Paper: arXiv 2604.16997

Authors: Andrew Y. Chen

Abstract

AI stocks trade at extraordinary valuations. We develop an asset pricing model in which investors use AI stocks to hedge against an AI singularity that displaces their consumption. Because markets are incomplete – investors cannot trade private AI capital – AI stocks command a premium. Market incompleteness distorts both valuations and the efficient development of AI, creating a rationale for government transfers that becomes compelling when singularity-driven growth overwhelms deadweight costs. This paper was generated by AI, using https://github.com/chenandrewy/ralph-wiggum-asset-pricing/.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents an intriguing theoretical asset pricing framework examining market incompleteness and AI singularity risks with a solid mathematical foundation. However, it lacks empirical validation, relying entirely on stylized theory and AI-generated model frameworks without backtests or real-world data validation.

Research Flowchart

  flowchart TD
    A[Research Goal: Explain extreme AI stock valuations via singularity hedging] --> B[Inputs: Consumption displacement risk & Private capital market incompleteness]
    B --> C[Methodology: Develop an incomplete-market asset pricing model]
    C --> D[Computational Process: Model AI stock premiums & optimal AI development timing]
    D --> E[Outcome 1: Incompleteness creates AI stock valuation premium]
    D --> F[Outcome 2: Government transfers justified when growth exceeds deadweight costs]

Cited References (24)

  1. Displacement Risk and Asset Returns (2012)
  2. The AI Dilemma: Growth versus Existential Risk (2024)
  3. Rare Disasters and Asset Markets in the Twentieth Century (2006)
  4. Can Time-Varying Risk of Rare Disasters Explain Aggregate Stock Market Volatility? (2013)
  5. Technological Revolutions and Stock Prices (2009)
  6. The Simple Macroeconomics of AI (2025)
  7. Are We Approaching an Economic Singularity? I nformation Technology and the Future of Economic Growth (2021)
  8. Growth Opportunities, Technology Shocks, and Asset Prices (2014)
and 16 more
  1. Left Behind: Creative Destruction, Inequality, and the Stock Market (2020)
  2. Automation and the Displacement of Labor by Capital: Asset Pricing Theory and Empirical Evidence (2023)
  3. Artificial Intelligence and Economic Growth (2019)
  4. Artificial Intelligence-Powered (Finance) Scholarship (2026) — link
  5. The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2024) — link
  6. Incomplete Contracting and AI Alignment (2018) — link
  7. AI and Catastrophic Risk (2023)
  8. Scenarios for the Transition to AGI (2024) — link
  9. Labor-Technology Substitution: Implications for Asset Pricing (2019) — link
  10. Artificial Intelligence and Firms’ Systematic Risk (2024) — link
  11. Does Peer-Reviewed Research Help Predict Stock Returns? (2022) — link
  12. When AI Builds Itself: Our Progress toward Recursive Self-Improvement, and Its Implications (2026)
  13. How Anthropic’s Product Team Moves Faster than Anyone Else (2026)
  14. Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering (2026) — link
  15. Ralph Wiggum Loop (2025) — link
  16. Do Markets Believe in Transformative AI ? (2025) — link

0 of 24 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.