Talk, Walk, and Market Response: Multimodal Measurement of AI Washing and Its Capital Market Consequences in China
Wen Zhanjie, Guo Jingqiao

TL;DR
This paper develops a multimodal AI Washing Risk Score to measure AI exaggeration in Chinese firms and examines its market consequences, revealing that genuine AI investment improves innovation while rhetoric can mislead investors.
Contribution
It introduces a novel multimodal measurement of AI Washing using text-image consistency and analyzes its impact on capital markets in China.
Findings
AWRS does not predict future real AI investment.
Substantive AI investment enhances patent quality.
AI rhetoric can mislead investors, leading to valuation corrections.
Abstract
As artificial intelligence and generative large language models drive industrial upgrading, capital markets increasingly focus on AI-themed listed firms. Information asymmetry and technological opacity lower the cost of exaggerating AI capabilities relative to genuine R&D, spurring widespread AI Washing. Using China's A-share market from 2018Q1 to 2025Q2, we advance literature in measurement and mechanism testing. We construct a multimodal AI Washing Risk Score (AWRS) via Qwen-VL to assess text-image consistency in annual reports and roadshows, and a Material Real-Investment Matching Index (MRMI) from patent quality, AI intangible asset capitalization, and technical personnel compensation using PCA. Four findings emerge: (1) AWRS lacks predictive power for future MRMI, with a wider rhetoric-action gap among financially constrained firms; (2) substantive AI investment boosts high-quality…
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