AI-assistance disclosure
This book was researched and illustrated with substantial assistance from artificial intelligence — principally Anthropic’s Claude — working under the author’s direction and editorial control. Every effort has been made to verify the claims in this book against the published sources linked at the end of each chapter, and readers are encouraged to consult those sources directly. Responsibility for any errors that remain rests with the author.
Preface
This book makes one promise: the confusing parts of the AI story — the spending, the hype, the job fears, the chip wars — become legible once you look at them through the ordinary tools of economics. Not advanced economics. The tools in this book are the ones taught in any first course: supply and demand, production, cost, competition, externalities. They are old, and that is their virtue. They were built to explain steam, electricity and computing, and they explain this too.
A warning before we start. The specific numbers in this book will date quickly. Capital spending figures, model prices, employment data — all of them move fast, and some will be stale by the time you read this. Where a figure is time-sensitive, it is marked “as of mid-2026” so you know exactly what it claims and when it claimed it.
More fundamentally, the effect estimates are best guesses. Serious economists’ estimates of AI’s impact on growth differ by a factor of twenty, and the history of technology is full of confident predictions that missed in both directions. The honest position, which this book takes, is that we might be wrong. What we offer is not a forecast but a framework: the economic dynamics each chapter teaches hold regardless of how the numbers turn out. If AI fizzles, the framework explains why. If it transforms everything, the framework explains that too.
How to read the book: each chapter opens with one concrete puzzle from the news and resolves it with one economic tool. The chapters build on each other — they follow the ladder of an economics course, from what AI is as a good, through who supplies it, to what it does to work, growth and the rules of the game — but each also stands alone. At the end of every chapter, two short sections keep the book honest. “What to watch” lists observable indicators — things you can check yourself — that will tell you whether the chapter’s argument is holding up or breaking down. “Dig deeper” links to the sources, so you can test the claims rather than take them on trust.
The numbers will change. The way of thinking, I believe, will not.