The book

Math, Not Magic

Name: Math, Not Magic: A Founder’s Guide to AI in Business and What’s Really Under the Hood
Availability: InStock

A Founder’s Guide to AI in Business and What’s Really Under the Hood

By Andrii Bondarenko, founder and CEO of TruckRoute.AI. In English, published independently.

Paperback: 143 pages, published 30 June 2026, ISBN-13 979-8198372849. Kindle edition published 11 June 2026. Both on Amazon; a copy can also be ordered directly from the author on this page.

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About the book

What artificial intelligence really is, in plain language: how AI “thinks” – and why it doesn’t think at all. Why the same question returns different answers, why some languages cost twice as much to process, and where AI lies with total confidence – and how to manage that.

The thesis of the book in one line: AI is not magic. It’s math. Whoever understands the math makes money on AI; whoever believes in the magic pays for it.

It is written for founders, CEOs, COOs, CMOs, CFOs, CTOs, product managers, marketers, analysts, lawyers and operations leaders – everyone whose work touches AI without them being an engineer. Ten chapters, eight worked cases, seven worksheets.

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The page carries an order form for ordering the book directly – a request, not a checkout: nothing is charged on the site and I answer every one personally. The form needs JavaScript; without it, the book is available on Amazon, or reach me on LinkedIn.

Why it matters: four scenarios from the introduction

Believing in magic has a price, and it shows up on the invoice. Each of these repeats every week in real companies, and each is avoidable once you know what is happening under the hood.

The financial report. The numbers looked plausible, so nobody checked them. One of them was produced from the statistical middle of a distribution, not from your data. Understand the guessing machine and every figure gets tied to a source and a validation step.
The client contract. A generated NDA adapts a clause to a law that does not exist, and the dispute surfaces six months later. Understand hallucination and nothing legal, financial or medical leaves the office without a licensed human on it.
The provider bill. You budgeted twenty thousand and the invoice says eighty, and nobody can say which part is eating tokens. Understand tokenization and the token cost of a user session is in your unit economics before the feature ships.
The competitive edge. A competitor ships something that looks like magic and the board asks why you haven’t. Understand the architecture and you see the engineering behind it – tuned retrieval, real caching, surgical fine-tuning – and design your own around your own data.

Free preview

Nine pages from the beginning of the book can be read on this page, in a reader built into it: the opening of the introduction and the full table of contents, before you buy anything.

Ten ideas the book leaves you with

Five of the ten on the book’s own cheat sheet:

Tokens are currency, and languages weigh differently in it.
Output costs 3–5× more than input. A chatty AI is an expensive AI.
Temperature isn’t quality – it’s determinism.
The context window is a desk, not a warehouse.
AI is a copilot, not an autopilot.

How every chapter is built

Concept – a simple analogy: the shredder, the caveman, the GPS, the mixing board, the waiter, the spotlight.
Under the hood – the technical and mathematical core, with formulas but without academic jargon.
Business impact – the concrete hit on P&L, ROI or competitive position.
Real case – a worked example from American logistics, retail or SaaS.
Founder’s checklist – five actions for this week.
Worksheet – an exercise filled in against your own product.
Practical tips – sidebars with tactical advice tied to the chapter.

Introduction. The Great Illusion of Intelligence
Beyond the Alphabet: Why AI Sees the World in Codes
Caveman Mode: Why AI Is So Fast and Lazy
Geometry of Meanings: Mapping Ideas with Vectors
A Mixing Board with a Billion Sliders: The Magic of Weights
The Hyperactive Waiter: Anatomy of a Prediction
Temperature: Managing Chaos and Hallucinations
The “Goldfish” Problem: Limits of the Context Window
The AI-Literate Founder: Strategy in the Age of Generative Models
Conclusion. From ARPANET to a Pocket Predictor

What you will be able to do

See your prompt turn into numbers, and follow those numbers through a mixing board with billions of sliders.
Model the unit economics of an AI feature before you ship it, not after the bill arrives.
Ask an AI engineer the right questions in an interview, and tell a real answer from a bluff.
Tell cheap hallucinations from expensive ones, and put guardrails exactly where they pay for themselves.
Decide on model, context window, temperature, fine-tuning and retrieval from the math of cost, not from a vendor deck.

What it is not: it won’t teach you to train neural networks from scratch, it won’t replace your CTO, and it won’t hand you prompts to copy – prompts go stale in months, architecture stays with you for years.

The author

Andrii Bondarenko is the founder and CEO of TruckRoute.AI, a logistics platform for commercial trucking that builds safe, economical routes around height, weight, weather, tolls and DOT restrictions. Every claim in the book is a decision he walks his own team through – a book from a practitioner, for practitioners.

More about his work