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

Here are some misconceptions around GEO:

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.

GEO is following the same pattern. “We’ve all lived through this a million times, and that’s why it’s been frustrating for us,” said Lily Ray, vp of SEO strategy and research at performance marketing agency Amsive.

Naturally, there are some differences. For example, query fan-out is a specific technical method used in how LLMs retrieve and process information at query time. This is a totally different retrieval architecture than the crawl-index-rank model that SEO was built around. 

The end goal is predominantly the same: SEO is designed to help webpages rank higher in search results, while GEO focuses on getting content cited or summarized inside AI-generated answers, which prioritize extractable information over clickable links.

However, the value exchange is different: traditional search is primarily a referral traffic channel, while AI search serves as a branding channel due to the limited traffic it sends to other sites, said Michael King, founder and CEO of a content marketing and SEO agency iPullRank.

Myth: If you’ve mastered SEO, you’ve already mastered GEO

Although some of what’s being sold as GEO optimization genuinely is repackaged SEO (content clarity, authority, signals, citation building), the more technical end of GEO – particularly anything touching retrieval architecture (RAG) for LLM queries, is legitimately new territory and requires expertise and additional resources from SEO teams.

But the basics of GEO and SEO are the same: clear content structure and headings, authoritative sources and topical expertise help both search ranking and AI visibility – as do brand mentions across different sites and platforms, and compliance with search spam policies. Good SEO practices should reward publishers and brands in AI search visibility, Ray said.

And yet, there are some key differences. Publishers and brands should consider how their content is being ingested by LLMs, whether or not they want to block AI crawling and scraping (such as around paywalled content), and how accessible their content is for LLMs to understand. For example, having too much JavaScript on a webpage can make it difficult for an LLM to understand the content there. 

Other unique tactics include “content chunking” (or breaking down content into sections to make it easier for LLMs to ingest) and videos and content shared on platforms like YouTube and Reddit to boost AI citations, King said. 

Myth: There are proven GEO tactics that guarantee AI mentions

How LLMs choose when and how to mention and cite certain publishers or brands remains a black box. AI-generated summaries vary by prompt and model. LLMs are probabilistic, not deterministic, and there is no stable ranking system within LLMs that’s comparable to Google search rankings, for example.

Companies claiming they know how to optimize for AI, or even measure AI visibility for a brand or publisher, gather prompt data and create specific prompt or topic categories and see how often companies are mentioned through citation tracking. But it’s still quite unpredictable.

Traditional SEO involves monitoring a site’s ranking position on search engine pages for specific keywords. Citation tracking measures how AI systems generate answers and which sources they choose to link to, meaning companies are competing for influence rather than slots in search rankings.

AI-generated summaries are responses to open-ended prompts, not fixed keywords like in traditional SEO, meaning user inputs can vary widely. There’s plenty of data on popular search keywords, but far less clear data on prompts that trigger a citation in an AI-generated answer. That makes it a lot harder for companies to understand why or when they’re being cited. While content licensing deals with AI platforms were seen as part of a strategy to improve publishers’ citations in AI search, referral traffic data shows that even companies with those deals are seeing click-throughs drop.

Myth: AI visibility is the new traffic

Getting mentioned or cited in LLMs does not translate into any notable amount of referral traffic (at least, not yet). 

Data from a recent Similarweb report shows that publishers like Reuters and The Guardian are the most mentioned news outlets in LLMs like ChatGPT and Perplexity, but referral traffic from those platforms amounts to less than 1 percent of their overall traffic.

So no, having high visibility in AI is not going to solve any referral traffic woes many publishers are facing amid search and social volatility (though it could help improve brand awareness).

There are some signs that audiences coming from AI search chatbots are more likely to convert and spend more time on-site. The Washington Post’s chief revenue officer Karl Wells said people to The Washington Post’s site from AI platforms spend more time on-site than those coming from search or social, and have a four-to-five-times higher subscription conversion rate compared to traditional search.

Myth: GEO is just a search problem

While some tools promise “GEO-optimized content”, the fundamentals are the same as standard SEO. Clear question and answer structures, concise declarative sentences, well-defined entities, structured data markup, and clean unambiguous prose do help LLMs retrieve and summarize content accurately. 

But that’s not to say this will be the case for long. King argued that traditional SEO focuses on optimizing content for specific keywords, while AI search retrieves information across a variety of keywords. That means publishers and brands need to be thinking about reputation management –where and how their brands are appearing across a variety of platforms that get scraped by LLMs. Direct integrations with LLMs are also unique to GEO and require more technical and engineering resources, as LLMs can then draw information from feeds they have access to and may prioritize that content as a result, he added.

King likened it to the evolution of social media strategies specific to each platform. “When social media first became a thing, it was like a marketing channel or capability, you could have easily just… had content strategists do it,” he said. “But social media marketers were smart enough to be like, ‘No, it’s a different thing. Each channel needs its own love. It needs its own team. It needs its own content. All the content needs to be different per channel.’”

Myth: GEO vendors can track AI visibility clearly

This is a murky business. Many AI visibility tools don’t have access to the actual prompts that users enter into AI search tools. Instead, they often rely on working backwards – analyzing patterns and outputs to infer which brands, publishers or content are being surfaced. 

To do this, they often use synthetic data, which simulates searches and queries designed to model user behavior. They also use data modelling with algorithms that estimate which content is likely to appear in responses. They’ll also use clustering, which is grouping similar outputs together to identify trends in which publishers or brands are mentioned most frequently.

They do not have access to real prompts inputted by users in AI search tools.