AI citation patterns differ across platforms because each engine retrieves and ranks sources differently. Studies show Wikipedia leads ChatGPT citations, while Reddit is prominent in Google AI Overviews and Perplexity. Results also change by intent and format, making platform-specific strategies essential.
Highlights:
- Engines favor different sources
- Intent shapes citation patterns
- One strategy cannot fit all platforms
New Data Reveals How AI Citation Patterns Differ Across Platforms
Recent studies increasingly point to the same broad conclusion: AI engines have individual citation ecosystems.
The exact leading source changes depending on the dataset, time period, platform, vertical, and type of query being measured. Wikipedia may dominate ChatGPT citations in one analysis, while Reddit becomes more prominent for support-related prompts. Perplexity may show a high concentration of Reddit citations in an aggregate dataset but favor YouTube for specific educational and recommendation queries.
The apparent contradictions are important. They do not necessarily mean that one study is incorrect. They show that citation behavior changes depending on what researchers measure.
A study of overall citation volume answers a different question from an intent-level analysis. Similarly, measuring the leading domain across all queries will produce different results from measuring the most-cited source within only commercial, educational, or navigational prompts.
Key Findings at a Glance
The combined source material supports several central findings:
- AI citation patterns vary by platform, industry, query intent, content format, and time period.
- ChatGPT and ChatGPT Search do not always display the same citation behavior.
- Google AI Overviews, Google AI Mode, and Gemini have different source preferences despite belonging to the same company.
- Wikipedia remains strongly associated with ChatGPT and ChatGPT Search in several datasets.
- Reddit is highly visible in some Perplexity, ChatGPT, and Google AI Overview datasets, but its influence varies widely by platform and vertical.
- YouTube performs strongly across Perplexity and several Google AI surfaces, particularly for educational, recommendation, purchase, and support queries.
- Claude appears more inclined toward formal, technical, institutional, or primary-source content in some analyses, although the supplied studies do not fully agree on its sourcing behavior.
- AI mentions and AI citations are separate outcomes. A platform may mention a brand without linking to its website.
- Crawler access can directly affect whether a website remains part of an AI platform’s source set.
- Aggregate citation benchmarks should be used to create hypotheses, not universal content rules.
What the AI Citation Data Measures
AI citation research can measure several different behaviors. Understanding those measurements is essential before applying the findings to an SEO or AI visibility strategy.
Some analyses measure total citations across a large dataset. Others focus on the share held by the leading sources, the most-cited domain for each query intent, citation overlap with search rankings, or the difference between brand mentions and direct links.
Because each methodology answers a different question, headline statistics should not be treated as interchangeable.
Platforms, Queries, and Time Period Analyzed
The five sources reviewed for this article cover different combinations of platforms, dates, query categories, and industries.
One study analyzed 680 million citations across ChatGPT, Google AI Overviews, and Perplexity between August 2024 and June 2025. It measured both overall citation volume and the distribution of citations among each platform’s top 10 sources.
Another study tracked ChatGPT, ChatGPT Search, Perplexity, Google AI Overviews, Google AI Mode, Gemini, and Claude. It covered seven search intents over seven months, from September 2025 through March 2026, producing 1,056 platform, intent, and month combinations. Claude was included for only two months, making its findings early indicators rather than a long-term pattern.
A third analysis examined high-commercial-intent prompts across nine verticals and seven major AI platforms over four months ending in January 2026.
The combined material therefore covers broad aggregate citation behavior, platform-level source preferences, intent-specific trends, industry variation, and commercial impact.
How Citations Were Identified and Compared
The first major distinction is between overall citation volume and top-source concentration.
Overall citation volume measures what percentage of all tracked citations points to a particular source. For example, Wikipedia represented 7.8% of ChatGPT citations in the August 2024 to June 2025 dataset.
Top-source share measures how citations are distributed only among a platform’s leading sources. In the same dataset, Wikipedia represented 47.9% of citations among ChatGPT’s top 10 sources.
These figures describe two different relationships. The first measures Wikipedia’s position across the full dataset. The second measures its dominance within a smaller group of leading domains.
Other studies identified the top-cited domain for every platform, intent, and month. This approach reveals whether an engine changes its preferred source depending on whether the user is seeking education, recommendations, comparisons, pricing, purchasing information, support, or navigation.
The Difference Between AI Citations and AI Mentions
An AI mention occurs when a platform names a company, product, or brand in an answer without linking directly to its content.
An AI citation occurs when the platform provides a link to a webpage used as a source.
The distinction matters because the two outcomes have different business effects. Mentions may build familiarity, reinforce category association, and influence future searches. Citations can generate direct referral traffic and provide users with an immediate path to the source.
The supplied research indicates that brands frequently achieve one without the other. Fewer than 30% of AI responses in one analysis mentioned and cited the same brand, while fewer than one in five brands were reportedly mentioned frequently and cited consistently.
This means a brand visibility strategy should track mentions and citations separately rather than combining them into a single metric.
Why AI Citation Patterns Differ Across Platforms
AI platforms differ because they use different retrieval systems, indexes, trust signals, citation interfaces, and source-selection rules.
Some platforms retrieve live web content before producing an answer. Others rely more heavily on existing model knowledge unless browsing is activated. Some consistently link to every source, while others provide citations less predictably.
The result is that the same query can produce different source sets across platforms.
Each AI Platform Uses a Different Retrieval System
Retrieval-Augmented Generation, or RAG, allows an AI system to retrieve information from external sources before generating an answer. The retrieved passages provide evidence the model can reference, helping ground the response and reduce hallucination risk.
RAG systems do not simply reproduce traditional search rankings. They evaluate whether content is semantically relevant, clearly structured, extractable, and supported by reliable information.
One source cited research finding that only 12% of URLs used by AI tools overlapped with Google’s top 10 results. The remaining citations came from pages outside the first page of Google results.
ChatGPT, Claude, Perplexity, and Google’s AI products also differ in how they connect to web indexes and retrieve current information. Those architectural differences influence which sources are available before any authority or relevance assessment takes place.
Source Authority, Relevance, and Freshness
AI engines do not define authority in exactly the same way.
ChatGPT and ChatGPT Search frequently associate authority with encyclopedic or well-established sources such as Wikipedia. Claude may give more weight to technical precision, primary sources, institutional material, and formal citations. Perplexity often prioritizes recent, directly retrievable web content.
Freshness can be particularly important for platforms with continuous or real-time retrieval. A source that is authoritative but outdated may lose visibility to a newer page that answers the same question more directly.
Authority therefore depends on the platform’s editorial identity. It may be represented by an encyclopedia, a community discussion, a video, a recognized media outlet, an institutional source, or a brand’s own website.
How Search Intent Influences Source Selection
Search intent changes the type of source an AI engine is likely to retrieve.
Educational queries may favor Wikipedia, YouTube, or institutional content. Recommendation queries may surface comparison articles, community discussions, videos, and third-party listicles. Support queries may rely more heavily on Reddit discussions, brand documentation, or demonstration videos.
Navigational queries are more likely to cite brand-owned domains because the user is actively looking for a specific company, product, or website.
The seven-month analysis found that ChatGPT Search consistently favored Wikipedia for Education, Recommendations, Comparison, and Purchase queries. ChatGPT itself showed a more divided pattern, with Wikipedia leading Recommendations and Reddit frequently leading Support.
The relationship between intent and source preference explains why a domain can perform strongly for one query category and remain largely absent from another.
The Role of Crawler Access and Content Availability
AI platforms cannot reliably cite content they cannot access.
Crawler restrictions, robots.txt directives, paywalls, rendering problems, and technical accessibility can all influence whether a page enters an AI platform’s retrievable source set.
The Amazon and Walmart example illustrates this relationship. Amazon restricted numerous AI user agents, including OpenAI crawlers. As Amazon’s presence in ChatGPT citations declined, Walmart’s citation share increased.
Amazon continued to perform strongly in Google AI Overviews because it allowed Googlebot while blocking Google-Extended, which is associated with other Google AI uses. This produced different citation outcomes across ChatGPT, Gemini, AI Mode, and AI Overviews.
Crawler policy is therefore not only a technical issue. It can reshape the competitive source set available to an AI platform.
How Citation Patterns Compare by AI Platform
The combined data does not identify one universally dominant source. Instead, it reveals platform-specific tendencies that vary further by intent and time period.
ChatGPT Citation Patterns
ChatGPT showed the strongest association with Wikipedia in the August 2024 to June 2025 dataset.
Wikipedia accounted for 7.8% of all tracked ChatGPT citations and 47.9% of citations among its top 10 sources. Reddit ranked second, followed by Forbes, G2, TechRadar, NerdWallet, Business Insider, the New York Post, Toxigon, and Reuters.
Other research suggests ChatGPT citations align strongly with Bing results, reinforcing the importance of visibility in Microsoft’s search ecosystem.
However, ChatGPT should not be treated as identical to ChatGPT Search. The seven-month study found that ChatGPT’s source preference changed by intent. Wikipedia led Recommendation queries, while Reddit often led Support queries.
This suggests that ChatGPT visibility can depend on a combination of brand recognition, training data, search retrieval, source authority, and the type of answer being requested.
Google AI Overviews Citation Patterns
In the 680-million-citation dataset, Reddit was the leading source for Google AI Overviews at 2.2% of total citations. YouTube followed at 1.9%, with Quora, LinkedIn, Gartner, NerdWallet, Forbes, Wikipedia, Business Insider, and Medium also appearing in the top 10.
Unlike ChatGPT’s concentration around Wikipedia, Google AI Overviews displayed a more distributed source mix.
A later intent-based study found that YouTube was the leading source across five of seven intents during the September 2025 to March 2026 period. Navigational prompts were the major exception, with brand-owned domains taking the leading position for much of the study period.
The difference between the studies may reflect changes over time, differences in query composition, or the distinction between overall citation volume and intent-level leadership.
Google AI Mode and Gemini Citation Patterns
Google AI Mode and Gemini should not be grouped together with Google AI Overviews as one citation channel.
Google AI Mode consistently directed Purchase queries toward Google-owned properties, including services such as Google Shopping and Google Maps. For Education queries, it shifted toward institutional sources for several months before returning to YouTube.
AI Mode was also the only engine in one dataset to cite LinkedIn as the leading source for Education intent.
Gemini displayed a more stable YouTube preference. YouTube led across every intent in the seven-month analysis and held the top position for Support queries throughout the entire study period. Brand-owned and media domains appeared more often for Navigational queries.
These findings demonstrate that shared ownership does not produce shared citation logic.
Perplexity Citation Patterns
Perplexity is strongly associated with real-time retrieval and visible source linking.
In the August 2024 to June 2025 dataset, Reddit represented 6.6% of all Perplexity citations. Among its top 10 sources, Reddit held a 46.7% share, followed by YouTube at 13.9%.
Other leading sources included Gartner, Yelp, LinkedIn, Forbes, NerdWallet, TripAdvisor, G2, and PCMag.
However, the seven-month intent-based analysis found that YouTube led Perplexity’s Education and Recommendations queries every month.
The two findings can coexist. Reddit may dominate Perplexity’s leading source set in aggregate while YouTube performs better for particular intents.
Claude Citation Patterns
The supplied material presents a less consistent picture of Claude.
One source describes Claude as conservative, technically focused, and dependent on formal authority. Its citation system uses inline links, while its Citations API allows answers to be grounded in provided documents. Early testing cited in that source associated the API with reduced source hallucinations and more references per response.
The seven-month study contained only two months of Claude data. During that limited period, Claude did not place YouTube, Wikipedia, or Reddit in any of the tracked leading citation positions. It instead favored brand domains, institutional sources, and compliance-grade material.
Another source states that Claude uses Brave Search and may cite smaller publications, reviews, and social sources more frequently than competing models.
Because these findings come from different methodologies and limited datasets, they should not be forced into one universal rule. The strongest combined signal is that Claude may reward precise, well-sourced, technically credible content, particularly in regulated or specialist categories.
AI Platform Citation Comparison Table
| Platform | Prominent source tendencies in the supplied data | Important qualification |
|---|---|---|
| ChatGPT | Wikipedia, Reddit, established media | Source preference changes by intent and browsing behaviour |
| ChatGPT Search | Wikipedia and reference-style content | Separate retrieval product from standard ChatGPT |
| Google AI Overviews | Reddit, YouTube, Quora, LinkedIn | Later intent-level data shows stronger YouTube leadership |
| Google AI Mode | Google-owned properties, institutions, LinkedIn, YouTube | Preferences shifted by intent and month |
| Gemini | Strong YouTube preference | Navigational prompts provide more room for brand-owned domains |
| Perplexity | Reddit and YouTube | Aggregate and intent-level findings produce different leaders |
| Claude | Institutional, technical, primary, niche, and formal sources | Available studies disagree and include limited observation periods |
Which Sources AI Platforms Cite Most Often
The leading source depends on the platform and dataset, but several source categories appear repeatedly: encyclopedic websites, community platforms, video platforms, media publications, review sites, institutional domains, and brand-owned pages.
Top-Cited Domains by Platform
Wikipedia is most strongly associated with ChatGPT and ChatGPT Search.
Reddit appears prominently in ChatGPT, Google AI Overviews, and Perplexity datasets, particularly for support, product discussion, and community-driven information.
YouTube performs strongly across Perplexity, Google AI Overviews, and Gemini, particularly for education, recommendations, purchasing, and support.
LinkedIn appears in Google AI Overviews and Google AI Mode data, while review and comparison platforms such as G2, Yelp, TripAdvisor, Gartner, and NerdWallet also appear among leading sources.
Claude may place more emphasis on brand, institutional, compliance, technical, and niche media sources, although the findings remain less stable.
Citation concentration varies substantially by platform.
Within the first study’s top 10 source sets, Wikipedia held nearly half of ChatGPT citations. Reddit held almost half of Perplexity’s. Google AI Overviews distributed citations more evenly among Reddit, YouTube, Quora, LinkedIn, and other domains.
A highly concentrated platform creates a different competitive environment from a diversified one. When one source dominates, brands may need to earn visibility through that source’s ecosystem. When citations are more distributed, multiple content and distribution channels may contribute.
Citation Patterns by Domain Type and TLD
The TLD analysis in the first study found that .com domains accounted for more than 80% of ChatGPT citations.
The .org extension represented 11.29%, while country-specific domains such as .uk, .au, .br, and .ca collectively represented a smaller but meaningful share.
Technology-focused extensions including .io and .ai also appeared in the dataset.
These figures suggest that established commercial domains remain dominant, but TLD alone does not determine citation success. Content quality, authority, relevance, structure, and accessibility remain more direct factors.
Differences Across Websites, Forums, Videos, and Social Platforms
Different content formats serve different citation needs.
Wikipedia provides structured entity definitions and reference-style summaries. Reddit provides first-person experience, peer discussion, troubleshooting, and community consensus. YouTube provides demonstrations, explanations, reviews, and visual guidance.
Journalistic websites provide independently produced reporting and third-party validation. Review platforms supply comparative and reputation-based information. Brand-owned domains provide official product, company, pricing, support, and navigational information.
An effective strategy should therefore consider both topic coverage and format coverage.
Citation Patterns Also Vary by Industry and Search Intent
Platform-level data becomes more useful when it is segmented by category and intent.
A source that is influential in apparel may have little impact in transportation, healthcare, manufacturing, or enterprise software. Similarly, a source that performs strongly for Support queries may not lead Purchase or Navigational prompts.
Why There Is No Universal Top Source
One source reported that Reddit citation share grew by at least 73% across the tracked period and more than doubled in some industries.
However, the same analysis found large category differences. Reddit represented approximately 10% of apparel citations in January but only 2% in transportation and logistics.
Platform differences were equally large. Reddit held more than 5% share on ChatGPT in the cited period but only 0.1% on Gemini.
A brand using the aggregate Reddit growth figure without checking its own category and platform mix could therefore invest in a channel that has little influence on its customers’ AI research journey.
How Brand Categories Produce Different Citation Results
AI engines select sources partly according to the information needed within a category.
Healthcare and finance queries may favor institutional or compliance-grade content. Consumer product queries may rely more heavily on reviews, videos, comparisons, and community experiences. Local queries may surface maps, directories, review platforms, and local media.
Brand category affects which sources are trusted, which formats are useful, and which prompts customers ask.
For that reason, brands should use broad citation studies as directional evidence and validate the findings against their own query set.
Informational, Commercial, and Transactional Query Differences
Informational prompts often reward definitions, research, guides, and educational videos.
Commercial prompts frequently surface comparisons, reviews, listicles, media coverage, and community discussions. One cited analysis found that listicles were particularly prominent for commercially focused prompts, with third-party listicles receiving considerably more citations than self-promotional versions.
Transactional and Purchase prompts may favor ecommerce pages, product databases, videos, Google properties, or comparison sources.
Navigational prompts are the clearest opportunity for first-party content because the user is already looking for a specific brand or destination.
AI Citation Preferences Change Over Time
AI citation patterns are not fixed.
Platforms update retrieval systems, change indexes, alter ranking logic, introduce shopping or search features, and expand the types of sources they can access. Source preferences can therefore shift over weeks or months.
Source Growth Does Not Affect Every Brand Equally
A platform-wide increase in citations from Reddit, YouTube, or another domain does not produce equal gains for every industry.
Growth may be concentrated in categories where users value peer experience, visual demonstrations, or community discussion. Other sectors may continue to favor institutional, technical, or first-party sources.
Brands should evaluate changes relative to their own vertical and query set rather than reacting to the headline growth rate alone.
Citation share shows how visible a source or brand is at a particular point in time.
Volatility shows whether that visibility is stable.
A source may hold a large share but be declining. Another may have a smaller share while growing quickly. Sudden changes in leading sources can reveal that an AI engine is redefining what it considers useful for a particular intent.
The seven-month analysis argues that these shifts provide earlier strategic signals than citation share alone.
How Platform Updates Can Reshape Source Visibility
Platform updates may affect citation diversity, preferred formats, and the relationship between AI results and traditional rankings.
By January 2026, Google AI Mode was reportedly citing 143% more unique domains than Google AI Overviews, even though the gap had been much smaller two months earlier.
Crawler-policy changes can also reshape visibility. Amazon and Walmart’s changing ChatGPT citation positions show how technical access decisions can alter the competitive source set without any corresponding change in traditional SEO rankings.
What Different Citation Patterns Mean for Brands
The central business implication is that AI visibility must be managed as a multi-platform discipline.
A brand may rank well in Google, appear in ChatGPT answers, earn Perplexity citations, and remain nearly absent from Gemini or Claude. Visibility on one platform does not automatically transfer to another.
The Business Impact of Being Cited by AI Platforms
Citations can generate referral traffic by giving users a direct path from an AI response to a website.
The supplied sources report positive conversion effects from AI-referred traffic, although the size of the effect varies considerably. One analysis cited a 2.4-times conversion rate compared with conventional search traffic.
Another reported a 23-times conversion rate but acknowledged that this result was an outlier and that other studies showed more conservative improvements.
The most defensible conclusion is that AI referrals may attract high-intent visitors, but brands should measure their own conversion rates rather than relying on a universal benchmark.
Why Brands Need Platform-Specific Visibility Strategies
Each AI platform favors a different combination of sources, formats, and trust signals.
A Wikipedia-oriented strategy may improve ChatGPT Search visibility but do little for a YouTube-led Gemini result. A Reddit strategy may support Perplexity or ChatGPT Support visibility while having limited impact on Gemini.
Similarly, technical institutional content may align with Claude but perform less strongly in systems that favor peer discussion or video.
A shared foundation is still valuable. Clear structure, reliable sourcing, entity consistency, and direct answers can support retrieval across multiple engines. The final layer, however, should reflect the platform’s specific citation identity.
How AI Citations Can Influence Traffic, Trust, and Conversions
A citation can affect several stages of the customer journey.
It can introduce a source during early research, reinforce trust during comparison, provide evidence for a product recommendation, and create a direct visit during purchase consideration.
Citations from third-party media, reviews, or community discussions may also influence brand perception even when the linked page is not owned by the brand.
This is why owned content strategy and earned visibility should be treated as connected parts of the same AI search strategy.
How to Improve Visibility Across AI Platforms
There is no single tactic that guarantees citations everywhere. The strongest approach combines clear content structure, platform-specific distribution, third-party authority, technical accessibility, entity consistency, and ongoing measurement.
Match Content to Each Platform’s Source Preferences
Before producing more content, brands should analyse the leading sources for their priority platform, industry, and query intent.
Reference-style prose may align with ChatGPT Search. Videos may perform more strongly in Perplexity, Gemini, and Google AI Overviews. Formal technical material may be better suited to Claude. Authentic community discussions may influence Reddit-heavy citation environments.
The goal is not to imitate a source superficially. It is to understand the information format the engine repeatedly treats as citation-worthy.
Create Clear, Quotable Answer Sections
AI retrieval systems often extract sections rather than entire articles.
Pages should therefore contain self-contained passages that answer specific questions without requiring extensive surrounding context.
Clear H2 and H3 headings, concise definitions, direct opening statements, comparison tables, bullet points, and focused FAQ sections can make relevant passages easier to retrieve.
One source recommends opening content with a short bottom-line-up-front summary that identifies the entity and directly answers the main question. It also recommends structuring content into passage-sized sections suitable for retrieval.
Strengthen Entities, Schema, and Topical Relationships
AI systems need to understand what an organisation, product, person, or concept is and how it relates to other entities. A clear model of SEO entities helps search and AI systems distinguish those subjects, their attributes, and their relationships.
Brands can reinforce these relationships through consistent naming, clear definitions, descriptive internal links, and structured data such as Organization, Product, Article, and FAQ schema.
The page copy should also state important relationships directly. For example, it should clearly identify what a company does, which market it serves, which product belongs to the company, and how the product differs from alternatives.
Schema does not replace strong content, but it can help clarify entity relationships already present on the page.
Build Third-Party Validation and Brand Authority
AI systems frequently rely on third-party sources to confirm brand relevance and credibility.
Useful third-party signals may include journalistic coverage, expert references, reviews, industry directories, customer discussions, comparison articles, and community participation.
The goal should not be to manufacture mentions. Authentic, useful, independently verifiable coverage is more likely to support durable visibility.
Reddit citations, for example, frequently point to individual discussion threads rather than corporate profiles or subreddit pages. That suggests the citation opportunity lies in useful conversations, not merely maintaining an account.
Keep Important Information Current and Consistent
Freshness matters when AI platforms retrieve live or recently indexed information.
Important pages should show accurate publication or update dates, especially when they cover pricing, product features, statistics, regulations, or market changes.
Core brand facts should also remain consistent across the company website, LinkedIn, Wikipedia, review platforms, directories, and media profiles.
Conflicting information can weaken entity confidence because an AI system may be unable to determine which version is accurate.
Diversify Content Formats and Distribution Channels
Topic coverage alone is not enough when platforms prefer different formats.
A single research finding can be developed into a reference article, short answer sections, a comparison table, a video explanation, a visual summary, a technical document, and a community discussion.
This does not mean publishing identical content everywhere. Each format should serve the expectations of the channel and audience.
The seven-month analysis concluded that format coverage is necessary to enter the citation set across a broader range of AI engines.
How to Measure AI Citation Performance
Traditional rankings and organic traffic do not provide a complete picture of AI visibility.
Brands need a measurement framework that tracks whether they are mentioned, whether they are cited, which pages receive citations, where those citations appear, and whether the resulting traffic produces business value.
Citation rate measures the percentage of tracked prompts in which a brand earns at least one citation.
A brand can create a representative query set based on the questions customers ask during research, comparison, purchase, onboarding, and support. The same prompts should then be tested repeatedly across relevant AI platforms.
AI share of voice compares a brand’s citations with the total citations earned by competitors for the same query set.
This can reveal whether an AI platform associates the brand with the category, even when absolute citation numbers are low.
AI Referral Traffic and Conversion Performance
Referral traffic should be segmented by source wherever possible.
Analytics and customer relationship management systems can be used to track visits, trials, inquiries, demos, purchases, and revenue associated with ChatGPT, Perplexity, Claude, Gemini, and other AI services.
Conversion rates should be measured separately for each platform because audiences and query types may differ.
The business goal is not simply to maximize mentions. It is to determine which visibility leads to qualified traffic, pipeline, and revenue.
Citation Changes by Platform, Topic, and Competitor
Citation monitoring should record more than a single monthly total.
Brands should segment results by:
- AI platform.
- Prompt or query
- Search intent
- Topic or product category
- Cited domain
- Cited page
- Content format
- Competitor
- Mention versus citation
- First-party versus third-party source
- Gain, loss, or change over time
This approach makes it possible to identify whether a decline is brand-specific, category-wide, or caused by a broader platform shift.
AI Citation Data Should Inform Strategy, Not Create Universal Rules
The combined evidence shows that AI citation patterns differ across platforms, but it does not support a universal source hierarchy.
Wikipedia may dominate one ChatGPT dataset. Reddit may dominate one Perplexity dataset. YouTube may lead specific intents across Perplexity, Gemini, and Google AI Overviews. Claude may favor technical or institutional sources, but the available studies remain less consistent.
The practical lesson is not that every brand should immediately prioritize Wikipedia, Reddit, or YouTube. It is that every brand should identify the platforms its audience uses, the prompts that shape its customer journey, and the source types those platforms currently reward.
Aggregate research is valuable for identifying possible patterns. Brand, category, platform, and intent-level data is required to turn those patterns into a strategy.
AI visibility is therefore not one optimization problem. It is a network of relationships between platforms, retrieval systems, source types, search intents, content formats, crawler policies, entities, and business outcomes.
Brands that understand those relationships will be better positioned to create content that is not only discoverable, but citation-worthy.
His academic background includes a master's degree in economic growth and innovation, as well as studies in statistics and the philosophy of language. Johan is fluent in Swedish, English and Spanish.
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