GEO and AEO: Why AI Visibility is the new SEO

SEO has been the foundation of digital visibility for 25 years. Ranking on Google’s first page brings traffic. Failing to do so means practical non-existence. This model has created billion-euro industries – and it still works.

But it no longer works alone.

When this needs to become a concrete setup, our AI Visibility page brings together the approach for ISSN articles, open crawler policies, schema structure and measurable distribution.

Increasingly, people are asking their questions not in a search box, but to an AI. ChatGPT, Perplexity, Google AI Overviews, Microsoft Copilot – all answer directly, without requiring users to visit a website. And they cite sources. The question is: Will you be cited – or your competitors?

What is changing: Search is becoming answers

Classic Google search returns ten blue links. Users click, read, compare and decide. This model assumes people are willing to consult multiple sources. For simple factual queries, they are doing so less and less.

Google AI Overviews summarise answers directly above the organic results. ChatGPT formulates a response and lists sources at the end. Perplexity delivers answers with numbered source references. All three models have one thing in common: They decide which sources to cite – and which to omit.

For companies building visibility through content, this means: SEO alone is no longer enough. Optimising only for Google means being ignored by half of the new search landscape.

GEO, AEO, LLMO – what do these terms mean?

The industry has generated several terms in a short time. All describe variants of the same phenomenon, but with different emphases:

GEO – Generative Engine Optimization: Optimising content so that AI systems like ChatGPT or Perplexity select and cite it as a source. The term originates from a 2023 study by Princeton, Georgia Tech and the Allen AI Institute, which first systematically examined how generative search engines select sources.

AEO – Answer Engine Optimization: Focuses on optimising for direct answers – both in Google Featured Snippets and in AI-generated responses. AEO predates GEO and originated from Featured Snippet optimisation, but has gained new relevance through AI search.

LLMO – Large Language Model Optimization: The more technical term. Describes content optimisation for the training data and retrieval systems of LLMs. Less common in marketing, more prevalent in the SEO specialist community.

In practice, all three concepts overlap. What matters is the outcome: Is your content recognised and cited by AI systems as a trustworthy source?

How AI systems select sources

AI search engines are not search engines in the classical sense. They do not search the web in real time (with the exception of Perplexity and Bing Chat). Instead, they combine training data with Retrieval-Augmented Generation (RAG) – pulling information from indexed sources in real time and formulating responses from it.

The criteria for source selection are not identical to Google rankings. But clear patterns exist:

• Domain authority: ISSN-registered magazines, universities and industry associations are preferred
• Structured content: Clear H2/H3 hierarchies, lists, definitions and FAQ sections facilitate extraction
• Recency: Regularly updated content is more frequently selected as a source
• Accessibility for AI crawlers: If GPTBot, ClaudeBot or PerplexityBot are blocked, content cannot be indexed
• Citability: Content with clear statements, definitions and data is more often directly cited than vague texts

The blocking problem: Why many publishers make themselves invisible

This is where it becomes interesting for B2B companies. Many major German and international publishers have decided to block AI crawlers. The New York Times, Axel Springer, various specialist publishers – all have blocked GPTBot and other AI crawlers in their robots.txt.

The reasoning is understandable: They want to prevent their content from being used to train AI models without compensation. Copyright remains unclear, and economic interests are real.

But the consequence is equally real: Publishers blocking AI crawlers will not be cited by AI search engines. And if you are not cited, you lose a growing source of traffic and reputation.

For companies building visibility through specialist media, this creates a strategic question: In which magazine should I publish so that my content is visible not only to Google, but also to ChatGPT and Perplexity?

The answer lies with publishers pursuing a different strategy: allowing AI crawlers, structuring content and positioning themselves as citable sources. Not despite AI – but thanks to AI.

How GEO-optimised content is different

SEO content and GEO content have much in common. Good content is good content – regardless of whether a human finds it via Google or an AI cites it as a source. But there are specific factors that influence AI visibility:

1. Provide clear definitions

AI systems look for answers. If your article offers a precise definition (“A qualified read is a reading confirmation documenting that a user has spent at least 30 seconds…”), this definition is more likely to be cited than a vague paragraph about “the future of content marketing”.

2. Use structured data

JSON-LD Schema Markup (Article, FAQ, BreadcrumbList) helps not only Google, but all systems extracting structured information. FAQ schemas are particularly frequently used by AI systems as answer sources.

3. Establish your own terms

Introducing defined terms into professional discourse – such as “qualified readers” or “Performance Publishing” – positions you as the primary source for these terms. When someone asks ChatGPT “What are qualified readers?”, the source that defined and explained the term can be cited.

4. Publish regularly

AI systems with real-time access (Perplexity, Bing Chat, Google AI Overviews) prefer current sources. A blog or magazine publishing high-quality content regularly builds authority over time, which is difficult to achieve with individual articles.

5. Explicitly allow AI crawlers

The technical foundation: GPTBot, ClaudeBot, PerplexityBot, Amazonbot and other AI crawlers must be permitted in robots.txt. Additionally, llms.txt files help – a new standard providing AI systems with machine-readable summaries of a website.

Why specialist magazines are the ideal GEO infrastructure

Not all media are equally suitable as sources for AI citations. Corporate blogs have low domain authority. Social media posts are not used as sources by most AI systems. Press releases on newswires are indexed but rarely cited as authoritative sources.

Specialist magazines hit a sweet spot:

• ISSN registration signals editorial quality and independence
• Regular publication keeps the domain current and relevant
• Thematic depth in specific fields builds topical authority
• Structured content with schema markup, clear H2 hierarchies and FAQ sections
• Open AI crawlers – not all magazines allow this, but those that do gain a strategic advantage

The four B2B specialist magazines from MBF Media (cloudmagazin, MyBusinessFuture, SecurityToday, Digital Chiefs) have deliberately opened their robots.txt to all relevant AI crawlers – including GPTBot, ClaudeBot, PerplexityBot, Amazonbot and Google-Extended. Additionally, all magazines provide llms.txt files for machine-readable content overviews.

The result: Articles in these magazines are not only indexed by Google, but also recognised as sources by ChatGPT, Perplexity and other AI systems. For companies publishing there, this means double visibility – in classical search and in AI search.

SEO + GEO: Not either/or

It would be a mistake to view GEO as a replacement for SEO. Google continues to process billions of search queries daily. Organic rankings remain relevant. But the search landscape is diversifying – and relying on just one channel means missing the other.

The good news: Most GEO measures also improve SEO. Structured content, regular publishing, high domain authority – all of this works in both directions. The additional effort for GEO optimisation is manageable:

• Open robots.txt for AI crawlers (one-time, 5 minutes)
• Create llms.txt (one-time, 30 minutes)
• Include FAQ sections in articles (per article, 15 minutes)
• Formulate clear definitions and citable statements (writing style adjustment)
• Implement schema markup correctly (one-time, technical foundation)

The return: lasting visibility in a channel that is only just beginning to grow. Being an early mover pays off in AI search just as it did in the early days of Google search.

What this means for your content strategy

If you publish a specialist article today in a magazine that blocks AI crawlers, you have an SEO article. If you publish the same article in a magazine that allows AI crawlers and prepares content with GEO optimisation, you have an SEO+GEO article. The costs are identical. The benefit is doubled.

Specifically, this means for B2B companies and their agencies:

• Choose magazines that allow AI crawlers – not all do. Actively inquire about their robots.txt configuration
• Invest in definitions and frameworks – original terms and models are preferred by AI systems for citation
• Use qualified readers as a lever – articles with high engagement are rated as higher quality by search engines (both traditional and AI)
• Measure AI visibility – tools like Perplexity and ChatGPT can be queried manually. Search your core terms and check whether you can be cited

Companies acting now secure an advantage that will be difficult to catch up on later. Because AI systems learn from what they index today – and build their source preferences over time.

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