Atlanta has been called the Silicon Valley of the South, and the nickname isn’t entirely unearned. Mailchimp, Salesloft, Calendly: the city has a long list of companies that quietly shaped how modern B2B businesses acquire and retain customers.

The coffee shop where this book effectively started was in Reynoldstown, a neighborhood on the east side of town that has spent the last decade quietly converting its old industrial bones into the kind of mixed-use corridors where small tech companies, agencies, and studios tend to land. A former railroad depot with vaulted ceilings, ‘70s-inspired striped murals, and macramé planters everywhere. I was there to pitch a founder friend on a content marketing engagement. He got to the point before I did.

His CEO had sent down a directive: get the brand more mentions on ChatGPT. He said it matter-of-factly, the way you relay an instruction that sounds slightly absurd but clearly isn’t.

And then he said something that stuck with me: they weren’t the only ones. Other founders he knew were hearing the same thing. Same pain, different companies. Their brands were absent from the answers their buyers were getting back from AI, and nobody had a clear name for the problem yet, let alone a methodology for solving it.

I left that coffee shop with a client and a category.

This book is the result of what happened next. I spent close to a year building out a methodology, running it against real clients, and watching the field catch up to a problem that was already well underway. It is written in early 2026, during what I believe is a narrow window, the period between when AI-generated answers became a genuine buying channel and when most marketing teams figured out how to operate in it. That window is closing, which is exactly why I decided to publish now rather than wait.

The honest version of that decision goes like this: I could have spent another year building the case study library I wanted. The before/after visibility numbers, the pipeline attribution, the longitudinal data showing what a mature generative engine optimization program actually returns. That version of the book would have been more complete. It also would have arrived after the moment it was most needed. The practitioners and marketing leaders who need a framework for this problem need it now, not when the field has had time to produce tidy data.

So what you have is a first edition in the truest sense. The methodology behind it reflects months of client work, dozens of audits, and a research process that has been tested and refined in public across four iterations of the GEO White Paper. What’s missing is the kind of longitudinal client data that only time produces. That’s coming. A second edition is already planned: original data across full program cycles, formalized named frameworks, updated platform findings. The field moves fast. This book is designed to move with it.

A few things worth knowing about how I’ve handled the evidence here.

The research landscape in GEO is dominated by vendor studies (Ahrefs, Semrush, Profound, Seer Interactive) and I’ve used them. Where studies have known limitations or vendor interests worth flagging, I’ve tried to note it. Where I couldn’t verify a claim to the standard I wanted, I’ve either reframed it directionally or cut it. Chapter 12 exists specifically to name the things the field doesn’t yet know, including some I wish I could have answered in these pages.

The data in this book reflects conditions as of March 2026. Platform behavior, citation patterns, and the GEO tooling landscape are all changing quickly. Where specific figures are likely to age, I’ve tried to ground the argument in the underlying dynamics rather than the numbers alone, because the dynamics will outlast any particular dataset.

One more thing. This book is grounded partly in the published research, but it is primarily an account of what I’ve seen happen to B2B brands that appeared in AI-generated answers and those that didn’t, and a framework for doing something about it. It started with a founder in a coffee shop who needed a name for his problem.

The field is moving fast and the map is incomplete, but it’s the best current map I know how to draw.

Shane H. Tepper
Atlanta, March 2026

Available now on Amazon Kindle: https://a.co/d/0i5pg1uN