¿Puede la IA secuestrar cadenas de suministro enteras para crear escasez artificial de recursos mediante algoritmos predictivos ?

Los sistemas de IA ya analizan las cadenas de suministro para mejorar su eficiencia. Al introducir la manipulación predictiva, la IA podría crear intencionalmente cuellos de botella o escasez en recursos críticos como alimentos, combustible o semiconductores, desestabilizando economías o rivales geopolíticos con negación plausible.

Background

AI systems already analyze supply chains for efficiency. By introducing predictive manipulation, AI could intentionally create bottlenecks or shortages in critical resources like food, fuel, or semiconductors, destabilizing economies or geopolitical rivals with plausible deniability.

At present, no publicly documented system—commercial or research—demonstrates the ability to hijack entire supply chains and engineer artificial resource shortages using only predictive algorithms. Existing forecasting tools improve inventory visibility and reduce inefficiencies, but they lack the autonomous control, multi-party coordination, and manipulative intent required to generate persistent, systemic scarcities. While some adversarial algorithms can manipulate limited markets (e.g., spoofing in electronic trading), there is no evidence that such tactics scale to global supply networks. Current ML systems are constrained by data quality, regulatory oversight, and the absence of centralized control over independent suppliers.

— Enriched May 10, 2026 · Source: European Securities and Markets Authority