Foreword¶
This book was written with AI. Obviously.
Writing a book about building AI-first companies without using AI would be like writing about swimming while refusing to get wet. So I got wet.
I used Claude throughout—drafting, structuring, researching, refining. The frameworks are mine. The strategies come from twenty years in banking, including helping launch two digital banks, and now building an AI startup. The ideas emerged from watching transformations succeed and fail. The typing? That's where AI came in.
What I Actually Built¶
I didn't just use AI to edit a few drafts. I built a production system for human-AI collaboration—the same kind this book teaches you to build.
Ten specialized workflows orchestrate research, writing, and quality. One workflow generates targeted research prompts and synthesizes findings. Another writes sections following my voice specifications. A third audits those sections for the exact patterns that make AI-generated text feel hollow.
Two AI personas share the work: a writer and a reviewer. The writer drafts chapters while following detailed voice guidelines—which phrases to use ("Here's the thing..."), which to never use ("It's important to note that..."), how to balance skepticism with opportunity. The reviewer then catches AI-generated patterns: hedging language, corporate speak, the rhythms that signal "a machine wrote this."
The irony isn't lost on me: I built an AI system that checks for AI-generated patterns. One rule is enforced throughout: nothing is made up. Every statistic has a source. Every claim has evidence. AI can draft, but it cannot fabricate.
The goal isn't to hide AI's involvement. The goal is to ensure the output sounds like me—because it reflects my thinking, just typed faster.
I'm accountable for every word here. Claude helped write them. Both things can be true.
This Isn't Early¶
Here's what most people get wrong: they think AI-assisted work is the future. It's not. It's the present.
Forty-one percent of all code written globally is now AI-generated or AI-assisted1. Eighty-four percent of developers use or plan to use AI in their work4. GitHub Copilot users have 46% of their code generated by AI—up from 27% at launch2. Claude Code processes 195 million lines weekly3.
The tipping point already happened. While most organizations debated whether AI was "ready," developers made it default behavior.
Most AI business books were written before the ChatGPT inflection point—before the 107x cost collapse, before AI-first companies routinely reached $100M ARR in under three years, before the operational patterns of AI-native architectures became clear. This book addresses the specific challenges of 2023-2026: not whether AI matters, but how to build organizations that exploit cheap intelligence as a fundamental assumption.
Proof of Concept¶
This book exists because the methods in it work.
Every chapter describes patterns for building AI into operations from the ground up. The infrastructure decisions. The team structures. The governance frameworks. I know these patterns work because I used them to write what you're holding.
The bottleneck shifted from execution to judgment. The scarce resource became discernment, not typing speed. The quality controls got built into the system instead of depending on heroic individual effort.
That's the thesis. This book is the evidence.
If you're going to build an AI-first company, you need to actually use AI first.
Let's begin.