There's a huge amount of tactical crossover between traditional SEO and AI Search (or GEO). We are not starting from scratch.
Technical SEO fundamentals (structured data, clean semantic HTML, entity mapping, crawlability) remain key for AI discoverability.
The pages that rank well in organic search are, overwhelmingly, the pages that get cited in AI responses, because many AI systems ground their answers in live web searches that still hit Google and Bing indexes.
The fan-out query mechanism makes this especially clear. When an LLM breaks a prompt into sub-queries and searches for grounding information, backlinks, domain authority, and rankings are doing exactly the same work they always did. Strong technical foundations, rigorous content strategy, and sustained Digital PR are the drivers of success in both traditional and AI search.
What AI Search adds, rather than replaces: prompt-aware content structure, self-contained content sections that work in isolation and a focus on topical comprehensiveness rather than keyword targeting – but also SEO tactics targeting visibility for fan-out queries rather than high-volume keywords.
The additional work is focused and deliberate, not a complete rebuild.
The best foundation for AI visibility is strong SEO, strong content, and strong Digital PR. AI Search or GEO is a layer on top, not a replacement.
Recommended Reading
Semrush: "What Is Query Fan-Out & Why Does It Matter?"
A clear explanation of how LLMs decompose a single prompt into multiple sub-queries, each pulling from different sources.
Seer Interactive: "Gemini 3 Query Fan-Outs Research"
Original research showing Gemini generates an average of 10.7 sub-queries per prompt, with 21.3% including a specific year.
Keywords Everywhere: "How to Find and Optimize for Fan-Out Queries"
A practical guide showing how fan-out queries hit the same Google and Bing indexes that traditional SEO targets.