Google Gets The Thank-You via @sejournal, @pedrodias

Google Gets The Thank-You A buyer can arrive through Google after making the important part of the decision somewhere else. Imagine a buyer asks an AI assistant which supplier fits a particular problem. Your business makes the shortlist. The buyer discusses the alternatives, checks a concern, then opens Google and searches for your company.

They visit and buy. Depending on the attribution setup, Google may receive the credit. The assistant need never have sent a visit at all. What happened before the visit we managed to record? In GEO is a distraction, Marie Haynes reports that ChatGPT sessions amounted to 0. 62% of Google organic sessions across her sample of 100 sites.

Her conversion analysis also favours Google on total volume. Those are useful observations about the properties she examined. I haven’t inspected the underlying GA4 data. Marie also recognizes the prospect of transactions happening without a site visit and argues for preparing for agentic commerce.

I share her concern about chasing mentions or treating weak evidence as the basis for a new discipline. Where I disagree is how far referral figures can take us in judging the broader effort to influence how AI represents a business. A report about arrivals cannot, by itself, settle what influenced the people arriving.

The Visit Happens After The Question Around me, I see people increasingly asking an assistant first and using Search afterwards. That’s an observation about the people I encounter, rather than evidence that everyone behaves this way. It does make me question what a Google visit means. Someone searching for a company could be starting their investigation.

They could also be following a recommendation or checking a shortlist made during an earlier conversation. A buyer might use an assistant to understand a problem before they even know which category of product they need. The assistant could introduce a company, or describe an alternative so poorly that the buyer rules it out.

In either case, the answer could influence the decision without sending the buyer to a website. That gives us a reason to investigate. It doesn’t tell us how often this happens or how much business it creates. I’m also wary of treating difficult attribution as evidence that a large amount of invisible value must exist.

The gap leaves us with a question about what happened before the visit. It doesn’t give us a revenue estimate, however plausible the journey sounds. I’d want to see that evidence before putting a revenue number in the proposal. What The Model Learned, And What It Looked Up I’ve been skeptical of using new initialisms for work SEO already covered.

When someone asks what GEO does that SEO doesn’t, I think that’s a fair question. If we’re going to use a separate name, I want to know what work I couldn’t already describe as SEO. The distinction that makes GEO useful to me is parametric memory: the knowledge and associations a model has learned through training, represented in its numerical weights.

Influencing what it learns about a brand, and how it can use that knowledge in relevant searches and answers, is the separate job I mean by GEO. Retrieval supplies external information while the system is answering. In retrieval-augmented generation, or RAG, the model uses retrieved material alongside what it has already learned.

The original RAG paper explicitly combines the two. Imagine asking a specialist colleague for advice. They bring what they’ve already learned, and they can open a reference folder to check something they need for your question. Changing the folder changes the information available for that consultation.

Changing what the colleague has learned involves a different process. A model’s weights don’t work like a person’s memory, and its answer doesn’t neatly identify which contribution came from which source. SEO addresses the web discovery and retrieval work: making relevant sources discoverable, accessible and suitable for the question.

A page being available, being retrieved and contributing to an answer are separate outcomes. SEO doesn’t own the entire answering system. For Google’s AI search features, Google itself says established SEO practices remain relevant and no special optimization is required. Calling familiar SEO work GEO gives it a new label.

I’d still like to know what changed in the work. My working shorthand is AI visibility = SEO + GEO: information available through retrieval, alongside associations already learned. Instructions and user context also matter, so the shorthand doesn’t describe everything that shapes an assistant’s response.

A page could be retrieved today and become candidate training material later. Separating the two routes helps explain how the information reaches the answer, even when the same page might eventually contribute through both. Memory Can Shape The Search