On 15 May 2026, Google published an official guide to optimising for AI search on the Search Central Blog - and the document's central claim has reshaped the conversation in the SEO community: AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) are not separate disciplines. They are an extension of traditional SEO. For SEO professionals worldwide, the message is clear: the fundamentals have not changed, but the emphasis has.
In brief: Google confirms that the same content quality principles and E-E-A-T signals that drive organic rankings also determine visibility in AI Overviews. There is no separate "AI-SEO" - there is SEO with a sharper focus on structured answers and schema markup.
What Google Published: Key Theses of the Official AI Search Guide
The document, titled "A new resource for optimizing for AI search", was published on Google Search Central Blog on 15 May 2026. Its central argument: the recommendations for optimising for AI search are identical to those for traditional search.
Three main theses of the guide:
1. AEO and GEO = SEO. Google states directly: optimising for AI search systems is the same SEO you already practise. The new terms (AEO, GEO, LLMO) describe the same process with emphasis on AI-generated results.
2. E-E-A-T remains the core signal. Content demonstrating real Experience, Expertise, Authoritativeness and Trustworthiness receives priority in both organic results and AI Overviews. Google has not introduced new "AI-specific signals".
3. Structured data bridges content and AI. Google explicitly recommends FAQPage, HowTo and Article Schema as tools that help AI systems understand and extract information from pages.
According to SEJ (Search Engine Journal) and Semrush analysis, the SEO community's reaction was mixed: many practitioners saw the guide as confirmation of existing strategy; others pointed to significant omissions.
Why Google Says "AEO and GEO = SEO": The Logic Behind the Position
Google's position is technically sound. AI Overviews and search agents operate on the same infrastructure as traditional search - GoogleBot indexes the same pages, the same Quality Raters evaluate the same content against the same E-E-A-T criteria.
Why separate "AI signals" are not needed:
- AI Overviews extract information from pages that already rank well organically
- Quality content with clear, structured answers wins both in organic and in AI citation
- Domain authority within a niche directly correlates with AI citation frequency
"Optimisation for AI search" is not an additional layer of work - it is focusing existing SEO on specific formats: direct answers to questions, FAQPage Schema, clear H2/H3 structure.
What Google Specifically Recommends for AI Visibility
Content:
- Clear, direct answers at the start of each section (the "answer first" principle)
- Factual accuracy with links to primary sources
- Depth of topic coverage without filler
- Original data, research and expert perspective
Structured Data:
- FAQPage Schema for pages with questions and answers
- Article Schema with publication date, author and image
- HowTo Schema for step-by-step instructions
- BreadcrumbList for navigational context
Technical Factors:
- Page loading speed (Core Web Vitals)
- Accessibility for GoogleBot crawling
- Mobile optimisation
- Internal linking for topical authority
E-E-A-T Signals:
- Author biography with verifiable credentials
- External links to authoritative sources relevant to your market
- Brand and team transparency
What Google Did NOT Say: Critical Analysis of the Guide
The official document is informative, but equally notable for what it omits.
Google did not explain:
- Specific ranking factors within AI Overviews (what exactly determines whose content appears in an AI answer)
- The role of backlinks in AI citation - quality and E-E-A-T are mentioned, but the link profile is not
- The impact of llms.txt and robots.txt on AI indexing - the topic is deliberately avoided (separate Google statement: llms.txt is not needed for search)
- The algorithm for selecting sources for AI Overviews when multiple sources provide competing answers
What this means in practice: The guide confirms the strategic direction ("content quality") but does not provide tactical formulas for guaranteed placement in AI Overviews. This is a deliberate position - Google avoids creating manipulation checklists.
Practical Optimisation Plan Based on Google's Official Guide
Step 1: E-E-A-T Content Audit Review each strategic page: does it have an author with a biography? Are there links to primary sources? Does it include original data or an expert position?
Step 2: Structured Data Implement or update: FAQPage Schema on informational pages, Article Schema on the blog, BreadcrumbList sitewide, HowTo Schema on instructional content.
Step 3: Reformat Content for AI Overviews Each H2 section should begin with a direct answer of 30-60 words. The remaining text expands the answer with evidence and examples.
Step 4: Topical Authority Build complete niche coverage: publish a series of articles covering all subtopics. If your core topic is e-commerce SEO, cover Product Schema, Merchant Center, AI Shopping, Local SEO for retailers - all as interconnected content.
Step 5: Monitor AI Visibility Track whether your content appears in AI Overviews via Google Search Console and tools such as Semrush AI Toolkit or SE Ranking.
FAQ
How does AEO differ from SEO according to Google?
According to Google's official position, AEO (Answer Engine Optimization) is not a separate discipline - it is SEO focused on formats that AI systems can easily process. The same E-E-A-T principles, structured content and technical accessibility that drive organic rankings also determine AI Overviews visibility.
What is GEO optimisation and is it separate from SEO?
GEO (Generative Engine Optimization) is the term for optimising content for generative AI systems such as ChatGPT and Perplexity. Google's official guide treats GEO as part of SEO, not a separate set of techniques. The strategies overlap: quality content with clear answers works across all AI systems.
Do I need to change my SEO strategy after Google's AI guide?
Fundamentally - no. Adjust and supplement - yes. Key additions: FAQPage Schema, the "answer first" format in H2 sections, stronger E-E-A-T signals. If your current strategy is built on quality content, you are already on the right track.
How do I get featured in AI Overviews according to Google?
Google does not provide a guaranteed formula, but lists the factors: E-E-A-T, structured data, clear direct answers and niche authority. Content that ranks well organically has a high probability of appearing in AI Overviews.
Does llms.txt help with AI search visibility?
No. Google has separately clarified that llms.txt is not used by Google Search and does not affect AI Overviews. The official guide makes no mention of llms.txt as a recommended optimisation step. Focus on content quality and structured data instead.
Conclusion
Google's official guide confirms what experienced SEO professionals already knew: AI search is an evolution, not a revolution. Quality content, expertise and structured data remain the central ranking factors.
The practical takeaway: implement the guide's recommendations - FAQPage Schema, E-E-A-T signals, the "answer first" content format - and build topical authority in your niche. The channel for delivering answers has changed; the underlying quality signals have not.
Related: What Is AEO and How to Optimise for AI Search | AI Overviews: CTR Impact and Strategy
