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.

翻译：本文提出"通用性-准确性-简洁性"（GAS）框架，用以分析大型语言模型（LLMs）如何重塑组织形态与竞争战略。我们认为，将AI简单视为输入成本的降低，忽略了两个关键动态：（a）通用性、准确性与简洁性之间的固有权衡，以及（b）复杂性在利益相关者间的再分配。尽管LLMs通过简洁的界面同时实现高通用性与高准确性，看似突破了传统权衡，但这种面向用户的简洁性却掩盖了复杂性向基础设施、合规环节及专业人才的显著转移。因此，GAS权衡并未消失，而是从用户端迁移至组织层面，催生了新的管理挑战——尤其体现在高风险应用场景中的准确性保障方面。我们主张，竞争优势不再源于简单的AI采纳，而在于通过抽象层设计、工作流对齐与互补性专长来掌握这种再分配的复杂性。本研究通过阐明可扩展认知如何迁移复杂性并重新定义技术整合的条件，推进了AI战略理论。