BrightEdge AI Catalyst analysis across five AI search engines shows that the brands AI engines recommend converge tightly in some categories and diverge sharply in others. Where consumers transact, the engines agree. Where consumers research, the engines develop distinct preferences for which brands belong in the answer.

BrightEdge AI Catalyst analysis reveals that ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews recommend a strikingly similar set of brands at the aggregate level. Pairwise overlap in top-named brands across the dataset falls in a tight 36% to 55% band. But that aggregate average masks meaningful category-level variance. In retail, travel, and tech, brand agreement across all five engines runs between 88% and 97%. In healthcare and finance, it drops to 60% and 71%. The same engines that converge on the same retailers, hotels, and consumer electronics brands disagree substantially about which hospitals, financial institutions, and authoritative publishers to recommend.

This is the follow-up to our recent BrightEdge AI Catalyst research on cross-engine source and brand convergence. The original study found that AI engines pull from wildly different sources but recommend the same brands. That finding holds at the dataset level. This installment splits the same dataset by category to show where the agreement is real, where the divergence concentrates, and what each engine's editorial signature looks like inside the categories where the engines part ways.

Data Collected

Key Finding

The aggregate brand convergence finding is real, but it is not evenly distributed across categories. When the same dataset is split by industry vertical, the spread of pairwise top-30 brand overlap looks very different from category to category. Retail shows 97% average pairwise brand agreement across all five engines. Travel shows 94%. Tech shows 88%. Finance drops to 71%. Healthcare drops to 60%, with a low-to-high range of 40% to 77% across engine pairs. The categories where consumers transact directly with brands (retail, travel, tech) draw from a smaller universe of well-established brands, and the engines pick from that same shared pool. The categories where consumers conduct deep informational research (healthcare, finance) have a much wider universe of credible brands, and each engine has developed its own preferences for which brands belong in the answer. The implication for brand strategy is that one playbook works across all five engines in transactional categories, while research-driven categories require engine-by-engine measurement and category-specific positioning.

Brand Agreement Sits at Different Points by Category

The pairwise top-30 brand overlap across all five engines varies materially by category. Where the buying decision is the dominant query intent, the engines converge. Where the research process is the dominant query intent, they diverge.

In retail, every engine pulls from a small, stable universe of major retailers and consumer brands. Amazon, Walmart, Target, Best Buy, Home Depot, and a handful of category leaders dominate every engine's top 30, leaving little room for divergence. Travel behaves the same way: a tight cluster of online travel agencies, hotel groups, and airlines (Expedia, Booking, Marriott, Hilton, Delta, United) carries across all five engines. Tech sits one step behind at 88% because the pool of relevant tech brands is slightly larger and the engines start to differentiate on which platforms, tools, and services they elevate. Finance and healthcare are the two categories where the divergence becomes the story.

Same Brand. Same Study. Different Treatment.

Inside the high-divergence categories, the same brand often receives dramatically different treatment from one engine to another. The gap is not subtle. Several flagship brands show share-of-mention differences of 9x to 22x across engines.

Mayo Clinic. Mayo Clinic is the most universally recognized healthcare brand in the dataset. Gemini directs 13.1% of its healthcare brand mentions to Mayo Clinic. Google AI Overviews directs only 1.5%. The same flagship authority commands roughly 9x more share on Gemini than on AI Overviews.

Cleveland Clinic. Cleveland Clinic captures 6.5% of Perplexity's healthcare brand mentions. On ChatGPT, that figure drops to 0.3%, a roughly 22x gap. Two engines querying the same healthcare prompts treat the same major medical institution very differently.

Goldman Sachs. Goldman Sachs ranks in the top 10 most-mentioned finance brands on Google AI Mode. On ChatGPT, Gemini, and Perplexity, Goldman Sachs does not crack the top 100. Institutional banking presence on AI Mode is materially different from institutional banking presence on the other engines.

Nasdaq. Nasdaq holds 4.5% share of finance brand mentions on ChatGPT. On AI Mode, that figure drops to 0.4%, an 11x gap. Engines that draw from exchange and regulatory sources elevate Nasdaq strongly. Engines that draw from institutional banking sources do not.

Bloomberg. Bloomberg captures 2.9% of finance brand mentions on Gemini. On AI Mode, Bloomberg has zero presence in the top 100. Editorial finance brands are heavily represented on Gemini and largely absent from AI Mode.

These are not measurement noise. They are systematic patterns that reflect the editorial signature each engine has developed inside high-divergence categories.

Each Engine Has a Personality Inside High-Divergence Categories

Inside the categories where the engines part ways, each engine favors a recognizable type of brand. The patterns hold up across the dataset and offer a usable mental model for predicting how each engine will treat brands in any high-divergence category.

ChatGPT favors specialty associations and exchanges. ChatGPT's healthcare top brands include the American Academy of Orthopaedic Surgeons (AAOS), the American Urological Association (AUA), the American College of Gastroenterology (GI), the American Academy of Pediatrics (AAP), and the American Academy of Family Physicians (AAFP). Its finance top brands lead with Nasdaq, the SEC, and major brokerages (Fidelity, Schwab, Vanguard). The pattern is consistent: ChatGPT elevates professional associations, regulatory bodies, and exchanges that carry institutional weight within their specific subcategory.

Perplexity leans on consumer health publishers and trading research tools. Perplexity is the only engine where consumer health publishers (Healthline, WebMD, Medical News Today) appear prominently in healthcare mentions. In finance, Perplexity uniquely elevates trading research tools like Stock Analysis, http://Investing.com , and Marketbeat alongside the standard exchanges. The pattern reflects Perplexity's broader posture as a research-oriented engine that surfaces both institutional and consumer-facing reference sources.

Gemini hyper-concentrates on flagship authorities and editorial finance. Gemini directs 13.1% of healthcare brand mentions to Mayo Clinic and 9.5% to NIH, two brands that account for nearly a quarter of all Gemini healthcare mentions. In finance, Gemini elevates Bloomberg, Wall Street Journal, Reuters, Forbes, and Investopedia, the editorial finance media set. The pattern is consistent: Gemini behaves like a concentrated authority recommender that leans heavily on a small set of flagship brands rather than producing broad lists.

Google AI Mode quietly favors institutional banks. AI Mode's finance top brands lead with JPMorgan, Wells Fargo, UBS, Goldman Sachs, Barclays, and Citigroup. The institutional banking presence is unique to AI Mode and is not mirrored on any other engine in the dataset. This pattern is not visible in aggregate share-of-voice reports, but it is clearly visible when finance brand mentions are isolated by engine.

Google AI Overviews spreads thin across providers. AI Overviews has the lowest brand concentration in high-divergence categories. No single brand commands more than 1.5% of healthcare mentions, and no finance brand exceeds 0.8%. The engine's top-15 lists in these categories include a wider variety of brands at lower share-of-voice levels, consistent with AIO's broader UGC-first sourcing posture.

What Marketers Need to Know

Brand agreement is real, but it is category-dependent. The aggregate finding that AI engines mostly agree on brands holds up. But the agreement is not evenly distributed. Retail and travel converge at 94% to 97% pairwise overlap. Healthcare and finance drop to 60% and 71%. Where your category sits on that spectrum determines whether one strategy works across all five engines or whether you need to look engine by engine.

Where consumers buy, engines converge. Where consumers research, engines diverge. Transactional categories pull from a small pool of well-established brands, and the engines pick from the same pool. Research-heavy categories have a much wider universe of credible brands, and each engine has developed its own preferences inside that universe. The strategic implication is straightforward: a single AI search strategy is portable across engines if you operate in retail, travel, or tech. If you operate in healthcare, finance, or other research-driven categories, you need to think harder about which engine is treating your brand how.

Each engine has a recognizable personality inside high-divergence categories. ChatGPT favors specialty associations and exchanges. Perplexity favors consumer health publishers and trading research tools. Gemini favors flagship authorities and editorial finance media. AI Mode favors institutional banks. AI Overviews spreads thin across providers. Knowing the editorial signature of the engines that matter to your buyers is half the work of building visibility in a high-divergence category.

Audit by engine within your category, not just in aggregate. The aggregate share-of-voice number can hide a lot if you are operating in a high-divergence category. A flagship competitor "missing" from one engine but dominant on another is not a measurement error. It is that engine's editorial signature in your space, and it is actionable. Brand teams operating in healthcare, finance, or any research-driven category should be measuring share of voice engine by engine and treating the divergence as a strategic input rather than a reporting inconvenience.

Match your authority strategy to the engines that matter. If your buyers rely heavily on Gemini, your strategy should prioritize being covered by the flagship authorities and editorial media that Gemini elevates. If your buyers use ChatGPT, your priority is presence in the specialty associations, regulatory bodies, and exchanges relevant to your subcategory. If your buyers use AI Mode in finance, institutional banking visibility is the lever that moves the needle. The three-layer source framework from the prior study still applies. The category-level engine personalities tell you where to put the emphasis.

A flagship competitor missing from one engine is signal, not noise. When a major brand in your category dominates one engine and is absent from another, that is meaningful information about how each engine has built its editorial signature. Mayo Clinic at 13.1% on Gemini and 1.5% on AIO is not random. Goldman Sachs in AI Mode's top 10 and absent from ChatGPT, Gemini, and Perplexity is not random. These patterns reflect each engine's source layer weighting and editorial posture. They are also the most actionable input for prioritizing PR, content, and authority-building investment.

Technical Methodology

Key Takeaways

BrightEdge AI Catalyst analysis reveals that ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews operate with fundamentally different editorial personalities when selecting the sources they cite. At the same time, the brands named in AI-generated answers remain far more consistent across engines than the sources those engines use to construct those answers. The gap between how engines source and what engines recommend is the single most important pattern for any brand building an AI search strategy.

The prevailing assumption in AI search is that each engine requires its own playbook because each engine behaves differently. The data confirms the engines do behave differently, in some cases by close to two orders of magnitude. But the consistency on the output side, which brands get named in the final answer, tells a different story. The playbook does not need to be fragmented by engine. It needs to be organized by source layer.

This is the latest installment in our BrightEdge AI Catalyst research series. We analyzed citations and brand mentions across ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews, drawn from prompts spanning ten industries including B2B technology, education, entertainment, finance, healthcare, insurance, restaurants, travel, and ecommerce. The patterns that emerged are directly relevant to any brand planning AI visibility at scale.

Data Collected

Key Finding

AI search engines are often discussed as if they behave similarly because they produce a similar kind of output: a synthesized answer with citations. The BrightEdge AI Catalyst data shows that behind the surface, the five engines pull from meaningfully different parts of the web. The share of citations coming from authoritative sources ranges from 10% to 26%, depending on the engine. The share coming from user-generated content ranges from 0.2% to 18%, roughly a 90x spread across engines answering the same categories of questions. Despite that divergence in sourcing, the brands those engines recommend cluster in a much tighter range. Pairwise top-100 overlap in named brands across engines falls between 36% and 55%, a 19-point spread, while pairwise top-100 overlap in cited sources ranges from 16% to 59%, a 43-point spread. Source agreement between any two engines varies widely and inconsistently. Brand agreement is consistently steady. The implication for brand strategy is that the path AI takes to reach its answer matters less than most strategies assume, but being present across the three distinct source layers that feed those paths matters more than strategies typically account for.

Five AI Engines, Five Sourcing Personalities

Gemini functions as a formal institutional recommender. Gemini shows the strongest bias toward authoritative sources of any engine in the dataset. Approximately 26% of Gemini's citations come from government domains, academic institutions, and major industry institutional bodies combined. UGC and social content makes up only 0.2%. The authority-to-UGC ratio is roughly 130 to 1, the highest in the study. Gemini also shows the highest .gov share of any engine at roughly 13%, paired with a .org share of 23%. The engine behaves as a conservative, list-oriented recommender that leans on trusted institutional voices and tends to produce longer, more inclusive brand lists than other engines.

ChatGPT acts as a long-tail editorial engine. ChatGPT cites the flattest source distribution of any engine in the study. Its top 10 most-cited domains account for only 18.5% of total citations, meaningfully lower than Perplexity (26.7%), Gemini (26.3%), or AI Mode (19.4%). ChatGPT also has almost no UGC presence (0.5%) and pulls heavily from government and .org domains (12% and 20% respectively). The engine reads as a formal editorial assistant with a long, diverse tail of corporate, institutional, and government sources.

Perplexity behaves like a research librarian. Perplexity concentrates more of its citations in institutional medical, government, encyclopedic, and medical publisher sources than any other engine. Combined, those four categories account for approximately 30% of Perplexity's citations. Perplexity shows the highest share of .edu citations (3.2%) and the highest share of international country-code domains (4.4%) in the dataset, reflecting a more formal and globally sourced material mix. It also names brands earliest of any engine, with 86% of its brand mentions landing in position 5 or earlier. Perplexity behaves like an engine that commits to a short, authoritative shortlist rather than producing an exhaustive list.

Google AI Mode operates as a broad commercial aggregator. Google AI Mode pulls from a wider catalog of unique domains than most other engines, with a long-tail distribution that spreads citations across far more sources than its siblings. It also distributes its citations more evenly across source types than any other engine in the study, showing the strongest mix of review aggregators, finance data sources, and news media citations in the dataset. UGC exposure is moderate at roughly 7%, well above ChatGPT or Gemini but well below AI Overviews. AI Mode's top 10 citation concentration is among the lowest at 19.4%, reinforcing its identity as a long-tail, balanced commercial surface.

Google AI Overviews is a UGC-first engine. Google AI Overviews stands apart from every other engine in the study. Approximately 17.5% of its citations come from user-generated content platforms, 35x higher than ChatGPT (0.5%) and 87x higher than Gemini (0.2%). A single video platform accounts for roughly 10.6% of all AI Overviews citations on its own, and a single forum platform adds another 2.9%. Authoritative sources, including government, academic, and major institutional bodies, account for only 9.5% of AIO citations combined. AI Overviews is the only engine in the dataset where UGC citations outweigh authoritative citations.

Authority Share Versus UGC Share, by Engine

The Two Google Engines Are Not the Same Engine

Among the five engines studied, the two most similar are Google AI Mode and Google AI Overviews, with a top-100 citation overlap of roughly 59%. But Gemini, also a Google product, behaves very differently from its siblings. Gemini's top-100 citation overlap with AI Mode is only 27%, and with AI Overviews only 34%. Gemini actually has more in common with ChatGPT (39% overlap) than with the Google search-embedded surfaces. In practical terms, "Google AI" is not one thing. The search-embedded surfaces lean heavily on commercial and UGC content, while standalone Gemini behaves like a conservative, authority-heavy reference engine. Any brand strategy that treats all three Google AI surfaces as interchangeable will miss the actual sourcing patterns driving visibility on each.

The Brand Convergence Signal

The most consequential finding in the study is not the divergence in sources. It is the convergence in brand recommendations despite that divergence. Pairwise top-100 overlap in cited sources across engines ranges from 16% to 59%, a 43-point spread. Pairwise top-100 overlap in named brands ranges from 36% to 55%, a 19-point spread. In every pairwise comparison, brand overlap falls in a tighter, more predictable range than source overlap. The engines disagree substantially and inconsistently about where to pull information from. They agree more consistently about which brands belong in the final answer. That pattern is what makes a unified strategy viable across all five engines, rather than five separate playbooks.

Sentiment Is Overwhelmingly Positive Across Every Engine

Brand sentiment in AI-generated answers skews positive on all five engines, but not uniformly. Gemini is the most positive at roughly 96% positive sentiment, with only 0.3% negative. ChatGPT sits at 94% positive with effectively zero negative mentions. Perplexity shows the highest neutral share at 11%, consistent with its more journalistic, reference-oriented posture. The Google search-embedded surfaces (AI Mode at 93% and AI Overviews at 89%) show slightly higher negative sentiment (1.7% and 2.1%), which reflects their deeper pull from UGC and commercial commentary sources where critical framing more commonly appears. Across the dataset, negative brand mentions remain a marginal share of total volume, which reinforces that visibility in AI answers is almost always presented in a positive or neutral frame.

What Marketers Need to Know

AI engines pull from three distinct source layers, and every engine uses all three. Authoritative sources include government, academic, and major industry institutional content. Commercial and editorial sources include review sites, comparison content, trade press, news media, finance data, and retailer listings. UGC includes video content, forum threads, community discussion, and creator coverage. No engine uses only one layer. The engines differ in how they weight each layer, not in whether they use it. A brand visibility strategy built around only one layer, no matter which layer, will underperform on engines weighted toward the other two.

Authority is category-relative. "Authoritative" does not mean .gov or .edu for every brand. Not every company can or should aim to be cited by federal agencies or academic institutions. Every category has its own authoritative layer: trade associations, analyst firms, expert publishers, standards bodies, professional associations, and institutional voices trusted within the vertical. The strategic question is which authoritative sources serve as the backbone of AI citations in your specific category, and whether your brand is covered by those sources.

Commercial and editorial presence is the widest visibility lever. Across all five engines, the brand/corporate and commercial/editorial source layer accounts for the largest share of citations, ranging from roughly 37% on Gemini to 51% on AI Overviews. Review sites, comparison content, trade press, retailer listings, and finance data are the sources AI most frequently reaches for. Investment in PR, trade coverage, review site visibility, and category comparison content translates into visibility across every engine, not just one.

UGC is non-negotiable for AI Overviews and still meaningful elsewhere. The AI Overviews surface draws roughly 18% of its citations from user-generated content, but UGC is not zero on other engines either. Perplexity pulls 1.5% of its citations from UGC, AI Mode pulls 7%, and both represent real retrievable impressions in categories where community and creator content is strong. A UGC strategy does not mean "produce short-form video." It means understanding which videos, forum threads, and community discussions AI is already citing in your category, and being present in that conversation with authority.

Weight investment based on which engines matter most to your buyers. The three-layer framework is universal. The emphasis is not. A B2B SaaS brand whose buyers rely heavily on ChatGPT and Perplexity will prioritize authority and commercial coverage, with UGC as a supplemental layer. A consumer brand whose buyers use AI Overviews heavily will prioritize UGC and commercial presence, with authority as reinforcement. Brand tracking at the engine level, not just in aggregate, is how those priorities get set and validated.

Engine overlap patterns should inform where you measure first. The two Google search-embedded surfaces share roughly 59% of their top-cited sources, so visibility gains on one frequently translate to the other. Gemini behaves more like ChatGPT than like its Google siblings, so brand teams should not assume that a Gemini strategy is a Google strategy. These overlap patterns are not intuitive, and brands that map their measurement plan against actual engine behavior will catch gaps that aggregate tracking hides.

Technical Methodology

Key Takeaways