SEO vs GEO: Why 'Generative Engine Optimization' Is a Misnomer

Artificial intelligence is reshaping almost every industry, and digital marketing is no exception. Marketers and content creators are rushing to adapt their methods, even though they don’t have much tangible evidence to base their new strategies on, and while search engine optimization (SEO) was never an exact science, it has been a long-evolving discipline that produced a wealth of metrics and proven techniques, processes, and results. AI-companies, on the other hand, are still very secretive with their information and it would be difficult to gauge any practice that claims to influence AI models.

The gold rush towards the new generation of SEO, which is being termed “generative engine optimization”, or GEO, has started and most SEO agencies are scrambling to join the hype. While we don’t have a problem with that (we can’t exclude ourselves from this trend), we do take issue with the terminology. “Generative engine optimization” is not just linguistically lazy – it reveals a fundamental misunderstanding of how AI systems actually work and may be hindering our ability to develop effective “optimization” strategies.

To understand why, we need to examine each component of both the original SEO acronym and its proposed AI successor.

From ‘Search’ to ‘Generative’: A Fundamental Shift in Function

The word “search” in Search Engine Optimization accurately describes what these systems do: they search through indexed content to find relevant results. Search engines like Google or Bing operate as sophisticated retrieval systems. They crawl the web, index billions of pages, and then search through these indexes to find content that matches user queries.

The process is fundamentally about discovery and retrieval. When you type a query, the search engine searches its database, applies ranking algorithms, and returns a list of existing web pages. The content already exists – the engine simply finds it and presents it in ranked order. Despite the fact that search engines evolved and today produce more than just the blue link list, does not change their underlying function.

“Generative,” however, describes a completely different process. AI systems like ChatGPT, Claude, and Bard don’t search for anything. They generate responses by synthesizing patterns learned from training data. When you ask a question, these systems create new text that appears relevant to your query. They’re not finding existing content; they’re producing, or generating, original responses based on learned patterns.

This distinction is crucial for optimization strategies. Search optimization involves making existing content more discoverable and rankable. Generative optimization involves making information more likely to be incorporated into AI-generated responses. The former is about visibility; the latter is about influence on synthesis processes.

The ‘Engine’ Metaphor: Mechanical vs. Organic Processes

This is where it gets tricky. The term “engine” makes perfect sense when applied to search systems. These platforms function mechanically, processing queries through deterministic algorithms, indexing web pages, and returning ranked results based on calculated relevance scores. The metaphor of an engine – a machine that converts input into predictable output through mechanical processes – accurately describes their operation.

From a computer science perspective, traditional search engines operate through deterministic processes. Given the same input and index state, they produce the same output. They follow explicit rules and algorithms to transform queries into ranked results. The computational process is mechanical, predictable, and rule-based.

AI systems have a completely different architecture and operate through probabilistic processes that are fundamentally different from mechanical engines. They don’t follow explicit rules but instead make predictions based on learned statistical patterns. Given the same input, they may produce different outputs depending on various factors. AI operates more like sophisticated pattern recognition systems than mechanical engines.

The engine metaphor also implies a level of control and predictability that doesn’t exist with AI systems. You can optimize for a search engine because its rules are relatively transparent and consistent. AI systems operate through emergent behaviours that arise from complex neural networks, making them more organic than mechanical in nature.

Beyond ‘Optimization’: Is This Even the Right Goal?

In SEO, the word “optimization” makes sense because search engines provide clear optimization targets: rankings, visibility, click-through rates. You can optimize your content to rank higher for specific keywords, improve your site’s authority, and increase your chances of appearing in search results.

But what exactly are we optimizing for with AI systems? The goal isn’t to rank higher in a list of results, nor to increase click-through rates – these are not AI metrics. The same objectives of optimization don’t apply to generative AI systems. Instead, the goal is to get referenced, to shape the model’s output.

The goal is to influence how AI systems generate responses and to increase the likelihood that your information is integrated into AI-generated content. This is less about optimizing for specific outcomes and more about shaping the synthesis process.

The Problematic Linguistic Evolution

While it is still early to tell, the term GEO is gaining traction, and it might actually stick. What this reflects more than anything, however, is the marketing industry’s tendency to rebrand existing concepts rather than develop new frameworks. And while this linguistic conservatism may be comforting because it implies that we won’t have to learn a new discipline from scratch, it’s not just intellectually dishonest (the potency of which shouldn’t be underestimated) but it is practically counterproductive.

Language shapes thought, and imprecise terminology leads to imprecise thinking. By calling AI systems “engines,” we import assumptions and strategies that don’t apply to these new technologies. The linguistic carryover has already led to several problematic approaches in early GEO attempts.

Many marketers are trying to “optimize” for AI systems using SEO-like tactics: keyword stuffing, link building, and technical optimizations designed for web crawlers. These approaches often fail because they’re based on the assumption that AI systems operate like search engines, when they actually operate like sophisticated pattern recognition systems.

Alternative Terminology

There are several alternatives that would reflect the process of influencing AI responses more accurately. Based on the above, however, there is one that fits the bill more than all others: Generative Model Shaping.  In this scenario, the “generative model” replaces the “search engine” as the object the activity is aimed at, and the “shaping” better describes the goal than “optimization”. There is a case to be made for Generative System Positioning or Generative Pattern Integration or any combination of these.

The choice of terminology has practical implications beyond academic precision. Investors, business leaders, and marketing professionals make decisions based on their understanding of these systems. If we describe AI systems as engines, we encourage mechanical thinking about systems that, as mentioned, operate organically. This isn’t just about semantics – it’s about developing frameworks that enable effective strategy development and implementation.