Ask ChatGPT to recommend a project management tool and it will list the same five or six names every time. Ask it about a niche player with a better product and fewer mentions across the internet, and you'll get silence. That should worry you if you're not one of the five or six.
For twenty years, SEO meant convincing Google your page deserved to rank for a specific query. You picked keywords, optimized pages, built backlinks, and watched your position on a results page. Plenty of problems with that system, but at least the rules were clear. AI-powered search throws most of them out.
When someone asks an LLM a question, there's no results page. No position one through ten. Just a single synthesized answer, and your brand is either in it or it isn't. The question stops being "how do I rank higher?" and becomes "how do I become the answer?"
How LLMs Form Opinions About Your Brand
Large language models get trained on huge datasets scraped from the web. Books, articles, forums, social posts, product reviews, news coverage, Wikipedia, industry publications. All of it gets compressed into the model's parameters. When someone asks a question, the model doesn't go search the internet. It generates a response based on patterns it absorbed during training.
So the model already has an "opinion" about your brand before anyone asks about you. If your company shows up consistently across industry publications, respected blogs, news outlets, and conference talks, the model has a strong signal about what you do. If your brand exists mostly on your own website and a few directory listings, the model barely knows you're there.
That changes things. In traditional SEO, you could compensate for low brand awareness with good on-page optimization and solid backlinks. In AI search, the model's training data is the playing field. You can't optimize your way into an answer the model doesn't already associate with your brand.
The Data: What Gets Cited in AI Overviews
Google's AI Overviews (the AI-generated summaries that now appear at the top of many search results) pull from specific types of sources. SEMrush and BrightEdge have both published research on which sites actually earn citations in these summaries.
Top Source Types Cited in Google AI Overviews
Analysis of AI Overview citation patterns across thousands of queries
Editorial sites and established brand domains account for over half of all AI Overview citations. Newer or lesser-known sites rarely get referenced.
The numbers tell a clear story. Well-known, authoritative sources dominate. If your brand doesn't show up across editorial sites, forums where real people talk about you, and industry publications, AI Overviews have almost nothing to draw from. You're invisible to the algorithm that now sits above the traditional search results.
Brand Queries vs. Non-Brand Queries: The CTR Gap
Even in traditional search, branded queries (searches that include your company name) crush non-branded queries on click-through rate. When AI search layers on top, the gap gets worse for unknown brands.
Click-Through Rate: Brand vs. Non-Brand Queries
Organic CTR for position 1 results, comparing branded vs. non-branded search queries
Branded searches convert to clicks at roughly 5x the rate of non-branded searches. When users already know your name, they click. When they don't, they scroll.
People click on brands they recognize. AI search engines recommend brands they recognize. If nobody's heard of you, both systems work against you at the same time.
What GEO Actually Means in Practice
Generative Engine Optimization (GEO) means optimizing your brand's presence for AI-generated answers, not just traditional search rankings. The term is new. The idea behind it isn't complicated.
GEO boils down to one thing: make your brand impossible for AI models to ignore. You need a footprint across the web that's broad enough and credible enough that when a model puts together an answer to a relevant question, your brand shows up naturally.
Here's where it splits from traditional SEO:
- You're not optimizing pages. You're building a narrative. The model doesn't care about your meta description or your header tags. It cares about the total picture of everything written about you across the internet.
- Third-party mentions matter more than your own content. Your blog posts help, but what really moves the needle is when other sites and people mention your brand in context.
- Consistency of information matters a lot. If your brand is described differently on every source, the model has a weak signal. If every mention reinforces the same positioning, the model has confidence.
Why Smaller Brands Have to Work Harder Now
If you're a well-known brand with years of press coverage and community discussion behind you, AI search works in your favor. The model already knows who you are. Your job is maintenance.
If you're a smaller brand (a startup, a local business, a niche player), the model might not know you at all. And unlike traditional search, where you could rank on page one with good content and smart link building regardless of brand size, AI search leans hard toward brands with large, established web footprints.
That doesn't mean smaller brands are locked out. But the work looks different. You can't just publish good content on your own site and hope for the best. You need to actively build brand signals across the web.
Practical Steps: Building Brand Signals for AI Search
Here's what we tell clients who want to show up in AI-generated answers. We run all of these ourselves.
Digital PR with a purpose. Get your brand mentioned in publications that AI models respect. Industry blogs, news outlets, trade publications. It doesn't need to be a full feature. A quote in a roundup or an expert comment in a news piece still registers. You want volume and consistency across authoritative domains.
Earn community discussion. Reddit, Quora, and niche forums are goldmines for LLM training data. When real people discuss your product in these spaces, the model picks it up. You can't fake this with bot accounts (models are trained to spot and discount spam). But you can build something worth discussing and make it easy for customers to share their experience.
Structured data and entity optimization. Help search engines and AI models understand what your brand is. Schema markup, a complete Google Business Profile, consistent NAP (name, address, phone) across directories, and a Wikipedia page if you qualify. These structured sources give models high-confidence data about who you are and what you sell.
Publish original research. Original data gets cited more than almost anything else. Run a survey. Analyze your own customer data (anonymized, obviously). Publish findings that other writers will want to reference. When your research gets cited across third-party articles, the model starts treating your brand as an authority in your space.
Consistent brand positioning everywhere. If your LinkedIn says "AI-powered analytics platform" and your homepage says "business intelligence tool" and your Crunchbase says "data visualization software, " the model has three conflicting signals. Pick your positioning and use it everywhere. Repetition creates clarity for humans and machines alike.
"SEO used to be about pages. AI search is about your brand's entire presence across the internet. The page isn't the unit of competition anymore. The brand is."
The Shift: From Rankings to Recommendations
SEO teams have spent two decades thinking in terms of rankings. Position one for this keyword. Page one for that phrase. AI search doesn't work that way. There is no position one. There's the answer, and you're either part of it or you're not.
That feels binary and unforgiving, because it is. But it also creates opportunity. The brands that figure out GEO early will have a real advantage, because building the kind of brand authority AI models respect takes time. It compounds. The difference is that the investment isn't just in content and backlinks. It's in your brand's total footprint across the web.
Companies that start building these signals now will be the ones AI search engines recommend next year. Wait too long and the gap gets harder to close.
