Anton Shenk

I study the economics of AI – from market monopolies to national security risks.

I'm a quantitative researcher at RAND – where I publish work on AI market dynamics (featured in Marginal Revolution) and the national security implications of frontier systems. My research combines econometric modeling, data science, and policy analysis to help leaders navigate our AI-driven future.

RESEARCH

Featured Publications
Evaluating Natural Monopoly Conditions in the AI Foundation Model Market
RAND Corporation, 2024
Featured in Tyler Cowen's Marginal Revolution
Paper | Blog PostEvaluating AI for National Security and Public Safety
RAND Corporation, 2024
PaperCharting Multiple Courses to Artificial General Intelligence
RAND Corporation, 2025
PaperCode Samples
Modeling the Economic Viability of AI Development
Interactive simulator modeling profitability of AI models based on compute trends, training costs, and usage growth. R, Shiny.
Appendix | Code | DashboardFormal Models for Robust AGI Governance
Dynamic model of strategic interactions between states under uncertainty around AGI development, safety, verification, and risk. R, Differential Equations.
Appendix | CodeExplore my full RAND corpus, archive, and GitHub for additional writing and code.

About

I'm a quantitative research assistant at RAND specializing in the economics of artificial intelligence. My work examines how AI is reshaping markets, regulatory frameworks, and security risks.

I hold a B.S. in Quantitative Economics and Mathematics from Tufts University (GPA: 3.86/4.0, summa cum laude), where I completed graduate coursework in econometrics, statistics, and game theory. At RAND, I've authored work featured in the organization's annual report, including solo work on model evaluation and collaborative analysis of monopoly conditions in AI markets.Research Interests
• AI market dynamics and competition
• Economic impacts of frontier AI systems
• AI safety and evaluation frameworks
• Economic statecraft and financial system resilience