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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.10590 |
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| _version_ | 1866911049028993024 |
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| author | Fang, Xinmin Tao, Lingfeng Li, Zhengxiong |
| author_facet | Fang, Xinmin Tao, Lingfeng Li, Zhengxiong |
| contents | Recent breakthroughs in artificial intelligence (AI) have triggered surges in market valuations for AI-related companies, often outpacing the realization of underlying capabilities. We examine the anchoring effect of AI capabilities on equity valuations and propose a Capability Realization Rate (CRR) model to quantify the gap between AI potential and realized performance. Using data from the 2023--2025 generative AI boom, we analyze sector-level sensitivity and conduct case studies (OpenAI, Adobe, NVIDIA, Meta, Microsoft, Goldman Sachs) to illustrate patterns of valuation premium and misalignment. Our findings indicate that AI-native firms commanded outsized valuation premiums anchored to future potential, while traditional companies integrating AI experienced re-ratings subject to proof of tangible returns. We argue that CRR can help identify valuation misalignment risk-where market prices diverge from realized AI-driven value. We conclude with policy recommendations to improve transparency, mitigate speculative bubbles, and align AI innovation with sustainable market value. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10590 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anchoring AI Capabilities in Market Valuations: The Capability Realization Rate Model and Valuation Misalignment Risk Fang, Xinmin Tao, Lingfeng Li, Zhengxiong Computers and Society Artificial Intelligence Recent breakthroughs in artificial intelligence (AI) have triggered surges in market valuations for AI-related companies, often outpacing the realization of underlying capabilities. We examine the anchoring effect of AI capabilities on equity valuations and propose a Capability Realization Rate (CRR) model to quantify the gap between AI potential and realized performance. Using data from the 2023--2025 generative AI boom, we analyze sector-level sensitivity and conduct case studies (OpenAI, Adobe, NVIDIA, Meta, Microsoft, Goldman Sachs) to illustrate patterns of valuation premium and misalignment. Our findings indicate that AI-native firms commanded outsized valuation premiums anchored to future potential, while traditional companies integrating AI experienced re-ratings subject to proof of tangible returns. We argue that CRR can help identify valuation misalignment risk-where market prices diverge from realized AI-driven value. We conclude with policy recommendations to improve transparency, mitigate speculative bubbles, and align AI innovation with sustainable market value. |
| title | Anchoring AI Capabilities in Market Valuations: The Capability Realization Rate Model and Valuation Misalignment Risk |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2505.10590 |