AI Doesn't Have Opinions. It Has Sources.

Reddit, Wikipedia, YouTube, LinkedIn — the studies mapping millions of AI citations agree on the headliners. They also show that every engine reads a different web, that your vertical has its own map, and that the map gets redrawn overnight.

When an AI assistant recommends a brand, it isn't expressing a preference. It's synthesizing one — from reviews, comparisons, forum threads and articles it has read about the market. We've argued before that mentions are won across surfaces you don't own. The obvious next question: which surfaces? Where does the machine actually read?

That question is no longer speculative. Between mid-2025 and early 2026, at least five independent research teams tracked millions of AI citations across ChatGPT, Gemini, Perplexity, Google's AI Overviews and AI Mode. Put their findings together and you get something genuinely useful: a source map of the answer layer — the supply chain of the machine's opinion.

Four things stand out. Each one changes how you should spend your PR and content budget.

1. The headliners are real — but smaller than the headlines

Every study lands on the same names at the top: Reddit, Wikipedia, YouTube, LinkedIn, trailed by editorial brands like Forbes and the big review platforms. Similarweb's analysis of nearly 600,000 citation events (Jan–Feb 2026, US) puts Wikipedia at 13.2% and Reddit at 12.0% of ChatGPT's citations.

Now look at the other side of that ledger. On most platforms, the top domain rarely exceeds 5% of total citations — and everything outside the headliners spreads across thousands of domains. The answer layer is not winner-take-all; it's a long tail with a famous head. Which means the niche industry blog, the specialist reviewer and the category community you've been ignoring are not rounding errors. Collectively, they are most of the map.

2. Every engine reads a different web

Ask four engines the same buying question and you get four different bibliographies. The starkest example is Reddit. In January 2026, Reddit supplied 24% of Perplexity's citations. On Google's Gemini, it supplied 0.1% (Tinuiti). Same forum, same month — a 240× difference in how much two engines trust it. In between: Google's AI Overviews drew 44% of their social citations from Reddit, while ChatGPT held around 5% overall.

The pattern generalizes. ChatGPT leans on commerce, news and professional sources. Google's AI Mode favors YouTube, fan communities and Google properties. Perplexity's web-heavy retrieval elevates community and specialist content. We saw the same divergence in our Turkey e-commerce index: ChatGPT recommends market leaders, Perplexity foregrounds category specialists.

The strategic consequence is the same one we keep returning to: a mention on one engine is not a mention on all — because each engine is reading a different web about you.

3. The map is redrawn overnight, without notice

Semrush tracked 230,000+ prompts and over 100 million citations for 13 weeks in 2025. Until mid-September, Reddit appeared in roughly 60% of its weekly ChatGPT citation snapshots and Wikipedia in about 55%. Then, over a few weeks, Reddit fell to roughly 10% and Wikipedia below 20% — while Forbes, PR Newswire and Medium climbed. Other engines were stable over the same period: this wasn't the web changing. It was one model quietly re-weighting whom it trusts.

No announcement, no changelog. Brands that had spent a year building Reddit presence for ChatGPT visibility woke up with a devalued asset — and most never noticed, because they weren't measuring.

Treat your source graph like a portfolio, not a monument. Concentration in one source is exposure, and drift is detectable — but only if visibility is monitored continuously rather than audited annually.

4. Your vertical has its own map

The aggregate rankings hide the most actionable finding. Scrunch's December 2025 industry breakdown shows that within verticals, the "Reddit era" looks very different:

Reddit dominates in aggregate partly because a handful of Reddit-heavy categories inflate the average. In finance, the models read NerdWallet. In healthcare, they read the NIH. If you optimize for the global map instead of your category's map, you're spending on the wrong surfaces.

The multipliers point the same way: review platforms like G2, Capterra and Trustpilot carry a 4.6–6.3× citation multiplier for brands, and YouTube — whose transcripts the models read like articles — shows the strongest correlation with AI visibility of any single source in Contently's 2026 roundup.

The playbook: find your graph, then feed it

Global source maps tell you how the answer layer works. They don't tell you where your mentions come from. That's a brand-specific, prompt-specific question — and it's empirical:

• Map your source graph. When models talk about you and your category, which third-party pages do they actually draw on? Those pages are your ranking factors now.
• Invest by vertical, not by aggregate. The right subreddit, the right review platform, the category authority — as the data shows, these beat the famous headliners in most verticals.
• Cover the engines separately. A Perplexity strategy built on community presence does nothing for Gemini. Measure each engine on the prompts that map to your revenue.
• Watch for drift. September-style recalibrations will happen again. The brands that notice in week one, not quarter three, keep their visibility.

The Lens maps exactly this: your mention share, and the source graph behind it, per engine and per prompt — so you know which pages are writing the machine's opinion of you, and which ones to feed next.

Sources

• Similarweb — AI Citation Analysis, ~600K citation events, Jan–Feb 2026
• Semrush — Most-cited domains study, 230K+ prompts / 100M+ citations, Jul–Oct 2025
• Tinuiti — Citation tracking across seven AI platforms, Oct 2025–Jan 2026
• Scrunch — Industry breakdown of most-cited AI sources, Dec 2025
• Contently — "Top 10 Sources LLMs Cite Most in 2026", synthesizing Peec AI, Profound and SE Ranking data, Apr 2026