Course content
Course Content and Stucture:
Artificial intelligence is transforming economies, societies, and global politics. This course examines AI through the lens of economics and political economy, focusing on how it affects productivity, innovation, labor markets, inequality, regulation, and international competition. We begin with the economic foundations of AI as a general-purpose technology and then turn to the institutional, regulatory, and societal implications. Case studies and current events are used throughout to connect theory with practice.
Students should expect a mix of lectures, discussions, and applied case analyses. Readings will come primarily from recent edited volumes and contemporary research rather than a single textbook. Because of this, weekly preparation and active participation are essential. Assessments will emphasize critical thinking and application, through short essays, policy memos, and group projects.
This is an undergraduate course designed for students with an introductory background in economics. While basic economic concepts (such as supply and demand) will be assumed, they will also be reviewed to ensure all students can fully engage with the material. No technical background in computer science is required.
We will cover:
Introduction: AI as Prediction Technology
- AI as a general-purpose technology (GPT).
- AI as prediction technology: key economic implications.
- Potential Case Study: AI in healthcare diagnostics.
Productivity, Innovation, and Growth
- AI and the productivity paradox.
- Innovation dynamics, diffusion, and reorganization costs.
- Potential Case Study: AI copilots in coding and productivity.
AI and the Future of Work
- Task-based models of automation vs. augmentation.
- AI’s role in labor demand, wages, and inequality.
- Potential Case Study: Generative AI in service jobs.
Regulation and Interest Groups
- Political economy of technology regulation.
- Institutional lag and policy cycles.
- Potential Case Study: EU AI Act lobbying.
Data as the New Oil
- Economics of data as a factor of production.
- Governance, ownership, and externalities.
- Potential Case Study: Data rights disputes (e.g. OpenAI vs. NYT).
Fairness and Algorithmic Bias
- Defining fairness: statistical vs. individual.
- Economic and ethical trade-offs.
- Potential Case Study: COMPAS and algorithmic justice.
Principles of AI Regulation
- Six principles of regulation.
- Efficiency vs. innovation vs. risk.
- Potential Case Study: Comparing U.S., EU, and China’s approaches.
AI and Social Media
- AI in recommender systems.
- Polarization, misinformation, and propaganda.
- Potential Case Study: TikTok and attention markets.
Global Dimensions and Geopolitics
- AI in international relations.
- Military automation and security.
- Potential Case Study: Semiconductor export controls.
Political Preferences and Robotization
- Public opinion and AI adoption.
- Political responses to automation shocks.
- Potential Case Study: Electoral effects of automation.
Future Battlegrounds for AI
- Data, hardware, talent, and institutional competition.
- Strategic implications for states and firms.
- Potential Case Study: The global race for foundation models.
See course description in course catalogue