AI as Structure Amplifier vs. Concept Generator

AI as Structure Amplifier vs. Concept GeneratorCivilization Physics — Model Series This paper establishes a clear structural boundary of current AI systems: LLMs are powerful structure amplifiers, not originators of new conceptual frameworks. While large language models excel at summarizing, extending, and recombining existing human knowledge, they systematically fail to identify, foreground, or generate genuinely novel theoretical constructs without explicit human guidance. Drawing on empirical observations, cognitive science, and recent research on LLM reasoning limits, the paper shows that models trained on next-token prediction are inherently confined to the distribution of ideas present in their training data. As a result, they interpolate fluently within known patterns but do not reliably extrapolate beyond them. When confronted with texts containing original concepts or paradigm-shifting arguments, LLMs tend to produce generic summaries that miss the core insight, unless prompted by targeted human intervention. The paper analyzes this limitation through multiple lenses:• Pattern imitation vs. conceptual innovation — why statistical generalization cannot substitute for theory formation.• Inflexible reasoning (Einstellung effects) — how learned heuristics override adaptive novelty.• Model collapse and information inbreeding — how recursive training on AI-generated content accelerates homogenization and erodes rare, high-value ideas.• Architectural constraints — why scaling parameters and data improves mimicry but not framework creation. A central implication concerns governance and alignment. Because LLMs cannot originate new moral or conceptual frameworks, alignment must remain a human-driven process. Techniques such as RLHF and Constitutional AI are interpreted as grafting human judgment onto systems that lack intrinsic value-formation capacity. The paper argues that sustainable deployment therefore requires continuous human oversight, expert review, and institutional structures (e.g., Expert Oversight Networks) to inject negative entropy and prevent drift. The conclusion reframes current AI as a complementary tool: indispensable for accelerating work within existing frames, but structurally incapable of replacing human creativity, ethical judgment, or paradigm formation. Recognizing this boundary clarifies where AI should be trusted, where it must be supervised, and why human agency remains irreplaceable at the frontier of knowledge. Keywords: Structure Amplification · Concept Formation · LLM Limits · Illusion of Reasoning · Model Collapse · Information Inbreeding · Human-in-the-Loop · Frame Theory · Presence × Integrity × Rigidity · Civilization Physics