Paper Type
ERF
Abstract
Technology labels are reshaping digital asset markets, yet little is known about what happens when those labels lose credibility. We examine whether regulatory enforcement against AI washing in traditional finance spills over to unregulated cryptocurrency markets, where disclosure is voluntary and technology claims are unverified. Employing an event study around three escalating SEC enforcement actions (2024–2025), we analyze AI-branded tokens against matched controls. Our findings reveal that AI tokens experience significant negative abnormal returns following early enforcement events, well beyond control token reactions. Notably, the market response attenuates by the third event, suggesting rapid investor learning. A pooled cross-sectional analysis confirms that the AI-specific penalty holds after accounting for token and platform characteristics. Together, these results demonstrate that technology category labels operate not merely as market descriptors but as strategic risk factors, with governance implications that extend well beyond formal regulatory boundaries.
Paper Number
1643
Recommended Citation
Zhang, Yuan and Damani, Ankita, "The Cost of AI Washing: Regulatory Spillovers to Crypto Markets" (2026). AMCIS 2026 Proceedings. 4.
https://aisel.aisnet.org/amcis2026/scuidt/scuidt/4
The Cost of AI Washing: Regulatory Spillovers to Crypto Markets
Technology labels are reshaping digital asset markets, yet little is known about what happens when those labels lose credibility. We examine whether regulatory enforcement against AI washing in traditional finance spills over to unregulated cryptocurrency markets, where disclosure is voluntary and technology claims are unverified. Employing an event study around three escalating SEC enforcement actions (2024–2025), we analyze AI-branded tokens against matched controls. Our findings reveal that AI tokens experience significant negative abnormal returns following early enforcement events, well beyond control token reactions. Notably, the market response attenuates by the third event, suggesting rapid investor learning. A pooled cross-sectional analysis confirms that the AI-specific penalty holds after accounting for token and platform characteristics. Together, these results demonstrate that technology category labels operate not merely as market descriptors but as strategic risk factors, with governance implications that extend well beyond formal regulatory boundaries.
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