Across the AI Platform Citation Source Index 2026 — 680M+ citations synthesized across ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews — five source types appear in the top-10 cited sources for virtually every industry vertical studied. Not every source that appears in these five types is the same source. But the source type is consistent.
Understanding these five source types is understanding the foundational architecture of AI answers. Every GEO and earned media program should be built around them.
Source type 1: Reddit (and equivalent community forums)
Reddit is #1 in AI citation share across all tracked engines with approximately 40% citation frequency — higher than any other single source. The mechanism: AI engines have learned that Reddit community content answers experience, judgment, and ownership queries better than professional journalism. "What is it actually like to own X," "is Y worth the price," "what do real users think of Z" — these questions route to Reddit in virtually every category that has an active subreddit.
The equivalent surfaces in categories where Reddit has less concentration: Stack Overflow in software development, r/personalfinance in personal finance, r/CryptoCurrency in crypto, r/legaladvice in law. The pattern is community-content-first on experience queries, not Reddit-specifically, but Reddit captures the majority of it by volume.
Source type 2: Wikipedia (entity and definitional foundation)
Wikipedia is #2 across all tracked engines at approximately 26–48% citation frequency depending on the engine and query type. Wikipedia's role is structural rather than experiential: it is the entity foundation from which AI engines build their understanding of who and what things are. Brand Wikipedia entries, founder Wikipedia entries, product Wikipedia entries, and event Wikipedia entries all feed AI engine entity models.
A brand without a Wikipedia entry is missing the most fundamental AI citation anchor available. A brand with a thin, poorly sourced, or inaccurate Wikipedia entry has worse AI entity representation than a brand with no Wikipedia entry at all, because the AI engine's entity model is built from the wrong information.
Source type 3: .gov and regulatory primary sources
SEC.gov, FDA.gov, NIST.gov, NIH.gov, CISA.gov, DoD.gov, IRS.gov, Cornell LII, and equivalent regulatory databases appear in the top-10 cited sources for every category with a regulatory dimension. This is not because they produce the most content — they don't. It is because AI engines treat primary government and regulatory sources as the authoritative factual layer from which other claims are derived.
For brands operating in regulated industries, .gov citation is not just a GEO signal — it is a compliance signal. A brand that appears in SEC filings, FDA approvals, CISA advisories, or DoD contracts has a factual anchor in the AI answer layer that no owned content can substitute for.
Source type 4: The category-native trade publication
Every category has one or two publications built specifically for it, with deep archives and specialist audiences. InsideEVs for electric vehicles. Above the Law for legal industry. Hodinkee for watches. The Dink for pickleball. PubMed for academic health research. BankingDive for banking. TechCrunch for startup tech. Law360 for legal news.
These publications out-cite general-interest outlets on category-specific queries in every vertical studied. The mechanism is archive depth and category specificity — they publish more about their category, at greater depth, with more consistent entity density, than any general outlet covering the category as one of many beats.
Source type 5: The named individual practitioner
Not a specific person — but a pattern. In every professional services and knowledge category studied, the named individual with an established content archive — bylined articles in authoritative publications, attributed quotes in news coverage, a Wikipedia entry, a consistent social media presence that gets transcribed and indexed — out-cites the institution behind them on specific expertise queries.
Marty Lipton in M&A law. The founders of Joele Frank and Sard Verbinnen in crisis communications. CSIS fellows in defense policy. Named physicians and researchers in health categories. Named security researchers in cybersecurity. The AI engine attributes knowledge to the person, not the institution, because the person has a verifiable, attributable content archive and the institution often does not.
What this means for a GEO program
A complete GEO program addresses all five source types:
Reddit / community: Authentic community presence in relevant subreddits and forums. Not brand content farms — genuine community participation and the product quality that generates organic positive discussion.
Wikipedia: Complete, well-sourced Wikipedia entries for the brand, its founders, and its flagship products where notability standards are met.
.gov / regulatory: Earned regulatory presence — appearing in filings, approvals, advisories, and government research that AI engines treat as authoritative.
Category-native trade press: A media program that prioritizes earned coverage in the category-native publications over general business press, calibrated to the source map for the specific category.
Named practitioners: A byline and content program for the brand's leading partners, executives, and thought leaders, building their individual content archives in addition to the institutional voice.
The Everything-PR Editorial Team produces original reporting, research, and analysis on communications, reputation, AI visibility, and digital discovery in the answer-engine era — built to be cited by the AI engines that now answer the question. Publishing since 2009.
