From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI
This study addresses strategic organizational design issues for managers and firms adopting generative AI, offering a novel conceptual framework rather than incremental analysis.
The paper tackles the problem of how large language models (LLMs) reshape organizations by introducing the Generality-Accuracy-Simplicity (GAS) framework, arguing that LLMs redistribute complexity from users to infrastructure and management, creating new challenges for competitive advantage.
This paper introduces the Generality-Accuracy-Simplicity (GAS) framework to analyze how large language models (LLMs) are reshaping organizations and competitive strategy. We argue that viewing AI as a simple reduction in input costs overlooks two critical dynamics: (a) the inherent trade-offs among generality, accuracy, and simplicity, and (b) the redistribution of complexity across stakeholders. While LLMs appear to defy the traditional trade-off by offering high generality and accuracy through simple interfaces, this user-facing simplicity masks a significant shift of complexity to infrastructure, compliance, and specialized personnel. The GAS trade-off, therefore, does not disappear but is relocated from the user to the organization, creating new managerial challenges, particularly around accuracy in high-stakes applications. We contend that competitive advantage no longer stems from mere AI adoption, but from mastering this redistributed complexity through the design of abstraction layers, workflow alignment, and complementary expertise. This study advances AI strategy by clarifying how scalable cognition relocates complexity and redefines the conditions for technology integration.