Generative Engine Optimization (GEO) builds on SEO fundamentals. Learn why good SEO practices—content, links, UX—still power visibility in AI search.
AI Overviews and zero-click search are changing how people find information and how brands get discovered. As search shifts from links to answers, one question keeps coming up: What happens to SEO?
Generative Engine Optimization (GEO) builds on the same foundation that has guided effective SEO for decades. As Danny Sullivan recently explained, “Good SEO is good GEO.” The core principles of clear, authoritative, user-focused content still determine visibility—whether the answer appears in a ranked list or an AI-generated response.
In this post, you’ll learn:
- What GEO means and how it fits into the evolution of search
- Why SEO fundamentals still matter for AI-driven visibility
- How to adapt your content and metrics for generative search
The acronym problem: the ABCs of search optimization
Search terminology keeps evolving, but every acronym shares one goal—helping people find and understand answers.
Search Engine Optimization (SEO) was first, prioritizing how to show up in search engines to drive traffic to your site.
Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) emerged, with GEO gaining some traction. Since then, the field has become more nuanced with subcategories like AIO, LLMO, etc.
AI SEO is the umbrella term for all of these emerging fields and can be understood as optimizing digital content so it can be discovered, understood, and cited by AI-powered search systems.
Several different subcategories of AI SEO exist, including:
- Answer Engine Optimization (AEO): Optimizing for direct answers through generated-by-AI systems and search engines. Initially focused on earning featured snippets, AEO has evolved within the broader framework of AI SEO to include visibility in AI-generated responses from platforms such as Google AI Overviews, ChatGPT, and Perplexity.
- Artificial Intelligence Optimization (AIO): Optimizing a brand’s presence across AI-powered search and discovery platforms. It encompasses the same strategies and objectives as AI SEO, serving as an alternate term for the broader effort to increase visibility within AI-driven search environments.
- Generative Engine Optimization (GEO): Enhancing brand visibility within AI-driven and generative search engines. It focuses on optimizing content so platforms like Google AI Overviews, Bing Copilot, and Perplexity can surface, cite, or reference a brand in their generated responses. GEO is a key subdiscipline within the broader field of AI SEO.
- Large Language Model Optimization (LLMO): Structuring and publishing content so large language models (LLMs) such as ChatGPT, Gemini, and Claude can accurately retrieve, summarize, and cite it.
The terminology differs, but these terms all have the same goal of making content discoverable and useful. This is true regardless of which platforms customers search on, and it comes down to optimizing both for how people search and how they consume answers.
Speaking at the 2025 SEO conference, Danny Sullivan reminded the audience that SEO has never been solely about “ten blue links.” Search evolved from directories to natural language queries to mobile-first experiences. Each transition sparked predictions that “SEO is dead,” yet the core practice remained constant.
These shifts show that SEO evolves with how people find answers—not against it.
GEO isn’t replacing SEO. It’s expanding what search optimization means, as we’re in an era where answers come from multiple sources, including large language models, not just ranked web results.
Discover how you appear across LLMs like ChatGPT, Perplexity, and Google AI, and get AI-powered strategy recommendations.
Good SEO = Good GEO
Content that performs well in traditional search can also gain visibility in generative search as both reward the same qualities: Clarity, authority, and relevance.
GEO refers to optimizing content for generative and AI-driven search experiences on platforms like ChatGPT, Perplexity, Google’s AI Overviews, and Bing’s Copilot. These systems synthesize information from multiple sources to generate direct answers.
The good news? They rely on the same quality signals that traditional search algorithms have always valued.
Write for people, not algorithms
Generative search systems prioritize content written for humans, not algorithms. And in recent years, Google has been doing the same.
Google’s Helpful Content System penalizes thin, keyword-stuffed pages while rewarding comprehensive, well-structured information. Good SEO already prioritizes addressing search intent and providing direct answers based on what users are looking for with each query.
AI models trained on high-quality text naturally favor the same characteristics.
Content that explains concepts clearly, uses plain language, and organizes information logically gets cited more frequently in AI-generated answers. Complex jargon and dense paragraphs make content harder to parse for both humans and machine learning models processing billions of data points.
Here’s what that looks like in practice:
- Keyword-stuffed: “Best running shoes for runners who run marathons are marathon running shoes that provide running comfort for marathon runners.”
- Human-readable: “Marathon runners need shoes with extra cushioning to absorb impact over 26.2 miles. Look for models with at least 30 mm of midsole foam.”
The second version explains concepts clearly, uses plain language, and organizes information logically, which can help it get cited more frequently in AI-generated answers.
Why? Because if your writing is accessible to readers, it’s accessible to the algorithms selecting sources for AI responses.
Dig deeper: How to Optimize a Website for SEO
Publish content that only you can produce
There’s evidence that suggests that LLMs and generative search engines favor content that contains unique and authoritative information. This is similar to traditional search best practices, which have long rewarded trusted sites with topical authority and unique content.
Authority signals matter because generative systems need to distinguish reliable sources from low-quality content.
According to Semrush’s AI Visibility Index research, pages appearing in AI Overviews often seem to have higher domain authority and more backlinks than average results. The Index found that 25% of the most mentioned brands were also sourced the most frequently.
Brands that publish proprietary research, case studies with specific results, and expert analysis also have a competitive advantage. Generic advice that restates common knowledge doesn’t differentiate your content. It won’t help you stand out for AI, and it rarely helped you stand out in traditional search.
Google recently stressed the importance of unique content in their guidelines for how to appear in AI overviews. They shared that brands should focus on “unique, non-commodity content that visitors will find helpful and satisfying.”
Unique content that builds authority may include:
- Proprietary research
- Case studies with results
- Expert interviews or commentary
Keep your site fast, stable, and user-friendly
Page experience has long impacted traditional SEO, and it still influences visibility in AI search.
Google’s systems assess content quality partly through user engagement signals and technical performance. Slow-loading pages, intrusive interstitials, and poor mobile experiences all hurt rankings. Google has even shared that a good on-page experience was critical for brands wanting to appear in AIOs.
These same factors affect whether your content gets crawled and ultimately referenced by AI systems. A site that’s difficult to navigate or has broken internal links sends negative quality signals and could even prevent LLMs from accessing your content entirely.
Core Web Vitals, mobile-friendliness, and HTTPS remain foundational. Not because they directly improve AI citations, but because they reflect overall site quality.
Help AI understand your entities
Generative search currently relies heavily on entity recognition. This means understanding not just keywords, but the people, places, concepts, and relationships those keywords represent.
AI systems use entities to understand context and connections. If you mention “Apple,” for example, does your content refer to the fruit or the tech company?
Entity markup clarifies this. It also helps AI connect your brand to relevant topics in Knowledge Graphs, building associations between your company and the concepts you want to be known for.
Structured data markup can help AI systems identify entities and their attributes accurately. Schema.org markup for articles, products, organizations, and FAQs provides explicit context that language models can parse. For example:
If you’re writing about “iPhone 16 battery life,” structured data can explicitly identify:
- Product entity: iPhone 16
- Manufacturer entity: Apple Inc.
- Attribute: Battery capacity (measured in mAh)
- Related concept: Battery life optimization
This clarity helps AI systems retrieve your content when users ask related questions across different phrasings.
Only well-implemented schema will move the needle, but it seems promising that it can make an impact with AI visibility. Google has also noted that the structured data must match visible content to increase visibility in their AIOs, so it’s important to take the time to get it right.
Structured data doesn’t guarantee inclusion in AI results, but it increases your eligibility by helping AI systems understand what your content covers and how it relates to user queries.
The helpful content evolution started long before AI Overviews
While AI search feels revolutionary, it’s really the next step in Google’s long journey toward rewarding people-first content. That focus began decades ago with PageRank, which used links from authoritative sites as a proxy for trust and value.
Over time, Google’s updates have consistently reinforced quality:
- Panda updates in 2011 targeted thin content farms.
- Hummingbird in 2013 shifted focus from exact keyword matches to semantic understanding.
- The 2022 Helpful Content Update made the philosophy explicit: Content should be created primarily for people, not search engines.
Now in 2025, AI Overviews: The newest iteration of the same idea. Google continues to prioritize content that answers questions directly, provides unique value, and comes from credible sources.
The ranking criteria haven’t changed—only the presentation. This progression proves that “good SEO is good GEO.” Optimization strategies built around user intent and authenticity have always outlasted tactics meant to game the system.
The great decoupling: impressions vs. clicks
Visibility and traffic are separating in what’s called “the great decoupling:” Impressions are rising while clicks fall. Your content can now build significant visibility and authority without driving proportional traffic—a fundamental shift that requires rethinking how we measure SEO success.
During Danny Sullivan’s keynote, audience member Angie Drake shared that her site’s content had recently experienced falling click-through rates after the release and expansion of AI overviews, even though her impressions were going up.
AI Overviews contribute by providing synthesized answers that reduce the need to click through to source pages. This reflects the increasing user expectations for instant facts.
For publishers and SEOs, this creates a measurement challenge. And for those who depend on clicks for revenue, it causes concern around profitability and even ROI.
Traffic has traditionally been the primary KPI, but visibility increasingly matters more than visits. Your content might appear in thousands of AI-generated answers without driving corresponding clicks.
Sullivan’s response acknowledged the tension but offered limited reassurance: Google won’t abandon publishers, but the balance between search engine and content creator value is shifting.
What this means for your SEO strategy
Measuring visibility and brand influence matters more than counting clicks when your content appears in AI-generated answers.
Here’s how to adapt:
- Prioritize visibility-first SEO: Prioritize appearing in answer boxes, AI Overviews, and other SERP features, even when those placements don’t generate clicks. Brand awareness, authority signaling, and influence matter as much as direct traffic.
- Account for impression-based influence: If your content consistently appears in AI-generated summaries for high-value queries, you’re building brand recognition and establishing topical authority. The value isn’t captured in Google Analytics, but it’s real nonetheless.
- Implement evolved attribution modeling: Content that introduces prospects to your brand through AI Overviews might not get credit for a conversion that happens days later through a direct visit or branded search. Multi-touch attribution and incrementality testing can help measure this indirect impact.
- Monitor share of voice in AI search: Use AI Visibility tools to measure how often your brand appears in generative answers compared to competitors.
Is SEO deprecated, and GEO the future?
SEO continues to evolve. Michael King, founder of iPullRank, describes traditional SEO as “deprecated,” meaning the methods that once dominated search no longer reflect how AI-driven systems retrieve and rank information.
King calls this evolution Relevance Engineering. This approach aligns content with how large language models process and evaluate text. His team reports stronger performance across AI Overviews, ChatGPT, and AI Mode when optimizing for query fan-out, sub-passage relevance, and semantic precision. These practices support how AI retrieves and presents content.
Why GEO defines the next phase of search
GEO represents the next phase of optimization in an AI-first landscape. It focuses on building content that performs in both classic and generative environments through clear structure, entity precision, and authoritative information.
Traffic remains valuable, but visibility across multiple search experiences now defines reach and influence. Success comes from creating material that is easy to retrieve, interpret, and connect to relevant topics.
How to optimize for the future of search
Search is moving toward conversational, answer-driven experiences. Organizations that design content for this behavior will sustain visibility as user habits change.
Key practices to prioritize:
Structure for AI thinking, write for people
Large language models (LLMs) segment and score pages for relevance. Design each section to stand on its own.
- Query fan-out: AI systems break queries into smaller questions and assemble responses. Cover subtopics that match these patterns. Semrush saw a 150% increase in AI Overview appearances after accounting for query fan-out.
- Passage chunking: Keep sections 100–300 words long with descriptive subheads so each part can rank independently.
- Retrieval relevance: Use precise, context-rich language that aligns with the way people phrase questions.
Pro tip: Test how your pages perform across generative platforms using the Semrush AI SEO toolkit.
Strengthen your entity signals
AI systems need to understand not just what content discusses, but which specific people, companies, products, and concepts are mentioned. Optimizing for entities means building clear, consistent references across your site and external sources.
Think Knowledge Graph profiles, Wikipedia entries, and authoritative backlinks all strengthen entity signals.
Make content answer-ready
AI models trained to generate answers naturally favor content that’s already formatted for easy comprehension and citation.
This means:
- Using clear subheadings
- Defining key concepts explicitly
- Organizing information in a logical hierarchy
Treat citations as the new backlinks
When ChatGPT or Perplexity references your content as a source, it builds brand authority even without generating traffic. Users may not visit your site immediately, but they associate your brand with expertise on that topic. Over time, this recognition can influence purchasing decisions and branded searches.
Good news, though: If users do click through an LLM, they’re 4.4x more likely to convert than those using traditional search, as clicks from organic traffic.
Built for how people search today. Track your brand across Google rankings and AI search in one place.
Key takeaways for advanced SEOs
The transition from traditional search to AI-driven discovery introduces new performance signals but keeps proven fundamentals in place. Visibility now depends on adapting those fundamentals for generative environments.
GEO builds on SEO, not beyond it
GEO, and AI SEO as a whole, extends established SEO methods. Pages that perform in organic search often perform in generative search because both reward clarity, authority, and depth. The tactics that continue to drive performance include:
- Comprehensive topic coverage
- Logical internal linking
- Authoritative backlinks that signal trust
Example: A guide on keyword research that previously ranked in organic results can now appear in AI Overviews, featured snippets, People Also Ask boxes, or LLM chat results when structured for clarity and completeness.
To expand visibility across both environments:
- Answer questions directly to qualify for featured snippets
- Write in a conversational tone and include unique data to improve citation likelihood
- Organize topics with a clear, hierarchical structure for easier crawling
- Build authoritative backlinks from credible domains
Visibility and authority matter as much as traffic
Direct visits no longer represent the full measure of success. Appearances in AI Overviews, featured answers, and other answer surfaces grow recognition and authority even without clicks. These new visibility channels include:
- Generative summaries in AI search
- Featured answers and zero-click results
- AI citations and mentions in LLM platforms
This shift requires expanding measurement beyond traditional traffic metrics. Marketers now need to find ways to track new metrics.
Measurement strategies need to evolve
Rankings and CTRs still matter, but they no longer capture the whole picture. Focus on metrics that reveal visibility, influence, and authority:
- Impression tracking: Track how often pages appear in search and generative results. Flat clicks with rising impressions indicate broader exposure.
- Share-of-voice metrics: Compare your brand’s presence in AI Overviews and SERP features with competitors.
- Entity tracking: Monitor how consistently AI associates your brand with key topics.
- AI citation monitoring: Identify how often AI tools reference your content in responses.
You can use Semrush’s AI Visibility Index and Position Tracking to monitor these metrics side-by-side with traditional rankings.
Dig deeper: Discover how to measure and maximize visibility in AI search
The open web still rewards people-first content
High-quality, user-focused writing continues to perform across every type of search experience. Google depends on a strong network of credible publishers. Advertisers need results that invite clicks, and users rely on information they can trust.
AI Overviews often answer quick questions, but detailed research still drives readers to full articles. Many users remain cautious about AI-generated results. A recent Exploding Topics study found that 42.1% of users have seen inaccurate AI Overviews, and only 8.5% always trust them.
Traditional search remains valuable. Generative platforms expand where visibility can happen. The goal stays the same: publish content that meets real search intent and provides clear, accurate answers. Brands that focus on depth, clarity, and usefulness will continue to earn visibility through both organic rankings and AI citations.
Don’t panic, but adapt
The principles behind search visibility remain the same: clear writing, authoritative information, and a strong user experience. The field of play has changed. “Good SEO is good GEO” means applying those fundamentals across AI-driven results.
Visibility now extends to AI-generated answers as well as ranked links. Success depends on tracking impressions, share of voice in AI search, and entity strength. Content should be easy to extract and cite as well as easy to read.
Models evolve, and so should optimization strategies. Teams that take a visibility-first, entity-led, and answer-ready approach will continue to grow through the next phase of search.
Do this next:
- Audit entities: Add accurate schema, consistent naming, and reputable external profiles.
- Make pages answer-ready: Use clear H2/H3s, concise sections, and scannable lists.
- Track modern KPIs: Measure impressions, AI citations, and share of voice along with rankings and traffic.
- Fix experience gaps: Review Core Web Vitals, mobile UX, crawl paths, and internal links.
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