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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchIn a 300-run AI visibility panel replay, citation-positive observations rose from 35 to 50—but that total concealed a sharp platform reversal: Google AI Mode fell from 29 citations to 11, while Perplexity rose from 5 to 35. The overall change is descriptive, not proof of a reliable improvement: the study’s prompt-paired 95% interval includes zero.
What the panel measured
Bertrand Morel’s 2026 report describes a repeated panel for Edikka, built around 25 fixed, non-branded French prompts about measuring visibility in AI-generated answers. Each prompt was run three times in four environments: ChatGPT, Claude, Perplexity, and Google AI Mode. That made 300 planned runs per wave.
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Five planned runs at each wave were excluded for documented validity reasons, leaving 295 valid observations at J0 and 295 at J+30. The prompt wording, corpus, coding rules, planned run count, and comparison method were held constant. One supplemental calibration row appears in the later archive but is outside the panel. Recorded fields included brand mentions, linked citations, cited URLs, visible-source counts, technical validity, and conditions needed to interpret answers. Morel’s report and public materials concern one brand, one French prompt set, and two dates—not a market-share estimate or a ranking of AI platforms.
Why the total hid the important change
Across all valid observations, citation-positive results increased from 35/295 (11.86%) at J0 to 50/295 (16.95%) at J+30, a reported rise of 5.08 percentage points. But the composition changed dramatically: Google AI Mode supplied most of the positive observations initially, while Perplexity supplied most at the second wave.
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| Platform | J0 citations | J+30 citations | J0 mentions | J+30 mentions |
|---|---|---|---|---|
| ChatGPT | 0/75 (0%) | 2/71 (2.82%) | 0 | 2 |
| Claude | 1/74 (1.35%) | 2/75 (2.67%) | 1 | 2 |
| Perplexity | 5/74 (6.76%) | 35/74 (47.30%) | 6 | 3 |
| Google AI Mode | 29/72 (40.28%) | 11/75 (14.67%) | 25 | 11 |
At J0, Google AI Mode accounted for 29 of the 35 citation-positive observations. At J+30, Perplexity accounted for 35 of 50. A dashboard showing only the aggregate would make this redistribution easy to miss.
What changed beyond citations
Several related metrics moved in different directions. Mentions—answers naming Edikka—fell overall, while the number of distinct prompts producing a citation edged upward. The share of visible sources attributed to Edikka also increased slightly.
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| Measure | J0 | J+30 |
|---|---|---|
| Brand mentions across valid observations | 32/295 (10.85%) | 18/295 (6.10%) |
| Edikka share of visible sources | 36/1,752 (2.05%) | 51/1,974 (2.58%) |
| Prompts with at least one mention | 14/25 (56%) | 7/25 (28%) |
| Prompts with at least one citation | 14/25 (56%) | 15/25 (60%) |
These measures answer different questions. A mention means the generated answer named the brand; a citation means the interface exposed a brand URL as a source. Neither establishes that a person visited the site or took a useful action. For example, Perplexity cited an Edikka URL 35 times at J+30 but named Edikka only three times. Referral visits and business outcomes require separate attribution data.
Does the rise demonstrate improved visibility?
No—not by itself. The reported prompt-paired 95% interval for the citation-rate change was −1.45 to +11.82 percentage points, so it includes zero. Morel describes the increase as an observed change in these waves; calling it a reliable improvement would go beyond the evidence. The report used 10,000 bootstrap replications clustered by prompt, rather than treating every repeated run as an independent strategic topic.
The report gives a different result for prompt coverage with a mention: it fell from 14 of 25 prompts to 7 of 25, with a reported interval of −52 to −4 percentage points. That does not turn mentions into a substitute for citations; it shows why each metric should be reported separately.
The counts establish that the platform distribution reversed in this panel. They do not establish that editorial work caused the change. The evidence is limited to one brand, one French corpus, and two waves, and the public package does not redistribute raw answer text or screenshots.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How much confidence to place in the coding
A documentary quality check used a separate AI-assisted pass to examine 30 J+30 rows. Twenty-nine could be fully recoded; one archived answer was empty. Citation presence agreed across all 30 rows. Mention presence agreed in 27 of the 29 comparable rows. Morel reports Cohen’s κ of 0 because positive mentions were rare and the sample had no positive-positive agreement. This was not an independent second human review.
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The public package includes structured observations, methodology, limitations, a manifest, and checksums, while the evidence archive remains controlled. That supports scrutiny of the reported structure and coding documentation, but readers cannot independently inspect the full answer text and screenshots from the public materials.
How to report an AI visibility panel responsibly
The panel’s main practical lesson is to keep the detail that an aggregate can erase. A useful report should make it possible to see what was tested, what counted as a result, and how much uncertainty surrounds a change.
- Show platform-level results next to the overall total.
- Keep mentions, citations, source share, and prompt coverage as separate measures.
- Publish numerators, denominators, and invalid-run counts; do not silently recode an invalid run as either success or absence.
- Compare at the prompt level and account for prompts as clusters when estimating uncertainty.
- Retain dates, run conditions, coding rules, and evidence so later measurements can be interpreted as a time series.
What to examine in the next measurement
Morel’s proposed follow-up is to inspect prompt-platform pairs that changed repeatedly, confirm that cited URLs are the intended pages, and check source fidelity as well as whether a source appeared. A small weekly sentinel panel can flag shifts between monthly replays of the frozen prompt set. Citations should be connected to referrals or outcomes only when attribution data exists.
For anyone assessing an AI visibility dashboard or monitoring approach, the relevant questions are whether it covers the platforms people actually use, preserves platform-level and prompt-level rows, defines mentions and citations distinctly, exposes run counts and invalids, accounts for prompt-level uncertainty, and retains dated conditions and evidence. The panel offers a case for asking those questions, not a tested comparison of commercial tools.
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