I hate to admit it, but as someone who has been doing SEO since 1998, I’m old enough to remember when ShoeMoney pronounced that SEO Was Dead in 2005. Of course, SEO has been the zombie that won’t die ever since.
This humorous history of “SEO Is Dead” comments reminds us that “past is prologue” and hyperbole is no stranger to the SEO industry. So when I start hearing people in 2025 proclaim SEO Is Dead (again) because of all the new AI-powered changes, it makes me laugh a little.
I’m certainly not the only one who doesn’t believe the death of SEO is near. For further context, you may want to check out Lilly Ray’s recent article on Search Engine Land.
That said, I have never seen the amount of change and disruption to this industry at any time in the last 27 years. And yes, most of the turmoil has been driven by AI technology advances and the resulting changes to user behavior.
This year has even given birth to the rise of two new acronyms (for better or for worse): Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). I understand the intention behind these terms, and we use them today to categorize the changes we are seeing, but to me, this is all still SEO.
Fundamentally, not much has not changed. Your potential customers are searching for things. When they’re searching for your products or services, you want them to find you. My job is to make sure they do. That’s SEO.
I’m also old enough to know when swimming upstream is pointless. These new terms will be adopted to draw distinctions between strategies, tactics and outcomes (as well as for marketing purposes). From that perspective, it's useful to define them, especially as they relate to how SEO and findability has and is changing this year.
For the record, I like Mike King’s umbrella definition of all these terms: Relevance Engineering. But since we already have enough to discuss, I’ll leave that out of our vernacular for today.
There are a lot of articles right now that explore these changes to SEO. I’m going to try as much as possible (although it's not totally possible) to stay high level on technical issues and stay as focused as possible on strategic direction for B2B SEO marketers.
First, let’s dive into these new acronyms, what they mean, and what it means for SEO.
Answer Engine Optimization (AEO) is the practice of optimizing content to appear directly in AI-generated responses (most often in search engines) and featured snippets. It focuses on providing immediate, authoritative answers to specific user queries.
Unlike traditional SEO that drives traffic to websites, AEO also optimizes for zero-click visibility, where users get complete answers without leaving the search platform. In some cases these ranking opportunities feature links that can drive traffic, but in many instances they do not. The zero-click nature of these results adds an additional layer of complexity to measuring success.
Traditional metrics like organic traffic need to be expanded in AEO strategies. With AEO, success measurements focus on visibility within AI-generated responses and brand authority establishment through citations.
It’s important to understand that these metrics measure potential brand awareness opportunities that can lead to direct traffic, search traffic through a subsequent brand related query, or influence a purchasing decision based on perceived authority.
Additional AEO success metrics include:
By taking a wider view of what constitutes success, we can recognize activities that contribute value to the sales funnel, even though leads and revenue may not be directly attributable to them. This is especially true for B2B customer journeys, where attribution is already challenging because there are multiple influencers within a single organization.
Generative Engine Optimization (GEO) can be defined as the strategic process of formatting, structuring and marketing content so AI platforms like ChatGPT, Perplexity, Claude, Grok, and Gemini can easily understand, extract and cite it when generating responses. GEO represents a fundamental shift from optimizing for page-level search engine rankings to engineering relevance for passages within AI reasoning systems.
GEO operates on different principles than traditional SEO systems (although there is an overlap with modern search engine architecture in terms of relevance). Instead of targeting keyword rankings with webpages, GEO is based on semantic relevance of passages within the vector-based retrieval systems that AI platforms use to identify and synthesize information.
Successful GEO content engineering focuses on creating information that supports AI reasoning processes, rather than just human reading patterns. In other words, there needs to be a balance between writing for human beings and writing for visibility within these systems. Content must survive passage-level competition in probabilistic retrieval systems to be effective.
Core GEO technical concepts (some are also applicable to AEO) include:
Much like AEO, many of the citations or brand mentions that occur in the output of these GEO systems have much lower CTRs than traditional search results. That’s because they either don’t have links or have links that aren’t prominently displayed.
The upside is that initial research suggests that the traffic that does come from these citations is more likely to be engaged and result in conversions, so it might be more valuable. Additionally, traffic from LLMs, especially ChatGPT, continues to rise monthly for most of my clients.
However, just like AEO, we need to look beyond direct traffic metrics to get a complete picture of the impact GEO systems are having on our customers and potential customers. The issue is that citations in these systems are harder to measure.
Currently there is no data available as to what user input prompted a referral. Essentially, there is no keyword data (although keywords are an oversimplification of how most users are interacting with these systems).
Also, the same prompt tends to elicit different results for different users and they are highly volatile in terms of which sites are listed. This recent study by Profound illustrates how often these results change.
This underscores the main challenge for GEO right now: It’s very much a moving target with limited signals that show success, not much historical data to compare those signals to, and a high rate of change for the results we’re trying to measure.
Understanding the distinctions between traditional SEO, AEO and GEO enables B2B marketers to develop targeted strategies that address both current search behavior and emerging AI-driven discovery patterns.
Traditional SEO optimizes webpages (with text, video and image content) to rank higher in search engine results pages, driving traffic through improved visibility for targeted keywords. Success measures include organic traffic growth, keyword rankings and conversion rates from search visitors.
Answer Engine Optimization (AEO) structures content for direct inclusion in AI-generated responses, featured snippets and voice search results. Success measures focus on zero-click visibility, citation rates, brand authority establishment through AI references as well as traffic from links where they occur.
Generative Engine Optimization (GEO) engineers content for citation across multiple AI platforms simultaneously, focusing on semantic relevance within vector-based retrieval systems. Success measures include cross-platform citation rates, brand mentions in AI responses, source authority recognition (links to your website) and traffic from those links.
|
Optimization Type |
Primary Platforms |
Content Strategy |
Success Metrics |
|
SEO |
Google, Bing traditional search |
Keyword-rich, comprehensive content with backlinks |
Rankings, traffic, conversions |
|
AEO |
Featured snippets, voice assistants, AI answer engines like AI Overviews |
Concise, question-focused answers with schema markup |
Snippet appearances, voice results, rankings, traffic, conversions |
|
GEO |
ChatGPT, Perplexity, Claude, Grok, Gemini, Google AI mode |
Conversational, fact-rich content optimized for AI synthesis |
Citations, brand mentions, traffic, conversions |
While here I’m trying to make a clear distinction between AEO and GEO, in practice they are often used interchangeably. I’m splitting hairs for the benefit of folks who want to make a distinction between the two, but ultimately, that distinction has limited value (except for discussions like this one).
In fact, Google has announced that AI overviews and AI mode are built from custom Gemini models that work with traditional search systems. There is a lot of shared technology or related concepts across all of these systems.
That’s why most of the optimization principles for AEO and GEO are based on the same concepts. These are relatively new acronyms and despite the distinction I am making between the two for academic purposes, it really isn't necessary. In fact, it’s still all just SEO to a lot of people like me. The differences between these acronyms are less important than what we do to optimize for them and how that might differ from traditional search.
Successful B2B organizations implement integrated strategies that maximize visibility across traditional search engines, AI-powered answer systems, and generative AI platforms. Let’s explore what that means.
AI search platforms operate on principles that fundamentally differ from traditional search engine algorithms. AI systems dynamically retrieve relevant content passages (sentences or paragraphs) from the web at query time, ensuring responses are grounded in real-time information rather than static model knowledge. This means that for AEO and GEO systems, passage optimization is more important than page optimization.
This has numerous implications for B2B content strategy. Google's AI Overviews, AI Mode and LLMs like ChatGPT use passage-level ranking models that evaluate specific document sections rather than entire pages. This means weak or unfocused content sections get ignored even if the overall page ranks well in traditional search results.
Key technical concepts to understand (or at least be aware of):
There is a lot of information out there about how these systems work, including this excellent article from Mike King, so I won’t dive too deep here. Ultimately, just understand that we need different tactics to optimize for these AI systems.
B2B buyers are fundamentally changing how they research solutions. Clickthrough rates in organic search results have gone down significantly as Google AI Overviews have been rolled out across more keywords over the last 14 months.
Conversely, traffic to LLM platforms like ChatGPT has increased significantly. This behavioral shift requires B2B marketers to understand new discovery patterns and content consumption preferences.
Emerging B2B research behaviors include:
Traditional authority signals like backlinks and domain authority are becoming less predictive of AI search visibility. AI citations cannot be explained by traditional website metrics because AI systems evaluate content quality through different mechanisms.
Admittedly, this is a moving target and measuring success is not completely straightforward. Various tools are being developed to measure optimization success, but they all have limitations.
Based on my observations to date, authority indicators for AI systems include:
AI search optimization is fundamentally altering B2B sales cycles by changing how prospects discover and evaluate solutions. Traditional lead generation through content downloads and form fills is being supplemented by AI-mediated discovery where prospects learn about companies through AI citations and references.
Early adopters report that AI search traffic demonstrates higher intent and conversion rates. A recent study by Amsive showed that LLM traffic converts at 3.76% versus 1.19% for organic search traffic—a 216% improvement in conversion performance.
So what are the implications for the B2B sales cycle?
Despite all the changes, SEO is not dead. SEO continues to be the number one acquisition channel for most of our clients. This means you should still pursue the following SEO tactics:
Despite lower clickthrough rates and decreases in overall SEO traffic, traditional SEO still outperforms LLMs for most companies. Traditional SEO still refers a significant amount of traffic, so you need to strike a balance between your SEO efforts and your AEO and GEO tactics. Implement slowly and test changes to your site in order to mitigate risk to existing performance.
As an additional note that underscores the importance of traditional SEO rankings, Ahrefs is reporting that 76% of AI overview citations pull from Top 10 pages.
Modern AI search success requires optimizing content at the passage level rather than page level. The relevance of a single sentence to a topic is now an important consideration for LLM citations. This represents a fundamental shift for some writers from the traditional page-focused approach to content engineering that supports AI reasoning processes.
Each content passage must function as a semantically complete unit capable of answering specific user questions without requiring additional context. This means restructuring existing content to create standalone information blocks that AI systems can independently evaluate and combine. The challenge is to do that without sacrificing your content’s ability to connect with users and effectively communicate brand value.
To optimize at the passage level, content creators need to ensure:
AI platforms determine content relevance through cosine similarity calculations between vector embeddings, rather than keyword matching. This mathematical approach to content evaluation requires understanding how AI systems convert text into numerical representations for comparison.
Vector optimization strategies include:
Tools have started to emerge that measure cosine similarity between content passages and keywords or topics. I won’t specifically endorse any of these at this time, but you should be experimenting with these tools and testing whether optimizing for cosine similarity improves your performance.
Using topic cluster architecture, semantically related terms, entity inclusion and natural language patterns are all tactics that have been important for SEO for a number of years. Those tactics are now working at the passage level instead of at the page level.
Google's AI Mode and ChatGPT both use query fan-out processes that generate dozens of related subqueries from single user inputs. Content must address not only the primary user question but also the comparative, exploratory and implicit queries AI systems generate during retrieval. These queries may change on a per-user basis based on personalization factors.
This requires content architects to think beyond individual keyword targets toward comprehensive query landscapes surrounding user intent. Successful content addresses multiple facets of user questions through structured sections covering definitions, processes, comparisons, benefits and implementation considerations.
Fan-out optimization tactics include:
If you want to start experimenting with fan-out queries, Screaming Frog is one of many interesting options. This article by Metehan Yesilyurt explains how you can use Screaming Frog to run query fan-out analysis.
Many of the tools on the market today are brand new. Stay tuned for the industry to release a ton of new tools specific to cosine similarity measurement and fan-out query identification.
Structured data serves as the critical bridge between human-readable content and AI interpretation, with specific schema types proving most effective for AI search optimization. Implementation goes beyond basic markup to creating comprehensive entity relationships that AI systems can confidently parse and utilize.
Priority schema implementations for B2B companies include:
In general, it makes sense to use whatever appropriate schema is available to describe your content with an emphasis on schema types that are directly supported by Google and Bing.
In fact, Bing has confirmed that their LLM models use structured data and so has Google.
Advanced schema implementation includes:
Another aspect of digital marketing to factor into your go-forward SEO/AEO/GEO strategy is how you’re being talked about on third-party sites. Some recent studies have shown that certain websites are most likely to appear in LLM citations and AI search results. These sites include:
Additionally, many of these sites have strong visibility in traditional organic search results, especially Reddit. Ensuring that your brand appears on these sites whenever possible lends itself to additional citations, even if it’s just a brand mention without a link.
You should really already be engaged in conversations on Reddit and Quora, where your potential and existing customers are having conversations about your brand. Any SEO/GEO/AEO benefit from those sites should be a bonus as opposed to the main focus.
Remember that these sites are not places to market your products and services. They are places to contribute value to the community. By doing this, you will earn the kinds of mentions and links that lead to citations.
Each of the sites that I listed (and there are even more) needs a dedicated strategy with an understanding of what components of that strategy influence SEO and LLM performance. Getting an SEO professional involved in those strategies will help ensure that you’re maximizing your return on investment.
Optimizing your ability to be indexed and cited by AI systems involves various technical considerations. One of today’s big issues is that LLMs have a hard time understanding Javascript. While this is likely to change soon, currently they may not be able to index your content if you’re using Javascript.
Here are some other technical considerations worth considering:
Here’s high level recap of everything we have discussed that I would recommend incorporating into your SEO/GEO/AEO strategy:
If all of this seems like a lot to consider, that’s because it is. The search landscape is changing very quickly and it continues to evolve at an unprecedented pace. For example, ChatGPT5 just launched (to underwhelming reviews), Claude recently launched Opus 4., and Google just launched the Nano Banana image editor. Those launches are just the tip of the iceberg across the AI landscape. Additionally, AI browsers are starting to come online with Perplexity launching Comet and Open AI about to launch their own browser. These browsers will fundamentally change how people interact with the web.
As if that wasn’t enough, ChatGPT Agent just launched. Additionally, the rise of MCP servers is changing the way that AI agents are working and what they can do, and that’s changing how people are working.
To say things are crazy right now is understating the obvious.
So that begs the question, how do we respond to the changes in a way that maximizes our chances of success? Embrace the change, stay curious, do your homework, and ultimately, I believe, there will continue to be opportunity for success.