What are “Generative Engine Optimization (GEO),” “Answer Engine Optimization (AEO),” “AI search” and Why they are so Important to Your Marketing in 2026

It’s pretty exciting to be witnessing the birth of a marketing discipline, so much so that we don’t seem to even have a consensus on what to call it. GEO, AEO, LLM optimization, AI search—all different ways of talking about the same thing, which is making sure you show up when and where you need to when people ask questions online.

As an agency owner with over 20 years of search optimization experience, I can tell you that GEO and AEO are on every marketer’s mind right now. Search is, and always has been, about visibility. And with any new technology, especially one that represents a seismic shift, there are new challenges and opportunities baked in.

The way Large Language Models (LLMs) work is incredibly complex. In fact, to varying degrees, the researchers who build them don’t entirely know how the magic in the machine happens. But as with all things, certain laws of the universe still apply.

In this series of dispatches, I’m going to dig into what GEO/SEO/AI Search really is and how it works. Along the way, we’ll delve into the fundamental aspects of LLMs that drive search functionality and that I believe will eventually shape any brand’s capacity for cultivating or maintaining visibility.

The Mechanics

The first thing to understand is the basics of how the various AI platforms gather, synthesize, and prioritize information.

These are not actually “answer” engines. They are statistical analysis machines capable of computing the likelihood of one word following another in close proximity, one after another, at a staggering speed.

So what is the basis for that calculation? Typically a data set, and modern LLMs rely on two primary data sources.

Model-Native Synthesis

We’ve all heard ad-nauseum about “training data” at this point. For tools that prioritize Model-Native Synthesis, that is the “large language model.” Their basis for “understanding” human language is a massive, somewhat static sample of text that can take any number of forms. 

What most started with was a finite amount of stuff like internet content, chat transcripts, book texts and articles. That’s the “base model.” From there, some platforms have added additional training data, but they tend to do so on an iterative basis. So a version you’re using may have “knowledge” that starts and stops at a certain point in time or volume of data. 

This is why iterative releases may produce wildly different results; the model itself may have access to new information or the model for synthesis of that information may have been updated (or both).

Retrieval-Augmented Generation

The other critical element of generative AI is “retrieval,” AKA, live web search. The variable in the relationship between Model-Native Synthesis and RAG is primarily newness. The latter is essentially a means of augmenting the massive compendium of language that is the “model” with new information based on what’s current on the internet.

Understanding these two modes of retrieval and analysis is the first step towards understanding your brand’s AI search visibility. The most important aspect is if and how the various tools deploy each, and specifically in which order. 

We’ll get into a detailed explanation in our next dispatch.

Wondering how visible your brand is in AI search today? Let’s talk.