GEO, AEO, AISO, SEO – the acronyms of the AI Search era. What do they really mean and do they actually replace classic SEO?

Over the last two years, the marketing industry has produced more acronyms than in the previous decade. GEO, AEO, AISO, AIO, LLMO, GSO – each one promises to be "the new SEO" and warns that without it your brand will go invisible in the era of artificial intelligence. Meanwhile, on 15 May 2026, Google published an official guide on optimizing for generative AI features in Search, stating bluntly: AEO and GEO are still SEO.

Who is right? The industry, which has been spinning up new disciplines for two years, or the biggest player on the search market? Or perhaps the answer lies somewhere in between – and that's exactly why specialized portals tracking this topic in real time keep emerging?

In this article, we break down each of these acronyms, show where they overlap and where they genuinely differ, and outline what marketers should actually be watching in the second half of 2026.

What's behind each acronym

SEO (Search Engine Optimization) is the discipline everyone knows: optimizing websites for ranking positions in classic search engines – primarily Google. The goal: a click from the SERP to the site. The signals that matter: links, content, search intent, user experience, and technical signals (Core Web Vitals, indexability).

AEO (Answer Engine Optimization) is optimization for so-called answer engines – features that, instead of showing a list of links, return a ready-made answer. The classic example is Google's Featured Snippets, and in the AI era – AI Overviews and AI Mode. AEO focuses on getting content selected as source material for the generated answer and, ideally, cited.

GEO (Generative Engine Optimization) extends this logic beyond Google. It's the practice of optimizing content so that generative models – ChatGPT, Claude, Perplexity, Copilot – cite the brand as a source or recommendation in their answers. The mechanism here is more complex, because each model has different training data, different update sources, and a different way of grounding answers in real-world references.

AISO (AI Search Optimization) is an umbrella term that combines AEO and GEO. It covers brand visibility across the entire AI-powered search ecosystem – whether the answer is generated by Google AI Overviews, ChatGPT, Perplexity, or any other tool. In practice, the Polish market increasingly uses exactly this term, because it captures the essence of the phenomenon better than the narrower AEO or GEO.

Other, less widespread terms also appear: LLMO (Large Language Model Optimization), GSO (Generative Search Optimization), AIO (AI Optimization). Wikipedia's "Generative engine optimization" entry openly notes that there is no definitional consensus between them yet – in practice they partially overlap, partially diverge in nuances that depend on the source.

What Google says – and whether it's right

On 15 May 2026, Google published a document titled "Optimizing your website for generative AI features on Google Search". The title sounds boring, but the document's thesis was an earthquake for the industry. Google states that AI Mode and AI Overviews do not run on a separate index nor on a separate algorithm – they use exactly the same ranking system as classic Search. The two technical mechanisms that the entire infrastructure rests on are RAG (Retrieval-Augmented Generation, which pairs answer generation with a prior step of retrieving documents from the index) and query fan-out (the automatic expansion of a single user query into many side queries).

Google's conclusion: if your site is well-optimized for classic SEO, it automatically stands a chance of also appearing in generative features. There is no need to build separate tracks for AEO and GEO. The document also includes a "Mythbusting" section in which Google explicitly discourages investing in llms.txt, content chunking for LLMs, and special schema versions dedicated to language models. From Google's perspective: if content is indexable and eligible for a snippet in classic results, it's also eligible to appear in AI Overviews.

The obvious gap in this argument is that Google only speaks for Google. ChatGPT, Perplexity, and Claude operate on completely different rules than Google AI Overviews, and their citation logic has little in common with the classic Google index. Where Google uses its own index for retrieval, OpenAI relies on its own mechanisms (including SearchGPT and partnerships with specific data providers), Perplexity builds a hybrid between a web index and curated source citation, and Claude works differently still.

Where SEO and AISO actually diverge

Contrary to what Google's guideline suggests, in practice the differences exist – and they are significant. The data published over the last several months by independent sources makes this clear.

First, the signal underpinning the whole mechanism is different. Classic SEO competes for a position in a results list. AISO competes for the brand being named in the model's response – even without a click-through. That's an entirely different metric: not ranking, but citation share and share of voice in AI responses.

Second, the overlap between what ranks on Google and what gets cited by generative models is minimal. eMarketer data shows that fewer than 10% of sources cited by ChatGPT, Gemini, and Copilot rank in Google's top 10 for the same query. A company can have phenomenal Google positions and at the same time be completely invisible to ChatGPT users – and vice versa.

Third, AISO operates at the level of the semantic entity, not the specific page. In classic SEO, we optimize a specific URL for a specific query. In AISO, the language model doesn't send the user to a specific URL – it names the brand, the product, the expert. What matters is how consistently the brand is described across reliable sources throughout the digital ecosystem, not whether a single subpage has a perfect meta tag.

Fourth, the way traffic is measured – and its business consequences – are different. On 16 May 2026, Google Analytics 4 added a native "AI Assistant" category for measuring traffic generated by chatbots. It was the first such move in a mainstream analytics tool – up until then this entire segment was a "grey zone" requiring manual regex rules and custom segments. It's telling that Google itself decided to break this traffic out in GA4. If there's no difference – why a separate category?

Fifth, market statistics show the classic ecosystem losing ground to the generative one. Ahrefs reported that AI Overviews cut CTR for content ranking at position one in Google by 58% (from the previous 34.5%). Traffic from AI platforms grew 527% year-over-year in the first five months of 2025. These are not forecasts – this is the operational reality marketers work in today.

What marketers should be doing in 2026

The pragmatic answer: don't choose between SEO and AISO – treat them as two layers of the same visibility strategy.

Solid SEO foundations are the starting point, not the goal. Indexability, healthy site architecture, content quality, E-E-A-T signals – all of this matters both for Google positions and for the chance of being cited by RAG-based generative systems. Here Google isn't lying: a site that's poor on SEO won't show up in AI Overviews either.

The second layer is work at the entity level. A brand has to be described consistently across many reliable sources – industry publications, topical portals, media. Mentions in generative models' responses come from how the brand is "understood" by the models as a semantic entity, not from whether a specific subpage has a perfectly written <title>. Contextual signals matter here: in what topical context the brand appears, which user problems it is associated with, which products and categories it serves.

The third layer is monitoring – many companies are still learning here. Measuring traffic from AI assistants (e.g. via the new GA4 category) is just the starting point. The real picture only emerges when you track whether and how the brand is mentioned in ChatGPT, Perplexity, Gemini, and Claude responses – with comparison to competitors. There is already a market for dedicated tools (LLM tracking, AI visibility monitoring), though none has yet become an industry standard.

The fourth layer is content format optimized for citation. Generative models more readily "extract" fragments that are unambiguous, well-structured, and contain concrete data: numbers, dates, definitions, short lists. This isn't new "AI magic" – it's the same practice that has worked for years for Featured Snippets, except now applied across the full length of a text rather than just the opening paragraph. Content that reads like "an expert explaining a topic" rather than "an SEO article packed with keywords" is naturally more citable. Independent research shows that pages cited by 4 or more AI platforms are 2.8 times more likely to also be mentioned in ChatGPT – visibility on one model reinforces visibility on the others.

What's not worth doing? In its May 2026 guideline, Google explicitly discourages investing in llms.txt, dedicated schema layers for generative models, or AI-specific rewrites of existing texts. Independent agency tests through 2025 and 2026 confirm this – the impact of these tactics on AI visibility remains unmeasurable.

The pace of change demands sources that can keep up with it

What we've written above may need updating in a few weeks. Google I/O 2026 introduced generative UI inside Search, AI agents operating within the SERP itself, and a brand-new "intelligent" search bar that changes how users interact with Google. ChatGPT, Perplexity, and Claude are developing their own search features in parallel. The underlying data – such as the overlap between content cited by AI and content ranking in Google – shifts month by month.

For anyone looking to follow this topic systematically, reading specialized sources works better than relying on generalist marketing blogs. In the Polish-speaking ecosystem, that role is filled by AISO.pl – a portal entirely dedicated to AI Search Optimization, language models, and their practical applications for marketers and SEO specialists. The site covers both current analyses of moves by Google and its competitors (including a detailed breakdown of the May 2026 generative-features document) and cross-cutting deep-dives on individual language models and their significance for AI Search visibility.

Conclusion

SEO isn't dying. Nor is it being replaced by GEO, AEO, or AISO. What's been happening for the last two years is more accurately described as an extension of SEO with a new layer – visibility inside the generative search ecosystem. Google is right that the foundations are shared. But independent practitioners are also right that the working methods, signals, and success metrics at the level of specific models all require their own approach.

The acronyms matter to the extent that they help name concrete practices. GEO reminds us that ChatGPT and Claude operate differently from Google. AEO reminds us that even inside Google itself, classic blue links are giving way to answers. AISO closes the loop holistically – as optimization for a search ecosystem that long ago stopped being any single player's monopoly.

Which acronym ultimately sticks? Not even Google knows.