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Is SEO dead yet in 2025? Or is AI Search really taking over?
Diagam showing how LLM search works

Is SEO dead yet in 2025? Or is AI Search really taking over?

Publish date
December 18, 2025
Author
Team Marketechy
Category
SEO
Is SEO really fading in 2025, or is AI search simply changing how visibility works? This piece breaks down SEO, AIO, GEO, and AEO, explains how AI-driven search reshapes user behavior, and outlines what brands need to do to stay visible when answers matter more than clicks.
Table of contents

First things first – let's navigate the terminology. SEO, AIO, GEO, what other acronyms are there?

  • SEO – an old-school search engine optimization, no longer description needed here.
  • AIO – Artificial Intelligence Optimization, which emerged as a result of the rising popularity of AI assistants. AIO can be referred to as the use of AI tools for better and more efficient search engine optimization – think of the use of ChatGPT for content writing and similar applications. Another AIO description comes from the growing popularity of using AI instead of search engines or as an add-on to search engines. In this case, we view AIO as an engine to optimize for, enabling AI to find our website's content easily.
  • GEO – generative engine optimization – is yet another acronym that refers to a process of website optimization for generative engines, so they (engines) could easily read and understand what the website and its content are about.

There is one other worth mentioning: AEO, which stands for answer engine optimization. The acronym is different, but in general, it means the same as the three others above.

For clarity, it is also essential to examine AI Search from three functional perspectives. 

  1. The first relates to searches conducted within generative AI tools like ChatGPT.
  2. The second perspective has to do with AI overviews that appear when a user actually searches on Google, but receives AI-generated results, which augment traditional SERPs by presenting machine-generated insights above organic listings.
  3. And the third involves AI-integrated browsers, such as Microsoft Edge with Copilot, Opera's AI features, and You.com's AI-enhanced search interface, which provide real-time insights, contextual suggestions, and interactive summaries as users navigate the web, blending browsing and AI assistance. 

Are people actually changing how they search?

Is AI actually going to dominate traditional search from now on? Or is it better to ask this question: Will people change their regular Google search behavior towards using AI-aided search?

Well, the answer is Yes, but it has a very valuable context. Recent studies by companies like Semrush, Ahrefs, and others sitting on actual traffic data show that users are actually changing their search behaviour. What's very interesting is that users are changing how they query –- previously, a typical user used to Google "wedding dresses NY" and now they are doing long-tail and more conversational queries like "What are the best wedding dresses stores in NY?" SEO experts were working on long-tail keywords long before AI entered the scene, but those queries weren't always conversational; instead, they were more like a set of keywords stuck together: "best wedding dress NY buy".

Traffic down, revenue up?

This is a fascinating phenomenon that began even before AI search flooded the market, but became more visible afterward.

Rand Fishkin from SparkToro describes this trend in detail in his post. Search engines and AI platforms increasingly answer queries directly (zero‐click searches), reducing the need for users to click through to a site. Therefore, AI engines influence users' decisions to buy, but not necessarily send traffic to their website. All of this results in more sales happening right after the first visit to the website, though we somewhat lose the value of awareness and educational content in regard to traffic. However, this type of content serves a purpose to train AI, and it means we still need it, actually, a lot of it.

What about Google's revenue?

At first glance, AI Overviews dominating the top of SERPs may look like Google is undermining its own business model. Traditionally, paid ads appeared above well-optimized organic results – a placement that has historically generated tons of Google's revenue. With AI Answers now occupying prime real estate and driving more zero-click searches, it would be logical to assume a significant loss in ad-driven revenue. However, Google isn't leaving monetization on the table. Google has begun integrating ads directly into AI Overviews and other AI-powered search experiences so that advertising still has visibility even when traditional search results are pushed down. 

According to Google's own marketing blog, Search and Shopping ads can appear directly within AI Overviews when relevant to the search query, complete with the standard "Sponsored" label to maintain transparency. These ads are designed to help users discover brands and products right at the moment they are asking questions.

Furthermore, Google has confirmed that this new ad placement is expanding beyond mobile to desktop in the U.S. and other markets, and that ads can appear above or below AI Overviews depending on relevance and query intent.

AI Search and its limitations: from user query to AI overview result

The nature of search in AI engines is different from what we are used to with conventional search engines. While Google and other traditional search engines heavily rely on keyword density, backlinks, and an index-based approach, AI engines retrieve information based on a mixture of user prompts, contextual probability, and semantic understanding. Instead of matching exact keywords, AI models interpret user intent, analyze relationships between concepts, and generate answers that synthesize information from multiple sources.

This is where Marketechy steps in to develop a plan of work tailored to your growth marketing, metrics, and budget.
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LLM‑based search engines interpret a user's natural‑language prompt not by matching keywords in an index, but by converting the text tokens into numerical representations – –vectors that are processed by LLMs to generate a response. These vector embeddings are produced by neural networks trained on massive amounts of text, enabling the model to estimate the probability of sequences of tokens and to generate text that continues the prompt in a "reasonable" way. The model repeatedly predicts the next token based on all preceding tokens, effectively building an answer one piece at a time. Because LLMs predict tokens based on patterns rather than explicit logic or reasoning, they can produce plausible answers even without retrieving exact facts, but this can also lead to hallucinations when the model over‑generalizes.

Unlike traditional search engines, which return a ranked list of documents from an inverted index, LLM search may use retrieval‑augmented generation (RAG) to fetch relevant information from external sources and integrate it into the response, avoiding irrelevant or simply wrong information. 

In RAG, the system first searches a database or search index for relevant passages or documents, and then uses that information to guide the language model in providing an accurate, relevant answer.

Evaluation of LLM‑based search focuses not only on relevance but on the groundedness and accuracy of responses, because generative models are made to balance plausibility with fidelity to present information.

This fusion of deep statistical modeling, retrieval mechanisms, and natural‑language generation results in search experiences that feel conversational and contextually aware but require careful design to maintain factual reliability and usefulness. 

As a result, AI search is less about ranking web pages and more about producing the most contextually relevant, human-like response – often tailored to the user's past queries, style, or additional clarifications. Are AI answers contextually relevant, though? This one is tricky and heavily depends on the context window of a specific LLM.

How to compete in AI search?

Taking into account all limitations of LLMs, there is clearly no proven step-by-step algorithm on how to rank at the top of the AI answers. At this point, while LLMs are still evolving, we can outline a backbone framework to prep your website content for citation by AIs. Here are the steps you should be working on:

  1. Prepare a list of keywords specifically for AI to cite. You should gather a list of long-tail keywords for which an AI overview is already available.
  2. Conduct a competitor gap analysis to identify which content and pages are receiving AI citations. This step will help you determine where you should move your content.
  3. Build your content strategy around these new AI-targeted keywords. Since most of the keywords that appear in the AI overview are informational, website owners should work on the educational content pieces. This is very similar to traditional SEO, where to generate demand, you first need to inform and educate users about various aspects of the topic.
  4. Write the content. This is a self-evident step, but you need to write your content to be a) highly quotable; b) respond to questions that exactly correlate to AI overview keywords; c) use exact AI overview keywords in page sub-headers.

Do the off-page optimization. This is one of the most important steps: to give your content a boost with AI-generated answers, you should not only prepare your website and content, but also distribute them and your brand across third-party websites. Digital PR comes in handy at this point.

Where this leaves SEO

SEO is not dead, but it is constantly reshaping. We again optimize our websites and brands to be found by search engines and AI. 

The changes in traffic patterns, driven largely by AI-enhanced search capabilities, have significant implications for the future of content marketing and online engagement. As users increasingly rely on AI for quick answers and insights, traditional metrics of success, such as website traffic, may no longer reflect the true effectiveness of content strategies. Marketers will need to pivot from solely focusing on attracting click-throughs to crafting content that engages users in a meaningful way, fostering brand loyalty, and encouraging direct interactions that happen beyond initial traffic visits.

This shift calls for a greater emphasis on high-quality, educational, and conversational content that aligns with user intent, as well as a strategic integration of AI tools to enhance personalization and relevance. Ultimately, businesses must adapt to this evolving landscape by redefining their content goals and leveraging innovative approaches to maintain relevance in a world where quick, AI-generated responses are becoming the norm.

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