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Exploring Vulnerability in AI Industry

Author

Listed:
  • Claudio Pirrone
  • Stefano Fricano
  • Gioacchino Fazio

Abstract

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.

Suggested Citation

  • Claudio Pirrone & Stefano Fricano & Gioacchino Fazio, 2025. "Exploring Vulnerability in AI Industry," Papers 2510.23421, arXiv.org.
    • Handle: RePEc:arx:papers:2510.23421
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    References listed on IDEAS

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    1. Jean-Charles Rochet & Jean Tirole, 2003. "Platform Competition in Two-Sided Markets," Journal of the European Economic Association, MIT Press, vol. 1(4), pages 990-1029, June.
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