标题： 探索人工智能产业中的漏洞 显示英文标题

标题： Exploring Vulnerability in AI Industry

摘要： 基础模型（FMs）的迅速崛起，得益于Transformer架构，推动了当前的人工智能生态系统。 以大规模训练和下游适应性为特点，FMs（如GPT系列）已实现广泛的公众采用，推动了一个由平台经济和激烈投资塑造的动荡市场。 由于数据限制，评估这一快速演变行业的脆弱性至关重要但具有挑战性。 本文提出了一种合成人工智能脆弱性指数（AIVI），专注于FM生产的上游价值链，优先考虑公开可用的数据。 我们将FM输出建模为五个输入的函数：计算能力、数据、人才、资本和能源，假设任何输入的供应脆弱性都会威胁该行业。 关键的脆弱性包括计算能力集中、数据稀缺性和法律风险、人才瓶颈、资本密集度和战略依赖性，以及不断上升的能源需求。 承认输入替代性不完美，我们提出了聚合子指数的加权几何平均值，使用理论或实证基准进行归一化。 尽管存在局限性和改进空间，这个初步指数旨在量化人工智能核心生产引擎中的系统性风险，并隐含地揭示下游价值链的风险。 显示英文摘要

摘要： The rapid ascent of Foundation Models (FMs), enabled by the Transformer architecture, drives the current AI ecosystem. Characterized by large-scale training and downstream adaptability, FMs (as GPT family) have achieved massive public adoption, fueling a turbulent market shaped by platform economics and intense investment. Assessing the vulnerability of this fast-evolving industry is critical yet challenging due to data limitations. This paper proposes a synthetic AI Vulnerability Index (AIVI) focusing on the upstream value chain for FM production, prioritizing publicly available data. We model FM output as a function of five inputs: Compute, Data, Talent, Capital, and Energy, hypothesizing that supply vulnerability in any input threatens the industry. Key vulnerabilities include compute concentration, data scarcity and legal risks, talent bottlenecks, capital intensity and strategic dependencies, as well as escalating energy demands. Acknowledging imperfect input substitutability, we propose a weighted geometrical average of aggregate subindexes, normalized using theoretical or empirical benchmarks. Despite limitations and room for improvement, this preliminary index aims to quantify systemic risks in AI's core production engine, and implicitly shed a light on the risks for downstream value chain.