大模型让用户界面变简单，但复杂性转移到组织内部，需重新设计管理架构。
From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI
- 提出GAS框架，分析通用性、准确性与简洁性的权衡关系。
- 揭示大模型将复杂性从用户转移至基础设施与合规环节。
- 适合关注AI战略与组织变革的管理者和研究者阅读。
本文提出通用性-准确性-简洁性（GAS）框架，分析大型语言模型（LLMs）如何重塑组织与竞争策略。传统观点认为AI仅降低输入成本，但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；(b) 复杂性在各利益相关方间的再分配。尽管大模型通过简单接口实现了高通用性与高准确性，这种用户端的简洁性掩盖了其向基础设施、合规要求及专业人员转移的复杂性。因此，GAS权衡并未消失，而是从用户端转移到组织内部，带来新的管理挑战，尤其在高风险应用中的准确性控制。我们认为，竞争优势不再源于简单的AI采纳，而在于通过抽象层设计、工作流程对齐及互补能力建设，驾驭这种重构后的复杂性。本研究深化了对可扩展认知如何转移复杂性并重塑技术整合条件的理解。
原文摘要 · Abstract (English)
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.
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