AI带来产出过剩,但需求不足,可能引发金融系统性风险。
Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption
- 分析AI替代人力导致收入下降、需求萎缩的恶性循环机制。
- 发现劳动份额下降将使货币流通速度持续降低,形成‘幽灵GDP’缺口。
- 指出高收入者受冲击最重,影响信贷与房贷市场,适合政策与金融研究者参考。
我们构建了快速采纳AI的宏观金融压力测试框架。不同于生产力崩溃或生存风险,核心矛盾是产出过剩与需求不足并存:经济制度仍基于人类认知稀缺性设计。三个机制揭示此通道:第一,置换螺旋与再安置效应竞争:企业理性用AI替代人力,导致总劳动收入下降,进而削弱总需求,加速更多AI采纳;推导出在AI能力增长速率、扩散速度与再安置率条件下,反馈为自我限制或爆发式扩张的临界点。第二,幽灵GDP:当AI产出替代人力产出时,若无补偿转移,货币流通速度随劳动份额下降而单调递减,造成度量产出与消费相关收入之间的裂口。第三,中介崩溃:能降低信息摩擦的AI代理压缩中间商利润至纯物流成本,引发SaaS、支付、咨询、保险及金融顾问等领域的价格重估。由于美国前20%收入者贡献47%-65%消费且面临最高AI暴露,其对私人信贷(全球2.5万亿美元)和抵押贷款市场(13万亿美元)的传导作用尤为显著。论文提出十一项可检验预测并附明确证伪条件。基于FRED时间序列与BLS职业层级数据校准的模拟,量化了稳定调整过渡至爆炸性危机的条件。
原文摘要 · Abstract (English)
We formalize a macro-financial stress test for rapid AI adoption. Rather than a productivity bust or existential risk, we identify a distribution-and-contract mismatch: AI-generated abundance coexists with demand deficiency because economic institutions are anchored to human cognitive scarcity. Three mechanisms formalize this channel. First, a displacement spiral with competing reinstatement effects: each firm's rational decision to substitute AI for labor reduces aggregate labor income, which reduces aggregate demand, accelerating further AI adoption. We derive conditions on the AI capability growth rate, diffusion speed, and reinstatement rate under which the net feedback is self-limiting versus explosive. Second, Ghost GDP: when AI-generated output substitutes for labor-generated output, monetary velocity declines monotonically in the labor share absent compensating transfers, creating a wedge between measured output and consumption-relevant income. Third, intermediation collapse: AI agents that reduce information frictions compress intermediary margins toward pure logistics costs, triggering repricing across SaaS, payments, consulting, insurance, and financial advisory. Because top-quintile earners drive 47--65\% of U.S.\ consumption and face the highest AI exposure, the transmission into private credit (\$2.5 trillion globally) and mortgage markets (\$13 trillion) is disproportionate. We derive eleven testable predictions with explicit falsification conditions. Calibrated simulations disciplined by FRED time series and BLS occupation-level data quantify conditions under which stable adjustment transitions to explosive crisis.
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