Goldman Sachs: World Models May Drive New AI Infrastructure Demand Alongside Large Language Models

According to Goldman Sachs on July 6, world models may become a second engine for AI infrastructure demand. Unlike large language models that process text and images, world models aim to understand causal relationships in physical and social systems—such as simulating friction, material behavior, supply chain reactions, policy impacts, and competitive strategies. Physical world models could support robotics, logistics, autonomous vehicles, and industrial design, while social models may enable strategy simulations, investment decisions, governance stress tests, and policy scenario analysis. Goldman Sachs noted that world models will not replace large language models but rather add new computational requirements alongside them.
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