The Economics of AI: Classroom Bootcamp

An eight-hour classroom bootcamp on what AI applications really cost, and what they are worth. Students measure the cost of real requests in Smarter, compare models on cost and quality, build a business case, and design a budget policy that Smarter enforces. No programming is needed. See Course Formats and Grading for the standard bootcamp day, the grading scale and the course policies, and The Economics of AI for an overview of the subject.

At a Glance

Students

Business, economics, finance and management students, and professionals who must approve or budget for AI projects.

Deployment

A shared Smarter instance run by the instructor, with at least three models from two or more providers configured.

Cost

Expect less than $3 USD of model charges per student, capped by a per-student Budget. That cap is itself a teaching example.

Learning Objectives

By the end of the bootcamp, students can:

  1. Explain how model providers price requests (input and output tokens, cached tokens) and what drives the cost of a conversation.

  2. Measure the cost, latency and quality of an AI task, and compare models on all three.

  3. Estimate the full cost of an AI application, including model charges and infrastructure, per user per month.

  4. Build a business case that weighs those costs against measurable benefits.

  5. Design a budget policy that turns spending limits into enforced controls.

Technical Prerequisites

  • Required: a laptop with a current web browser and a spreadsheet program (Excel, Google Sheets or Numbers), and experience of using a chat assistant.

  • Required: spreadsheet basics: formulas, references, charts.

  • Required: reading a YAML file well enough to change a value (Learn YAML in Y minutes).

  • Helpful: introductory microeconomics (marginal cost, fixed and variable costs) and a basic business case or net present value calculation.

  • Not needed: programming.

Schedule

Block

Min

Content

1

45

Lecture 1: The unit of AI cost. Tokens, input and output prices, context length, cached input, and why a long conversation costs more than its last message. Reading a provider’s price list.

2

45

Lab 1: Price a conversation. Hold three conversations of different lengths, read their tokens and costs in Smarter’s records, and rebuild the costs in a spreadsheet from the price list. Produces Assignment 1.

3

45

Lecture 2: Quality costs money, sometimes. Model tiers, reasoning models, tools and retrieval as cost multipliers. Latency as a cost. Why the cheapest model is sometimes the most expensive choice.

4

45

Lab 2: The model comparison. Run the same ten tasks on three models from different providers, and record cost, latency and a quality score for each. Produces Assignment 2.

5

45

Lecture 3: Infrastructure. Fixed and variable costs of running an AI platform. The baseline cost of a Kubernetes cluster (about $350 USD a month for Smarter), the marginal cost per user, and economies of scale. Self-hosting a model versus paying per token.

6

45

Lecture 4: The business case. Benefits that can be measured (time saved, tickets deflected, revenue), adoption curves, and sensitivity analysis. Productivity research and its limits.

7

45

Lab 3: A business case. Build a one-year cost and benefit model for an AI application for a fictional company, using the costs measured in Labs 1 and 2. Produces Assignment 3.

8

45

Lecture 5: Controlling spending. Budgets on users, applications, accounts and providers; warnings and hard limits; cost accounting and chargeback to departments. The built-in budget examples.

9

45

Lab 4: Hit the limit. Apply a small budget to yourself, use it up, and see what happens. Then design the warning that should have come first. Ungraded.

10

45

Lecture 6: The bigger picture. Prices falling over time, the data center build-out, vendor concentration, labor market effects, and who captures the value of AI.

11

30

Capstone kickoff. Students choose a capstone and a company, and sketch their model with the instructor’s feedback.

Graded Assignments

Assignment 1: Anatomy of a Bill

Students reconstruct the cost of three conversations of different lengths from the provider’s price list, compare their figures with Smarter’s records, and explain why cost grows faster than the number of messages.

Turn in: the spreadsheet and a half-page explanation.

Criterion

Pts

Exemplary

Developing

Reconstruction

8

Figures match Smarter’s records, or the differences are explained.

Figures do not match, and the difference is unexplained.

Spreadsheet

4

Uses formulas and references, not typed-in results.

Hard-coded numbers.

Explanation

6

Explains that each request resends the conversation so far.

Says only that longer conversations cost more.

Clarity

2

Labeled and readable.

Unlabeled.

Assignment 2: Model Comparison

Students compare three models on ten tasks, scoring quality with a simple rubric, and recommend one for a stated use.

Turn in: the spreadsheet of cost, latency and quality, a chart, and a one-paragraph recommendation.

Criterion

Pts

Exemplary

Developing

Data

6

Complete, from Smarter’s records, for all thirty runs.

Missing runs, or estimated figures.

Quality scoring

5

A stated rubric, applied consistently.

Impressions.

Chart

4

Shows the cost and quality trade-off clearly.

A table only, or a misleading chart.

Recommendation

5

Fits the stated use, with the trade-off explained.

Picks the cheapest or the best without reasons.

Assignment 3: One-Year Business Case

Students model the costs and benefits of an AI application for a fictional company over one year, for three adoption scenarios.

Turn in: the spreadsheet model and a one-page summary.

Criterion

Pts

Exemplary

Developing

Costs

6

Model charges from measured data, plus infrastructure and people.

Model charges only.

Benefits

5

Measurable, with stated assumptions.

Vague benefits (“better productivity”).

Scenarios

5

Low, expected and high adoption, with the break-even point.

One scenario.

Summary

4

A clear go or no-go recommendation, with the key assumption named.

No recommendation.

Capstone Projects

Students choose one. Capstones are due one week after the bootcamp.

Capstone A: An Investment Proposal for an AI Application

Learning objective. Build an evidence-based investment proposal for an AI application, from measured unit costs to a three-year financial model.

Students choose a use case in a real or realistic company, such as customer support, document review or sales email. They build a prototype assistant in Smarter, measure its cost and quality on twenty realistic tasks, and use those measurements in a financial model.

What students turn in:

  • The prototype LLMClient manifest, and the task set with measured results.

  • A three-year financial model with costs, benefits, scenarios and sensitivity analysis.

  • A three-page investment memo for a chief financial officer.

How it is graded (100 points):

Criterion

Pts

Exemplary

Developing

Measured unit costs

25

From the prototype, on realistic tasks, with quality checked.

From price lists, not measured.

Financial model

25

Complete costs and benefits, scenarios and sensitivity.

Missing cost categories or scenarios.

Assumptions

15

Stated, sourced, and tested for sensitivity.

Hidden or unsupported.

Risks

15

Covers quality, adoption, price changes and vendor risk.

Not discussed.

Memo

20

Clear recommendation a CFO could act on.

Data without a recommendation.

Capstone B: A Departmental AI Budget Policy

Learning objective. Design and implement a spending policy that balances access to AI with cost control, and test it.

Students design a budget policy for a fictional university department or company division of around two hundred users and four AI applications: who gets how much, how limits renew, who is warned, and how costs are charged back. They implement it in Smarter and simulate a month of use.

What students turn in:

  • A two-page policy document.

  • The budget manifests.

  • A simulation spreadsheet of a month’s use, showing who hits limits and when, and the total cost.

  • Evidence (screenshots) that the manifests behave as the policy says.

How it is graded (100 points):

Criterion

Pts

Exemplary

Developing

Policy design

25

Limits based on expected use, fair and explained.

Arbitrary or one-size-fits-all.

Implementation

25

Manifests apply and enforce the policy.

Manifests do not match the policy.

Simulation

20

Realistic use patterns and a credible monthly total.

Unrealistic or missing.

User experience

15

Warnings before limits, and clear messages at them.

Users cut off without warning.

Writing

15

Clear enough for a department head.

Unclear or overlong.

Further Study

  • Price and performance data: Artificial Analysis, which compares models on quality, speed and price, and the providers’ own pricing pages.

  • Cloud cost management: the FinOps Foundation, its framework, and its working group material on the cost of AI.

  • Reports: the annual AI Index from Stanford HAI, with chapters on the economy and on the cost of training and running models.

  • Podcasts: Exponential View with Azeem Azhar, on technology and the economy, Odd Lots from Bloomberg, which regularly covers AI and the data center build-out, and the a16z Podcast.

  • Writing: Ethan Mollick’s One Useful Thing on AI and productivity at work, and Benedict Evans’s newsletter and presentations on technology markets.

  • YouTube: talks by Ajay Agrawal, Joshua Gans and Avi Goldfarb on the economics of prediction, and Daron Acemoglu’s lectures on automation and inequality.