Digital marketers and search engine optimization professionals face a persistent strategic dilemma: Should you keep your content strategy strictly focused on a few core topics where you possess deep expertise, or should you broaden your reach into peripheral topics to widen the marketing funnel? In traditional organic search, the concept of topical authority has long suggested that search engines favor sites that demonstrate deep, structured knowledge within a specific domain. However, as search behavior shifts toward Artificial Intelligence (AI) and Large Language Models (LLMs)—a discipline often referred to as Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO)—the rules of visibility are being rewritten.
When an AI model generates an answer, it handles domain authority and brand recognition through two distinct mechanisms: citing a website as a supporting source link and explicitly recommending or mentioning a brand by name in its response. Understanding whether topical authority operates similarly in AI search requires looking beyond simple rankings and examining how LLMs surface brands across both core and adjacent categories over time.
Recent research analyzing extensive data from Semrush’s AI Visibility Toolkit provides fresh empirical insight into this dynamic. By evaluating brand performance across hundreds of categories in ChatGPT, the data demonstrates that while broad category coverage can earn source citations, earning explicit brand recommendations requires concentrated topical depth within a brand’s established sphere of expertise.
The Data and Methodology Behind the Study
To evaluate how topical focus influences brand visibility in generative AI models, research was conducted using U.S. ChatGPT data provided by Semrush via their AI Visibility Toolkit. The dataset tracked domain performance across 1,094 distinct categories from January through June 2026, with each category evaluated using 5 unique prompt variants per monthly snapshot.
The statistical analysis rested on three core experimental tests:
- The Relatedness Test: This model compared a brand’s appearance in new, expansion categories against categories where it had already established deep expertise (defined as appearing in at least 3 out of 5 prompt variants within a category). This test analyzed 45,578 expansion appearances across 1,458 mapped brand entities from February through May.
- The Future Performance Test: This longitudinal model traced domain citations and brand mentions over a six-month window to observe how early category dominance impacted subsequent visibility.
- The Breadth vs. Depth Test: This framework evaluated whether brands that appeared across multiple categories experienced any performance penalty when spreading their content across breadth versus depth. The test utilized a dataset of 283,215 citations and 76,493 named-brand-mention observations, pairing each current-month status with its next-month performance outcome.
All statistical findings represent associations after applying controls for baseline organic traffic, Semrush Authority Score, branded search volume, existing citation/mention share, and category-month variances. Because these are observational models, they indicate strong directional patterns rather than definitive causal proof that content publishing directly caused the observed output.
Mentions vs. Citations: The Topical Authority Split
In traditional search engine optimization, a ranking is binary: a web page either appears in a search result or it does not. In generative AI search, visibility operates on two distinct tiers: link citations and named brand mentions. The study reveals that topical authority affects these two visibility types in fundamentally different ways.
When measuring brand appearances in categories that were semantically distant from their established expertise, domains frequently served as reference sources but were rarely recommended by name. Specifically, in distant categories:
- 50% of brand appearances resulted in citations only.
- 25% of appearances resulted in named brand mentions.
- 9% of appearances managed to capture both a citation and a named mention.
- Citation-only presence remained virtually unchanged regardless of topical distance, sitting at 41% in distant categories compared to 40% in close categories.
Conversely, when a domain expanded into topically close categories aligned with its proven expertise, its ability to earn explicit recommendations increased substantially:
- 74% of appearances earned source citations.
- 44% of appearances generated named brand mentions.
- 34% earned both a citation and a named mention simultaneously.
These findings illustrate a critical distinction in Answer Engine Optimization: an LLM will readily index and cite a credible domain as a supporting reference across virtually any topic, provided the content meets standard source requirements. However, when the model generates active recommendations, it favors entities that have built strong topical relevance within their primary vertical.
The Myth of Being Spread Too Thin: Depth Beats Breadth
A common concern among content strategists is that publishing content across diverse topics dilutes domain authority, causing a “spread too thin” penalty. The empirical data indicates that breadth alone does not penalize a domain; rather, shallow coverage within target categories is what undermines long-term visibility.
To understand this dynamic, the study differentiated between three metrics:
- Presence: Whether a brand appears at all within a given category answer.
- Depth: The number of prompt variants (from 1 to 5) in which the brand surfaces within that category.
- Category Performance: The brand’s total share of citations or named mentions across the category’s answer set.
For domain citations, spreading out across multiple categories yielded no measurable downside. The positive association with future citation share rose steadily from +0.012 when a domain appeared in 1 out of 5 category prompts to +0.062 when it appeared across all 5 prompts. Being cited across broad categories does not trigger an authority penalty in generative AI responses.
However, named brand mentions displayed a far more sensitive relationship with content depth. When a brand surfaced shallowly—appearing in only 1 out of 5 prompt variants across its target categories—it experienced a negative association with next-month mention share (-0.051). When a brand achieved complete depth by appearing in 5 out of 5 prompts, the association turned slightly positive.
This reveals that the perceived penalty for spreading a brand too thin across topics is actually a penalty for shallow performance. Brands that establish true category dominance—surfacing consistently across multiple variations of a topic—can successfully leverage that depth to expand into adjacent verticals. Trying to be present everywhere with minimal depth leads to diminishing brand mentions, while mastering core topics provides a stable foundation for growth.
Industry Variations in AI Topical Authority
Topical authority in AI search does not function identically across every industry sector. The data highlights pronounced operational differences between low-risk informational sectors and high-stakes verticals, such as those governed by Your Money Your Life (YMYL) guidelines.
Finance and Real Estate: Strong Citation Scalability
Financial services and real estate demonstrated the strongest repeat-citation patterns when expanding topical coverage. In these industries, building broad, repeated coverage consistently correlated with higher source visibility in AI responses:
- In the Finance category, the correlation between citation breadth and next-month citation share increased from +0.054 at 1 prompt variant to +0.139 when coverage reached all 5 prompt variants.
- In Real Estate, the association expanded from +0.021 at 1 prompt variant to +0.135 at full 5-prompt depth.
For brands operating in finance and real estate, category expansion serves as a viable citation strategy. When a domain systematically covers prompt variations across related financial or property topics, it reliably secures a larger share of source citations in subsequent months without suffering performance degradation.
Legal and Healthcare: The High-Stakes Mentions Threshold
The legal and healthcare verticals presented a starkly different dynamic. Because these fields involve high-stakes decision-making with direct consumer impact, LLMs maintain a significantly higher threshold for making explicit brand recommendations.
- In the Legal sector, the association between mention breadth and future mention share remained negative at -0.058, even when domains achieved full 5-prompt coverage.
- The Healthcare sector reflected the same pattern, maintaining a negative association of -0.037.
In legal and medical topics, achieving domain citations through thorough documentation is achievable, but converting those citations into direct brand recommendations by the AI model is far more difficult. Generative engines demand higher thresholds of verified entity trust, external mentions, and off-page authority before actively recommending a specific legal firm or healthcare provider to users.
Strategic Implications for Content and AEO Strategy
The empirical patterns identified in the Semrush ChatGPT dataset offer actionable guidance for modern content strategists, SEO directors, and brand marketers adapting to AI-driven search environments.
1. Differentiate Your Citation Goals from Your Mention Goals
Content teams must distinguish between strategies designed to capture link citations and those designed to earn brand recommendations. If your goal is top-of-funnel reach through source links, expanding content into peripheral, topically relevant categories is a validated tactic. However, if your goal is securing direct product or service recommendations in AI answers, you must maintain deep coverage in topics directly aligned with your primary business focus.
2. Audit Category Depth Before Expanding Scope
Before launching content campaigns targeting new verticals, evaluate your coverage depth within existing core topics. Earning a single mention across a scattered array of prompts signals shallow coverage, which correlates with reduced overall visibility. Ensure your content assets comprehensively address the primary variants of your core topics—aiming to satisfy multiple prompt formats—before broadening your editorial calendar.
3. Tailor Tactics to Vertical Risk Profiles
Adapt your expectations based on your industry’s regulatory and risk profile:
- Finance, Real Estate, and E-Commerce: You can utilize source citations as an early key performance indicator (KPI) when expanding into adjacent topics. Broadening content touchpoints offers measurable downstream advantages.
- Healthcare, Law, and High-Risk Services: Recognize that capturing source citations is merely a prerequisite, not the final objective. Earning LLM brand recommendations requires building authoritative off-page validation, third-party press mentions, and verified entity signals alongside on-page content depth.
Methodological Framework and Research Scope
To ensure proper interpretation of these findings, it is important to review the structural parameters, measures, and data boundaries governing the study.
Dataset Composition
The core findings are derived from Semrush’s U.S. ChatGPT dataset within the AI Visibility Toolkit spanning January through June 2026. The dataset encompasses 1,094 individual categories, with 5 standardized prompt variants monitored per category in each monthly data collection snapshot.
The predictive models evaluated 283,215 domain-category citation instances and 76,493 named-brand-mention instances from January through May, tracking each observation against its corresponding performance in the following month. Domains that lost visibility within a category in subsequent months were recorded with a share value of zero to account for churn.
The semantic relatedness analysis monitored 45,578 expansion appearances generated by 1,458 mapped brand entities. Because evaluating expansion requires baseline performance metrics, this model evaluated data snapshots from February through May.
Measurement Standards
The metrics utilized across the models include:
- Citation Share: The proportion of total source link citations captured by a single domain within a category’s answers.
- Brand-Mention Share: The proportion of explicit, named brand references captured by a mapped entity within a category’s answers. Citations and mentions were calculated independently, as ChatGPT generates significantly more source links than direct brand recommendations.
- Depth and Breadth: Depth measures domain appearance across the 5 prompt variants within a single category. Breadth tracks the total number of unique categories in which a domain surfaces during a monthly snapshot.
- Demonstrated Expertise: Established when a brand surfaces in at least 3 out of 5 prompt variants within a category prior to the expansion observation. Semantic relatedness was evaluated using multilingual embedding models comparing target category titles and prompts against the brand’s verified expertise categories.
Model Controls and Scope Boundaries
To isolate the relationship between topical focus and AI visibility, multivariate regression models applied controls for domain Authority Score, monthly organic search traffic, baseline branded search demand, concurrent depth and share, and fixed category-month variations.
Entity resolution mapped web domains and named text entities to verified first-party brand entities wherever possible. As a result, relatedness metrics reflect mapped corporate entities rather than unverified raw web text. Finally, while these models demonstrate clear statistical relationships between content depth, topic distance, and AI search presence, they measure observational associations rather than direct causal attribution.