A time for monsters: Organizational knowing after LLMs

TL;DR

This paper explores how Large Language Models (LLMs) act as boundary-crossing entities that challenge traditional organizational knowledge, highlighting their potential and risks in reshaping inquiry, validation, and agency.

Contribution

It introduces the concept of LLMs as Haraway-ian monsters, analyzing their role in generating analogies and transforming organizational epistemology.

Findings

LLMs expand organizational knowing through analogical connections.

They pose epistemic risks by destabilizing established categories.

The paper identifies challenges in inquiry, validation, and agency due to LLMs.

Abstract

Large Language Models (LLMs) are reshaping organizational knowing by unsettling the epistemological foundations of representational and practice-based perspectives. We conceptualize LLMs as Haraway-ian monsters, that is, hybrid, boundary-crossing entities that destabilize established categories while opening new possibilities for inquiry. Focusing on analogizing as a fundamental driver of knowledge, we examine how LLMs generate connections through large-scale statistical inference. Analyzing their operation across the dimensions of surface/deep analogies and near/far domains, we highlight both their capacity to expand organizational knowing and the epistemic risks they introduce. Building on this, we identify three challenges of living with such epistemic monsters: the transformation of inquiry, the growing need for dialogical vetting, and the redistribution of agency. By foregrounding the entangled dynamics of knowing-with-LLMs, the paper extends organizational theory beyond human-centered epistemologies and invites renewed attention to how knowledge is created, validated, and acted upon in the age of intelligent technologies.