As large language models become integrated into intellectual work, they increasingly participate in the generation, synthesis, and refinement of ideas. While this collaboration often improves output quality and speed, it can also introduce a subtle failure mode: the loss of traceability in the observer’s own cognitive trajectory(the lived path of understanding). This paper introduces the concept of trace-preserving co-creation, arguing that understanding is not defined solely by the final artifact produced, but by the observer’s ability to reconstruct the path by which meaning emerged. We propose that meaning is observer-dependent and reconstructed along a traceable sequence of representational shifts, and that AI systems optimized primarily for compression and coherence can collapse this trace. We outline the conceptual structure of emergence maps as a missing design primitive for human–AI systems and argue that preserving cognitive traceability is a critical condition for sustained epistemic agency under accelerated co-creation.
Kirandeep Kaur (Tue,) studied this question.