量化基础模型行业的系统性脆弱性
Quantifying Systemic Vulnerability in the Foundation Model Industry
AI总结：
本文提出AIIVI指数，基于O-Ring理论量化基础模型行业脆弱性，发现计算基础设施和能源系统是主要约束因素，揭示了行业在数据透明度低和技术快速演变下的极端脆弱性。
摘要：
基础模型行业在关键输入上表现出前所未有的集中：半导体、能源基础设施、精英人才、资本和训练数据。尽管有广泛的行业分析，但尚未有全面的框架来评估整体工业脆弱性。本文开发了基于O-Ring生产理论的人工智能工业脆弱性指数（AIIVI），认识到基础模型生产需要同时具备不可替代的输入。鉴于数据透明度极低和技术快速演变，我们采用经过验证的人工在循环方法，利用大型语言模型系统提取分散的灰色文献指标，并完全人工验证所有输出。应用于六个最先进的基础模型开发者，AIIVI等于0.82，表明由计算基础设施（0.85）和能源系统（0.90）驱动的极端脆弱性。尽管产业政策目前强调半导体产能，但能源基础设施代表新兴的约束因素。该方法证明适用于其他快速演变、信息不透明的行业，其中传统数据来源不足。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
