Paper
Token Taxes: mitigating AGI's economic risks
Authors
Lucas Irwin, Tung-Yu Wu, Fazl Barez
Abstract
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.
Metadata
Related papers
Fractal universe and quantum gravity made simple
Fabio Briscese, Gianluca Calcagni • 2026-03-25
POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan
Marta Moscati, Muhammad Saad Saeed, Marina Zanoni, Mubashir Noman, Rohan Kuma... • 2026-03-25
LensWalk: Agentic Video Understanding by Planning How You See in Videos
Keliang Li, Yansong Li, Hongze Shen, Mengdi Liu, Hong Chang, Shiguang Shan • 2026-03-25
Orientation Reconstruction of Proteins using Coulomb Explosions
Tomas André, Alfredo Bellisario, Nicusor Timneanu, Carl Caleman • 2026-03-25
The role of spatial context and multitask learning in the detection of organic and conventional farming systems based on Sentinel-2 time series
Jan Hemmerling, Marcel Schwieder, Philippe Rufin, Leon-Friedrich Thomas, Mire... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.04555v1</id>\n <title>Token Taxes: mitigating AGI's economic risks</title>\n <updated>2026-03-04T19:38:29Z</updated>\n <link href='https://arxiv.org/abs/2603.04555v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.04555v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CY'/>\n <published>2026-03-04T19:38:29Z</published>\n <arxiv:comment>Accepted at the ICLR 2026 Post-AGI Science and Society Workshop (OpenReview: https://openreview.net/forum?id=03tzkncv2c)</arxiv:comment>\n <arxiv:primary_category term='cs.CY'/>\n <author>\n <name>Lucas Irwin</name>\n </author>\n <author>\n <name>Tung-Yu Wu</name>\n </author>\n <author>\n <name>Fazl Barez</name>\n </author>\n </entry>"
}