes: usage-based surcharges on model inference applied at the point of sale. We situate token taxes within previous proposals for robot taxes and identify two key advantages: they are enforceable through existing compute governance infrastructure, and they capture value where AI is used rather than where models are hosted. For enforcement, we outline a staged audit pipeline -- black-box token verification, norm-based tax rates, and white-box audits. For impact, we highlight the need for agent-based modeling of token taxes' economic effects. Finally, we discuss alternative approaches including FLOP taxes, and how to prevent AI superpowers vetoing such measures.

翻译：通用人工智能的发展可能侵蚀政府税基、降低生活水平并削弱公民权利——这些风险使得第一次工业革命期间长达四十年的工资停滞相形见绌。尽管人工智能安全研究主要关注能力风险，但针对如何缓解通用人工智能经济风险的研究相对匮乏。本文提出，后通用人工智能时代的经济风险可通过代币税有效缓解：即在销售点对模型推理按使用量征收附加费。我们将代币税置于先前机器人税提案的框架中，并指出其两大优势：可通过现有计算治理基础设施强制执行，且能在人工智能使用端而非模型托管端实现价值捕获。在实施层面，我们构建了分阶段审计流程——黑盒代币验证、基于规范的税率设定及白盒审计。关于影响评估，我们强调需采用基于智能体的建模方法来分析代币税的经济效应。最后，我们探讨了包括浮点运算税在内的替代方案，以及如何防止人工智能强国对此类措施的否决。