SEO vs. GEO vs. AEO: Differences and Commonalities

In B2B marketing in 2026, SEO vs. GEO is not an either-or question. SEO optimizes for classic rankings, GEO for citations in AI answers, and both share the same foundation. This article clarifies the definitions of SEO, AEO, and GEO, shows the shared technical foundation, explains the different citation mechanisms, and assesses which levers matter in the DACH region and in Austria in particular.

What is the difference between SEO and GEO?

SEO (Search Engine Optimization) optimizes content for rankings in the classic list of search results; success shows in position, clicks, and organic traffic. GEO (Generative Engine Optimization) optimizes content to be cited in AI-generated answers from ChatGPT, Perplexity, or Google AI Overviews; success shows in mentions and cited sources. The technical foundation is identical, the output format is not.

The full definition of terms, including AEO and LLMO, follows below. The foundational article What is GEO? explains what GEO does in detail, and What is SEO? covers the basics of classic search engine optimization.

Why the distinction between SEO, AEO, and GEO matters

Search behavior is shifting measurably. In Germany, half of internet users now use an AI chat at least occasionally instead of classic internet search, as a survey by Bitkom Research shows (Germany survey). This figure shows that AI search is no longer a US niche phenomenon, at least in the largest German-speaking market.

At the same time, click behavior in classic search is changing. A US analysis by Pew Research documents that users who see an AI summary click a classic search result in only 8 percent of visits, compared with 15 percent without an AI summary (international survey, USA). Only 1 percent of users click the link inside the AI answer itself (international survey, USA).

For B2B companies, this creates a dual task. They must stay present in classic rankings and get cited in AI answers at the same time. If you draw clear lines between the three disciplines, you avoid two costly mistakes: dismissing GEO as a mere buzzword or declaring classic search dead too soon.

Definitions: What distinguishes SEO, AEO, and GEO

The three terms describe optimization for three different output formats of the same search engines.

• SEO (Search Engine Optimization): Optimization for classic search engine rankings, meaning the organic blue links on the search results page. The goal is the highest possible position, which brings qualified traffic to your own website.
• AEO (Answer Engine Optimization): Optimization for direct answers, for example in featured snippets, the knowledge panel, or voice search. What counts here is the one precise answer that the search engine serves directly, not the position in a list.
• GEO (Generative Engine Optimization): Optimization for citations in AI-generated syntheses. Systems such as ChatGPT, Perplexity, Google AI Overviews, or Gemini combine several sources into one answer. GEO aims to appear as a cited source in that synthesis.

In practice, more acronyms circulate as synonyms or subsets. LLMO (Large Language Model Optimization) and AI Optimization essentially mean the same as GEO: visibility in AI systems. SISTRIX founder Johannes Beus calls this proliferation of acronyms a sham, driven primarily by sales interests. Google's Danny Sullivan said at WordCamp US in August 2025: Good SEO is good GEO (international source). The two statements are compatible. GEO describes a real shift in output format but does not call for an entirely new methodology.

Commonalities: The technical foundation remains identical

The most important and most often overlooked point: GEO builds on SEO instead of replacing it. The fundamentals are the same for all three disciplines.

• Crawlability: What a search engine cannot crawl, an AI system cannot cite either. A technically clean, accessible website is the prerequisite for any form of visibility.
• Structured data and schema: Schema markup helps both classic search engines and AI systems classify content correctly. Machine-readable markup is an advantage in all three disciplines.
• E-E-A-T: Experience, expertise, authoritativeness, and trustworthiness are the quality criteria by which good content can be measured. AI systems prefer trustworthy sources for reasons similar to those behind classic rankings.
• High-quality content: Content that answers a question precisely and completely works in classic search, as a direct answer, and as a cited AI source.

Google itself confirms this continuity. According to Search Engine Land, Google's Gary Illyes stated that normal SEO is enough to appear in AI Overviews and no separate GEO or LLMO is needed (international source). If you have a solid SEO foundation, you do not start from zero with GEO.

Differences in the citation mechanism

The foundation may be the same, but the mechanisms that create visibility differ. This is the real core of the distinction.

• Classic SEO (blue links): The search engine lists ten or more results, and the user chooses. Relevance, backlinks, technical signals, and user behavior count. Traffic flows directly to the website.
• AEO (featured snippets and voice): The search engine extracts a single answer from one source and displays it prominently. What counts is whether content is precise, clearly structured, and phrased directly as an answer.
• GEO (synthesized AI answers): The AI system processes several sources and generates a new, summarized answer with source citations. What counts is whether a brand or piece of content is present in the sources consulted, clearly phrased, and trustworthy.

The decisive difference lies in the click. In classic search, the click is the goal. In AI answers, it often becomes unnecessary because the answer already appears in the interface. This zero-click trend is not new; it is accelerating. As early as 2024, according to a SparkToro analysis, 59.7 percent of EU Google searches and 58.5 percent of US searches ended without a click (international survey, EU and USA). AI Overviews amplify this effect. An Ahrefs analysis of 300,000 keywords shows that the presence of an AI Overview correlates with a 58 percent lower click-through rate for the top-ranked result (international survey).

GEO-specific optimization levers from practice

Precisely because the mechanism is different, GEO develops its own levers that go beyond classic SEO. The scientific basis comes from the GEO study by Aggarwal et al., which shows that GEO methods can increase a source's visibility in generative answers by up to 40 percent (international research). The following levers have emerged from the study and from practice.

• Front-loading and answer islands: The core answer belongs at the start of a section. AI systems prefer to extract clearly delimited, self-contained answer blocks. A paragraph that answers the question directly is more likely to be cited than an answer that only becomes clear from context.
• Statistics and quotes in content: Documented figures and traceable sources increase the likelihood of being cited. AI systems prefer statements that can be verified.
• Source citations and authority: If you cite carefully yourself and back up your statements, you signal trustworthiness. This affects AI systems much as it affects human readers.
• Schema for AI: Structured data makes content machine-readable and makes it easier to classify correctly in a synthesis.

llms.txt deserves a section of its own: a Markdown file proposed by Jeremy Howard in 2024 that gives language models a compact overview of a website with links to its most important content. It does not control which content crawlers may access; that remains the job of robots.txt. A reality check is needed here. Google does not support llms.txt and, according to Gary Illyes, has no plans to, as Search Engine Land reports (international source). If you use llms.txt, treat it as an optional signal for individual systems, not as a reliable lever for Google.

Brand mentions instead of backlinks: The changed off-site signal

Perhaps the biggest strategic difference concerns off-site signals. In classic SEO, backlinks are the central currency. For AI visibility, the weight shifts toward brand mentions, including unlinked ones.

An Ahrefs study of 75,000 brands provides clear correlation data. According to it, a brand's web mentions correlate with visibility in AI Overviews at 0.664, far more strongly than backlinks at 0.218 (international survey, Spearman correlation). The three strongest factors are all off-site signals: web mentions at 0.664, branded anchor text at 0.527, and branded search volume at 0.392 (international survey).

In practice, this means a shift in priorities. Link building is no longer the only focus: reviews, expert articles, industry directories, and consistent brand mentions gain importance. A brand that is mentioned frequently and consistently in its relevant environment has a better chance of appearing in AI answers. Important: correlation is not causation. The data show a strong relationship but do not prove a direct ranking factor.

Target systems at a glance

GEO is not a uniform discipline. It addresses several systems with different reach and ways of working.

• Google AI Overviews: According to Google, the AI summary directly in Google Search reached 2 billion monthly users in July 2025 (international source). Here, classic SEO carries particular weight as the foundation.
• Google AI Mode: Google's conversational search entry point had more than 100 million monthly active users in July 2025 (international source, USA and India). Since October 2025, AI Mode has been available in more than 200 countries and territories, including many in Europe, according to Google.
• ChatGPT: According to Sam Altman, OpenAI's system reached 800 million weekly users in October 2025 (international source). With its search feature, ChatGPT is becoming a serious entry point for search.
• Perplexity, Gemini, and Claude: These systems cite sources with varying transparency. Perplexity displays sources prominently, Gemini is tightly integrated with the Google ecosystem, and Claude is often used in professional contexts.

Optimization differs by system in degree, not in kind. If you focus on clear structure, documented statements, and strong brand signals, you cover the essential requirements of all systems.

Business value: Why AI referral traffic is worth more than average

A common objection: AI traffic is still too small to be worth it. The volume is indeed still limited, but the value per visitor is above average.

A Semrush study based on more than 500 digital marketing topics shows that measured by conversion rate, an AI search visitor is worth 4.4 times as much as a classic organic search visitor (international survey). The same study projects that AI search could overtake classic search for these topics by early 2028 (international survey).

Growth is substantial, too. An Adobe Analytics analysis of US retail documents a 1,200 percent increase in traffic from generative AI sources between July 2024 and February 2025 (international survey, USA). These visitors also behave in more valuable ways: according to Adobe, they show 8 percent higher engagement, 12 percent more page views per visit, and a 23 percent lower bounce rate (international survey, USA). For B2B lead generation, this means that fewer but more purchase-ready visitors can contribute more than a larger, less qualified volume.

Measurement and KPIs: From ranking to share of voice

The different mechanisms call for different metrics. If you measure GEO with classic SEO metrics, you only see part of the picture.

• Classic SEO KPIs: Keyword rankings, organic traffic, click-through rate, and conversions from organic traffic remain the basis. The data sources are Google Search Console and web analytics.
• GEO KPIs: Share of voice in AI answers, meaning how often a brand is mentioned in the relevant syntheses. Added to this are citation and mention tracking as well as analysis of AI referral traffic and its conversions.

The tool market is evolving quickly. Vendors such as Ahrefs with Brand Radar or Semrush with AI visibility features capture brand mentions and visibility in AI systems. It is important to segment AI referral traffic cleanly in web analytics, for example by tracking referrals from AI sources as a separate channel. Only then can you prove the business contribution instead of assuming it.

Common mistakes with GEO, AEO, and SEO

In the shift to AI search, some mistakes keep recurring that cost visibility and budget.

• Treating GEO as a replacement for SEO: If you neglect the technical foundation because you are betting on AI, you take away the basis GEO depends on. No crawlability, no citation.
• Using manipulative GEO tactics: Attempts to manipulate AI systems with hidden instructions or artificial content are risky. Google consistently penalizes manipulative practices, and AI systems are developing similar defenses.
• Betting everything on llms.txt: A file that the dominant search provider ignores is no substitute for a strategy.
• Not measuring AI visibility: Without share-of-voice and citation tracking, success remains invisible and impossible to steer.
• Abandoning classic search: It remains the largest channel. AI answers mostly draw on content that is already well optimized.

The DACH and Austria perspective

For the German-speaking B2B market, two factors are decisive: market maturity and language.

The market is mature. According to DataReportal, Austria has 8.69 million internet users at a penetration rate of 95.3 percent (Austria survey, January 2025). This high penetration means that almost the entire relevant target group can be reached. In AI usage, Austria is above the EU average: according to Eurostat, around 39 percent of 16- to 74-year-olds in Austria used generative AI tools in the three months before the survey in 2025, compared with just under 33 percent across the EU (Austria and EU survey). By the same measure, Germany is slightly below the EU average at 32.3 percent. The higher Bitkom figure measures something else: half of internet users aged 16 and over in Germany use an AI chat at least occasionally instead of classic search (Germany survey, fall 2025). In Austria and Germany, the market is therefore ready for GEO.

Language is a lever of its own. AI systems answer in the language of the query and prefer sources in the matching language. German-language, technically precise content with a clear DACH focus therefore has a structural advantage for German-language prompts. For local B2B GEO strategies, this means: publish consistently in German, clearly name regional entities such as locations and industry ties, and build brand mentions in German-language professional circles.

Going further: Toward Search Everywhere

The medium-term trend does not lead to three separate disciplines but to one integrated strategy. Machine-readable websites are becoming standard equipment because AI agents are becoming a new user group. In agentic commerce, software agents research and act on behalf of people. They read structured data, compare offers, and make preselections.

For companies, SEO, AEO, and GEO thus merge into one shared task: being present and citable wherever answers are generated. Sensible next steps are a technical audit of crawlability, expanding structured data, enriching content with documented statistics and clear answer blocks, and setting up mention tracking. The shared foundation remains: high-quality, trustworthy content that answers a question better than others do.