Paper: arXiv 2606.30583

Authors: Nicola Borri, Yukun Liu, Aleh Tsyvinski

Abstract

We study 380 trillion tokens of realized AI consumption across more than four hundred LLMs. We build a high-frequency AI factor and show that a long-short strategy based on firms’ AI exposure earns significantly positive returns. The average strategy return is larger based on intensive, frontier-oriented AI consumption but smaller based on casual or open-weight usage. Internationally, the return spread is significant in developed countries but insignificant in emerging markets. Examining occupational AI exposure, we find more positive exposure in occupations intensive in nonroutine interactive tasks and more negative exposure in those intensive in nonroutine analytical tasks.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 9.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper demonstrates high empirical rigor through its use of a massive, proprietary dataset and robust statistical methodologies. Its novelty lies in constructing an AI factor from real-time consumption data and linking it to stock returns and occupational exposure, offering fresh insights into a rapidly evolving area.

Research Flowchart

  flowchart TD
    A[Research Goal: Cross-Section of Stock Returns & AI Exposure] --> B{Key Methodology: AI Factor Construction & Long-Short Strategy};
    B --> C[Data/Inputs: 380T Tokens Realized AI Consumption, >400 LLMs, Firm Financials];
    C --> D(Computational Processes: Factor Building, Portfolio Sorting, Regression Analysis);
    D --> E{Key Findings: Significant Positive Returns from AI Factor};
    E --> F[Outcomes: Stronger Returns from Frontier AI Consumption; Returns Significant in Developed Markets; Occupational AI Exposure Varies];