SEO isn’t dead, it just now has a roommate.

Let’s be honest, the way people find information online looks pretty different than it did even three years ago. Google’s still massive—obviously. But more and more, people are skipping the blue links entirely and just… asking. ChatGPT, Perplexity, Gemini, whatever their tool of choice, they want an answer, not a results page they have to sift through themselves.

That changes things for anyone trying to get their content in front of people.

There are now effectively two games running in parallel. SEO, which most marketers know inside and out by now, and something newer called GEO, or Generative Engine Optimization. They’re related, but they’re not the same thing. Getting clear on the difference (and honestly, the overlap) is worth your time.

So, what does SEO actually look like today?

The fundamentals haven’t gone anywhere. Good structure. Headings that make sense. Content people actually want to read. A website that loads without making someone question their life choices. Consistent publishing. These things still matter, arguably more than ever, because the bar for “quality” keeps rising.

What SEO does, at its core, is help search engines understand what your content is about and when it’s the right answer to surface. That hasn’t changed. The platforms evaluating your content have just gotten a lot smarter about spotting the difference between content written for humans and content written to game an algorithm.

GEO is the newer kid on the block

Generative Engine Optimization is what happens when you extend that same content logic into AI-powered environments. Perplexity, AI Overviews in Google, Bing’s Copilot integration, and whatever’s coming next (and something is always coming next). If you’re just getting started, our GEO overview for 2026 is a good place to get your bearings.

Here’s the key difference: generative platforms don’t rank pages. They synthesize them. They pull from multiple sources, stitch together a coherent answer, and present it, often without the user ever clicking through to read your actual article. Which means the question isn’t just “does Google think my page is relevant?” It’s “is my content written clearly and credibly enough that an AI can actually use it?”

That shift in framing matters a lot. GEO is less about keyword density and more about whether your content answers real questions, holds together logically, and comes from a source that seems trustworthy. It rewards clarity. Specificity. Writing that actually explains something rather than gesturing vaguely in its direction.

Why you can’t just rely on SEO anymore

Search results themselves have changed. Google’s rolling out AI Overviews more aggressively. Users are getting comfortable with conversational tools that skip rankings altogether. The traditional “rank #1 and you win” model is getting complicated by discovery pathways that don’t look anything like a search results page.

Organizations that are only optimizing for rankings are quietly losing ground in discovery environments they haven’t even thought to track yet.

It’s not that SEO stops mattering, it doesn’t. It’s that optimizing for search alone leaves a growing gap.

The goal isn’t to abandon what’s working. It’s to build on it.

Structure does more heavy lifting than most people realize

This is probably the biggest practical takeaway. Well-organized content, real headings, consistent terminology, modular sections that can stand somewhat independently, benefits both SEO and GEO at the same time.

Search engines use structure to interpret meaning. Generative systems use it to extract context. A piece of content built with clear architecture is just easier to work with, whether “working with it” means indexing it or pulling a specific passage into a synthesized answer. It gets found more easily. It gets reused more easily. Those two things aren’t unrelated.

Trust, which has always mattered, now matters even more

Generative systems are, by design, selective about their sources. They’re not going to pull from a thin, undated, anonymous page when there’s a well-sourced, consistently updated, clearly authoritative alternative available. That’s just how they’re built. Understanding how LLMs actually index and use content helps clarify why this matters so much for your publishing strategy.

Which means the things that have always built search authority, original insights, accurate and current information, strong topical depth across related content, are now also the things that determine whether you show up in AI-generated answers. Publishing original research. Keeping information current. Building out topic clusters rather than one-off articles. Establishing a recognizable point of view over time.

None of this is new advice, exactly. But the stakes for ignoring it are higher than they used to be.

Where is this all going?

SEO isn’t going away. But “search” as a category is expanding, into conversational tools, into AI-generated summaries, into interfaces that don’t look like search at all but functionally replace it for a lot of use cases.

GEO is how you prepare for that. Together, the two disciplines create something more durable than either one alone: content that can rank and get referenced, get indexed and get synthesized. Visibility that holds up as user behavior keeps shifting, which, if the last few years are any indication, it absolutely will.

Ready to build a content strategy that works across both? We help organizations in healthcare, higher education, government, and nonprofits get found where it matters, whether that’s a search results page or an AI-generated answer. Let’s talk about your content strategy.

Generative Engine Optimization (GEO) is making organizations scramble — our clients have been asking “Are we ready for the new ways LLMs crawl, index, and return content to users? Does our site support evolving GEO best practices? What can we do to boost results and citations?”  

Large language models (LLMs) and the services that power AI summaries don’t “think” like humans but they do perform similar actions. They seek content, split it into memorable chunks, and rank the chunks for trust and accuracy. If pages use semantic HTML, include facts and cite sources, and include structured metadata, AI crawlers and retrieval systems will find, store, and reproduce content accurately. That improves your chance of being cited correctly in AI overviews.

While GEO has disrupted the way people use search engines, the fundamentals of SEO and digital accessibility continue to be strong indicators of content performance in LLM search results. Making content understandable, usable, and memorable for humans also has benefits for LLMs and GEO.

How LLM systems (and AI-driven overviews) get their facts

Understanding how LLMs crawl, process, and retrieve web content helps us understand why semantic structure and accessibility best practices have a positive effect. When an AI system generates an answer that cites the web, several distinct back-end steps usually happen: 

1. Crawling — Bots visit URLs and download page content. Some crawlers execute javascript like a browser (Googlebot) while others prefer raw HTML and limit their rendering.
2. Chunking — Large documents are split into small, logical “chunks” of paragraphs, sections, or other units. These chunks are the pieces that are later retrieved for an answer. How a page’s content is structured with headings, paragraphs, and lists determines the likely chunk boundaries for storage.
3. Vectorization — Each chunk is then converted into a numeric vector that captures its semantic meaning. These embeddings live in a vector database and enable systems to find chunks quickly. The quality of the vector depends on the clarity of the chunk’s text.
4. Indexing — Systems will store additional metadata (URL, title, headings, metadata) to filter and rank results. Structured data like schema metadata is especially valuable. 
5. Retrieval — A user asks a question or performs a search and the system retrieves the most semantically similar chunks via a vector search. It re-ranks those chunks using metadata and other signals and then composes its answer while citing sources (sometimes). 

The Case for Human-Accessible Content

There are many more reasons why digital accessibility is simply the right thing to do. It turns out that in addition to boosting SEO, accessibility best practices help LLMs crawl, chunk, store, and retrieve content more accurately.

During retrieval, small errors like missing text, ambiguous links, or poor heading order can fail to expose the best chunks. Let’s dive into how this can happen and what common accessibility pitfalls contribute to the confusion.

For Content Teams — Authors, Writers, Editors

Lack of descriptive “alt” text

While some LLMs can employ machine-vision techniques to “see” images as a human would, descriptive alt text verifies what they are seeing and the context in which the image is relevant. The same best practices for describing images for people will help LLMs accurately understand the content. 

Out-of-order heading structures

Similar to semantic HTML, headings provide a clear outline of a page. Machines (and screen readers!) use heading structure to understand hierarchy and context. When a heading level skips from an <h2> to an <h4>, an LLM may fail to determine the proper relationship between content chunks. During retrieval, the model’s understanding is dictated by the flawed structure, not the content’s intrinsic importance. (Source: research thesis PDF, “Investigating Large Language Models ability to evaluate heading-related accessibility barriers”) 

Descriptive and unique links

All of the accessibility barriers surrounding poor link practices affect how LLMs evaluate their importance. Link text is a short textual signal that is vectorized to make proper retrieval possible. Vague link text like “Click here” or “Learn More” does not provide valuable signals. In fact, the same “Learn More” text multiple times on a page can dilute the signals for the URLs they point to.

Using the same link text for more than one destination URLs creates a knowledge conflict. Like people, an LLM is subject to “anchoring bias,” which means it is likely to overweight the first link it processes and underweight or ignore the second, since they both have the same text signal. 

Example of the duplicate link problem: <a href=“[URL-A]”>Duplicate Link Text</a>, and then later in the same article, <a href=“[URL-B]”>Duplicate Link Text</a>. Conversely, when the same URL is used more than once on a page, the same link text should be repeated exactly.

Logical order and readable content

Simple, direct sentences (one fact per sentence) produce cleaner embeddings for LLM retrieval. Human accessibility best practices of plain language and clear structure are the same practices that improve chunking and indexing for LLMs

For Technical Teams — IT, Developers, Engineers

Poorly structured semantic HTML

Semantic elements (<article>, <nav>, <main>, <h1>, etc.) add context and suggest relative ranking weight. They make content boundaries explicit, which helps retrieval systems isolate your content from less important elements like ad slots or lists of related articles. 

Lack of schema

This is technical and under the hood of your human-readable content. Machines love additional context and structured schema data is how facts are declared in code — product names, prices, event dates, authors, etc. Search engines have used schema for rich results and LLMs are no different. Right now, server-rendered schema data will guarantee the widest visibility, as not all crawlers execute client-side Javascript completely. 

How to make accessibility even more actionable

The work of digital accessibility is often pushed to the bottom of the priority list. But once again, there are additional ways to frame this work as high value. While this work is beneficial for SEO, our recent research uncovers that it continues to be impactful in the new and evolving world of GEO.

If you need to frame an argument to those that control the investments of time and money, some talking points are: 

• Accurate brand representation — Poor accessibility hides facts from LLMs. When customers ask an AI  assistant for “best X for Y,” your content may not be shown — or worse, misrepresented. Fixing accessibility reduces brand risk and increases content authority.
• Engagement boost — Improvements that increase accurate citations and AI visibility can increase referral traffic, feature mentions, and lead quality. In a landscape where AI Answers are reducing click-through rates, keeping the traffic you have on your site for longer and building brand trust becomes vital.
• Increased exposure — Digital inclusion makes your content widely accessible to machines and the machines that assist humans. Think about a search engine as another human-assistive device, just like a keyboard or screen reader. 
• Multi-pronged benefits — Accessibility improvement improves traditional SEO, can benefit mobile performance, and reduces the risks associated with accessibility compliance policies. 

Staying steady in the storm

Let’s be clear — this summer was a “generative AI search freak out.” Content teams have scrambled to get smart about LLM-powered search quickly while search providers rolled out new tools and updates weekly. It’s been a tough ride in a rough sea of constant change.

If you continue to have questions within this rapidly evolving landscape, talk to us about your questions around SEO, GEO, content strategy, and accessibility conformance. Ask about our training and documentation available for content teams.

Additional Reading

• AHREFs.com: Is SEO Dead? Real Data vs. Internet Hysteria
• SearchEngineJournal.com: How LLMs Interpret Content: How To Structure Information For AI Search
• InclusionHub.com: SEO and Web Accessibility: What You Need to Know (from 2020, but still relevant)

THE CHALLENGE

OUR APPROACH

THE RESULTS