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2025/2026  BA-BHAAI1114U  Economics and Political Economy of AI

English Title
Economics and Political Economy of AI

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration Summer
Start time of the course Summer
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 30
Max. participants 100
Study board
Study Board for General Management
Programme Bachelor of Science in Economics and Business Administration
Course coordinator
  • Moira Daly - Department of Economics (ECON)
For academic questions, pleases contact Moira Daly (moda.eco@cbs.dk).
Main academic disciplines
  • Innovation
  • Political Science
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 20/11/2025

Relevant links
Learning objectives
By the end of the course, students will not only gain a broad understanding of how artificial intelligence is reshaping economies and societies, but also develop the analytical tools to critically evaluate these changes. More specifically, students will be able to:
  • Explain AI as a technology and apply economic models to predict effects on things such as productivity, diffusion lags, labor demand, wages, and distributional outcomes, being sure to state key assumptions.
  • Evaluate regulatory and political-economy trade-offs regarding AI (such as efficiency, innovation, risk, fairness, data externalities) using (welfare) economic analysis.
  • Construct and defend a clear, evidence-based position on a contemporary AI issue, integrating relevant economic models and political-economy perspectives, and acknowledging limitations.
Course prerequisites
Introductory level economics
Examination
Economics and Political Economy of AI:
Exam ECTS 7.5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Aids Limited aids, see the list below:
The student is allowed to bring
  • USB key for uploading of notes, books and compendiums in a non-executable format (no applications, application fragments, IT tools etc.)
  • Any calculator
  • In Paper format: Books (including translation dictionaries), compendiums and notes
The student will have access to
  • Canvas
  • The personal drive (S-drive) on CBS´ network
  • Advanced IT application package
Make-up exam/re-exam
Same examination form as the ordinary exam
The number of registered candidates for the make-up examination/re-take examination may warrant that it most appropriately be held as an oral examination. The programme office will inform the students if the make-up examination/re-take examination instead is held as an oral examination including a second examiner or external examiner.
n/a
Course content, structure and pedagogical approach

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.

 
Research-based teaching
CBS’ programmes and teaching are research-based. The following types of research-based knowledge and research-like activities are included in this course:
Research-based knowledge
  • Classic and basic theory
  • New theory
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Development of research questions
  • Analysis
  • Discussion, critical reflection, modelling
  • Peer review including Peer-to-peer
Description of the teaching methods
The course combines lectures, interactive discussions, and case-based learning. Lectures will introduce the main theoretical frameworks and situate them within the broader economic and policy debates on artificial intelligence. Discussions will encourage students to actively engage with the readings, question assumptions, and connect concepts to contemporary developments.

Case studies and/or news articles drawn from sectors such as healthcare, finance, and social media will be used to illustrate how AI technologies generate both economic opportunities and societal challenges. Students will also analyze real-world policy debates, such as the EU AI Act and global competition in semiconductor technologies.
Feedback during the teaching period
Feedback is an integral part of the course and takes place in several forms:
1. In-class dialogue – Lectures and discussions are interactive, and students are encouraged to ask questions and test their understanding. Immediate verbal feedback will be provided during class discussions and case study analyses.
2. Group work – Feedback will also be provided during group presentations and case study sessions, both from the instructor and peers.

Student workload
Precourse activity 20 hours
Classroom attendance 38 hours
Preparation 128 hours
Examination 20 hours
Further Information

6 week course.
Expected literature

These are both freely available

  • Ajay Agrawal, Joshua Gans, and Avi Goldfarb (eds.), The Economics of Artificial Intelligence: An Agenda (University of Chicago Press, 2019).
     
  • Ajay Agrawal, Joshua Gans, Avi Goldfarb, and Catherine Tucker (eds.), The Political Economy of Artificial Intelligence (University of Chicago Press, 2024).

 

  • Joshua Gans, The Microeconomics of Artificial Intelligence (MIT Press, 2025).

 

 

We will also use case studies.
Last updated on 20/11/2025