Mean Articulation Machines
本文分析了大语言模型作为文本模式检测机器的架构特性，基于概念分析和基准数据，划分了LLM可靠胜任的战略任务与人类认知不可替代的领域，帮助战略决策者有效部署AI工具。
The performance of large language models (LLMs), both good and bad, derives from their core architecture as text pattern detection and generation machines that are sensitive to the frequency of the data upon which they are trained. They are amazing “mean articulation machines” in this sense. Using conceptual analysis and recent benchmark data, the paper identifies those strategic tasks that fall within the reliable competence of LLMs and those that remain fundamentally misaligned with LLM’s associationistic architecture. The result is a practical continuum identifying where LLMs offer genuine leverage and where human cognition remains indispensable. The most challenging tasks—novel scientific and strategic breakthroughs—are currently out of reach for LLMs because of inherent limitations in their architecture. Because breakthroughs are described with text does not imply that we can simply mine text for the next novel breakthrough. In clarifying the boundary of current LLM capabilities, the paper aims to help strategic decision makers deploy these tools more effectively as powerful assistants for the majority of tasks that lie on the tractable side of the continuum. History: Accepted for the Special Issue: Can AI Do Strategy? Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2025.0439 .