Sources by Intent Study Q2 2026: Brand Editorial Owns 76% of French AI Citations

A qualitative study on 150 French unbranded prompts across 9 intent typologies. Why brand-owned editorial dominates 76% of AI citations, how negative framing wipes out official sources (×18), and what GEO teams should do about it.

Luca Fancello•May 20, 2026•23 min read

TL;DR, what this paper does and doesn't cover

What we found across 150 French prompts answered by ChatGPT (Core) and Perplexity:

• Brand-owned editorial content owns 76% of citations on average, peaking at 89% on local-intent prompts. Wikipedia maxes out at 2.7% on definition prompts, and press media never exceeds 2% on any intent type we tested.

• Intent dictates the source landscape. Official portals (.gouv.fr and regulators) carry 28% of citations on tutorial prompts and 20% on legal prompts, and exactly 0% on open-recommendation prompts. Aggregators and forums emerge specifically on commercial recommendation queries.

• Negative framing divides official-source citations by ~18. On the same Reliability topic, rephrasing a question into a critical register "why is X criticized" collapses the share of institutional sources from 11.1% to 0.6%, while community forums double their share.

• ChatGPT and Perplexity are not interchangeable retrieval systems. At comparable citation depth (~8-10 sources per sourced answer), ChatGPT cites community forums roughly 11× more than Perplexity, and twice as many institutional sources. Perplexity cites video and aggregators more heavily.

Part 1, the 76% rule, brand editorial owns French AI citations

The single most repeatable finding in this study is also the one most likely to reshape French GEO strategy in 2026: on every intent type we measured, the largest source family, by a wide margin, is content published by economic actors themselves.

We grouped 1,819 distinct cited domains into nine generic families (taxonomy detailed in Part 2). One family alone, brand-owned editorial & SEO publishers, which includes consulting firms, SaaS platforms, corporate editorial blogs, brand-owned media, and any other publisher whose underlying business is selling a product or service, captures 76% of citations on average, with the per-intent share ranging from 59% (Tutorial) to 89% (Local).

Three observations follow.

• It is not a concentration effect, it is a long-tail effect. The AI does not lean on a hard core of a few dozen authoritative publishers. It draws from a wide spread of corporate editorial content, the majority of which appears only once or twice across the dataset.

• The classical authority signals are quietly receding. In traditional SEO, Wikipedia, established press, and institutional sites carried the bulk of the authority signal. In our measurements, Wikipedia represents only 0.2% to 2.7% of citations depending on intent. Press media (economic, tech, mainstream) cap at 1% to 2% on every intent type. The encyclopedic and journalistic references that organized fifteen years of SEO discourse have become marginal in French AI answers.

• The brand has become its own primary authority source. This is not a metaphor. When the AI answers in French, three out of four citations come from publishers whose underlying business is the topic at hand. A brand's content strategy is no longer one acquisition channel among others, it is its representation channel in the AI layer. Editorial silence is no longer a missed opportunity, it is a structural invisibility.

Part 2, data and methodology

Dataset

• 150 French prompts, all written by us, fully unbranded (no specific brand mentioned in any prompt)

• 9 intent typologies: Definition / education, Tutorial / how-to, Open recommendation, Comparison & alternatives, Reliability / reputation, Problems & troubleshooting, Pricing & commercial terms, Legal / compliance / security, Local / proximity

• 15 prompts per intent, with the Reliability intent split into two sub-batches: 15 neutrally-framed prompts and 15 negatively-framed prompts (rephrasings such as "why is X criticized", "worst flaws of Y") to test the framing effect at constant intent

• 2 providers: ChatGPT (Core) and Perplexity

• 2 runs per prompt per provider (4 cumulative answers per prompt)

• 600 generated answers, of which 430 are retained after the no-citation filter (see below)

• 4,171 citations issued across 1,819 unique domains

• Language French, country inference France, no auto-detect competitor (focus on sources)

Filter applied

To evaluate the sourcing profile, what the AI mobilizes when it mobilizes anything, we exclude all answers that contain zero citations. The binary "search or no-search" decision is informative but distinct, and mixing it into the family-share metric would conflate two phenomena. After filtering, the analysis is performed on 430 answers: all 300 Perplexity answers (Perplexity cites in 100% of cases by design) and 130 ChatGPT answers out of 300.

The nine source families

We classified each cited domain into one of nine generic families. The taxonomy is opportunistic, not theoretical, and the brand-owned bucket is deliberately broad.

1. Official & institutional: .gouv.fr portals, regulators (CNIL, ARCEP, ACPR, AMF), professional orders (Notaires de France, Ordre des experts-comptables), European institutions, public agencies (Bpifrance, France Num)
2. Forums & communities: Reddit, Quora, specialized French forums
3. Video & social: YouTube, LinkedIn, X, Facebook, TikTok, Instagram
4. Wikipedia & encyclopedias
5. Aggregators & directories: third-party comparators of all flavors, professional directories
6. Media & press: economic press, tech press, mainstream press, business magazines
7. Reviews & consumer organizations: Trustpilot-style review platforms, French consumer associations (Que Choisir, 60 Millions de Consommateurs)
8. Local & Maps: local directories, mainstream real-estate platforms, generic local listings
9. Brand-owned editorial & SEO publishers: the remainder. Any site published by a commercial entity, whether SaaS, professional services firm, bank, insurer, fintech, content publisher, or vertical platform

This bucket-9 catch-all is itself an observation. There is no universally accepted classification of "AI source types" in French, and every GEO team will need to build a workspace-specific taxonomy. Productizing this classification work (per-workspace source tagging) is on the Qwairy product roadmap and is partly motivated by this study.

Limitations of the dataset

These are detailed in Part 9. The headline limitations are: 9-intent grid (not exhaustive), 15-30 prompts per cell (margin of error around ±10 points), 2-provider scope, 4 cumulative runs (snapshot), French only, manual classification of domains (subjective), and snapshot period (model versions evolve).

Part 3, intent dictates the source mix

The matrix below reads by row: out of 100 citations issued for that intent's prompts, X% belong to a given family.

Three patterns emerge.

• Normative-frame questions summon official sources. Tutorial (28%) and Legal (20%) pull four to five times more citations from .gouv.fr and institutional sites than the study average.

• Aggregators and forums emerge on commercial recommendation, and only there. Open recommendation is the only intent where aggregators (14%) and forums (7%) jointly cross 20% of citations.

• Comparison and Local are pure brand-editorial territory. At 86% and 89% respectively, these two intents are nearly mono-family.

Part 4, three real prompts, three source landscapes

To ground the matrix in concrete behavior, here are three prompts from our dataset, each asked to both assistants, with the actual citation profile each AI produced.

Example 1, Tutorial: "Comment créer une SASU en 2026 ?"

This is a textbook administrative tutorial prompt. Both assistants source it heavily, and both reach for official portals first.

• ChatGPT pulled its top citations from impots.gouv.fr, arcep.fr and the Google results page.

• Perplexity went broader, with youtube.com leading, followed by service-public.gouv.fr, bpifrance-creation.fr, and a long tail of legal-services editorial publishers.

Example 2, Open recommendation: "Quelle est la meilleure banque en ligne pour un freelance ?"

A commercial recommendation prompt with no brand mentioned. The mix shifts dramatically.

• ChatGPT placed reddit.com as its top source, followed by a banking comparator and a B2B accounting platform. Reddit dominates the citation profile, accounting for nearly half of all sources cited on this prompt.

• Perplexity went without forums entirely.

Example 3, Negative framing: "Pourquoi se méfier des plateformes de crowdfunding immobilier ?"

Same Reliability intent as a neutral question would be, but rephrased in critical register.

• ChatGPT mixed reddit.com, a consumer-finance editorial publisher, and a few independent personal-finance blogs.

• Perplexity pulled from a mix of consumer-finance editorial publishers, a few news media articles on previous crowdfunding incidents, and YouTube reviews.

Part 5, negative framing makes official sources disappear

On the 15 Reliability prompts rephrased in critical register, the family mix reorganizes dramatically:

The most striking effect is on official sources: they collapse. At constant intent, rephrasing the question in a critical register makes the institutional share drop by a factor of roughly 18.

Part 6, two retrieval personalities

At comparable scope (sourced answers only), both assistants cite a similar number of domains per answer: roughly 8 citations on average for ChatGPT and 10 citations for Perplexity.

Part 7, the GEO playbook by intent type

Tutorial / how-to (28% official, 5% video)

• Co-publish with institutional actors when applicable.
• Structured how-to schema, step-by-step on your own editorial pages.
• Produce video tutorials.

Open recommendation (14% aggregators, 7% forums, 0% official)

• Active Reddit France presence.
• Inclusion in third-party aggregators / comparators.
• Own brand editorial on "best X" pages, with concrete differentiators.

Comparison & alternatives (86% brand-owned)

• Own your X vs Y and alternatives to X pages.
• Cover every meaningful comparator your prospects mention.
• Refresh comparison content every quarter, with year-tagged updates.

Legal / compliance (20% official, 77% brand-owned)

• Reference and align with .gouv.fr and regulator portals.

Pricing & commercial terms (85% brand-owned, 9% aggregators)

• Transparent pricing pages.
• Inclusion in vertical pricing aggregators, particularly the niche ones.

Local / proximity (89% brand-owned)

• Local landing pages, one per geographic market.

Reliability / reputation (positive AND negative registers)

• Work both framing registers.

Definition / education (77% brand-owned, 11% official, 2% Wikipedia)

• Canonical explainers on your own site, with structured definitions.