A time for monsters: Organizational knowing after large language models
将大语言模型视为哈拉维式的怪物，分析其如何通过大规模统计推理生成类比，重塑组织认知，并探讨对探究、对话验证和能动性再分配带来的挑战。
Large language models are reshaping organizational knowing by unsettling the epistemological foundations of representational and practice-based perspectives. We conceptualize large language models 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 large language models 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-large language models, the article 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.