A note before we start

You should know who’s talking to you before you give a book your weekend.

I’m a geek and a tinkerer. I’ve spent my working life building data platforms for banks, telcos, and airports, and these days my day job is building the technical AI foundations for organizations. Back in my big data years, around 2012, a conviction settled in that has shaped everything I’ve built since: the world itself is inherently event-driven. That conviction carried me to the front of event-driven architectures, the art of building software around things that happen rather than things that are, and eventually to Synadia, where I built and researched event-driven AI on top of NATS, a messaging system that moves events between machines.

What I love most sits between fields, taking an idea that’s ordinary in one area and finding out it’s radical in another. This book is that habit pointed at the brain: how it might work, which parts of the excitement around AI are hype, and which parts could quietly become real. Because a brain, whatever else it is, is the most event-driven system there is. Things happen to it, constantly and from every direction at once, and it is never quite the same afterward.

I am not a scientist. I don’t have a PhD, a lab, or a grant. I have a laptop, a stubborn streak, and, lately, an unusual collaborator.

This book is a joint project between me and an AI, and I mean that concretely, not as a confession buried in the acknowledgments. The theory grew out of dialogue: I push an idea, the machine pushes back, and what survives gets written down. Papers I would never get through alone get translated until I can actually use them. Code and prose get drafted faster than I could ever type them. And then comes the part that is entirely mine: deciding what’s true. Nothing in this book survived because it sounded good. Every claim had to pass a bar that was written down before the experiment ran, on runs that reproduce byte for byte, in a record that keeps the failures filed next to the successes. The repository this book grew out of is open, the rules we work by are in it, and a good part of what you’ll read is me being wrong first.

I know how this looks from a distance. A tinkerer, an AI, and a theory of learning: the internet has a shelf ready for that, right between perpetual motion and secret cancer cures. That shelf is exactly why the method is strict. You are not asked to trust me anywhere in this book. Where I claim a measurement, you can rerun it. Where an idea is borrowed, I say whose it was. Where something is unproven, the text says so and moves on.

At the same time, I don’t feel like I have something to prove. I’m convinced there is something in this; you don’t spend this many evenings on a whim. But if smarter people with better tools read it and see further, that isn’t a defeat, that’s the point. I’ll read them gladly. Then I’ll feed them into an AI until they’re understandable to me, because that’s how I read hard things now.

So what qualifies me to work on this? I’m convinced I can do something most other people cannot, and I’m still finding out what that is. This book is part of the finding out.

The story starts with a lawnmower.