马奇和西蒙提出，组织理论发展的核心议题在于，如何通过提升组织智能克服人类有限理性的约束？在AI时代，这个问题的答案似乎正在浮现。 对组织理论的研究者来说，一个最基本的问题是：组织是如何应对环境的？所谓组织设计，就是要厘清组织内部的分工协作问题，进而建立起一个应对环 境变化的机制。 当下，许多组织已经成长为横跨各个专业领域且内外兼容的大型生态系统，复杂度不断提升。然而，组织设计面临一个无奈的现实：人类的理性存在天花 板。正如詹姆斯·马奇（James March）与赫伯特·西蒙（Herbert Simon）在管理学经典著作《组织》（Organizations）一书中所言：应将组织视为有限理性 的决策与学习系统，它们以惯例为基础、依赖历史路径且具有目标导向性。 正因为"有限理性"，企业需要分化出不同的部门，部门内需要设置不同的岗位，并由此形成各种各样的工作流程。一旦出现分工，就产生协调的需求；分 工越细致，协调越困难，而循环重复的流程让企业开始形成路径依赖，管理问题不断涌现。 AI参与组织决策 通常，人们会从决策的角度观察组织设计的过程。我们对环境的认知很多时候也来自对所谓专业化决策的解读。 决策的过程一般分为三个 ...

Core Insights - The article discusses the potential onset of a third AI winter, drawing parallels with historical AI downturns due to unmet expectations and market realities [4][7]. Group 1: Current AI Market Situation - Many AI products launched earlier this year are now facing declining interest as they fail to address real business problems, leading to increased operational burdens and costs for companies [1][5]. - The high costs of training large models and their limited applicability in vertical markets have resulted in low return on investment, causing many AI projects to become mere showcases rather than practical solutions [5][6]. Group 2: Historical Context of AI Winters - The first AI winter occurred from 1974 to 1980, characterized by overly optimistic predictions that were not met due to technological limitations, leading to reduced funding and interest in AI research [2][3]. - The second AI winter from 1987 to 1993 was marked by the limitations of expert systems, which could not scale or adapt, resulting in a loss of market confidence and funding [3][4]. Group 3: Factors Contributing to Potential Third AI Winter - There is a significant gap between technological capabilities and market expectations, leading to a lack of sustainable business models for many AI products [6][7]. - Many companies are rushing into AI projects without a clear strategy or understanding of market needs, resulting in products that do not align with customer requirements [6][7]. - The urgency for immediate returns from both enterprises and investors is causing a lack of patience for long-term AI development, which may lead to a withdrawal of capital and support [7].