AI 市场恐慌指南：从中国低价模型到资本开支周期

AI has become a structural driver of the United States economy and stock market rather than a narrow technology theme.

AI market freakouts repeatedly rotate through a small set of narratives about price, demand, financing, capability, and infrastructure.

Cheaper Chinese and open models can pressure frontier-lab token margins even when overall demand for digital cognition keeps expanding.

Sanctions and entity lists may raise friction, but they do not by themselves stop Chinese laboratories from improving their models.

Legitimate efficiency-oriented distillation should be distinguished from covert industrial-scale extraction of proprietary model behavior.

The DeepSeek episode showed how markets can mistake a cheaper reasoning product for proof that frontier compute demand has collapsed.

Circular financing concerns matter because investors want to know whether AI infrastructure revenue reflects independent end demand.

Hyperscalers face a moving investor threshold in which rising AI capital expenditure must be matched by increasingly visible revenue and productivity gains.

Physical constraints, delays, cancellations, and local opposition slow the data-center buildout and stretch the adjustment timeline.

Recurring corrections can act as pressure releases that moderate excess before a fully runaway AI bubble forms.