This course analyzes Artificial Intelligence through the lens of microeconomics, industrial organization, and applied econometrics, following Joshua Gans' The Economics of Artificial Intelligence. Core topics include:
Infrastructure & Inputs: Data centers, environmental resources, data as a non-rival good, privacy, and intellectual property.
Competition & Regulation: Network effects, loss-leading, open-source dynamics, and the EU AI Act.
Economic Impacts: Labor market displacement/augmentation, firm productivity, algorithmic bias, consumer search, and the economics of generative art.
A core component of the course is the empirical replication and extension of an existing economics paper on AI. Students will utilize the LLM and computational tools taught in the TD sessions to process data and run econometric analyses. Potential data sources include the AIOE Database, employment surveys, AI-simulated datasets for algorithmic bias, or web-scraped data from AI platforms.
Grading is based entirely on group work (groups of 5):
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50% — Literature Presentations: Graded on analytical comprehension, presentation materials, discussion engagement, and a 1-page (A4) synthesis.
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50% — Empirical Replication Project: Graded on the originality of the empirical variation, correct implementation of LLM/econometric tools, and critical assessment of the original paper's methodology. The final session (Session 6) is dedicated to presenting these projects.
