[論文レビュー] Abundant Intelligence and Deficient Demand: A Macro-Financial Stress Test of Rapid AI Adoption

Xupeng Chen|arXiv (Cornell University)|Mar 10, 2026

Complex Systems and Time Series Analysis被引用数 0

ひとこと要約

要約: The paper formalizes a macro-financial stress test showing how rapid AI adoption can coexist with, or trigger, a macroeconomic contraction via distribution-and-contract mismatch, displacement-reinstatement dynamics, velocity effects, and intermediation compression.

ABSTRACT

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.

研究の動機と目的

• Formalize how rapid AI adoption interacts with aggregate demand and financial markets through a distribution-and-contract mismatch.
• Develop three mechanisms—displacement spiral with task reinstatement, Ghost GDP/velocity collapse, and intermediation collapse—to assess fragility.
• Provide falsifiable predictions, empirical grounding, and calibrated simulations to identify stability versus explosive crisis regimes.
• Connect micro-level AI productivity effects to macro-financial fragility using a formal framework and testable propositions.

提案手法

• Adopts a task-based production framework where AI automates a measurable set of tasks with a CES aggregation across tasks.
• Defines AI capability growth, diffusion speed, and substitution costs with a logistic diffusion function for adoption dynamics.
• Introduces three core mechanisms: displacement spiral with reinstatement effects, Ghost GDP (velocity-driven demand mis-measurement), and intermediation margin compression.
• Derives propositions identifying conditions for stability, reinforcement, or explosive decline in the labor share and demand.
• Calibrates the model using FRED time series and BLS occupation data to simulate scenarios and generate testable predictions.

Figure 1 : Macro preconditions for the displacement spiral. (a) Labor share (BLS, FRED series PRS85006173) has declined $\sim$ 19% from its 1960 peak. (b) M2 velocity (FRED series M2V) has declined 36% from its 1997 peak, consistent with Proposition 2 . (c) Ghost GDP proxy: divergence between real G

実験結果

リサーチクエスチョン

• RQ1 AI 主導の置換と再統合が自己制限的な労働分配動態を生む条件は何か、 explosive な動きに至るのか？
• RQ2 AI による産出置換が貨幣速度と測定GDPと消費関連所得のギャップ（Ghost GDP）にどのように影響するか？
• RQ3 セクター間の金融仲介マージン圧縮はどの程度発生し、マクロ金融伝達とどう相互作用するか？
• RQ4 上位所得階層の消費集中がマクロ金融ショックを拡大する exponentially なリスク経路は何か？
• RQ5 危機から安定的調整へシステムを動かす政策・制度的対応は何か？

主な発見

• 三つのメカニズムがマクロ的ストレスを生み出す：再統合の可能性を伴う置換スパイラル、Ghost GDP の速度崩壊、サービス分野を横断する仲介マージンの圧縮。
• 再統合が置換を打ち消す安定的置換経路、あるいは explosive な置換経路を AI 能力成長と再統合率に依存して内包する。
• Ghost GDP は労働所得分配が低下すると貨幣 velocity が低下することを意味し、急速な AI 移行期には従来の需要指標が誤解を招く。
• 情報摩擦を低減する AI により仲介マージンが固定床へ圧縮され、SaaS、決済、コンサルティングなどの摩擦駆動セクターで露出が高まる。
• 消費集中は上位階層の賃金労働者が大量の消費を牽引し、AI の露出が高いため信用・住宅ローン市場への伝播を強め、マクロショックを増幅する。
• 加速的な AI 導入が安定的調整から explos ive 危機へ移行する条件を政策対応と適応速度に依存して示す。
• このフレームワークは FRED および BLS データの実証モーメントに基づく 11 の検証可能な予測と反証可能条件を生み出す。

Figure 2 : Occupation-level evidence: AI exposure and wage outcomes. (a) Nominal wage growth (2019–2023) vs. AI exposure ( $\beta=-6.53$ , $p<0.01$ ). (b) Real wage growth after adjusting for 18% CPI inflation. (c) Pre-trend placebo (2015–2019): the significant negative slope ( $\beta=-3.86$ , $p<0.