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AI washing: Strategic disclosure and backlash

Xiapeng Song et al.

Finance Research Letters2026https://doi.org/10.1016/j.frl.2026.109684article
AJG 2ABDC A
Weight
0.37

Abstract

As firms increasingly exaggerate artificial intelligence (AI) adoption in disclosures, we analyze market responses to unsubstantiated AI claims. Using BERT-based text classification and AI patent data for U.S. firms (2018-2023), we find that such narratives initially attract investors but ultimately yield negative market reactions and sustained underperformance. Results demonstrate market penalties for AI overclaiming through both short-term investor responses and long-term operational outcomes, extending narrative economics to emerging technologies. The study highlights that while narratives can mobilize attention, markets ultimately punish rhetoric that outpaces implementation.

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https://doi.org/https://doi.org/10.1016/j.frl.2026.109684

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@article{xiapeng2026,
  title        = {{AI washing: Strategic disclosure and backlash}},
  author       = {Xiapeng Song et al.},
  journal      = {Finance Research Letters},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.frl.2026.109684},
}

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AI washing: Strategic disclosure and backlash

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.