AI Token Economics

Week 1 | February 5, 2027

AI Today: Setting the Stage

The landscape, the players, and why this is now a board-level problem

Before you can argue about what AI costs, you need a shared picture of what it is and who sells it. This opening session maps the territory: how modern AI systems work, who the players are at each layer of the value chain, and why cost, pricing, and competition policy have all landed on the board agenda at once. You leave with the vocabulary and the mental map the rest of the program builds on.

IN THIS SESSION

• Modern AI in plain terms: foundation models, agents, and what each actually does
• The value chain: chipmakers, cloud platforms, model providers, and application builders
• Where the money moves between those layers and who captures it
• Why economics, pricing, and antitrust now arrive as one problem, not three

Balaji Padmanabhan, PhD
Associate Dean of Strategic Initiatives
Director, Center for Artificial Intelligence in Business
Dean's Professor of Decisions, Operations & Information Technologies

Week 2 | February 12, 2027

The Macro-Finance of the AI Boom

Capital, energy, and what Washington sees

Token prices are the visible tip of the largest capital investment cycle in modern history. Hundreds of billions in data centers, chips, and power are being financed on terms that will eventually have to be compensated — what you pay for AI sits downstream of those investments. This session examines how and when the buildout is financed, how capital costs and energy constraints flow through to AI prices, whether this wave resembles past technology booms, and how policymakers balance AI competitiveness against its risks.

IN THIS SESSION

• How the AI infrastructure buildout is financed and how that flows through to what you pay
• Capital markets, financing structures, and systemic-risk questions in the AI boom
• Chips, energy, and industrial policy: the constraints shaping AI's cost curve
• Reading Washington: policy signals on AI investment and competition

Michael Faulkender, PhD
Former Deputy Secretary of the U.S. Treasury
William A. Longbrake Professor of Finance

Week 3 | February 19, 2027

Algorithmic Pricing: Strategy, Platforms, and Value

Where pricing by machine creates value — and where it invites scrutiny

Algorithms now set prices for airlines, retailers, ride-sharing platforms, and a growing share of the economy. This session examines algorithmic pricing as a business strategy: where it creates genuine value, how platforms deploy it, and what happens to competition when firms on both sides of a market price by machine. It also sets up the question the next two sessions answer: when does pricing by machine stop being a strategy and start being a legal problem?

IN THIS SESSION

• How firms use algorithms to set and optimize prices
• Platform pricing, self-preferencing, and competition among algorithms
• Where algorithmic pricing creates value, and where it invites scrutiny
• Dynamic and personalized pricing: what customers will accept, and what they won't

Bo "Bobby" Zhou, PhD
Associate Professor of Marketing

Week 4 | February 26, 2027

Working Session: Outline Your AI Cost, Pricing, and Risk Map

Review your organization’s AI costs, pricing opportunities, and potential risks to create a plan for your final project. Connect what you have learned so far and clarify what you need to complete the final project.

IN THIS SESSION

• Final project goals and expectations
• Applying token economics, pricing, and labor concepts to your organization
• Identifying the data and questions your map should address
• Work time and guidance to shape your project plan

Week 5 | March 5, 2027

The Other Antitrust Frontier: Algorithms, Labor Markets, and Talent

The exposure organizations most often overlook

Algorithms are now affecting wages the way they affect product prices. This session examines the rapid expansion of antitrust into labor markets — no-poach and wage-fixing enforcement, the national battle over noncompete agreements, and the rise of algorithmic wage-setting and compensation benchmarking tools. Every participant is also an employer, and the practices in question are routine: a salary survey, a benchmarking subscription, a standard clause in an offer letter. You leave able to assess where your own talent practices sit.

IN THIS SESSION

• Antitrust comes to labor markets: no-poach, wage-fixing, and monopsony power
• Noncompete agreements: the evidence and the policy battle
• Algorithmic wage-setting & compensation benchmarking: the RealPage question, mirrored
• Practical steps to assess labor-market antitrust exposure

Evan Starr, PhD
Professor of Management & Organization

Week 6 | March 12, 2027

Algorithmic Pricing Meets Antitrust: Identifying the Red Lines

When does smart pricing become illegal coordination?

You can't agree with a competitor on price. Can your software? Led by a former Director of the FTC's Bureau of Economics, this session traces how enforcers think about pricing algorithms — from early cases involving computerized fare systems and e-commerce sellers to today's litigation over shared pricing software — and examines how competition questions are now reaching AI markets themselves. You will see how an agency actually builds a case: what evidence it looks for, what the economics have to show, and where current law runs out.

IN THIS SESSION

• A brief history of algorithmic coordination: from airline fare systems to e-commerce
• RealPage and the current enforcement landscape for shared pricing algorithms
• How agencies investigate: evidence, economics, and the limits of current law
• Competition in AI markets: concentration, partnerships, and what enforcers watch

Andrew Sweeting, PhD
Former Director, Bureau of Economics, Federal Trade Commission
Professor of Economics

Week 7 | March 19, 2027 | 10 a.m. to noon ET

Reading the Token Bill

What your AI actually costs and how to manage it

AI adoption can bring substantial usage costs that are difficult to anticipate. This session explains the main cost drivers, including input and output tokens, caching, reasoning, and how models are used within a workflow. Participants will examine the current model landscape and cost trends, compare the cost of completing tasks across models, and work through practical ways to reduce unnecessary usage while maintaining the quality they need. A live conversation and Q&A with Dominik Kundel, Developer Experience Lead at OpenAI, will bring an industry perspective to model choice, usage costs, and AI adoption at scale.

IN THIS SESSION

• Understand what drives cost per completed task, beyond the headline price per token
• Compare models and workflows across cost, quality, and speed
• Explore practical ways to reduce unnecessary usage and manage costs as adoption grows
• Put questions directly to Dominik Kundel during a live conversation and Q&A

Daniel McCarthy, PhD
Associate Professor of Marketing

Week 8 | March 26, 2027

The Hot Seat

Your organization's map, in front of the people who wrote the rules

You've spent six weeks building one document: a map of your own organization showing what your AI actually costs, where your pricing creates opportunity, and where your pricing or talent practices carry risk. In this closing session you present it, and the faculty push back. A former FTC Bureau of Economics director, a former Deputy Treasury Secretary, and the program's pricing and labor economists ask the questions your board will eventually ask. You leave with a tested map and a 90-day plan.

IN THIS SESSION

• Participant presentations: AI cost, pricing opportunity, and risk maps
• Live feedback from the full faculty panel
• Synthesis: connecting token economics, pricing strategy, and antitrust
• Your 90-day action plan