Course

AI Token Economics

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$1,750 Enroll

Full course description

Executive Certificate Program 

Registration Deadline: Friday, January 29
Course Dates/Times: February 5 - March 26
Audience: Finance, technology, product, pricing, legal, compliance, and policy professionals responsible for AI-related business decisions.
One-day registration and payment options: Reach out to crichey@umd.edu for one-day registration and alternative payment options.

Overview

Every AI interaction is metered in tokens, and the bill has grown large enough to reach the board. The strange part is which way it moves: the price of each token keeps falling while total spend keeps climbing, because agents and reasoning models consume far more tokens per task than a simple chat ever did.

Meanwhile, the pricing software companies rely on has become an antitrust target. Regulators have challenged shared pricing tools, states are writing their own rules, and the same scrutiny now extends to wages and hiring.

Over seven live weeks, this program puts those questions in one room: what AI will cost your organization, how to price the products you build on it, and where pricing — by people or by machines — crosses a legal line. You'll work alongside finance, product, and legal leaders from other organizations, learn from faculty who have served at the FTC and the U.S. Treasury, and leave with a cost model, a pricing framework, and a risk map built on your own organization.

Valuable Outcomes

1. Evaluate the Economics of AI Investment. Understand the AI value chain, infrastructure financing, and cost drivers influencing organizational AI spending and long-term financial planning.

2. Build a More Informed AI Cost Model. Analyze token-based pricing, model selection, task-level consumption, and scaling considerations to improve visibility into AI expenditures.

3. Identify Strategic Pricing Opportunities. Examine algorithmic, dynamic, and personalized pricing approaches to assess how AI-enabled pricing can create value and shape competitive positioning.

4. Recognize Competition and Antitrust Risks. Evaluate regulatory considerations associated with shared pricing algorithms, market coordination, wage-setting technologies, and employment practices.

5. Translate Insights into Organizational Action. Develop an integrated AI cost model, pricing framework, and risk map, strengthen it through faculty feedback, and establish a 90-day action plan.

Cancellation and Refund Policy

Cancellations made more than 14 calendar days before the first program session are eligible for a full refund. No refunds are available after that deadline.

Instead of canceling, participants may transfer their registration to another cohort beginning in the same program year, through June 30.