Google’s AI Mode does not favour content placed at the top of a webpage, a new analysis of more than 2,300 AI citations has found, indicating the system can pull answers from any part of a page. The findings challenge a long?held belief in search and publishing that placing key information “above the fold” boosts visibility in new AI-driven search experiences. Instead, the analysis points to how information is structured on a page as a more meaningful factor in whether AI Mode cites it.
The result matters for publishers, marketers, and product teams working on search visibility and content design. As AI summaries take a larger role in how users discover information, businesses have sought to understand which on?page elements influence whether AI-generated answers reference their content. The new data suggests a shift in emphasis: not where the content sits on the screen, but how clearly the page organises and signals relevant information.
AI Mode surfaces sources from across the page
The study assessed thousands of instances where AI Mode produced a response and cited sources to support its answer. Across this dataset, the analysis did not find a systematic bias toward content placed at the top of pages. Instead, AI Mode cited passages that appeared throughout the page, including sections not immediately visible without scrolling.
AI Mode is a feature within Google Search that generates concise answers to user queries and includes links to source material. It draws on content indexed by Google’s systems and presents citations alongside its summaries, enabling users to click through to original pages. The feature has prompted scrutiny from publishers who aim to understand how the system identifies which sites to surface.
‘Above the fold’ meets AI retrieval
“Above the fold” refers to the part of a webpage visible without scrolling. For years, digital teams have prioritised that area to capture user attention, emphasise key messages, and improve engagement. Traditional search practices have also stressed early placement of core information and headings to signal relevance to both users and crawlers.
The latest findings suggest that AI Mode’s retrieval and citation behaviour does not rely on this visual placement. Instead, the system appears to identify relevant passages regardless of where they sit on the page. This aligns with broader developments in search, where technologies such as passage understanding allow systems to evaluate discrete sections of a page, not just the page as a whole.
Structure over placement: why layout and signals matter
The analysis highlights the role of page structure in helping AI systems detect and extract information. Structure, in this context, refers to how content is organised and signposted—for example, whether information sits under clear headings, within distinct sections, or in formats that make relationships between ideas easy to parse.
Search engines have evolved to interpret context within a page, assessing headings, sectioning, and other cues to locate precise answers. As AI features summarise information, these structural signals can assist in identifying the most relevant passage to cite, even if that passage appears well below the top of the page. The emphasis on structure over screen position reflects this shift in how systems evaluate content.
Changing assumptions for SEO and content operations
Many SEO and content teams have treated top?of?page placement as a proxy for prominence. The new data challenges this assumption in the context of AI Mode’s citations. While visible placement still influences human behaviour, AI systems can isolate and cite passages from deeper within a page if the information is clearly delineated and contextually relevant.
This change affects how organisations interpret performance signals from AI-driven search features. If AI Mode surfaces answers from anywhere on a page, teams may scrutinise internal content models, heading logic, and information density rather than just repositioning content higher. The focus shifts from screen real estate to information architecture, with an eye on how clearly a page communicates its key facts and relationships.
Advertising and user behaviour remain distinct factors
Above-the-fold placement still holds value for advertising and human attention. Many ad buyers measure viewability using standards that depend on screen position and time in view. Content placed higher on a page often attracts earlier user attention and may support key revenue metrics tied to impressions and engagement.
AI Mode’s citation behaviour operates on a different axis. The system evaluates relevance at the passage level, which can decouple AI visibility from traditional placement strategies. Businesses that rely on ad-supported models may therefore see two parallel considerations: ensuring strong user-facing presentation for humans while maintaining page structures that help AI systems locate and reference authoritative information.
How the findings fit wider industry debates
Publishers and marketers continue to assess how AI summaries affect traffic distribution, brand visibility, and the shape of the search results page. AI Mode’s practice of citing sources offers a path for user referral, but the volume and quality of that traffic remain active areas of industry monitoring. The new analysis adds clarity on one part of the picture: where on a page AI Mode finds the passages it cites.
The findings also sit within broader discussions about transparency in AI-generated results. Clear citations help users trace information back to source material, while structured content can make those citations more legible. As search experiences evolve, the mechanics of how AI systems choose and display sources remain central to how publishers and users understand the provenance of information.
Methodology and limits of the dataset
The analysis examined more than 2,300 AI citations to identify patterns in source selection across a broad set of examples. This offers a data?driven snapshot of how AI Mode behaves across varied queries. The scale provides enough observations to highlight consistent tendencies in citation placement within pages.
As with any observational study, the dataset reflects the queries and scenarios sampled at a point in time. Google’s systems iterate frequently, and AI behaviour can vary by query type, language, device, and user context. The results, therefore, describe identifiable patterns in the tested set rather than fixed rules for all searches and regions.
Industry context: evolving search signals and content design
Search has steadily moved from page?level scoring to finer?grained understanding of passages and entities. This evolution enables AI features to assemble answers from multiple parts of a page or from different sources, and then attribute those pieces with citations. In such a system, how content is segmented and labelled can influence the machine’s ability to extract precise passages.
For businesses, this places a premium on clarity and coherence in content design. A page that cleanly separates topics, frames key facts within relevant sections, and avoids burying essential information in dense blocks can be easier for systems to interpret. While visual hierarchy still shapes human reading patterns, AI citation behaviour hinges on how well a page signals meaning at the structural level.
What this means
- AI Mode’s citations reflect passage?level relevance rather than the visual position of content on a screen.
- Page structure and clear organisation appear to play a larger role than top?of?page placement in whether AI Mode cites a passage.
- Advertising and user attention dynamics continue to value above?the?fold presentation, but AI visibility follows different signals.
- Operational focus may shift towards information architecture, consistent sectioning, and clear contextual cues that help systems identify relevant passages.
- As AI features in search evolve, organisations may monitor how changes in layout and structure correlate with citation frequency and referral patterns.
When and where
Industry analysis of more than 2,300 citations in Google’s AI Mode was published on 9 February 2026 by Search Engine Land: https://searchengineland.com/google-ai-mode-above-the-fold-content-study-468578
