NYU SternEconomics of AI
ECON-GB 3150 T1 · FALL 2026

Economics of AI

What are the economic impacts of AI?

This course explores both the macroeconomic implications of AI—for example, whether it will lead to a jump in growth and prosperity, labor-market dislocations and joblessness, or changes in inflation—and its microeconomic and industry impacts, including which firms and industries will win or lose and whether AI will ultimately become a commodity. We also explore related technology trends that will interact with AI, such as autonomous vehicles, the electric tech stack and robotics, and may ultimately have some of the largest impacts.

View course syllabus ↗Open NYU Brightspace ↗Team presentation topics ↗Begin with Session 1 →
01—06
Six meetings.
One evolving argument.

THE COURSE QUESTION

Capability is only the beginning.

We will ask not only what AI can do, but how quickly organizations change, who captures the value and which physical and political constraints shape the outcome.

Will productivity surge?Which workers gain?Where do rents accrue?Can the grid keep up?
01

THE INTELLECTUAL ARC

Six big questions

SESSION 1↗

AI as a General-Purpose Technology

Lessons from History

Is AI really different—or are we repeating the early stages of every major technological revolution?

SESSION 2↗

Winners and Losers at the Firm Level

Value Creation ≠ Value Capture

AI could create trillions of dollars of value. Who actually gets to keep it?

SESSION 3↗

AI and Labor Markets

Augmentation vs. Automation

Will AI replace workers—or make them valuable enough that we ultimately need more of them?

SESSION 4↗

The Productivity Puzzle

Will AI Deliver a Macro Payoff?

If AI is transforming firms, why is it so difficult to see in aggregate productivity statistics?

SESSION 5↗

The New Industrial Race

AI, the Electric Tech Stack, and Global Competition

Is the AI race really a race for models—or for chips, electricity, grids and physical infrastructure?

SESSION 6↗

Political and Regulatory Responses

AI Across Industries

What happens when technological change creates concentrated losers faster than institutions can adapt?

02

HOW WE WORK

The course rhythm

Before class

Read + watch
+ observe

Arrive with evidence, an example and a provisional view.

During class

Analyze + debate
+ apply economics

Move from the headline to mechanisms, tradeoffs and incentives.

After class

Reflect + revise
your beliefs

Record what changed—and what evidence would change you next.

YOUR FORECAST

Make two claims.
Then let the course test them.

Estimate AI’s effects on your own work and on the broader U.S. labor market. Responses are anonymous and may be compared with the class later in the course.

0150%
0250%

No names or identifying information are requested.

CLASS FORECAST · LIVE

See the results after answering!

Submit both forecasts to reveal the class results, or choose to view them without answering.

Two Different Visualizations of All the Jobs in America

BACKGROUND TO CONSIDER · 2014

(1) Every Job in America, in One Graph

This NPR Planet Money treemap shows the structure of roughly 137 million U.S. jobs as it looked in 2014. Use it as historical context—not as a current labor-market snapshot.

View the original NPR article ↗
NPR Planet Money treemap of approximately 137 million U.S. jobs, grouped by services, goods-producing industries and government
Source: Quoctrung Bui, NPR Planet Money, January 9, 2014. Data reflect the labor market at that time.

INTERACTIVE EXPLORATION · CURRENT BLS DATA

(2) Great Job Visualization from Karpathy.ai

Open the original interactive

Interactive visualization by Andrej Karpathy, using U.S. Bureau of Labor Statistics Occupational Outlook Handbook data. AI-exposure scores are rough estimates for exploration, not predictions that a job will disappear. If the interactive does not load here, use the link above to open the original site.