If you’re feeling lost amidst so many acronyms and technical terms, don’t worry: you’re not alone. Let’s simplify all of this and understand how AI is revolutionizing searches in search engines.<\/p>\n
Curious to learn how to adapt your Content Marketing<\/a> strategy to this new reality? Keep reading!<\/p>\n It’s an innovative approach to search engine optimization that adapts to the era of artificial intelligence-based<\/a> search engines, also known as generative engines (GE).<\/p>\n Never heard of the term GEO? Don’t worry: it’s quite recent, having been formalized in a collaborative study<\/a> conducted in November 2023 by Princeton University, Georgia Tech, the Allen Institute for AI, and IIT Delhi.<\/p>\n Before discussing optimization for generative engines, we need to understand what they are, right?<\/p>\n According to the researchers in the study, “Generative Engines typically satisfy queries by synthesizing information from multiple sources and summarizing them with the help of large language models (LLMs).”<\/p>\n So, GE not only searches for information but also generates a response to the user’s query from multiple sources, which can include text, video, infographics, e-commerce offerings, and whatever else seems relevant to answer the question.<\/p>\n Well-known examples include Gemini (formerly Bard), Bing Chat, and Google SGE<\/a>.<\/p>\n The perspective is that this change will have a significant impact on businesses and individuals, especially when considering traffic and search engine optimization (SEO)<\/a>. Giuseppe Caltabiano<\/a>, VP of Marketing at Rock Content, made the following observation:<\/p>\n “Generative Engines represent a transformative shift in the search engine paradigm, offering direct, comprehensive answers to user queries and thereby potentially reducing the need for users to visit websites directly. Which can lead to a drop in organic traffic to websites and severely impact their visibility.”<\/em><\/p>\n<\/blockquote>\n The decrease in traffic is not new in the world of digital marketing. According to the 2025 Digital Experience Benchmark Explorer<\/a> research by Contentsquare, in various industries, 55% of websites recorded less traffic in 2024 than the previous year.<\/p>\n With AI search, many believe there will be an even greater decline in traffic with the intensification of ‘zero-click’ results, especially those with less specific intentions, meaning more at the top of the funnel.<\/p>\n The logic is simple: the response given by AI to these more general inquiries will likely already satisfy the user, reducing the chances of them clicking and going to your site.<\/p>\n Considering that many businesses rely on traffic and online visibility<\/a> to generate conversion and sales, there is a discussion of how GEs have a very high potential to affect the economy of those working with Content Marketing in general.<\/p>\n That’s precisely why GEO emerged: to help this group better deal with this change we are going through and without much certainty about what the next steps will be. Quoting the same study: <\/p>\n “To address this, we introduce GENERATIVE ENGINE OPTIMIZATION (GEO), a novel paradigm to aid content creators in improving the visibility of their content in GE responses through a black-box optimization framework for optimizing and defining visibility metrics.”<\/em><\/p>\n<\/blockquote>\n The idea is that by understanding and implementing GEO methods, content creators can significantly increase visibility in AI-driven search environments.<\/p>\n After understanding the definition of each term, you might be wondering: but what about SEO? Does it no longer exist? Isn’t it the same thing? What’s the difference? Let’s delve deeper below.<\/p>\n GEO focuses on optimizing content for the AI algorithms of generative engines. Meanwhile, traditional SEO aims to improve rankings on search engine results pages (SERPs) based on various criteria like keywords, backlinks, content quality, user experience, and page loading speed.<\/p>\n As we’ve seen, generative engines produce multimodal responses, combining information from various sources and formats, contrasting with traditional SEO’s focus on link-based results most relevant to answering the query.<\/p>\n When conducting searches with long and detailed queries, search engine results pages (SERPs) often couldn’t address the entire specific demand. Thus, they opted to provide content they deemed more generally relevant to the query.<\/p>\n Right here’s where the game is starting to change in a subtle but noticeable way. Instead of hunting for that magic keyword density or crossing your fingers over a few juicy backlinks, writers now need to start thinking almost like the engines themselves\u2014what bits of content are actually going to catch an AI’s “attention” in a sea of sources? If you’ve ever had that sinking realization after spending hours crafting a blog post only to have AI read right past it, you’re not alone. Not everything that works for humans jumps out at these engines, and honestly, the trial-and-error process feels a bit like the early SEO days but with a lot more guesswork and a dash (or a bucket) of impatience.<\/p>\n Another thing that doesn’t get talked about enough is how this shift might change what “success” looks like for content creators. It’s not just about traffic counts or time-on-site anymore; there’s this weird in-between space where your content might help form someone’s answer\u2014summed up, mashed together with other sites\u2014without them ever clicking through. Sometimes, you’ll get credited as a source. Other times, it’s crickets and you’re left wondering if you were even in the mix. If you’re measuring brand awareness or trying to build a reputation in your field, that evolution means your strategy’s going to need a serious rethink, and, frankly, a thicker skin.<\/p>\n However, with the evolution of Generative Engines (GEs), these platforms now generate responses considering more specific long-tail keywords, offering more precise solutions tailored to user needs.<\/p>\n This implies a shift in content creation approach, requiring greater attention to detailed and specific audience queries.<\/p>\n In the traditional search engine model, visibility is often measured by a site’s average ranking on search results pages.<\/p>\n However, this metric is less relevant for generative engines, which prioritize structured, rich responses over a simple list of links.<\/p>\n Feeling overwhelmed by all the theories? It’s much simpler than it seems and has become quite familiar in our daily lives lately. As mentioned in the introduction, we have some very popular examples of GEs nowadays.<\/p>\n Gemini is an artificial intelligence tool developed by Google, initially named Bard. It functions as a generative language assistant, capable of understanding and responding to complex questions, as well as generating relevant and informative content.<\/p>\n Using advanced natural language processing and machine learning techniques, Gemini<\/a> aims to provide more precise and human-like responses. It can even be used in place of Google Assistant.<\/p>\n Below, you can see how a search and response work on Gemini.<\/p>\n<\/p>\nWhat is Generative Engine Optimization (GEO)?<\/h2>\n
But what is a Generative Engine?<\/h3>\n
What is the impact of GEO on SEO?<\/h3>\n
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GEO and SEO, what’s the difference?<\/h2>\n
Focus on AI Algorithms<\/h3>\n
Multimodal Responses<\/h3>\n
Complex Queries<\/h3>\n
SERP Positioning Relevance<\/h3>\n
What Are the Main Examples of Generative Engines?<\/h2>\n
Gemini (Formerly Bard)<\/h3>\n
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