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.