What question did this study set out to answer?

The aim is to delineate the capabilities and limitations of large language models (LLMs) in performing strategic tasks.

March 21, 2026

Mean Articulation Machines

Key Points

The aim is to delineate the capabilities and limitations of large language models (LLMs) in performing strategic tasks.
Conceptual analysis of LLM architecture and performance.
Evaluation of recent benchmark data to assess task competence.
Identification of tasks where LLMs align with human cognition.
A continuum of tasks indicating where LLMs excel or struggle.
LLMs can reliably assist with many tasks, but face challenges in novel scientific breakthroughs.
Strategic decision makers can benefit from understanding LLM limitations for effective tool deployment.

Abstract

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 .

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