Is Being Good at SEO Enough to Be Good at GEO?

For years, many believed that mastering SEO was sufficient to perform well in any search environment. It made sense: Google dominated, the rules seemed stable, and SEO represented most of the playing field. But today, this idea no longer holds. GEO (Generative Engine Optimization) is not simply an extension of SEO. It's an ecosystem shift. A profound transformation in how information systems find, interpret, and select content.

The problem is that by continuing to claim "GEO = SEO," we miss the transformation. We ignore the data showing a growing gap between AI engine behavior and traditional search engines. And above all, we deprive ourselves of essential skill development to remain relevant in a landscape where Google is no longer the sole gateway.

The Numbers Show a Massive Divergence Between SEO and AI Visibility

The numbers are unequivocal. Profound observes only 12% overlap between results cited by ChatGPT and Google's organic results. This divergence shows that LLMs don't simply draw from the best-positioned pages: they use other selection systems, based on signals that traditional SEO doesn't cover.

For its part, ZipTie states, in an analysis covering 25,000 real queries, that even a site ranked #1 on Google has only about a 25% chance of being cited in an AI response. And according to seoClarity, across more than 400,000 tested keywords, Google's AI Overviews include the #1 organic position only 54% of the time, while position 20 appears about 7% of the time.

This is no longer a theoretical debate. The data proves that SEO is no longer a guarantee of exposure in AI environments.

Generative Engines Operate by Different Rules

Generative engines work differently. They don't simply rank documents: they search them via query fan-out, transform them through RAG pipelines, reevaluate them based on contextual relevance, weight them according to memory, personalization, model preferences, and entity patterns. These are no longer the same rules of the game.

This is where SEO reaches its limit: it's still largely based on lexical logic, textual matching, link signals, HTML structure, and predictable intents. AI engines, however, operate through vectorization, embeddings, semantic clustering, entity hierarchies, internal architecture, and selection strategies specific to each platform. Traditional SEO tools don't capture these signals. They continue to simulate Google, not LLMs.

SEO Remains a Starting Point, But Is No Longer Sufficient

Solid SEO remains an excellent starting point, of course. It provides rigor, content mastery, entity expertise, and understanding of topics and their semantic depth. But this is not enough to operate in an environment where engines no longer search the same way.

Being good at GEO requires understanding how AIs calculate relevance, how they build their context, how they reinterpret sources, and why certain sources disappear or emerge depending on the models.

A New Fragmented Landscape Where Each Engine Has Its Own Rules

We must accept that the playing field has changed. Being well-positioned is no longer enough: you must be findable in vector space, coherent in an entity universe, relevant in a generative context, and selectable according to each model's internal criteria.

We're entering an era where Google is no longer the primary gateway to information. Each AI engine has its personality: ChatGPT favors certain sources, Perplexity amplifies forums and real-time content, Gemini promotes its own ecosystems, AI Overviews apply their own filters, and private enterprise RAGs create closed niches.

SEO prepares the ground; GEO builds the paths that lead to visibility in AI engines. In a world where engines no longer rank but select, interpret, and generate, continuing to believe that "SEO = GEO" is like playing a game that no longer exists.