提出评估大模型产业系统性脆弱性的新指数，发现能源基建是最大瓶颈。
Quantifying Systemic Vulnerability in the Foundation Model Industry
- 基于O-Ring理论构建工业脆弱性指数，整合不可替代的关键输入
- 六家头部企业脆弱性指数达0.82，能源系统脆弱性高达0.90
- 适用于数据不透明的新兴行业，方法可复用
大模型产业在半导体、能源基础设施、顶尖人才、资本和训练数据等关键投入上呈现前所未有的集中。尽管已有大量行业分析，但缺乏全面评估整体产业脆弱性的框架。本文基于O-Ring生产理论，提出人工智能产业脆弱性指数（AIIVI），认识到大模型生产需多种不可替代要素同时可用。鉴于数据高度不透明和快速技术迭代，采用人机协同方法，利用大语言模型系统提取分散的灰色文献指标，并对所有输出进行人工验证。应用于六家前沿大模型开发商，结果显示AIIVI为0.82，表明极端脆弱性，主要由算力基础设施（0.85）和能源系统（0.90）驱动。当前产业政策侧重芯片产能，但能源基础设施已成为新兴关键约束。该方法适用于其他快速演进且数据匮乏的行业。
原文摘要 · Abstract (English)
The foundation model industry exhibits unprecedented concentration in critical inputs: semiconductors, energy infrastructure, elite talent, capital, and training data. Despite extensive sectoral analyses, no comprehensive framework exists for assessing overall industrial vulnerability. We develop the Artificial Intelligence Industrial Vulnerability Index (AIIVI) grounded in O-Ring production theory, recognizing that foundation model production requires simultaneous availability of non-substitutable inputs. Given extreme data opacity and rapid technological evolution, we implement a validated human-in-the-loop methodology using large language models to systematically extract indicators from dispersed grey literature, with complete human verification of all outputs. Applied to six state-of-the-art foundation model developers, AIIVI equals 0.82, indicating extreme vulnerability driven by compute infrastructure (0.85) and energy systems (0.90). While industrial policy currently emphasizes semiconductor capacity, energy infrastructure represents the emerging binding constraint. This methodology proves applicable to other fast-evolving, opaque industries where traditional data sources are inadequate.
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