- Semrush’s AI Visibility Index, built from more than 126 million real US AI search prompts, suggests established brands often surface in LLM answers through their homepages rather than optimized blog posts or deep landing pages.
- Growth advisor Eli Schwartz argues that AEO behaves more like a branding channel than a traffic channel, meaning many brands are redeeming years of market presence, not just executing new SEO tactics.
For years, SEO teams were told that more content creates more visibility.
New data from Semrush suggests AI search may work differently.
For category leaders, the page showing up most often in AI answers is not always a blog post, comparison article or optimized landing page. In many cases, it is the homepage.
That observation, highlighted by growth advisor and Product-Led SEO author Eli Schwartz on X, cuts against the assumption that answer engine optimization requires a completely new content playbook.
The real story may be simpler and less comfortable: for established brands, AI visibility may already be heavily shaped by brand authority.
What the Semrush Data Shows
SEO managers keep telling me the same thing: leadership is panicking about LLM visibility.
You already know my opinion on that panic. Not the point of this post.
The point is what citation should actually mean for a brand once you stop chasing the panic and start thinking about… pic.twitter.com/dit7yqiLVj
— Eli Schwartz (@5le) June 18, 2026
Schwartz pointed to findings from the Semrush AI Visibility Index, which analyzed more than 126 million real US AI search prompts across 22 industries and four AI platforms.
The index tracks which brands and pages AI systems cite in their responses.
The pattern Schwartz highlighted centers on Indeed. According to the data, Indeed’s homepage is the primary driver of the brand’s AI visibility, not a support article, keyword-targeted landing page or long-tail blog post.
The homepage is doing what a homepage is supposed to do: represent the brand when someone asks an AI system about the best option in a category.
That is a meaningful signal. It suggests that when LLMs answer category-level queries for well-known brands, they may reach for the domain’s most authoritative page rather than the most recently optimized one.
Why This Is Different From Traditional SEO
Classic SEO rewards breadth.
More pages, more keywords, more entry points. The goal was to capture demand across thousands of queries by meeting searchers wherever they were in the funnel.
AI search does not always follow that logic.
When someone asks an LLM to recommend the best job board, it is not necessarily scanning hundreds of pages and picking the most keyword-relevant URL. It is drawing from what it understands about category authority, trust and brand recognition.
That is why Schwartz’s framing matters: AEO may behave more like a branding channel than a traffic channel.
Traffic channels reward output. Branding channels reward perception, consistency and market presence built over time.
Indeed is not winning that AI citation slot because the SEO team optimized a page last quarter. It is cashing a check that brand marketing wrote years ago.
The Problem With Treating AEO Like Another Content Project
The concern Schwartz raises is practical.
Many SEO and marketing teams are under pressure from leadership to show AI visibility metrics. In response, they launch AEO initiatives, create new content formats, optimize for structured answers or buy tools to monitor citations.
Some of that work can be useful.
But it often starts from the wrong diagnosis.
Before building a new content layer for AI visibility, the first question should be simpler: where does the brand currently show up in LLM answers, and which pages are being cited?
That gap between current AI representation and desired AI representation is the actual brief.
Semrush’s index tracks brand mentions, citations by source and co-occurrence with competitors across AI platforms. For teams that have not done this kind of audit yet, that is the starting point, not a new content calendar.
For context on how brands are approaching AI citation tracking more broadly, Semrush’s AI SEO statistics report shows that some sites are already seeing more than 1% of total sessions coming from platforms like ChatGPT, Perplexity and Copilot. That is still small, but growing fast enough for marketers to pay attention.
Content Still Matters, But It May Not Be the Main Lever
None of this means content stops mattering.
Content still helps AI systems understand a company’s products, services, expertise and positioning. It can support citations, explain use cases and fill gaps in how a brand is represented.
But the Semrush data suggests that category-level visibility often depends more on brand authority than publishing volume.
That is an uncomfortable shift for SEO teams because publishing more content is something they can control. Brand recognition is harder to manufacture quickly.
What Marketers Should Do Now
There are two different situations here.
For brands with strong market recognition, the immediate priority is auditing what AI systems already cite. If the homepage is already appearing, that is a useful signal. If comparison pages, review sites or outdated content are shaping the brand’s AI presence, that is a positioning problem.
For newer brands or companies with thin market presence, there is no shortcut.
The data suggests that AI visibility for category leadership queries is heavily weighted toward brands that LLMs already understand and associate with a market.
That means the work required is not just content. It is broader brand footprint: third-party coverage, industry mentions, customer demand, trusted references and the kind of market presence that builds over time.
The ongoing debate around AEO and AI visibility strategy has mostly focused on what to create next. Schwartz’s point reframes the problem: for many brands, what already exists may matter more than what gets built next.
The uncomfortable takeaway is that AI visibility may not be something marketers can manufacture with one new content project.
For many brands, the work that matters most may have started years ago through reputation, recognition and market presence.
AI search might simply be making those advantages easier to see.
