Why AI Engines Cite Same Brands but Different Sources

If a vendor sells you "one source playbook for every AI engine," they are selling you the wrong half of the problem.

On April 24, 2026, BrightEdge AI Catalyst published Same Brands, Different Sources, a cross-engine study covering ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews across nine verticals. The headline number is the cleanest argument yet for splitting GEO strategy in two directions at once.

Pairwise top-100 brand overlap across the five engines clusters at 36 to 55 percent. Pairwise top-100 source overlap spans 16 to 59 percent. Brands agree across engines more than the sources those engines consult.

That gap reframes the entire GEO budget question. The brand-perception layer is portable. The source-presence layer is not.

What BrightEdge actually measured

BrightEdge AI Catalyst ran top-100 brand and top-100 source comparisons pairwise across five AI engines and nine verticals: B2B tech, healthcare, finance, restaurants, travel, ecommerce, and three others. For every pair of engines and every vertical, they computed the percentage of brands and sources both engines surfaced.

Two distributions came back. One tight, one wide.

Brand overlap is a 19-point spread. Source overlap is a 43-point spread. The gap between those two numbers is the strategy.

The brand spread sits inside a narrow 36 to 55 percent band. Pick any two of the five engines and somewhere between a third and just over half of the recommended brands will be the same. The source spread is more than twice as wide. Some engine pairs share less than a fifth of their top sources. Others share nearly two-thirds.

Authority-source share ranged 10 to 26 percent across engines. User-generated content share ranged 0.2 to 18 percent, a 90x variance between the lowest and highest engine. Different engines weigh authority and community evidence on completely different scales.

Five reasons the brand-source gap reframes GEO budget

The aggregate cross-engine GEO program flattens a much more interesting story underneath. Five findings inside the BrightEdge report deserve separate budget lines.

Reason #1: Brand presence is portable across engines

A 36 to 55 percent brand overlap means the work that gets your name into one engine's recommendation pool meaningfully transfers to the others. Customer reviews on G2, executive bylines in industry press, named-vendor positioning in analyst briefs: this work compounds across all five engines because every retrieval pipeline eventually consults a brand-evidence layer.

The implication for GEO budgets is that brand-perception work earns the highest cross-engine return per dollar. One investment, five payouts. That is the rare unit-economics moment in AI search optimization.

Reason #2: Source presence does not transfer

The 16 to 59 percent source spread says the opposite. The PubMed paper that gets you cited by Claude has near-zero crossover to the Reddit thread that gets you cited by Gemini. The trade-publication interview that gets you cited by Google AI Overviews has near-zero crossover to the YouTube transcript that gets you cited by ChatGPT.

Source-presence work is per-engine work. There is no shared playbook. Pretending there is one is the single most common GEO budget mistake we see in B2B SaaS engagements.

Reason #3: Google's three AI surfaces are three independent surfaces

Gemini, AI Mode, and AI Overviews are not one optimization target. BrightEdge found that Gemini shares more pairwise similarity with ChatGPT than with its own siblings AI Mode and AI Overviews.

That finding is now triple-corroborated. Otterly's April 22 study found a 50-point share-of-voice differential between AI Mode and AI Overviews on identical YouTube content. We documented the same divergence in Google AI Overviews Changed Dramatically After Gemini 3. The retrieval pipelines are separate, the source pools are separate, and the optimization tracks are separate.

Stop writing "optimize for Google AI" in client briefs. Write "optimize for Gemini, AI Mode, and AI Overviews" as three line items.

Reason #4: Authority weighting varies by an order of magnitude

The 10 to 26 percent authority-source band is a 2.6x spread. A brand strategy that leans hard on PubMed-class authority will pay off disproportionately in Claude and Perplexity. A brand strategy that leans hard on news authority will pay off disproportionately in Google AI Overviews.

The 0.2 to 18 percent UGC band is the wider story. Reddit, Quora, and forum threads are 90x more important on some engines than others. A B2B SaaS brand that ignores community channels because "our buyers don't read Reddit" is making the budget call on the wrong engine's data.

Reason #5: Vertical effects compound the engine effects

Brand and source overlaps were measured pairwise per vertical, not pooled. That means the per-engine source playbook also has to be per-vertical. The healthcare source pool on Perplexity does not look like the B2B tech source pool on Perplexity, even though it is the same engine.

A nine-vertical, five-engine optimization matrix is 45 cells. A serious GEO program decides which 8 to 12 of those cells matter for the buyer mix and concentrates investment there. The brand strategy still runs across all 45 cells. The source strategy does not.

How to split brand work from source work in your GEO plan

The diagnosis is the gap. The prescription is a four-step split that matches the gap to the budget.

Step 1: Audit cross-engine brand presence first

Run the same 50 buyer prompts through all five engines. Record which brands surface in each engine's top-five vendor mentions. Compute pairwise overlap.

If your brand sits in two of five engines, you have a brand-presence problem. Source work will not fix it. The fix is named-vendor positioning in the analyst content, customer reviews, and industry-press coverage that all five engines consume.

If your brand sits in four of five but loses one, you have a per-engine source problem on the engine you are losing. The fix is targeted source-pool entry on that engine, not broad brand work.

This split decision sits upstream of every other GEO budget call. We walk through the audit mechanic in How to Run an AI Visibility Audit.

Step 2: Map the per-engine source pool for the engine you lose

Once you know which engine you lose, map the actual sources cited on that engine for your category. Ten to fifteen prompts is enough to surface the top 30 sources. That set is your per-engine target list.

The Writesonic GPT-5.5 citation study found that roughly 30 domains capture 67 percent of citations within a topic on ChatGPT. Otterly's power-law analysis found that the top 15.8 percent of URLs capture 50 percent of citations. The pool is not infinite.

You are not optimizing for the open web on that engine. You are optimizing for entry into a 30-domain shortlist. That is a tractable list, not a hopeless one.

Step 3: Place evidence at the per-engine sources, not on your own site

Most GEO budgets get spent on owned-site content. The BrightEdge data argues for redirecting a meaningful share of that budget to placement on the per-engine sources you identified in step 2.

For ChatGPT-leaning verticals: Reddit threads, helpful Quora answers, named contributions to industry-trend journalism. For Claude-leaning verticals: peer-reviewed contributions, white papers indexed by academic search, named research in NGO and government repositories. For Google AI Overviews: comparison-content placements in trade press and news authority. For Gemini: authority-aligned research that overlaps with what ChatGPT consumes. For AI Mode: long-form structured content with deep schema coverage.

We documented the underlying mechanic in How AI Platforms Choose Which Sources to Cite. The retrieval prior is the budget allocator.

Step 4: Reset measurement to track brand and source separately

Most GEO scorecards roll brand mentions and source citations into one number called share of voice. The BrightEdge data says that is the wrong unit of measure.

Track two layers. Brand mention rate per engine, which captures the portable layer. Cited-source domain count per engine, which captures the per-engine layer. The two numbers move on different timescales and respond to different budget interventions.

If brand mention rate is rising but cited-source count is flat, brand work is doing its job but source work is not. If brand mention rate is flat but cited-source count is climbing, the source work is reaching the engine but not yet the buyer-facing recommendation pool.

The two-layer view is the only way to attribute budget correctly. We use the same split in How to Measure GEO and AI Visibility.

Why a unified brand strategy still works

A 36 percent floor on pairwise brand overlap is not a small number. Across five engines, the recommended-brand pool converges meaningfully even when the source evidence diverges. That convergence is the cross-engine compounding asset GEO buyers should anchor budget around.

Three forces drive the convergence. Authority-domain coverage of named vendors. Customer-review aggregation that surfaces in every engine's training and retrieval. Industry-analyst language that becomes the canonical vendor short-list across press and content.

Brand-perception work is the only GEO investment that pays five times. Everything else pays once per engine.

That math reframes the budget conversation. Spend on brand-perception work first. Spend on per-engine source-presence work second, only on engines where the audit shows a gap.

The order matters. Per-engine source work without underlying brand presence often fails to convert because the engine's brand-evidence layer does not yet recognize the vendor as a candidate at all.

Why per-engine source strategy is the new lever

Three independent studies in the last 60 days now agree that the source pool inside each engine is narrower and more concentrated than it was at the start of 2026.

Resoneo and Meteoria reported that ChatGPT now pulls 19 unique domains per response, down from earlier baselines. The Writesonic study confirmed 15 domains under GPT-5.5. Otterly's power-law analysis showed the top 15.8 percent of URLs capturing 50 percent of citations.

The source pool is shrinking on every engine. That makes the per-engine source-presence lever more valuable, not less. Smaller pool means winner-take-most dynamics inside each engine's source list.

The brand-perception layer stays portable. The source-presence layer inside each engine becomes a higher-stakes per-engine concentration play. Both pressure points get sharper at the same time.

This is the same compression dynamic we documented in Why ChatGPT Cites 5 Sources but Claude Cites 13. Different engines, different shortlist sizes, but the same direction of travel.

FAQ

Why does brand overlap cluster more tightly than source overlap?

AI engines reach different sources but converge on a similar vendor short-list because the brand-evidence layer is consumed from a smaller, more authoritative pool. Customer reviews, analyst categorization, and named industry-press coverage feed every engine's brand-recognition stack. The source evidence layer is consumed from a much wider, more divergent retrieval pool that depends on each engine's specific retrieval prior.

Should I run one GEO program or five?

One brand-perception program, five per-engine source-presence programs. The brand layer earns cross-engine compounding returns. The source layer does not. Splitting the two budget lines is the single most actionable change most B2B SaaS GEO programs need to make in 2026.

How do I find which engine I am losing on?

Run the same 50 buyer prompts through ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews. Record top-five vendor mentions per engine. Compute your brand presence per engine. Any engine where you sit below your average is the engine to investigate first. The audit is the upstream decision for the entire budget.

Is Google AI one optimization target or three?

Three. BrightEdge found that Gemini, AI Mode, and AI Overviews share less pairwise similarity with each other than Gemini does with ChatGPT. Otterly corroborated the AI Mode versus AI Overviews split with a 50-point share-of-voice differential on identical content. Treat them as three separate optimization tracks with three separate source pools.

Does this change which content my own site needs?

Yes, by reducing the importance of owned-site source-pool entry and increasing the importance of named placement on per-engine sources you do not control. Owned-site content still anchors the brand-evidence layer because it powers analyst briefs, review snippets, and press citations. But for source-presence work, redirect a meaningful share of budget toward off-site placements at the 30-domain shortlist that captures most citations on each engine.

What this changes about your next 90 days

Three actions, in order.

Run the cross-engine audit this week. Five engines, fifty prompts, two metrics: brand mention rate and cited-source domain count.

Split the budget by next Friday. One brand-perception line that runs across all engines. Five per-engine source-presence lines, sized to the gaps the audit surfaced.

Reset the scorecard before the next quarter. Two columns per engine, not one. Brand mention rate is the portable column. Cited-source domain count is the per-engine column. Track them separately or you will keep paying for the wrong half of the work.

The BrightEdge data does not say AI search is harder than you thought. It says GEO budgets have been pointed at the wrong half of the problem. The fix is a split, not an escalation.