GEO vs. AIO vs. LLMO - What Are We Calling This New Organic Optimization Strategy? - Firebrand

Updated on 01/21/2026

Ahoy, matey! We’re entering uncharted waters — new lands where old maps are no longer accurate.

Just as Magellan once sailed beyond the horizon, naming newly discovered routes as he went, today’s marketers are navigating the unknown frontier of AI-driven discovery. In this vast, shifting landscape, the familiar search engine is no longer the only gatekeeper of organic web visibility.

AI chatbots powered by large language models are surfacing content directly to users, bypassing a growing share of the clicks and impressions that traditional SEO once delivered in spades. But what do we call this new discipline, this next organic optimization strategy? That’s the question at the heart of this post. We’ll explore the competing terms — AI Optimization (AIO), Large Language Model Optimization (LLMO), and Generative Engine Optimization (GEO) — and break down what we think each actually means by comparing them against a set of considerations to declare a winner.

Search Volume Comparison

When considering adoption of a term, either in general or for marketing purposes, we always like to get the Search Volume and other metrics from SEMrush to get a sense of how many others are looking for this term on Google (USA). Keyword difficulty (1-100 scale) can tell us how many other websites are targeting each term, which reveals that this term potentially already exists in a vastly different context.

Generative Engine Optimization

Large Language Model Optimization

Avg. Monthly Search Volume (Google)

Keyword Difficulty (0-100)

Top 10 Google Search Results and Semantic Considerations

Google Search of “AIO”

One major issue with adopting “AIO” as our new category of optimization is that this already exists as an abbreviation for “all-in-one” / “all-in-one liquid coolers” for computing systems as indicated from the Google AI Overview. Additionally, there is a company that makes an AI-powered lending platform. These findings explain the super-high search volume (74,000).

We don’t want to be competing with established products or companies. Even if we could wedge into the top rankings over time, there would be a large amount of irrelevant impressions when a user is looking for these instead of the new SEO strategy.

Google Search of “AI Optimization”

“AI optimization” has clearer search intent, yet Google — and by proxy the market — seems to think that it means two things. The AI Overview defines it more in terms of optimizing the underlying models and systems (AI infrastructure) that generate the answers and not the attempt to influence the answers (outputs) themselves. However, the top two organic results in Google differ. One (Digital Success) likens the term to optimizing your content for AI results and the ever-present Tech Target is more about optimizing AI models in the backend.

Google Search of “GEO”

“GEO” means a lot of different things which means this is not a great term to adopt. During our research, there was no AI Overview on Google, but there was a rich snippet for Gene Expression Omnibus from Wikipedia. The top five organic positions on Google consist of a variety of things: two non-profits (doing different things), a global sustainability organization, a stock symbol, and a dictionary definition for “earth/ground.”

This range of meanings is unfortunate because if we were to adopt the full spelling (“generative engine optimization”) the abbreviation would be almost useless compared to how unique and clear “SEO” has become.

Google Search of “Generative Engine Optimization”

A front-runner has emerged with “Generative Engine Optimization.” The current AI Overview is pretty much spot-on, and the top three organic results (and beyond) all define this term in the same vein that encapsulates the process and strategy we are trying to solidify. Using the word “generative” with “optimization” implies that the optimization that is being done is for the output of the AI models and not the overall performance of the model as “AI Optimization” can imply.

Google Search of “LLMO”

“LLMO” does not have any issues with already having other established meanings, which already makes this a solid choice. Additionally, Google’s top three results — and really all the results we saw — define this term exactly in the way we are looking for. The only pushback we can see in some of the results and what ChatGPT says is that LLMO is a more technical way to say GEO.

Google Search of “Large Language Model Optimization”

Now this is interesting. Despite “LLMO” being almost a perfect fit, when you search the full term of “Large Language Model Optimization” the results show a stark difference. The AI Overview is similar to “AIO,” where it becomes more about making LLMs more dynamic and capable instead of optimizing content to feed into them. The top organic results reflect this understanding as they are more geared towards LLM engineering. This difference between “LLMO” and “Large Language Model Optimization” is almost upsetting.

What Do Other Subject Matter Experts Say?

We compiled this short list of respected publications and what camp they are in.

What Does ChatGPT Think About This Debate?

We asked ChatGPT to produce a score sheet of websites that say it should be called “GEO” vs “AIO” vs “LLMO” by comparing the top 100 Google Ranked sites. And although it could not produce a nice scoresheet of data, this is what it returned:

  • GEO (Generative Engine Optimization): This term is prominently featured across multiple reputable sources, including Search Engine Land, Forbes, and HubSpot. It is also the subject of academic research, indicating a strong and growing consensus around its usage.​
  • AIO (AI Optimization): While mentioned in some contexts, AIO lacks widespread adoption among major publications and is less commonly used to describe strategies for optimizing content for AI-generated search results.​
  • LLMO (Large Language Model Optimization): This term appears primarily in academic discussions and has not been widely adopted in mainstream marketing literature.​

In summary, Generative Engine Optimization (GEO) stands out as the leading term embraced by industry professionals and scholars alike for optimizing content visibility in AI-driven search environments.

    And the Winner is… Generative Engine Optimization (GEO)

    Just as Darwin once encountered strange new species and was faced with the task of naming and classifying them to make sense of an unfamiliar world, marketers today are confronting a new digital ecosystem shaped by AI. 

    After surveying the emerging terminology and weighing clarity, relevance, and traction, we believe the strongest contender is Generative Engine Optimization (while also avoiding “GEO” by itself as an abbreviation because of the many existing and differing definitions).

    While LLMO was a close second and may serve as a useful synonym in the near term, its more technical phrasing and lower search volume (20 monthly searches in the U.S. compared to 590 for Generative Engine Optimization)—suggest it may not resonate as broadly or intuitively.
    The gap in meaning between the acronym LLMO and its full form further muddies the waters. Generative Engine Optimization, on the other hand, clearly conveys its purpose: optimizing for the generative engines that are now shaping how people discover, evaluate, and engage with content. 

    If we’re choosing a flag to plant on this new frontier, this is the one we believe is most likely to endure.

    No matter what you decide to call it, optimizing your content for generative AI platforms requires a precise strategy — one that’s very similar to traditional SEO. Firebrand’s Digital Marketing experts can chart a course that maximizes visibility for your tech brand.  

      Naming AI Search Optimization FAQs

      Why do some people call it AEO (Answer Engine Optimization) instead of GEO?

      Some marketers prefer “Answer Engine Optimization” (AEO) because it clearly conveys optimizing for answers to user questions. However, this terminology has a significant limitation: not every prompt is a question, and not every AI response is technically an “answer.”

      Users interact with AI platforms in many ways beyond asking questions – requesting creative content, analysis, or giving instructions. Since these prompts generate responses but aren’t questions seeking answers, the term “generative” is more accurate. This is why “Generative Engine Optimization” (GEO) has gained traction among industry leaders like Search Engine Land and Forbes. That said, AEO and GEO are often used interchangeably and describe essentially the same optimization strategy.

      What's the difference between AIO, GEO, and LLMO? Are they all the same thing?

      While all three address optimizing for AI-powered search, they have important distinctions and conflicts with existing terminology.

      • AIO (AI Optimization) has major problems. With 74,000 monthly searches, it already means “all-in-one” in computing. It can also refer to optimizing AI models themselves rather than optimizing content to appear in AI outputs—making it ambiguous.
      • GEO (Generative Engine Optimization) is the current front-runner with strong industry adoption and 590 monthly searches. However, the acronym “GEO” already means geography, geology, and several organizations, making it nearly unusable as shorthand.
      • LLMO (Large Language Model Optimization) has no conflicting acronym meanings but creates confusion: searching the full phrase returns content about engineering LLM systems rather than optimizing content for them.

      The marketing community hasn’t settled on a winner, but “Generative Engine Optimization” (spelled out fully) has the strongest adoption.

      Which term should I use when talking to clients or stakeholders?

      Use “Generative Engine Optimization” spelled out in full, not abbreviated. Industry leaders have aligned around this terminology, with ChatGPT’s analysis finding GEO prominently featured across Search Engine Land, Forbes, and HubSpot.

      Spelling it out avoids the confusion that comes with “GEO” as an acronym, which has multiple established meanings. The word “generative” accurately describes what AI platforms do – they generate synthesized responses from multiple sources rather than just returning links.

      You may encounter “AEO” or “LLMO” in your research, and they’re essentially describing the same strategy. But for clarity and professional alignment, stick with “Generative Engine Optimization” in your communications and strategy documents.

      Does it really matter what we call this, or should we just focus on the strategy?

      Both matter, but strategy should take priority. The underlying goal remains consistent: ensuring your content is discoverable, citable, and authoritative when AI platforms generate responses.

      However, terminology has practical implications. Clear, consistent language helps with internal alignment, stakeholder buy-in, and budget allocation. Using “AI Optimization” might signal confusion about what you’re actually optimizing, while “Generative Engine Optimization” demonstrates alignment with industry standards.

      Does it really matter what we call this, or should we just focus on the strategy?

      “Generative Engine Optimization” appears to be pulling ahead as the industry standard, with the strongest adoption among authoritative publications and growing search volume (590 monthly searches versus just 20 for “Large Language Model Optimization”).

      However, we predict that all three terms will likely coexist for the near future, similar to how “content marketing,” “inbound marketing,” and “demand generation” are used somewhat interchangeably. The acronym problem will probably force the industry toward always spelling out “Generative Engine Optimization” rather than abbreviating it.

      What’s more likely to evolve is the scope of what it means. As AI search platforms mature, we might see more specific terminology emerge – perhaps distinguishing between conversational AI (ChatGPT, Claude) versus hybrid search platforms (Google AI Overviews, Perplexity).