For years, those of us working in SEO had a very clear mission: appear on the first page of Google results. Yes, there were other search engines, but let’s be honest: no one could rival the Mountain View giant.
At least, until now.
After more than two decades of absolute dominance, Google is no longer alone. Tools like Chat GPT, Perplexity, and Deep Seek have changed how many people access information. They don’t work like traditional search engines: instead of showing a list of links, they generate complete answers from multiple sources.
Google, of course, hasn’t stayed out of this race. Its response has been AI Overviews: AI-generated summaries that appear at the top of many search results.
Until recently, the goal was to rank as high as possible among the ten organic results. Now, we compete to be included in a single AI-generated answer. There’s no visible ranking. No guaranteed clicks. Just an automated synthesis in which, if you’re lucky, your content gets cited.
Given this shift, some people are ready to replace SEO with acronyms like AIO, GEO, or LLMO (marketing loves acronyms). If you’re a bit lost, let me clarify.
What Are AIO, GEO, and LLMO?
These acronyms attempt to name new optimization strategies in the age of generative AI. While related, they don’t mean the same thing.
AIO (AI Optimization) is the broadest term. It refers, in theory, to optimizing content, products, or services to work well with AI tools. This can include everything from generating text with Chat GPT to structuring your pages so a virtual assistant can understand them. The problem is it’s so vague that it often doesn’t say much—more a label than a concrete methodology.
GEO (Generative Engine Optimization) is more specific. It refers to adapting content to appear in results generated by AI-powered engines, such as Google AI Overviews or Perplexity. It’s no longer about moving up a ranking, but being cited or included in a generative answer. GEO involves writing clearly, providing verifiable information, and having a presence on authoritative sites.
LLMO (Large Language Model Optimization) is more technical. It focuses on influencing large language models like GPT-4, Gemini, or Claude. It doesn’t aim to appear in a search engine but in the responses generated by the models themselves, which often rely on pre-existing training data. Therefore, being present in trusted sources like Wikipedia, reputable media, or specialized forums is key.
In short: AIO sounds good but is vague. GEO better describes what many of us are already doing to appear in AI-generated answers. And LLMO is useful if you work directly with models or datasets. You can use whichever term you like, but the important thing is to understand that SEO is evolving, not disappearing.
Does This Mean SEO Is Dead?
Wait, wait, wait.
I didn’t say that, not at all.
SEO is alive. Both GEO and AIO just describe, in new words, what SEO has always tried to do: gain organic visibility where people search for answers. You can still use “SEO” if you want. I do. Changing my title to “GEO Manager” would just make some clients think I work in a special police unit.
The Most Used AI “Search Engines”
These are the AI tools that provide complete answers and for which optimizing your content is worthwhile.
Google AI Overviews
AI Overviews is Google’s new feature that generates automatic responses using AI directly in the search engine. Instead of only showing a list of links, it creates a summary at the top of the results. This block may include citations to web pages, but not always. There are no positions like in classic SEO: if you appear, it’s within the generated text. To be included, your content must align with search intent, be clear, useful, and meet EEAT criteria (experience, authority, trustworthiness). It’s a direct evolution of SEO—but based on answers, not ranking.
Chat GPT
Chat GPT can access the internet via web browsing to answer questions with up-to-date information. While it doesn’t always do this by default, it can show sources when requested or when it deems it relevant. Sometimes it cites sites, includes direct links, or mentions the origin of data. Often, however, it generates answers without visibly showing sources. To appear in its responses, having updated, well-structured content that accurately answers common questions is crucial. Even without clicks, influencing its responses has real value if your content aligns with what the model considers useful, reliable, and clear.
Perplexity
Perplexity is an AI-based search tool that answers questions conversationally. Unlike Chat GPT, it always shows the links used to build its response, allowing users to verify sources. It doesn’t use a traditional ranking system; instead, it selects the most relevant and clear pages for each query. Accurate, recent, and concise content has real chances of being cited, potentially generating visible organic traffic. It’s one of the few environments where SEO impact can be measured transparently in generative systems.
Bing Copilot
Bing Copilot is the evolution of Bing, now integrated with GPT-4 to generate automatic responses. Like Perplexity, it presents a summary and links to sources, though with less clarity on selection criteria. It has lower adoption than Google but already generates traffic, especially in tech, science, health, or finance. It works well with specialized, structured content that answers specific questions. Its integration with other Microsoft services may increase relevance in corporate environments. It’s an emerging channel worth watching.
Gemini, Claude, and Other AIs
Gemini (Google), Claude (Anthropic), and models like Mistral or LLaMA don’t browse the web when answering questions. Their responses rely on training data or pre-loaded sources. They don’t show links or cite authors but do use data from public web pages, media, wikis, forums, or repositories. Being present in this ecosystem (Wikipedia, Reddit, Medium, GitHub, digital media) increases the likelihood that your content becomes part of the “world model” of these AIs.
Keys to Appearing in AI-Answered Queries
Clear, hierarchical structure (semantic, not just visual)
Language models interpret content through HTML, not visual design. Correct use of semantic tags (H1, H2, H3…), well-structured paragraphs, and ordered or unordered lists is more important than ever. Structured data (FAQPage, Article, HowTo, BreadcrumbList) helps AI understand the function of each content piece. Implementing JSON-LD increases the chances of models recognizing questions, answers, steps, and organization.
Direct language and clear purpose, without sacrificing readability
AI doesn’t waste time with roundabout sentences. It favors simple phrases, clear definitions, and well-delimited concepts. But don’t write like a robot: maintain readability. Short sentences, one idea per paragraph, avoid lexical ambiguities, and don’t overuse synonyms (if it’s called “CRM,” call it CRM throughout). Use HTML tags like <strong> to highlight key points or <code> for technical snippets to help models classify content.
Answer specific questions with real search intent
AI works on intent logic, not just keywords. Adapt your content to answer specific queries like “how to use Zapier with Gmail” or “best alternative to Google Analytics in 2025.” Use question-format headers, answer immediately, then elaborate. Structured data like FAQPage and HowTo helps content be recognized as a clear answer. Use HowToStep for steps and ItemList for benefits when possible.
Authority, EEAT, and real trust signals
Implement author metadata (author, sameAs, publisher), link to real profiles (LinkedIn, author page, external collaborations), include publication/modification dates (datePublished, dateModified), cite trustworthy sources, and link to reputable sites. Google and other models interpret context, domain, and authority signals. The more verifiable your content is (for humans and machines), the higher the chance of being used as a source.
Usability, accessibility, and performance matter for AI too
Although AI doesn’t browse like a human, it interprets technical quality. Slow, heavy pages with confusing structure or poorly managed scripts may be ignored. Ensure accessibility (correct ARIA tags, descriptive alt text, color contrast, logical mobile structure). Usable and accessible content is easier for models to interpret.
Presence beyond your blog: semantic distribution and indexability
Most language models learn from a broader ecosystem: Wikipedia, forums, media, open databases, collaborative platforms like GitHub or Stack Overflow. Your content should exist in multiple places. Participate in communities, publish excerpts on LinkedIn or Medium, link your blog from visible pages, and allow relevant bots to crawl your site. Ensure important content is accessible without heavy JavaScript or dynamically loaded elements.
Is This Very Different from Traditional SEO?
Not fundamentally, but the implications differ.
Traditional SEO competes for a position in a list. GEO competes to be inside a single answer, often without clicks.
This changes visibility logic: all or nothing.
Also, traditional SEO relies on relatively known ranking factors (PageRank, backlinks, speed). GEO depends on language models that don’t rank but build answers from semantic corpora. It’s harder to know why an AI chooses one source over another.
That said, the work is the same: create valuable, clear, updated, and trustworthy content. Want help adapting your current SEO strategy to this new scenario?
