Why the same social citation reads as the crowd on one engine and a credible authority on the other, and what that means for AEO strategy

Marketers have known for a while that AI search leans on social platforms. Reddit and LinkedIn in particular show up again and again in AI-generated answers. That much is not news. The useful question is not whether these channels get cited. It is where they get cited, what they get cited for, and which sources they get cited alongside. Those answers turn out to be very different depending on the engine.

We used BrightEdge AI Hyper Cube to pull the full universe of prompts where Reddit and LinkedIn earn citations on Google AI Overviews and ChatGPT, then classified each prompt by topic, query intent, functional job, and the other sources cited alongside the social channel. The pattern is clear: ChatGPT and Google AI Overviews do not use these two channels the same way. One engine treats a Reddit citation as the voice of the crowd. The other treats it as a credible reference, sitting it beside the most authoritative publishers on the web.

This is the kind of nuance an engine-agnostic view of AI search will miss. Earlier research in this series showed that AI engines assign functional roles to the biggest sites on the internet, citing Reddit less as a forum and more as a consumer-opinion and product-research layer. This installment goes one level deeper, into exactly how two engines diverge in the way they deploy the two social channels marketers ask about most.

What We Analyzed

We analyzed the prompt and citation universe for Reddit and LinkedIn across Google AI Overviews and ChatGPT, spanning both consumer and professional topics. Every prompt was classified four ways: by topical cluster, by query intent, by functional job-to-be-done (the kind of question being asked), and by co-citation neighborhood (the other brands and sources cited in the same answer). The goal was to understand not just that these channels get cited, but the specific conditions under which each engine reaches for them.

Data Collected

Key Finding

The same social channel plays a different role on each engine. On Google AI Overviews, Reddit is cited as part of a social pack: YouTube appears alongside it in roughly 36% of citations, with Facebook, TikTok, and Instagram close behind, while editorial authorities appear next to it only about 6% of the time. On ChatGPT, the pattern nearly inverts. Reddit is cited beside Healthline, Mayo Clinic, Cleveland Clinic, and Encyclopedia Britannica, with authoritative publishers flanking it about 36% of the time and other social barely registering. Same channel, opposite standing.

The functional picture reinforces it. Both engines cite these channels mainly for how-to, definitional, and verification questions, but ChatGPT leans on them far harder for procedural how-to and causal why answers, while Google AI Overviews is the engine that surfaces them for head-to-head comparison queries. The implication for marketers is that a Reddit or LinkedIn presence is not one asset with one value. It is an asset whose value depends entirely on which engine is reading it and what job the user is doing.

The Same Reddit Citation Lives in Two Different Neighborhoods

The clearest signal in the data is the company Reddit keeps. We measured how often a Reddit citation appears next to other social and UGC platforms versus next to editorial authorities, and the two engines come out as near mirror images of each other.

On Google AI Overviews, Reddit sits inside a crowd. YouTube is the dominant neighbor, and the rest of the pack is Facebook, TikTok, Instagram, and Quora. The engine is effectively grouping Reddit with other places where people post, treating it as one more voice in the user-generated layer.

On ChatGPT, Reddit keeps very different company. Its most frequent co-citations are Healthline (around 12% of Reddit-cited answers), Mayo Clinic (around 9%), Cleveland Clinic (around 8%), and Encyclopedia Britannica, with Medical News Today, Verywell Health, WebMD, and the CDC close behind. The engine is slotting Reddit into the same answers as the most trusted reference publishers on the web. For a marketer, that is the difference between background noise and borrowed credibility.

LinkedIn Keeps Professional Company on Both Engines

LinkedIn does not show the same dramatic flip, and that is itself a finding. On both engines its co-citation neighbors are professional: career and education platforms like Indeed (roughly 11% of LinkedIn citations on AI Overviews), ZipRecruiter, Coursera, Udemy, and LinkedIn Learning. Editorial authorities sit next to LinkedIn rarely on either engine, around 3% on AI Overviews and 5% on ChatGPT. The role is consistent rather than inverted: both engines file LinkedIn as a professional and career source.

One detail stands out. On ChatGPT, the single most common co-citation inside LinkedIn-topic answers is Reddit itself, appearing in roughly 15% of those answers. ChatGPT reaches for Reddit to round out professional answers far more than Google AI Overviews does, which means the two channels are not always competing for the same slot. Sometimes they share it.

What These Channels Get Cited For

Looking only at prompts that carry a clear question or intent, the functional jobs these channels perform are mostly shared, with a few sharp differences. The table below shows the share of intent-bearing citations by functional job.

How-to is the swing job, and ChatGPT leans on social much harder for it. Reddit how-to citations roughly double from AI Overviews to ChatGPT, and LinkedIn climbs from about 22% to 33% of intent-bearing prompts. ChatGPT also pulls Reddit for causal why questions (why something happens, why a symptom appears) more than twice as often as AI Overviews does.

Comparison is a Google AI Overviews specialty. Around 10% of social citations on AI Overviews are head-to-head comparison prompts (americano vs latte, premium vs Sales Navigator). On ChatGPT it is about 1%, because the engine tends to synthesize the comparison itself rather than pointing to the thread where humans debated it.

Verification is everywhere, and the two channels do it differently. It accounts for 14% to 24% of citations across the board. On LinkedIn it is platform-capability checking (Can I unsend a LinkedIn message?). On Reddit it is consumer permission and reassurance (Can dogs have corn? Is this normal?).

One caution on reading this table: the labels capture the shape of the question, not always the reason the social source was pulled. A how-to prompt about a home remedy or a product setup is procedural on its face, but the reason Reddit gets cited for it is often the lived experience in the thread underneath. The experiential value of these channels is real, and much of it hides inside the how-to and verification buckets.

The Topics Each Channel Owns

The topical split is the most intuitive part of the picture and it holds across both engines. LinkedIn earns its citations in professional contexts: careers and recruiting, professional skills and online learning, platform how-to, business-to-business and sales topics, and term definitions. Reddit earns its citations in broad consumer contexts, but the consumer mix shifts by engine. On ChatGPT, Reddit skews toward health and medical questions, money and finance, definitions, and food. On Google AI Overviews, it skews toward entertainment and media, gaming, food, and tech. The health and finance concentration on ChatGPT is what produces the authority-publisher neighborhood described above. When the question is medical, ChatGPT pulls Reddit and Mayo Clinic into the same answer.

Intent and Tone

Informational intent dominates everywhere, and ChatGPT leans into it harder, accounting for roughly 80% to 85% of its citations versus about 65% to 70% on AI Overviews. The more commercially interesting band is consideration intent, which runs roughly 9% to 14% across all four cuts. That is the slice closest to a buying decision and the one marketers should care most about. Transactional intent is thin everywhere, in the low single digits, so neither channel is earning citations at the point of purchase. They are upper and mid-funnel assets.

There is also a tone difference worth noting. When LinkedIn is named as a brand in an answer, ChatGPT speaks about it positively far more often than AI Overviews does, roughly 46% of the time versus about 31%. Reddit is cited more neutrally on both engines, as a reference point rather than an endorsement. ChatGPT, in other words, is more willing to frame LinkedIn as a recommendation.

What Marketers Need to Know

Reddit is your highest-leverage credibility play on ChatGPT. Because the engine cites it next to Mayo Clinic and Healthline, a strong, well-upvoted Reddit thread can punch at the weight of an editorial citation there, especially in health, finance, and other research-heavy categories. That same thread on Google AI Overviews mostly buys a seat in a crowded social pack. Different engines, different value from the exact same content.

Match the channel to the job, not to the logo. LinkedIn earns citations for professional how-to and capability questions. Reddit earns them for consumer how-to, comparison, and lived experience. Decide which channel to invest in based on the question you are trying to win, then build the asset that answers it.

Win the question, not just the brand name. Citations flow to content that answers how do I, can you, and is it worth it, not to a bare brand mention. Build for the underlying job and the brand mention comes with it. A thread or post that resolves the actual question is far more citable than one that simply names the product.

Audit by engine, not in aggregate. The same channel is an authority on one surface and background noise on another. A blended, cross-engine view averages that difference away and hides it. Look at ChatGPT and Google AI Overviews separately to see the real role each channel plays in your category.

Treat comparison content as a Google AI Overviews opportunity. If you produce head-to-head comparison content, AI Overviews is where the social version of that conversation surfaces. Seeding credible comparison discussion where the crowd debates pays off disproportionately on that surface.

Technical Methodology

Key Takeaways

The conventional wisdom said AI search would force marketers to build a parallel optimization discipline. AEO, GEO, llms.txt, content chunking, AI-specific rewrites. A whole new playbook for a whole new surface. The data tells a different story. Across seven major industries on Google organic search and AI search engines, the underlying jobs users are trying to accomplish are largely identical. Surface mechanics differ. The job universe does not.

This finding aligns directly with Google's updated AI Optimization Guide, published just before Google I/O. The guide states plainly: "From Google Search's perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO." Google's own one-line summary of how to succeed: "Focus on what your visitors would enjoy, find helpful, and feel satisfied with after visiting your website." A user-centered content strategy is the strategy. The surfaces sort themselves out.

BrightEdge Data Cube X organic keyword data and AI Hyper Cube prompt data, analyzed across seven dominant brands in seven major industries, show that the underlying user jobs are present on both surfaces. When we collapse the traditional five-bucket intent taxonomy into three jobs-to-be-done | Learn, Decide, and Act | 85% of queries fall into the same job-to-be-done bucket whether the user is on organic search or AI search. When we go deeper to an 11-job taxonomy that travels across categories, 8 of the 11 specific jobs appear meaningfully on both surfaces. Users are users. Same jobs. Different doors.

This is the third installment in our funnel-shape research series. The first installment examined how the four classical query intent categories survived into AI search, each reshaped to fit the medium. The second installment looked at the same data through the lens of the consumer journey and found that brand-owned content dominates the middle of the funnel. This installment steps back to the system level: across the entire query universe and both surfaces, do users come to organic and AI search for the same underlying reasons? The answer is yes, and the implications for content strategy are significant.

We analyzed the full keyword universe and AI prompt universe for top brands across seven industries: Retail, SaaS, Healthcare, Insurance, Finance, Travel, and Education. Each keyword and prompt was classified into one of 11 generalized jobs-to-be-done, then grouped into three job buckets. The findings are directly relevant to any brand building a unified content strategy that needs to perform across organic search and AI surfaces.

Data Collected

Key Finding

The job universe is the same on organic search and AI search. Across seven industries pooled, 85% of queries fall into the same job-to-be-done bucket whether the user is on organic search or AI search. Eight of the 11 specific jobs in the unified taxonomy appear meaningfully on both surfaces. The Learn bucket dominates both. The Decide and Act buckets are present on both. The implication for marketers is that there is no separate AI playbook to build. The same user-centered content strategy that wins on Google organic also makes a brand eligible across AI Overviews, AI Mode, ChatGPT, Perplexity, and Gemini. What changes between surfaces is the grammar of the query (keyword-shaped on organic, conversation-shaped on AI) and the mechanics of how each engine retrieves and presents the answer. The underlying user need is the constant.

What the 11-Job Taxonomy Looks Like

Users come to search to do one of three things: learn something, decide between options, or get something done. Within those three buckets, eleven specific jobs travel across every industry we measured. Healthcare's "find a clinic near me" and Finance's "atm near me" are the same job. Retail's "what is back to school sales" and SaaS's "what is CRM software" are the same job. The taxonomy generalizes.

Learn (the largest bucket on both surfaces).

Decide (the evaluation moment).

Act (the conversion moment).

The Learn bucket is the dominant share of activity on both surfaces. The Decide bucket is meaningfully present on both. The Act bucket is present on both, often expressed through different specific jobs (organic search carries more direct-transactional language, AI search carries more planning and managing language). In every case, the underlying user need is identifiable in both query sets.

What Changes Across Surfaces: Grammar, Not Goals

The traditional five-bucket intent taxonomy (informational, navigational, commercial, transactional, consideration) makes the two surfaces look more different than they are. Organic data is heavy with navigational queries because people type "walmart" into Google to get to the site. AI has near-zero navigational queries because nobody asks ChatGPT to navigate them anywhere. The standard taxonomy interprets this as divergent user behavior. It is not.

"Walmart hours" typed into Google and "what time does Walmart close" asked of ChatGPT are the same job: Get Hours / Specs / Status. The user is doing the same work. The grammar of the query is different because the surface is different. Organic search has trained users to drop articles, verbs, and natural language because the keyword-matching paradigm rewarded brevity. AI search has trained users to write full sentences because the conversational paradigm rewards specificity. The keywords look different. The job is the same.

This grammar difference is the single largest source of apparent misalignment between the two surfaces. When we collapse navigational and informational queries into a unified Learn bucket (because they answer the same underlying user need), the surfaces snap into alignment. 85% of queries fall into the same job bucket across both.

Decide-Stage Behavior Shows Up Differently

Within the strong alignment, one pattern stands out: Decide-stage queries are more visible on AI surfaces than on organic. Across the seven industries pooled, the Decide bucket accounts for a meaningfully larger share of AI prompt activity than it does of organic keyword activity. This is consistent with what BrightEdge's prior funnel-shape research found: the consideration stage of the funnel is real on both surfaces, and AI consolidates evaluation behavior in ways organic search distributes.

This is not a sign that user goals have changed. It is a sign that the consideration journey has changed shape. The buyer asking "what's the cheapest car insurance for a young driver" or "best CRM for small business" used to do that work across review sites, comparison aggregators, and forum threads. Now it consolidates into a single prompt and a single answer. The job is the same. The path through it is shorter and more visible.

For marketers, this is the most actionable finding from the cross-surface analysis. The brands that organize content around the full job taxonomy | including the Decide-stage jobs of Compare Options, Validate Choice, and Get Recommendation | are positioned to be retrieved by AI surfaces when that consolidation happens. The brands that have historically optimized only for the Learn-stage and Act-stage queries on organic, leaving consideration content to third-party reviewers and aggregators, have a gap to close.

Industry-Specific Patterns

The taxonomy travels across industries, but the mix of jobs varies by category in predictable ways. Some industries are Learn-dominant. Some carry meaningful Decide-stage volume. The shape of the user journey differs by what the user is shopping for.

Healthcare and Education. These categories are overwhelmingly Learn-dominant on both surfaces. Users come to search for symptoms, definitions, treatments, courses, degree programs, and how-to guidance. The Decide and Act buckets are small. The implication is that the user-centered content strategy here is depth and breadth of educational coverage. The brands that win are the ones whose content actually teaches.

Finance and Insurance. These categories carry meaningful Decide-stage volume on both surfaces, with AI carrying a larger share than organic. The user journey involves significant comparison and evaluation before commitment. Compare Options ("best high yield savings accounts"), Validate Choice ("is Otto insurance legit"), and Get Recommendation ("what credit card should I get") are core jobs. Brand-owned content that addresses these jobs directly | comparison tables, transparent product details, defensible claims about coverage or rates | is the leverage point.

Retail and Travel. These categories show a more even distribution across Learn, Decide, and Act. Users research, compare, and transact, often within the same session. Plan a Trip / Activity is a significant Act-stage job that is largely unique to Travel. The implication is that content needs to serve the full journey, from category exploration to specific destination planning to booking-stage information.

SaaS. B2B software shows a mix of Learn-stage definitional content ("what is CRM software," "what is account management") and Decide-stage evaluation content ("best project management tools," "small business CRM software"). The user journey is research-heavy and consideration-heavy, with the Act stage often deferred to a sales conversation outside the search session. Brand-owned product education and buyer's guide content does most of the work.

What Marketers Need to Know

The jobs are the same across surfaces. The brands that organize content around what users are trying to do, rather than around the surface they happen to do it on, build the most durable AI search strategy. Google's updated guide says it directly. The data confirms it across seven industries.

Your strategy does not bifurcate. There is one user, one job universe, and multiple surfaces. Foundational SEO is the cost of entry to AI visibility. A page that cannot be crawled, indexed, and retrieved by Google Search cannot be retrieved by an AI surface that draws from the same index. The technical and content fundamentals that make a brand visible on organic are the same fundamentals that make it eligible across AI surfaces.

Cover the full job taxonomy. The 11 jobs above are durable across categories. The brands that win in AI search are the ones whose content actually serves all of them, not just the head terms or the brand keywords. Audit your content coverage against Learn, Decide, and Act. Where you have gaps, you are invisible to AI surfaces when users do those jobs.

Build for users, not for surfaces. Google's own guidance is clear: focus on what visitors find helpful and satisfying. The same content that satisfies the job on organic search is what gets retrieved by AI surfaces. There is no separate AI playbook to build. Skip the llms.txt files, the content chunking, the AI-specific rewrites. Write for the user. The surfaces will follow.

Measure across surfaces, not against them. Yes, there are things you can do to make content more visible in AI experiences. But the fundamental content strategy does not change. What changes is your ability to see how that strategy performs everywhere it lives. That requires one platform connecting organic search, AI Overviews, AI Mode, ChatGPT, Perplexity, and Gemini into a single view of how users are finding you. This is what BrightEdge is built for.

Expect surface-specific grammar, not surface-specific intent. Users phrase queries differently on AI than on organic, but the underlying need is the same. Content that answers the job comprehensively, in plain language, with clear structure and entity clarity, satisfies the user regardless of how they ask. Optimizing for the job is the optimization.

Technical Methodology

Key Takeaways

The conventional wisdom said AI search would compress the funnel and shift citations toward third-party reviewers and aggregators in the consideration phase. The data tells a different story. Across eight industries on Google AI Overviews and ChatGPT, the middle of the funnel still represents meaningful volume, and brand-owned content takes the dominant share of citations there.

BrightEdge AI Hyper Cube analysis across Google AI Overviews and ChatGPT shows that the consideration stage of the funnel is alive, measurable, and varies dramatically by industry. The size of the middle ranges from 4% to 26% of AI search demand depending on the category and the engine. Inside that middle, brand-owned pages account for 42% to 79% of citations across every industry studied, while review and comparison aggregators account for 1% to 7%. The opportunity for brands is on their own domains.

This is the second installment in our funnel-shape research series. In the prior installment we examined how the four classical query intent categories survived into AI search, each reshaped to fit the medium. That analysis mapped consideration-stage queries to the classical commercial intent bucket. This installment looks at the same data through the lens of the consumer journey: top, middle, and bottom of the funnel. The terminology shifts from "commercial intent" to "consideration stage" because the questions we are answering this time are about consumer-journey shape, not classical SEO intent taxonomy. The underlying data is consistent with the prior piece.

We analyzed the full prompt universe across eight industries on both engines: B2B, Ecommerce, Education, Entertainment, Finance, Healthcare, Insurance, and Travel. Each prompt was classified by the BrightEdge Generative Parser into a funnel stage. Citations from consideration-stage prompts were categorized by source type. The findings are directly relevant to any brand planning content strategy for AI search across multiple industries.

Data Collected

Key Finding

The middle of the AI search funnel is real volume in every industry, and brand-owned content owns it. Volume-weighted, the consideration stage represents between 4% and 26% of AI search demand across the eight industries studied. Travel, Ecommerce, B2B, and Finance show the largest middles. Healthcare and Entertainment show the smallest. Inside that consideration stage, brand-owned commercial pages take between 42% and 79% of all citations across every industry on both engines. Review and comparison aggregators, the source type marketers most often assume dominates the research phase, take between 1% and 7% of citations in most categories. The implication for marketers is that the buying-guide content, category explainers, and comparison pages on a brand's own domain are the highest-leverage AI search assets in the middle of the funnel. Outsourcing the consideration phase to third-party reviewers leaves the dominant citation channel uncovered.

The Size of the Middle, by Industry

Volume-weighted share of AI search demand classified as consideration stage:

Two patterns stand out. First, the size of the middle of the funnel varies by a factor of six between the largest category and the smallest. Marketers who assume the consumer journey looks the same across industries are missing where the opportunity actually concentrates. Second, AI Overviews consistently shows a larger consideration stage than ChatGPT in commercial categories. In Travel, Ecommerce, B2B, Finance, and Insurance, AIO's middle is meaningfully bigger than ChatGPT's. In Education and Entertainment, the two engines look roughly the same. The pattern suggests AIO is woven into the buying journey in commercial categories in a way ChatGPT is not, despite the popular narrative that consumers have moved their research behavior to conversational AI.

What a Consideration Prompt Looks Like

Consideration prompts capture users who are comparing options or evaluating a category without committing to a specific brand or product. Examples drawn from the data, generalized for clarity:

In Travel: "best beach vacations for families," "top all-inclusive resorts in Mexico," "cheapest time to fly to Europe."

In Ecommerce: "best treadmill for home use," "treadmill vs elliptical for cardio," "best mattress for back pain."

In B2B: "small business CRM software," "best project management tools for remote teams," "top cloud storage providers for enterprise."

In Finance: "best high yield savings accounts," "Roth IRA vs traditional IRA," "top robo-advisors."

In Insurance: "term vs whole life insurance," "best homeowners insurance companies," "cheapest car insurance for new drivers."

These queries differ from branded queries (which name a specific product or company) and from transactional queries (which signal readiness to act). The defining characteristic is comparison and evaluation. The user is figuring out what to want, not which one to click.

Brand-Owned Content Dominates Consideration-Stage Citations

Across every industry studied and both engines, brand-owned commercial pages take the largest share of citations in the consideration stage. The range is 42% on the low end (Healthcare, where authority sites take a meaningful slice) to 79% on the high end (Travel ChatGPT, where the engine routes consideration queries heavily to brand domains and bypasses online travel agencies). Most industries land between 50% and 70%.

This finding pushes back on a widely held assumption in the AEO and GEO community. The assumption was that AI engines, when faced with a comparison query, would lean on third-party reviewers and aggregators to make the recommendation. The data shows the opposite. The brand's own buying guide, category explainer, or comparison page is more often the cited source than a review aggregator.

The pattern is consistent across engines, with one nuance. AIO concentrates citations across a small number of brand-owned domains. ChatGPT distributes citations across a wider set of brand-owned domains. The dominance of brand-owned content holds in both cases, but the competitive dynamics are different. On AIO, winning a consideration-stage citation in a given category means displacing a small number of established players. On ChatGPT, winning a citation is more accessible, but the citation share per win is smaller.

Review and Comparison Aggregators Are Not the Dominant Source

The source type that conventional AEO wisdom positioned as the natural winner of the consideration stage, review and comparison aggregators, accounts for between 1% and 7% of citations in most industries on both engines. The two exceptions are B2B on ChatGPT, where software review and comparison sites take a slightly larger share, and Finance on AIO, where financial product comparison sites cluster around the high end of the range. Even in those exceptions, brand-owned pages still take more citations than aggregators.

This does not mean third-party reviews are irrelevant. They influence the brand recommendations AI engines surface and they remain important for trust signals. But the citation slot, the actual source AI engines link to in the consideration stage, more often belongs to a brand's own domain. Marketers who have built their AI search strategy primarily around earning third-party reviewer mentions are competing for a small share of the citation channel.

Citation Concentration: AIO Concentrates, ChatGPT Distributes

Citation concentration in the consideration stage differs substantially between engines. On Google AI Overviews, a small number of domains accounts for the majority of citations in any given industry. On ChatGPT, citations spread across a much larger set of domains for the same industries.

The pattern means winning consideration-stage citations on AIO requires going deeper on a smaller number of pages within a category. The competitive set is narrow. Once a brand earns a citation slot, it tends to hold it across many related prompts. ChatGPT is the opposite. The citation pool is more democratic. Breadth of topical coverage, distinctive perspectives on a category, and content depth across many comparison angles matter more than dominance on a single page.

For content strategy, this means the optimization approach differs by engine. On AIO, the priority is identifying the small number of pages that win the highest-volume consideration queries in a category and concentrating optimization investment there. On ChatGPT, the priority is breadth, coverage across the full comparison landscape, and content depth that signals authority across many sub-topics within a category.

A Note on Google AI Overviews and When They Trigger

Google AI Overviews do not appear on every search. AIO is triggered only when Google decides an AI Overview is the right response format for a given query. Many consideration-stage searches return a traditional organic results page with no AIO at all. The analysis in this study measures the subset of consideration queries where Google has chosen to deploy an AIO.

This caveat actually strengthens the finding rather than weakening it. Even on the consideration queries Google has decided merit an AI Overview, the citation slots are not spreading across third-party reviewers and aggregators. They are concentrating on brand-owned pages. Whatever combination of signals Google uses to decide when to deploy AIO and what to cite inside it, the result is that brand-owned content is the dominant beneficiary in the consideration stage.

ChatGPT does not have an equivalent trigger condition. Every prompt receives a response. The full consideration-stage volume on ChatGPT is measured directly. The fact that brand-owned content dominates on both surfaces, despite the very different mechanics of how AIO and ChatGPT decide what to cite, reinforces the strength of the underlying pattern.

Industry-Specific Patterns

Healthcare. Healthcare authority sites (major medical centers, government health agencies, established medical reference sites) take a larger share of citations than in any other industry, between 26% on AIO and 36% on ChatGPT. Even so, brand-owned commercial pages still take the largest single share. The takeaway for healthcare marketers is that competing for citation slots means competing against highly credentialed authority sources, which makes E-E-A-T signals (expertise, experience, authoritativeness, trust) even more important in this category than elsewhere.

Travel. Travel shows the most divergent engine behavior. On AIO, online travel agencies and booking aggregators take a meaningful slice of consideration citations (around 24%). On ChatGPT, the engine bypasses OTAs and routes consideration citations directly to brand-owned destinations (79% brand-owned). For travel brands, this means a ChatGPT optimization strategy that targets brand-owned travel content can win significant citation share, while an AIO strategy needs to plan for OTA competition in the citation slot.

B2B. B2B shows the cleanest gap between AIO's larger middle of the funnel (22%) and ChatGPT's smaller middle (9%). The implication is that B2B buyers are using AIO for category exploration more than they are using ChatGPT for the same purpose, at least in the consideration stage. Software review aggregators have a slightly more prominent role here than in other categories, but brand-owned product pages and buyer's guides still take the largest share.

Education and Entertainment. These are the only two industries where ChatGPT's middle of the funnel is larger than or equal to AIO's. Both categories also show meaningful citation share for video platforms (10% to 17% on AIO) and UGC sources (10% to 13% on both engines). The pattern suggests that for educational and entertainment decisions, users are pulling in more diverse source types than in commercial categories.

What Marketers Need to Know

The middle of the funnel is real volume in AI search. The size varies by industry and by engine, ranging from 4% to 26% of total demand. In Travel, Ecommerce, B2B, and Finance, the consideration stage represents a meaningful share of AI search demand on both engines and should be a primary focus for content strategy.

Your own content is the opportunity. Brand-owned commercial pages take 42% to 79% of consideration-stage citations across every industry studied. The category guides, comparison pages, and buying guides on your own domain are doing the work. Investing in this content is more leveraged than chasing third-party reviewer placements.

Do not outsource the middle to third parties. Review and comparison aggregators take 1% to 7% of consideration citations in most categories. They remain important for trust signals and indirect influence on what AI engines recommend, but the citation channel itself belongs to brand-owned content.

Optimize for both engines differently. AIO concentrates citations across a small number of pages. ChatGPT distributes citations across a wider set. The same brand-owned content strategy serves both engines, but the tactical priorities differ. On AIO, win the small number of pages that own the highest-volume consideration queries. On ChatGPT, build breadth and topical depth across the full comparison landscape.

Audit your consideration coverage by industry. Some industries have much larger middles than others. If you compete in Travel, Ecommerce, B2B, or Finance, the consideration stage deserves significant share of your AI search investment. If you compete in Healthcare, Education, or Entertainment, the middle is smaller, but the source-type dynamics in those categories require category-specific strategy (authority signals in Healthcare, video and UGC presence in Education and Entertainment).

Expect engine-specific behavior, not engine-specific intent. The underlying user behavior in the consideration stage is the same across engines. The way each engine surfaces and cites sources for that behavior differs. A unified content strategy organized around the consumer journey, with execution tuned for each engine's citation dynamics, is more durable than separate engine-specific playbooks.

Technical Methodology

Key Takeaways