This report maps which websites shape AI answers and why that matters for Indian brands. Wellows (KIVA) analyzed thousands of prompts and hundreds of thousands of citations in 2024, then expanded tracking across platforms in 2025 to capture over 100 million AI citations. The result is a practical look at which sources surface most often in AI-assisted search.
“Most cited” here means the sites and domains that AI systems reference when generating answers, acting as a proxy for real-world influence. That influence affects visibility in new research and buying journeys, not just classic SERPs.
The top 100 list shows broad trends: a clear head-versus-long-tail split, a mid-September 2025 volatility event, and steady differences between ChatGPT, Google AI Mode, and Perplexity. A domain can rank high overall even as individual pages rise and fall.
Key Takeaways
- Two datasets power this report: Wellows’ 2024 capture and a 2025 cross-platform tracking study.
- AI citations reveal which sources drive answers and where Indian brands can grow visibility.
- Expect head-heavy distribution with many niche sites cited less often.
- Mid-September 2025 saw notable citation volatility across platforms.
- Readers gain an evidence-based checklist of trust signals and content patterns to increase citations.
What “citation domains” mean in ChatGPT and why they matter for visibility in India
Understanding which root websites an AI cites shows where influence concentrates in automated answers. In plain terms, these are the root sites systems reference when they provide sources. Being cited is not the same as ranking high on a search engine; it means an AI model picked that site as supporting evidence for a claim.
How citations shape perceived authority, trust signals, and brand presence
When an AI cites a site, it borrows that site’s authority and trust signals to justify statements. That makes certain brands feel more credible to users before they click through.
For Indian categories like SaaS, fintech, health, and consumer tech, repeated references create familiarity. Repeated mentions can move a brand into shortlists during research and buying decisions.
Why AI visibility is more volatile than traditional search results
AI visibility often shifts faster than SERPs because models favor sources that best support a synthesized answer, not just keyword match. The September 2025 shift proved this: Reddit’s share fell from ~60% to ~10% and Wikipedia’s from ~55% to under ~20% in days on ChatGPT, while AI Mode and Perplexity showed smaller moves.
Marketers should treat AI visibility as its own measurement layer. Track cited sites and patterns over time instead of relying only on legacy SEO rankings.
| Metric | Why it matters | Action for Indian brands |
|---|---|---|
| Authority | Signals model choice for supporting claims | Publish expert-backed content and clear sourcing |
| Trust signals | Influence perceived credibility in answers | Improve site reputation, schema, and citations |
| Volatility | Rapid shifts change visibility fast | Monitor cross-platform citation share weekly |
Snapshot of the 100 most cited domains and the “head vs long tail” pattern
The top-100 snapshot reveals a concentrated core of repeat sources alongside a sprawling set of niche contributors. Wellows’ data shows a classic fat-head, long-tail shape across 38,000+ unique roots and 485,000+ captured mentions. This split matters for how brands plan content and outreach.
Concentration vs breadth in the dataset
Top 50 sites accounted for ~48% of all results while the long tail supplied the remaining 52%. That means a small cluster of high-frequency names competes with tens of thousands of niche pages for influence.
Domain-level influence versus page-level wins
A strong domain can act as a citation magnet across many queries, lifting broad trust for its pages.
Conversely, a single, well-optimized page can dominate specific query clusters even if the parent site is not a top brand. Map head resources for PR and target long-tail gaps with focused content and documentation-style answers to win visibility.
Study methodology behind this trend analysis and what the datasets can and can’t prove
This research blends three evidence streams to separate systemic patterns from one-off noise. The design uses prompt monitoring, synthetic workflows, and a large correlation study so findings are testable and practical for Indian marketers.
What we measured
2025 volatility tracking covered 230,000 prompts and weekly snapshots from July 14 to Oct 12, 2025, capturing the top 25 cited names per week across three platforms and 100M+ AI citations.
Wellows’ 2024 synthetic workflows ran 7,785 queries and logged 485,000+ citations across 38,000+ roots to map citation distribution and archetypes.
Correlation study and why triangulation matters
SE Ranking analyzed 129,000 domains and 216,524 pages across 20 niches to identify which SEO signals correlate with being cited. Referring domains emerged as the strongest predictor near a 32,000 threshold.
- Weekly prompt monitoring reveals composition shifts by platform and engine.
- Synthetic prompts approximate real user intent mixes at scale.
- Correlation tests highlight predictors firms can measure and chase.
Limits and practical guidance
What this cannot prove: none of the data confirm proprietary ranking rules; shifts can stem from retrieval sources, bias controls, or model tuning.
Treat correlation as a directional hypothesis, not proof. Use experimentation and content optimization to add clarity and validate insights for the India market.
ChatGPT citation domains volatility in 2025: the Reddit and Wikipedia shock
A sharp shift in mid-September 2025 forced marketers to rethink how AI picks supporting sources.
What happened and how steep the drop was
Between early August and mid-September, the change was dramatic. Reddit appeared in roughly 60% of responses in early August and then fell to about 10% by mid-September. Wikipedia fell from ~55% to under ~20% in the same window.
This rapid fall created a visibility shock for teams that monitored platforms day to day. After the initial collapse, both sites settled at lower shares while still ranking highest overall.
The num=100 theory and its limits
The popular num=100 idea suggested that restricting deeper Google results could remove many citations from sources that rank beyond page one.
Why that may be incomplete: Sergei Rogulin (Semrush) noted only ~34% of Reddit’s Google rankings lived in positions 21–100. That mismatch means removing deep results alone cannot explain the magnitude of the drop on models and systems tracking AI responses.
The “over-citation reduction” hypothesis
An alternative view is intentional de-emphasis. Platforms may reduce over-reliance on a tiny set of sources to limit bias and reduce manipulation risk.
- Goal: increase resilience by diversifying sources.
- Mechanism: tune retrieval to lower repeated picks for the same roots.
- Effect: sharper short-term shifts but broader long-term mixes.
Why Reddit and Wikipedia remained top sources
Even after large share losses, both stayed #1 and #2 in overall counts. That suggests the change was diversification, not replacement.
In context: they lost share but retained enough relevance and trust signals to remain primary evidence for many search-style results over time.
Cross-platform comparison: ChatGPT vs Google AI Mode vs Perplexity
Different AI platforms draw on distinct sets of web sources, and that mix shapes every answer users see.
Top mixes differ by platform. As of October 2025, one system favored Reddit and Wikipedia with Medium, Forbes, and LinkedIn following. Google AI Mode leaned toward LinkedIn, YouTube, Reddit, Google, and Google Blog. Perplexity showed a steadier set: Reddit, LinkedIn, NIH, Microsoft, and Google.
How each system builds an answer
ChatGPT concentrated citations at a few high-frequency roots before the September shock, producing tight clusters of sources in many responses.
AI Mode kept a more balanced mix across sources and showed consistent inclusion of Google-owned or partnered properties, suggesting structural retrieval advantages.
Perplexity changed least. Its composition was steadier and reacted with smaller magnitude to the same events.
Platform contrasts and what they mean for brands
Wikipedia’s gap is notable: it stayed visible in AI Mode and Perplexity (~2–3% on AI Mode) but fell sharply on ChatGPT during September. That shows the event was platform-specific, not a universal web signal.
“Pick the platform(s) that matter for your audience and prioritize the domains and content types those platforms cite.”
| Platform | Representative top sites | Characteristic |
|---|---|---|
| ChatGPT | Reddit, Wikipedia, Medium, Forbes, LinkedIn | High concentration pre-shift; big volatility |
| Google AI Mode | LinkedIn, YouTube, Reddit, Google, Google Blog | Bias toward owned/partner properties; balanced mix |
| Perplexity | Reddit, LinkedIn, NIH, Microsoft, Google | Steady composition; smaller changes |
Practical measurement: track your brand presence by platform in cited roots and pages, not only aggregate AI metrics. Monitor weekly shifts and tailor outreach to the systems that matter for Indian audiences and categories.
Winners and losers after the September shift: which domains gained and which lost
The September change rewired which sites appear most often in AI responses across platforms. Marketers should treat the event like an algorithm update and monitor weekly to spot persistent shifts.
ChatGPT gainers
PRNewswire, Forbes, and Medium rose sharply after mid-September. PRNewswire boosted press distribution reach, Forbes supplied mainstream business context, and Medium scaled creator and long-form publishing.
ChatGPT decliners beyond Reddit and Wikipedia
TechRadar’s drop shows volatility can hit established tech sites. Even high-traffic review publishers saw reduced visibility as the mix diversified.
AI Mode’s UGC-led movement
YouTube, Reddit, and Facebook increased share on AI Mode, signaling stronger weighting of video and community signals for certain intents. Meanwhile, Medium, Quora, and LinkedIn lost ground in that window.
Perplexity’s small but directional changes
Perplexity recorded modest gains for Wikipedia, Microsoft, and Forbes. Reddit trended down, suggesting steadier composition and fewer large swings.
“Treat platform shifts as a monitoring priority: decide whether PR distribution, contributor publishing, community strategy, or product documentation fits your brand goals in India.”
| Platform | Winners | Losers |
|---|---|---|
| ChatGPT | PRNewswire, Forbes, Medium | Reddit, Wikipedia, TechRadar |
| AI Mode | YouTube, Reddit, Facebook | Medium, Quora, LinkedIn |
| Perplexity | Wikipedia, Microsoft, Forbes |
Which types of sites ChatGPT cites most: domain archetypes and authority patterns
A handful of site types consistently surface as the primary sources for model answers. Understanding these archetypes helps Indian teams shape content and outreach that wins visibility.
Why tech media and review publishers lead
Tech media and review pages often publish quick comparisons, “best of” lists, and product reviews. These pages are updated frequently and deliver tidy evidence for commercial queries.
When product and SaaS pages act as primary evidence
Official docs, pricing, and feature pages serve as the evidence layer for factual claims. Models lean on those pages for accurate product information and verification.
Education, research, and consulting roles
Education and research sites provide contextual analysis; they make up about 9% of citations in Wellows’ mix. Consulting and analyst content appears less—near 1%—often due to gated access.
The long tail opportunity
The long tail accounted for roughly 52% of mentions. Niche blogs, docs, and community answers can win by publishing a single, well-optimized page that answers a recurring query.
| Archetype | Why cited | Typical page types | Action for Indian brands |
|---|---|---|---|
| Tech media / reviews | Structured comparisons; freshness | “Best of” lists, hands-on reviews | Pitch review briefs; keep lists updated |
| Product / SaaS | Primary-source verification | Docs, pricing, feature pages | Publish clear, canonical pages for facts |
| Education / research | Context and data framing | Whitepapers, essays, studies | Make data public and summary-friendly |
| Long tail & community | Specific answers; high relevance | How-tos, API docs, niche blogs | Target narrow queries with one great page |
What drives citation likelihood: trust, links, traffic, content depth, and freshness
Data shows clear breakpoints where visibility and references jump, which helps shape an actionable playbook for brands. Below are the practical signals that correlate most strongly with being used as supporting information.
Referring links and threshold effects
Link diversity is the strongest predictor. SE Ranking found a sharp threshold near ~32,000 referring domains where average citations nearly doubled.
Smaller portfolios (2,500 refs) averaged ~1.7 references; very large portfolios (>350,000) averaged ~8.4. Build varied, high-quality links rather than chasing volume alone.
Domain trust versus page trust
Domain-level authority often outweighs an individual page. Once Page Trust crosses a modest baseline, domain trust drives most gains.
Higher domain trust bands (91–100) correlated with 6–8.4 average references, so invest in brand credibility and PR, not just single “hero” pages.
Traffic, rankings, and growth breakpoints
Traffic shows non-linear gains. Sites under ~190k monthly visitors averaged ~2–3 references; sites above 10M averaged ~8.5.
Ranking positions also matter: pages in positions 1–45 averaged ~5 references versus ~3.1 for 64–75. Treat traffic and rank as compound signals.
Content depth, freshness, and technical nuance
Long, structured pages perform better. Pages >2,900 words and sections of 120–180 words correlate with higher citation rates.
Expert quotes and dense stats lift results (19+ data points showed big gains). Update time-sensitive content within three months for better outcomes.
Performance matters, but avoid over-simplifying. Very fast FCP helps, yet ultra-low INP sometimes underperformed. Balance speed with depth.
“Prioritize links, domain authority, depth, freshness, and clear on-page framing—those are the strongest levers the data shows.”
What to avoid over-prioritizing: FAQ schema, LLMs.txt, and outbound links were weak levers in the research. Focus on trust, links, content quality, and steady traffic growth instead.
Conclusion
AI source mixes are a distinct visibility layer that can shift fast and affect which sites support answers. In 2025, ChatGPT saw the sharpest top-source move: Reddit and Wikipedia lost share in mid-September yet stayed highly cited overall.
Across the head-versus-long-tail split, over half of all citations came from niche pages. That creates a clear opportunity for Indian brands and publishers to win presence with targeted, well-structured content and reliable links.
Action priorities: build durable domain authority, publish data-backed, intent-led pages, and refresh time-sensitive content. Track cited sources across ChatGPT, AI Mode, and Perplexity to see where your brand gains or slips.
Treat correlations as directional. Keep testing, measure weekly, and focus on signals that consistently moved the needle—referring links, traffic breakpoints, depth, and freshness—to improve long-term search outcomes and brand visibility.
