AI-driven automation threatens to erode government tax bases, lower living standards, and disempower citizens--risks that mirror the 40-year stagnation of wages during the first industrial revolution. While AI safety research has focused primarily on capability risks, comparatively little work has studied how to mitigate the economic risks of AI. This position paper argues that technical governance researchers should prioritize the study of 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. We then present a research roadmap. For enforcement, we outline a staged audit pipeline--black-box token verification, norm-based tax rates, and white-box audits--and identify open technical problems at each stage. For impact, we highlight the need for economic modeling of cost pass-through and deadweight loss. Finally, we discuss why FLOP taxes may be preferable, token taxes could stifle innovation, and how to prevent AI superpowers from vetoing such measures.

翻译：人工智能驱动的自动化正威胁着侵蚀政府税基、降低生活水平并削弱公民权力——这些风险与第一次工业革命期间长达40年的工资停滞如出一辙。尽管人工智能安全研究主要聚焦于能力风险，但相对较少的工作探讨了如何缓解人工智能的经济风险。本文主张技术治理研究者应优先研究代币税：一种在销售环节基于模型推理使用量征收的附加费。我们将代币税置于先前机器人税提案的背景下，并指出其两大关键优势：可通过现有计算治理基础设施执行，且能在人工智能使用地而非模型托管地捕获价值。随后，我们提出研究路线图。在执行层面，我们勾勒出分阶段审计流程——黑盒代币验证、基于范数的税率及白盒审计——并识别各阶段存在的开放技术问题。在影响层面，我们强调需对成本转嫁及无谓损失进行经济建模。最后，我们探讨为何浮点运算税可能更优、代币税可能抑制创新，以及如何防止人工智能超级大国否决此类措施。