Human–AI Collaboration Requires a High-Order Dynamic Abstraction Substrate

This position paper argues that effective Human–AI collaboration in the LLM era requires a high-order dynamic abstraction substrate—one that externalizes stable human cognition into a form AI can reliably consult, and whose content evolves as AI and human capabilities reshape what is worth codifying. We analyze the substrate along three dimensions—what it must hold (five necessary knowledge categories of autonomous AI: Situation, Purpose, Action, Constraints, Evaluation; SPACE), where its content originates (a source-by-externalization analysis isolating the fabrication-codified failure mode behind LLM hallucination, and three practice-grounded routes that defend against it), and how it organizes Human–AI collaboration (two complementary insights: a three-phase temporal cycle of externalize–practice–summarize in which AI plays a different role at each phase, and a long-tailed abstraction hierarchy in which humans focus on the rare top while AI handles the tail). We name this substrate Knowledge Object (KO Network) and characterize it along three axes: content that occupies the moving band between the AI capability ceiling and the human articulability ceiling; structure that commits to five quality attributes (Understandable, Verifiable, Traceable, Controllable, Reusable; UVTCR) and SPACE-aligned subgraphs; and a bidirectional lifecycle in which entries are added as humans articulate previously tacit cognition and retired as AI absorbs the codified. Finally, we respond to five alternative views.