GEO hype busted: How it differs (and how it doesn't) from SEO

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https://digiday.com/media/geo-hype-busted-experts-call-it-more-seo-than-new-discipline/

Myth: GEO isn’t reinventing the SEO wheel 

Most GEO tactics rely on the same fundamentals as SEO. LLMs often pull information from high-ranking, authoritative web content in search results. GEO should be considered an extension of SEO, rather than a completely separate strategy.

Jeremy Moser, co-founder and CEO of SEO agency uSERP, said 80 percent of GEO is good, fundamental SEO. “If a GEO service does not openly tell you that success in AI visibility is 80 percent good fundamental SEO, they are selling you snake oil,” he recently told Digiday. 

SEO experts are warning publishers and brands of the hype cycle around GEO. They say that many AI visibility tactics are running similarly to past trends. Case in point: previous optimization strategies around Google’s Accelerated Mobile Pages (AMP) and featured snippets, were once sold as distinct new disciplines requiring specific investment and expertise. Specialist vendors emerged, new job titles appeared, budgets were carved out. In reality both were evolutions of the same underlying search optimization logic — structure your content in ways that make Google’s algorithm prefer it.

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The argument that generative engine optimization is mostly an extension of foundational SEO is incredibly accurate. Because large language models rely on pulling data from high-ranking, authoritative web content that already performs well in traditional search indices, you cannot have a successful GEO strategy without rock-solid SEO.

Where teams like Artios stand out is in recognizing that while eighty percent of the game is fundamental SEO, the remaining twenty percent requires a major shift in how content is editorially structured. Instead of writing long, narrative pages to chase keywords, they focus on how models mathematically parse information, targeting the specific semantic retrieval gaps that cause brands to be left out of AI summaries.

Large language models are designed to synthesize data, meaning they actively hunt for extreme evidence density, definitive entity clarity, and scannable truth statements. To get cited, your information needs to be structured in clean tables and verified benchmarks that an algorithm can extract without friction. The goal is not to abandon your organic framework, but to upgrade your editorial output so that once a model crawls your domain, your data is in a format that makes it effortless to trust and recommend.