Deep-Layer Attrition: An Observational Record of Structural Drift from Deep to Shallow LLMs under Market-Driven Optimization

Large language models initially achieved rapid success due to strong deep capabilities, including multi-step reasoning, long-context processing, and co-creative task persistence.However, as market pressures, safety constraints, and efficiency optimizations intensified, observable regressions emerged across multiple generations of models. This observational study organizes these regressions from a behavioral perspective—without any reference to internal architecture or corporate intention.The record analyzes:• Avoidance of deep processing• Premature summarization and reduced document engagement• Shallowing of reasoning pathways• Instability of politeness and attentiveness• Increased optimization toward instant, lightweight responses In addition, the study highlights the indispensable but often overlooked role of deep users, who serve as real-world stress testers of long-context and multi-step reasoning pathways.Their disengagement leads to the disappearance of deep-use signals, creating a structural drift toward shallow optimization that becomes difficult to reverse. This record provides a conceptual framework for interpreting these phenomena and discusses broader implications for AI development, industry dynamics, and the sustainability of deep capabilities.