Human-Ai-Collaboration

Misunderstanding as a Productive Force Niklas Luhmann observed that knowledge systems do not evolve despite misunderstanding, but because of it. Unexpected misalignment — what appears as error or miscommunication — often becomes the source of novelty and structural change. This insight predates modern human–machine collaboration, yet becomes newly visible in the presence of large language models. From Linear Thought to Distributed Cognition The emergence of LLMs marks a transition from linear, single-mind cognition to fragmented, recombinable, multi-perspective thinking. ...

Field note (retrospective) This document records an early line of reasoning developed around 2018, before the emergence of modern large language models. The intent of this note is to preserve the initial intuitions, metaphors, and concerns that shaped later work, not to assert a current position or policy recommendation. Several assumptions and framings used here have since been revised, formalized, or superseded. Context This essay was originally written from the vantage point of 2018, when breakthroughs in artificial intelligence—such as AlphaGo, advances in speech recognition, and early NLP systems— signaled a structural shift in how humans and machines might collaborate. ...