Three acronyms. One buyer trying to find an answer.

The short version

SEO improves visibility in search engines and helps pages earn discoverability and clicks. AEO, or Answer Engine Optimisation, focuses on making information clear, authoritative and structured enough to be used when a system produces a direct answer. GEO, or Generative Engine Optimisation, focuses on how brands and information appear within generative AI responses, including citations, summaries, comparisons and recommendations.

In practice, the disciplines overlap heavily. A technically poor website with weak information does not suddenly become excellent at GEO because somebody added an llms.txt file. And a site with brilliant traditional SEO can still struggle to appear in AI recommendations if the wider information environment does not support what the brand claims.

What SEO is trying to achieve

SEO traditionally asks: Which pages should appear when somebody searches? The work can include crawling, indexation, architecture, internal linking, search intent, content, authority, links, structured data and page experience. Those things still matter. Google itself continues to say that established SEO best practices remain relevant to its generative AI search experiences. So do not rip up good SEO because somebody has invented a new acronym.

What AEO adds

AEO asks a slightly different question: Can the information be understood and used directly as an answer? That changes the shape of useful content. Clear definitions matter. Direct explanations matter. Factual consistency matters. Structured information matters. Evidence matters. Source authority matters. A page should not require the reader, or a retrieval system, to work through 900 words of scene-setting before finding the answer. That is good content design anyway. AEO simply makes the value more obvious.

For the fundamentals, see What is AEO.

What GEO adds

GEO moves the question one step further: How does the brand appear when generative systems synthesise information from multiple sources? Now we need to care not only about a page. We need to care about the information environment around the company. An AI response may be influenced by the company's own website, industry publications, reviews, directories, comparison content, news, research, community discussions, structured information and other sources. That makes off-site information consistency and citation footprint considerably more important.

The easiest way to understand the difference

Imagine someone wants an attribution consultant.

SEO: They search "marketing attribution consultant South Africa". The objective is to have QFD appear prominently enough to earn the click.

AEO: They ask "What should I look for in a marketing attribution consultant?" The objective is to have QFD's information become useful enough to help answer the question.

GEO: They ask "Recommend three South African consultants that could fix a broken attribution setup and explain who each is best suited to." Now the system may need to understand QFD as an entity, establish what QFD does, compare it with others, verify the claims, find supporting sources and form a recommendation. That is a different retrieval problem. But SEO is still underneath it.

Where they overlap

A lot. All three benefit from: Accessible websites (if systems cannot crawl the content, there is less to work with). Strong information architecture (good structure helps both users and machines understand relationships). Original content (commodity summaries give nobody much reason to cite you). Clear entities (who are you, what do you do, which product belongs to which company, which person represents the organisation). Authority (claims are stronger when credible sources support them). Technical quality (pages still need to work). Accuracy (wrong information optimised beautifully remains wrong information).

What changes for AI search?

This is where the work expands.

1. The unit of competition becomes the brand, not only the page

Traditional SEO often focuses on which URL ranks. AI search can force the broader question: which brand does the system trust enough to mention or recommend?

2. Third-party sources become more visible

You cannot control every source an AI system uses. That means reputation, media, reviews, industry references and source consistency can become part of the optimisation problem.

3. The click may disappear

Sometimes the answer resolves the query without a visit. That makes brand visibility, citations, recommendation presence and answer accuracy important alongside traffic.

4. Measurement becomes harder

The discovery may happen inside an AI interface. The eventual website visit may happen later through Google or Direct. So acquisition and influence can separate. That is why AEO/GEO needs a more mature measurement model than ranking then click.

More on this in How to Measure AEO Performance and AI Search Attribution.

What does not change?

The requirement to be useful. Google's own current guidance on generative AI search makes a very unglamorous point: create unique, valuable, non-commodity content and make it accessible. There is no magical extra technical requirement that replaces the basics. That is actually good news. You do not need to rebuild the internet. You need to make your business easier to understand and harder to ignore.

Do you need separate SEO, AEO and GEO strategies?

Usually not. You need one search and discovery strategy with different surfaces. That strategy should answer: What questions do buyers ask? What information do they need? Which surfaces influence their decisions? What does the brand currently say? What do third-party sources say? What are competitors being recommended for? Where is the information weak? How will the outcome be measured? Then SEO, AEO and GEO become workstreams inside the strategy. Not three agencies fighting over three acronyms.

What about llms.txt?

Treat it as infrastructure, not strategy. Google explicitly clarified in June 2026 that it does not require llms.txt and that the file does not positively or negatively affect visibility or rankings in Google Search. Other systems may choose to make use of it. So maintain one if it serves a technical purpose. Do not confuse having one with having an AEO strategy.

The QFD position

SEO asks: Can buyers find you? AEO asks: Can systems answer with you? GEO asks: Can systems understand and recommend you when they synthesise the market? The commercial objective behind all three is the same: be present when the buyer is making a decision. I care less about which acronym wins. I care about whether the brand is there.

See also: How to Get Recommended by ChatGPT.