/ THE SHORT ANSWER
The studies suggest that generative search visibility is shaped by source authority, engine-specific retrieval behavior, semantic relevance, and the readability of source text. They do not reveal a universal GEO formula. The practical response is to publish original evidence, preserve strong SEO foundations, make each page easy to understand, and measure visibility across engines rather than assuming one ranking system.
- 01In the sampled brand-ranking prompts, earned sources supplied most citations across all four AI engines, but the exact mix varied materially by engine.
- 02In the Google AI Overview study, a one-standard-deviation decrease in source perplexity was associated with a nine percentage point increase in predicted citation probability.
- 03Engine overlap was low enough that a single-platform visibility check can produce a false sense of coverage.
- 04Google now describes GEO as part of SEO and explicitly rejects special files, excessive chunking, and scaled pages created only to capture query variants.
/ dotSuper point of view
GEO should be run as an evidence and distribution discipline, not a collection of AI-search hacks.
The evidence base at a glance
The first study evaluated how web-enabled Claude, ChatGPT, Gemini, and Perplexity cited sources across consumer ranking prompts, local service queries, languages, and paraphrases. Its core ranking dataset contained 1,000 prompts across ten categories.
The second study compared Google AI Overview citations with conventional Google results for 4,060 queries. It analyzed 98,477 unique websites, then ran controlled retrieval experiments and a 147-participant randomized trial.
RESEARCH SCALE
Two studies, four useful lenses.
The samples answer different questions. Read the figures as complementary evidence, not as one combined experiment.GEO engine study across ten consumer categories
Google organic and AI Overview observations
Website-level sample in the content study
Predicted probability after 1 SD lower perplexity
A percentage-point change is not the same as a relative percentage increase. The study reports a move from 47% to 56%.
View the chart data
| Measure | Value | Study context |
|---|---|---|
| Consumer ranking prompts | 1,000 | Four web-enabled engines plus Google comparison |
| Paired Google queries | 4,060 | AI Overview and first-page organic results |
| Unique websites | 98,477 | Website-level content analysis |
| Predicted citation probability | 47% to 56% | One standard deviation lower perplexity |
Where the papers converge
Both papers point toward retrieval compatibility. A page must be discoverable, relevant to the question, and easy for a system to use as support. The GEO study emphasizes third-party authority, scannable comparisons, explicit claims, and language-aware distribution. The content study finds that lower-perplexity source text and stronger query-to-source similarity are associated with citation selection.
That does not mean every page should be rewritten to sound generic. The content study tested AI polishing inside a controlled retrieval pipeline, while Google advises publishers to prioritize unique, non-commodity, people-first material. The sensible synthesis is clear writing around distinctive evidence.
| Question | Study signal | Practical interpretation |
|---|---|---|
| Who gets cited? | Earned sources dominate many sampled ranking prompts | Build evidence other credible sites have a reason to reference |
| What text gets selected? | Lower perplexity and higher semantic similarity correlate with citation | Answer the intended question clearly and keep the supporting evidence close |
| Does one engine represent all AI search? | Source mixes and domains vary by engine | Measure multiple engines and keep results separated |
| Do special GEO hacks replace SEO? | No | Maintain crawlability, indexing, useful content, and accurate structured data |
What this changes for dotSuper
dotSuper should use its Feeds library as an evidence system rather than a page-volume system. Each research cluster should begin with one original question, a direct answer, visible methodology, downloadable or inspectable numbers, limitations, and a clear route to the relevant product.
The immediate opportunity is to build a repeatable visibility baseline across Google generative AI reports, ChatGPT, Claude, Gemini, and Perplexity. The baseline should distinguish citation presence, cited URL, source type, answer accuracy, and assisted conversion. A single blended score would hide the engine differences shown in the research.
- Create original benchmarks from dotSuper delivery work using anonymized and permissioned data.
- Build one strong topic hub for each product, then connect narrow supporting pages only when each page answers a genuinely different question.
- Place the direct answer, evidence, definitions, and caveats early in the page.
- Track Google generative AI impressions in Search Console and maintain a separate monthly multi-engine citation test.
- Pursue authentic earned distribution through partners, events, associations, customer evidence, and expert contributions.
The five-part dotSuper research series
This report is the overview. Four focused field notes separate the mechanisms so readers and retrieval systems can reach the precise evidence they need.
| Page | Question answered | Primary metric |
|---|---|---|
| AI search source mix by engine | Which kinds of sources does each engine cite? | Earned, brand, and social share |
| Engine overlap and query volatility | How stable is visibility across engines and prompts? | Jaccard overlap and local-result overlap |
| Content readability and citation probability | What page-level characteristics correlate with citation? | Predicted probability and treatment effects |
| dotSuper GEO measurement playbook | How should this become an operating system? | Citation coverage, assisted demand, and freshness |
What this page cannot conclude
- 01Both papers are preprints. Their findings should be treated as evidence to test, not as a settled ranking formula.
- 02The GEO engine study collected data in August 2025. Models, retrieval systems, interfaces, and citation behavior can change quickly.
- 03Observed citation patterns do not prove that changing one page element will cause an engine to cite that page.
- 04Google states that there is no special schema, file, or content format required for its generative AI features. Core SEO and useful, original content remain the foundation.
Sources
- 01Generative Engine Optimization: How to Dominate AI SearcharXiv · accessed Sep 1, 2026
- 02When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search EnginearXiv · accessed Sep 1, 2026
- 03Optimizing your website for generative AI features on Google SearchGoogle Search Central · accessed Sep 1, 2026
- 04Introducing Search Generative AI performance reports in Search ConsoleGoogle Search Central · accessed Sep 1, 2026
- 05Google Search's guidance on using generative AI content on your websiteGoogle Search Central · accessed Sep 1, 2026