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So, how to optimize for AI versus classic Google search results is fundamentally a huge
issue for a ton of people, and there's skepticism about whether there are real differences.
I'm going to tell you right now, I shared that skepticism, because a lot of the people
that I follow in my LinkedIn feed and my threads and my Blue Sky feed were saying, right from
classic SEO world, which I haven't been in for a while, they were saying, hey, good optimizing
for AI is just good SEO practices.
And I was like, oh, okay.
Then last November, I was in Tokyo with Mike King, who is joining us today, and I'm watching
Mike present on stage, and Mike delivered an incredible talk overall, but there was
this one section of his talk that hooked me and kept me on the edge of my seat, and it
was about exactly this topic, how to optimize differently to appear in AI results versus
in classic Google search rankings.
And I was fully convinced, like, Mike does not BS around this stuff.
He is not coming from a place of theory.
He showed examples, he showed the research, he showed the data.
AI-generated overview
Marketing technologist Mike King presents evidence that optimizing for AI search platforms differs substantially from traditional SEO practices. King challenges the industry consensus that 'good AI optimization is just good SEO,' citing research showing only 25-39% overlap between traditional Google rankings and AI search citations. He introduces the concept of 'query fan out'—where AI systems generate 5-50 synthetic queries from a single user prompt—creating visibility gaps that traditional SEO tools cannot track. King demonstrates that content chunking (breaking information into atomic units) improves semantic relevance by 9-15% in vector space models. He emphasizes metadata importance for AI systems, notes that 28.3% of queries used in AI responses have zero search volume, and presents case studies showing 253-661% improvements in AI visibility. King positions AI search optimization as primarily a branding channel rather than performance marketing, with YouTube and Reddit being the most-cited sources across AI platforms.
Only 25-39% of content ranking in traditional Google search appears in AI-generated responses, indicating fundamental differences in how AI systems select and cite sources compared to classic search algorithms.
Query fan out—where AI systems generate 5-50 synthetic queries from a single user prompt—means 28.3% of queries driving AI visibility have zero traditional search volume, creating blind spots for conventional SEO tools.
Content chunking demonstrably improves AI relevance: splitting a single paragraph into topic-focused segments increased cosine similarity scores by 9.78-15.4%, making content more retrievable by vector-based systems.
Metadata (titles, descriptions, semantic URLs) serves as advertisement to AI systems, influencing whether they fetch and analyze page content, with semantic URLs generating 11.4% more citations in AI responses.
In this episode of SparkToro Office Hours, Michael King, Founder/CEO of iPullRank, talks with SparkToro's Rand Fishkin and Amanda Natividad about the differences between SEO and GEO — and what it takes for your content to be mentioned by the leading LLMs like ChatGPT, Gemini, and Claude.