The marketer’s guide to Google AI Overviews and the future of organic search
Google AI Overviews explained and what they mean for SEO
Google has changed the way people find information online. The new AI Overviews [AIO] now sit at the top of search results, summarizing what users need to know before they even reach the first link.
These summaries combine insights from several websites and present them as short, conversational explanations inside the results page.
This update shifts how search works. For years, ranking first was the ultimate goal. Now, visibility depends on whether your content gets included in the AI Overview itself.
Google is building these summaries using Large Language Models (LLMS) that rely on structured information, expertise, and trust signals from across the web.
The impact is already visible.
Several brands have noticed fewer clicks, even when they still hold strong positions. Others are being cited inside AI Overviews and gaining awareness without direct traffic.
Search has become a mix of visibility and validation, not just rankings and impressions.
In this guide, you’ll learn what Google AI Overviews are, how they show up in search, how they change traffic patterns, and what you can do to earn placement.
You’ll also see examples from live searches and what current data says about how this new layer is shaping the future of SEO.
What AI Overviews are and how they appear in search
Example of an AI Overview 👆
When you click on the ‘Show More’ button, the AI Overview expands further into a detailed answer.
AI Overviews is a search feature within Google that uses generative AI to create summarized answers directly inside search results. They appear at the top of the results page as a block of text that combines information from several web pages. The system analyzes content from trusted sources, generates a clear explanation, and includes citations so users can visit the original sites for more detail.
Unlike Featured Snippets, which lift a single text passage from one page, AI Overviews generate responses by analyzing information from multiple sources.
The system uses large language models to read, understand, and summarize relevant pages that align with the search intent. Instead of quoting one paragraph verbatim, it creates a short, cohesive explanation that blends insights from across the web.
This approach helps Google provide context, not just answers, and gives users a faster way to understand a topic without opening several tabs.
The feature looks different depending on the query. For some searches, you’ll see a compact paragraph with two or three citations. For others, Google displays a multi-card layout that breaks the summary into smaller pieces. Each card links to a source, usually one that provides strong expertise or clear, structured information.
AI Overviews show up most often for informational searches— things like “how programmatic advertising works” or “best ways to improve page speed.” They appear less for transactional or branded queries, where users are likely ready to buy or visit a specific site.
Right now, visibility inside these summaries is unpredictable. Some websites that already rank high get cited often. Others appear even when they don’t rank in the top ten results. That shows Google’s AI isn’t only looking at position, but also at trust, clarity, and how well a page answers the search intent.
How do AI Overviews work?
AI Overviews are powered by Google’s Search Generative Experience (SGE) and supported by the Gemini large language model. It combines classic information retrieval with generative summarization to provide users with quick, verified answers.
The process works in four main steps:
1. Retrieval of relevant pages
Google retrieves top-ranking web pages from its search index based on relevance, freshness, and authority. This step uses the same ranking signals that power standard organic results.
2. Content synthesis through large language models
The retrieved pages are processed by Google’s Gemini model, which identifies key ideas, definitions, and relationships across multiple sources. The model generates a natural-language summary that combines this information into a single, coherent response.
3. Grounding and citation
Google validates each statement in the generated text against the original sources. This step, known as grounding, ensures factual accuracy. The AI Overview then attaches citations to sentences or sections that reference those sources.
4. Presentation in the search results
The verified summary is displayed at the top of the search results page, often with a multi-card layout, embedded visuals, and clickable citations. The layout adapts to query intent—shorter for direct questions, and more detailed for complex or comparison-based topics.
AI Overviews act as a synthesis layer built on top of Google Search. They rely on structured, high-quality content that demonstrates expertise, accuracy, and context.
How AI Overviews impact SEO, traffic, and CTR
Google has moved the fight for attention to the very top of the page. AI Overviews now trigger on a meaningful slice of searches and push classic listings further down, which changes how users scan and click. In March 2025, Semrush and Datos measured AI Overviews on about 13% of U.S. desktop queries.
Google has publicly said that links cited inside AI Overviews receive more clicks than when those same pages appear as regular organic results.
Links cited inside AI overviews 👆
That’s true in a narrow sense: if your page is one of the few cited inside the summary, you might see a short-term lift in engagement.
The broader picture tells a different story. A July 2025 Pew Research Center study found that when an AI summary appears in search results, users click a traditional link only 8% of the time– compared with 15% when no summary is present.
Only 1% of visits included a click on a link inside the AI Overview itself. And in 26% of cases, users ended their session right after viewing the AI summary.
Both views are accurate. They just describe different realities. Google’s data focuses on the handful of sources featured inside the Overview. Pew’s data captures the entire search ecosystem. So while inclusion can help a few sites, the overall pool of organic clicks is shrinking.
What does this mean for brands?
Shift the goal from “rank first” to “earn inclusion and citation.”
Build pages that answer the core question cleanly, then support with short sections, lists, and tables that LLMs can lift.
Strengthen E-E-A-T signals on author, organization, and page level.
Track performance by separating queries that trigger AI Overviews from those that do not; judge CTR and clicks against that split, not sitewide averages.
Watch overlap between cited sources and your ranking URLs to see where content earns inclusion without position and where rank is not enough.
Examples from live queries
Seeing how AI Overviews behave in real searches makes their impact clear. The feature doesn’t activate on every query. When it does, it usually takes over the entire top fold, pushing the first organic result halfway down the page.
Let’s look at a few examples👇
1. “Best CRM for small business”
This query now triggers a full AI Overview that lists four CRM options— HubSpot, Zoho CRM, monday.com, and Pipedrive.
The summary breaks down each tool’s strengths: HubSpot for its free tier and all-in-one marketing suite, Zoho for scalability and affordability, monday.com for its customizable visual setup, and Pipedrive for its sales-pipeline focus.
Below that, the Overview expands into a visual module titled “Popular CRMs for small businesses,” featuring a YouTube video on the top five CRM tools, followed by links to Reddit and Zapier.
What it means: Google is layering formats inside AI Overviews— text summaries, video embeds, and discussion links. The content being cited isn’t limited to traditional blog pages. It pulls from trusted informational sources, comparison articles, and platforms that signal engagement (like Reddit). For content teams, this confirms that multi-format authority matters. Articles, videos, and community discussions now compete together for visibility.
2. “What is programmatic advertising”
The Overview starts with a short paragraph defining programmatic advertising as “the automated, technology-driven buying and selling of digital ad space.” It highlights how artificial intelligence and machine learning power real-time ad auctions and mentions benefits such as precision targeting, cost efficiency, and campaign optimization.
Immediately below that, the Overview embeds a YouTube video from AdRoll titled “What Is Programmatic Advertising?” and includes a visual summary with labeled diagrams showing how DSPs, SSPs, and ad exchanges interact. The summary then transitions into a clear “How It Works” section with numbered steps that describe the full process— from user visit to auction, bidding, and ad placement.
What it means: Google’s AI is now structuring answers in the same way a well-written explainer blog would. It’s pulling definitions, visuals, and procedural content into one cohesive response. That tells us the model is favoring multi-format educational content that clearly teaches a concept, not just defines it.
3. “Best VPNs for streaming”
For high-intent commercial queries, Google’s AI Overviews provide a structured, list-based response that resembles a mini-buying guide more than a typical search result.
In this case, the Overview lists ExpressVPN, NordVPN, Surfshark, Proton VPN, and Private Internet Access (PIA) as the top options for 2025. Each provider includes a short description highlighting its strongest feature— ExpressVPN for user-friendly design and fast unblocking, NordVPN for overall speed and advanced features, Surfshark for affordability, Proton VPN for privacy, and PIA for ease of use.
Below the summary, Google expands the answer with a section titled “Top Streaming VPNs for 2025.”
It uses bullet-point formatting that mirrors listicles published by tech media sites, followed by another section labeled “Key Factors When Choosing a Streaming VPN.”
That part outlines the specific criteria—speed, unblocking capability, and cost—that Google’s model uses to structure its explanation. The right-hand module surfaces additional sources such as CNET and TechRadar, further reinforcing the blend of expert publishers and product-review content.
What it means: Google’s AI layer now blends transactional intent with editorial authority. It not only ranks vendors but also synthesizes expert consensus from trusted publications and presents it in a clean, scannable layout. For content strategists, this marks a fundamental change: the overview favors neutral, comparison-driven content rather than brand pages, helping users make decisions.
AI Overviews vs Google’s new AI Mode (Gemini “Search Mode”)
Another major AI-driven feature is shaping the future of search at Google. It’s called the AI Mode.
If AI Overviews condense and surface trusted web content at the top of search results, then AI Mode provides a deeper, chat-style, model-powered interface that goes beyond classic links.
Understanding both gives search leads and marketers a strategic edge. One controls citation inclusion, the other governs brand narrative and long-form visibility.
Here’s a quick comparison👇
Feature | AI Overviews | AI Mode | |
|---|---|---|---|
Purpose | Create a concise, citation-backed summary of existing web pages for a query | Deliver a conversational, model-generated answer (via Gemini) with links and follow-ups | |
Trigger / Interface | Appears in the standard search results page, usually above blue links | Activated via a dedicated “AI Mode” tab or interface; chat-style interaction | |
Sources | Web index + Google metadata + citations from those pages | Gemini model output + optional grounding links; more extensive reasoning and multimodal input | |
User interaction | One-click expandable summary or multi-card response | User can ask follow-up questions, upload images, switch formats, chat deeper | |
Citations & attribution | Inline visible citations to original pages; derives from web content | Links may appear, but the primary output is a model-generated dialogue or narrative; less structured citation visible | |
Best use case | Quick answers, definitions, comparisons where the web already has strong coverage | Exploratory or complex tasks, deep research, decision-making, or multi-step flows |
How they work together in practice
AI Overviews can be seen as the first layer: summarizing the web and surfacing trusted sources quickly. AI Mode is the next layer: a richer experience that uses Gemini to reason, explore, and deliver deeper results.
According to Google’s blog, AI Mode “brings together advanced model capabilities with Google’s best-in-class information systems” and is “built right into Search.”
Essentially, AIO reinforces what the web says; AI Mode shapes how the web is asked, interpreted, and used.
Impact of AI Overviews & Google’s AI Mode on marketing teams
1. SEO is shifting from ranking to representation
Traditional SEO rewarded position. The new AI layer rewards representation— how accurately and consistently your brand is described across the web.
AI Overviews and AI Mode both rely on Google’s understanding of entities: your brand, your authors, and your topical expertise. If your pages are clear, structured, and supported by strong E-E-A-T signals, the model is more likely to use your content as part of its summary or conversation.
Ranking on page one matters less if you aren’t being referenced by the AI summary sitting above it.
2. Content strategy must serve two layers of search
AI Overviews extract answers from structured, high-trust web content. AI Mode builds narratives through conversation. To win in both, brands need to publish content that performs on two levels:
At the Overview level: Create pages that clearly define, list, or explain a topic. Use schema markup and concise formatting so the model can lift your answers directly.
At the Mode level: Build deeper assets such as data-driven explainers, use cases, and product insights that Gemini can reference in multi-turn chats.
Your content library should feel like an ecosystem, not a set of isolated pages. Each topic cluster should include one summary piece that answers the core question (to earn Overview inclusion) and several supporting resources that provide perspective (to surface in AI Mode).
3. Authority signals are becoming machine-readable
LLMs depend on structured patterns to detect trust. Author bios, organization schema, and citation hygiene now play the same role backlinks once did. Brands that clearly state who wrote the content, when it was last updated, and how facts were sourced will appear more “trustworthy” to Google’s AI systems.
SEO teams should treat E-E-A-T like metadata, not marketing copy. Add consistent author markup, link to verified profiles, and ensure each page connects back to an authoritative domain entity.
4. Reporting frameworks need a new metric: inclusion
Tracking rankings alone won’t reveal how much visibility your brand truly has. AI Overviews introduce a new layer called "inclusion or citation share," which measures how often your brand or URL appears in summaries.
Tools like Profound, Trakkr, Ahrefs’s Brand Radar, and more are beginning to track this metric by identifying which domains are cited in AI Overviews. The next evolution of SEO reporting will combine:
Organic rankings (classic visibility)
AI Overview citations (authority in the AI layer)
AI Mode mentions (brand perception in generative dialogue)
5. Brand reputation now happens inside search
AI Mode pulls from the same data Google uses for Knowledge Panels, reviews, and third-party content. That means your brand voice can appear inside AI conversations, even if you didn’t write the words.
For marketing leaders, this makes brand governance inside search critical. Messaging consistency across your website, press coverage, partner listings, and even community discussions (such as on Reddit & Quora) helps Gemini form a unified understanding of your brand.
Inconsistent claims or outdated information create noise that can weaken your representation in AI-generated summaries.
6. Winning means teaching, not selling
Both AI Overviews and AI Mode prioritize helpful, educational, and balanced perspectives. The more your brand contributes genuine expertise to the topic ecosystem, the more likely it becomes a trusted reference in Google’s AI output.
SEO teams should optimize for clarity and context rather than conversion copy. Marketing teams should think like educators. Answer questions, clarify misconceptions, and publish insight-driven content that builds topical trust.
At Authority Juice, we call this search representation management. You’re not optimizing for clicks. You’re training Google’s AI to describe your brand accurately and favorably.
10 ways you could optimize for AI Overviews
AI Overviews reward pages that are clear, well-structured, factual, and easy for models to parse. Ranking alone is not enough. Your goal is to make your content machine-readable and citation-ready.
The following has worked the best for our clients at Authority Juice.
1. Lead with a crisp answer, then expand
Open with a one-sentence definition or outcome. Follow with the 3–5 essentials a user must know. Close the intro with one actionable takeaway.
This mirrors how Google composes AI Overviews and makes your page a clean fit for summarization. Google describes AI Overviews as a synthesis layer that “prominently includes links to learn more,” which signals it is looking for tight, high-signal passages to cite.
Let’s take ‘What is programmatic advertising’ as an example.
Crisp answer (the definition):
Programmatic advertising is the automated process of buying and selling digital ad space using artificial intelligence and real-time bidding technology.
Expansion (3–5 key essentials):
It replaces manual negotiations with algorithms that match ads to audiences in milliseconds. The system uses demand-side platforms (DSPs) for buyers and supply-side platforms (SSPs) for publishers. This automation increases efficiency, reduces wasted spend, and allows advertisers to target based on audience data instead of guesswork.
Actionable takeaway:
To make programmatic campaigns perform better, marketers should focus on first-party data quality and transparency between ad exchanges and partners.
Why this works
The first sentence defines the concept cleanly — short enough for an AI Overview to quote verbatim. The second part expands with the core mechanics and value proposition, giving Gemini (or any LLM) more context for synthesis.
The last line offers a human, outcome-driven takeaway, making the passage more useful and citation-ready.
This structure mirrors how Google composes its AI Overviews:
→ short definition → supporting context → key insight or action.
2. Structure the page like data, not prose
Use predictable patterns models can lift:
H2/H3s that match common search phrasing: “What is…,” “How it works…,” “Benefits…,” “Steps…,” “Examples…”
Numbered steps for processes and checklists
Short tables for comparisons and factor lists
Tight FAQs that answer a single question per item
Google’s own guidance favors helpful, people-first content with clear organization. Follow it.
3. Add the right schema where it actually helps
Schema does not force inclusion, but it clarifies context and roles. Below is a clean playbook you can hand to your team.
Article / WebPage (use on most editorial pages)
Goal: Tell Google who wrote it, what it is about, and when it was reviewed.
Required core fields
@type: Article (or BlogPosting) and WebPage
headline and name
description
datePublished and dateModified
Author as a Person (link to your existing author entity)
Publisher as your Organization
mainEntityofPage (URL of the page)
Helpful fields
about and keywords for topical clarity
image with width and height
inLanguage
mentions to point at related entities or pages
FAQPage (use when the page has a real Q&A block)
Goal: Give AI a clean set of question–answer pairs.
Rules
👉 Each item is a real question and a direct answer.
👉 Do not stuff the FAQ with sales copy.
HowTo (use when the page teaches a process)
Goal: Mark up a numbered set of steps. This helps extraction.
Rules
👉 Use real steps that a person can follow.
👉 Include tools or supplies if they matter.
👉 Keep step text short and precise.
Organization (site-wide; include once per site template)
Goal: Establish your brand as a stable entity.
Key fields
name, URL, logo
sameAs links to official profiles
contactPoint if you support customers
foundingDate, address and phone when relevant
Product / SoftwareApplication (use on product or feature pages)
Goal: Give models structured details that match buying intent.
Product basics
name, description, brand
offers with price, priceCurrency, and availability
aggregateRating when you have real reviews
SoftwareApplication basics
applicationCategory and operatingSystem
offers with plan details or starting price
features in clear bullet form within the on-page copy
Make sure to validate and check for hygiene
👉 Validate every page in Rich Results Test and Schema Markup Validator.
👉 Keep dates fresh and consistent with on-page text.
👉 Do not duplicate types for the same role on the same page.
👉 Keep JSON-LD in the <head> and render it server-side when you can.
How schemas or structured data help AI Overviews
Article/WebPage gives the model a clear summary of the page, the author, and the topic.
FAQPage and HowTo give the model neat units it can lift into a summary.
Organization and Product/SoftwareApplication connect your page to a trusted brand entity.
Clean IDs, dates, and sources reduce ambiguity, which improves your odds of citation.
4. Design your pages like a data source
When Google’s AI scans a page, it looks for scannable logic.
Use clear sub-headings (H2, H3) that match common query phrasing (“What is…,” “How it works…,” “Benefits of…”).
Add bullet points, numbered lists, or comparison tables — Gemini prefers consistent patterns for extraction.
Include concise definitions or summary boxes.
Keep visuals labeled (alt text, captions, filenames that describe the concept).
Think of your page as a dataset, not a wall of text. The easier it is to parse, the easier it is to cite.
5. Strengthen E-E-A-T at every level
AI Overviews depend on trust. Pages that show real expertise and clear ownership rise higher.
Add visible author names, bios, and credentials.
Link to professional profiles or industry sources that validate experience.
Include dates of publication and revision for transparency.
Use references where you cite data or statistics.
The model maps authors, organizations, and topics together. The more consistent that triangle, the stronger your brand’s “authority graph” becomes in AI-driven search.
5. Build topical authority through clusters
Google’s AI doesn’t evaluate pages in isolation. It evaluates topic ecosystems. So, create interconnected clusters around key subjects:
One pillar page that defines and explains the core concept (e.g., “What Are AI Overviews?”).
Several supporting articles that go deeper into subtopics (“How to Rank in AI Overviews,” “AI Overviews vs Featured Snippets,” “Best Tools to Track AIOs”).
Internal links that show progression between them.
This structure trains the model to associate your domain with the entire subject area, not just a single query.
6. Maintain clarity between AI-suitable and sales content
AI Overviews tend to avoid overtly promotional material. If your page reads like a sales pitch, it’s unlikely to be cited.
Keep educational and commercial content separate:
Use your blog and resource center for explainers, guides, and frameworks.
Reserve conversion-focused copy for product or service pages.
Ensure the tone is informative first, persuasive second.
7. Monitor and measure AI Overview inclusion
Tracking AI Overview visibility is still new, but it’s measurable.
Use tools like Profound, Ahrefs Brand Radar, Trakkr, or AthenaHQ to identify which of your pages appear as cited sources.
Create a GSC filter to tag AI Overview queries (usually longer informational searches).
Track impressions and CTR differences between AIO-triggered and non-AIO queries.
Your new KPI isn’t just keyword positions. It’s citation share: how often your brand is trusted enough to appear in the summary layer.
8. Refresh content with precision
AI Overviews pull from the freshest and clearest results available. Keep content updated.
Review core educational pages every quarter.
Add new data, stats, or visuals that reinforce credibility.
Re-validate schema and internal links to avoid crawl gaps.
Timely updates help maintain inclusion once you’ve earned it.
9. Build backlinks that reinforce topical authority
AI Overviews pull from pages that Google already trusts. Backlinks still serve as one of the strongest indicators of authority and topical alignment, especially when they come from credible, contextually relevant domains.
We’ve seen it work wonders!
Here’s how to approach it:
Prioritize relevance over volume. One backlink from a reputable, topic-aligned site beats ten random mentions. The AI retrieval layer favors domains with thematic consistency—meaning if your backlinks come from sites that already rank for similar topics, your authority in that space strengthens.
Earn citations from educational and reference-style content. Guides, research pages, and explainer-style articles tend to be included in AI Overviews more often. Getting referenced in those strengthens your eligibility for inclusion.
Avoid link exchanges or low-quality directories. Those dilute your trust graph.
Track new referring domains being added, not just total backlinks. Focus on building a broad, diverse backlink profile that reinforces your brand’s authority across multiple topic clusters.
10. Maintain strong technical health and indexability
Before your content can appear in an AI Overview, it must first be retrieved during Google’s pre-generation stage. That process depends entirely on a site’s technical integrity. Even the best-written content can be invisible to Google/Gemini if crawling or rendering fails.
Key technical priorities:
Ensure fast load times. Pages with strong Core Web Vitals, especially LCP under 2.5s and CLS below 0.1, tend to rank higher and are more likely to be retrieved for summaries.
Fix crawl errors and canonical conflicts. Verify that the pages you want surfaced are included in your sitemap, properly canonicalized, and not blocked by robots.txt.
Use HTTPS and a clean URL structure. Security and clarity remain baseline trust signals.
Monitor indexation continuously. Pages that drop from the index (due to noindex tags, soft 404s, or canonical misfires) immediately lose eligibility for AI Overview inclusion.
Implement schema consistently. Use structured data to reinforce page type and content relationships, helping Google understand hierarchy and context.
Check mobile performance. Most AI Overviews are generated from mobile-first indexing data. Poor responsive design or slow mobile rendering can exclude your content from consideration.
Treat technical SEO as the foundation of generative visibility. AI Overviews can’t cite what Google can’t confidently retrieve.
Is optimizing for Google’s AI Mode any different?
It is. Slightly.
AI Overviews and AI Mode share the same foundation: both run on Google’s Gemini model and rely on the same web index for factual grounding. But their purpose, user flow, and ranking logic differ enough that your optimization strategy has to adapt.
1. AI Overviews surface information. AI Mode sustains conversation.
AI Overviews answer a question once. They summarize what’s already known and cite trusted web sources.
AI Mode, on the other hand, behaves like a dialogue. It lets users ask follow-ups, refine answers, or switch context entirely, without leaving the same search session. The content that performs well in this mode is content that fuels deeper exploration, not just a static definition.
Example:
In an AI Overview, Google might show a single paragraph defining programmatic advertising.
In AI Mode, the conversation could continue with:
“How does programmatic compare to direct buys?”
“What tools manage this process?”
“Which metrics prove ROI?”
If your content answers all three within one topical cluster, Gemini is more likely to reference you multiple times during the session.
2. Optimization priorities shift from clarity to continuity
For AI Overviews, optimization focuses on structured clarity: schema, headings, definitions, and factual precision.
For AI Mode, optimization expands into continuity and coverage. The model rewards content that helps users move from question to decision naturally.
Here’s how it differs in practice:
Optimization area | For AI Overviews | For AI Mode |
|---|---|---|
Search trigger | Query confidence and informational intent | User opts into “AI Mode” and begins multi-turn dialogue |
Content focus | Concise explanations and direct answers | Broader coverage, follow-up insights, and use-case examples |
Ideal page type | Educational pages, FAQs, how-tos | Full guides, frameworks, comparison pieces, and narrative explainers |
Schema priority | FAQPage, HowTo, Article | Article, Organization, Author, Product with detailed relationships |
Ranking signal | Page clarity and structure | Depth, topical coverage, and semantic consistency across the site |
Engagement signal | Clicks from citations | Brand mentions and reappearances in extended chat threads |
3. Content for AI Mode must anticipate the next question
Gemini’s conversational logic pulls from related entities and sub-topics. To appear in follow-ups, your pages should already answer questions users haven’t asked yet.
Use sub-headings like:
“Next Steps”
“When This Approach Doesn’t Work”
“Common Mistakes”
“Alternatives and Comparisons”
These structural cues give the model more options to continue the conversation using your content.
4. Build content that’s multimodal and explorable
AI Mode surfaces not just text, but also videos, charts, and images that enhance comprehension. That means content formatted like an interactive teaching tool with visuals, data tables, and summaries has a higher chance of being reused in longer conversational flows.
Upload supporting visuals with descriptive filenames and alt text; Gemini reads those signals.
5. Measure differently
There’s no separate metric yet for AI Mode visibility. Search Console doesn’t break out impressions from AI Mode sessions. The best approach is to track assisted visibility:
Brand mentions or citations in AI answers (visible snippets).
Referral patterns marked as “direct” but coinciding with AI Mode launches.
Session time and conversions from new landing pages referenced in generative search results.
In other words, AI Mode visibility is more about brand recall than click-through rate.
The growing curiosity (and confusion) around how to turn off AI Overviews
Source: Reddit
When Google’s AI Overviews started showing up in nearly every search, the internet did what it always does. It went to Reddit for answers.
A thread in r/techsupport, now with over 2,000 upvotes and more than 1,000 comments, became a running conversation about whether users can actually turn off AI Overviews or the newer AI Mode.
Most participants were curious, cautious, and a little unsure of what was happening behind the scenes.
Many said they had toggled off “AI in Search” inside Search Labs, yet still saw generative results on routine queries. The confusion stems from the distinction between hiding AI Overviews and disabling the underlying system. Google’s toggle only hides the summaries from view; it doesn’t stop the AI from processing the query.
Several users also noticed something else: when they searched “how to turn off AI Overview,” the AI Overview itself tried to answer, and got it wrong.
It pulled fragments from unrelated support pages and produced a made-up explanation that didn’t exist anywhere verbatim on the web.
That’s what AI researchers call a hallucination — when a model generates language that sounds factual but isn’t supported by real sources.
It’s not malicious; it’s mechanical. The system tries to synthesize an answer even when the data is ambiguous.
But for everyday searchers, that distinction doesn’t matter. It feels like Google’s AI just invented an instruction that doesn’t work.
By the way, here’s how I turned off Google’s AI overviews & AI mode. I used my mobile device for it.
To disable AI Overviews and AI Mode, you need to do it through Google Search Labs while logged into your Google account.
Open Google Search on desktop or mobile.
Tap the flask icon in the top left corner — that’s Search Labs.
(See image below.)
Select Manage experiments.
Toggle off both AI Mode and AI in Search.
Once disabled, AI Overviews and other generative responses will stop appearing in your personal search results. But this only applies when you’re signed in. If you use incognito mode, log out, or search from another device, Google may still show the AI-generated summaries.
In short, you can switch them off for yourself, but AI Overviews remain active across Search as a system feature.
AI hallucinations: The hidden problem inside AI Overviews
Even the smartest models get things wrong, and when they do it confidently, users rarely notice.
That’s the issue with AI hallucinations. This happens when large language models like Google’s Gemini generate answers that sound factual but aren’t supported by real sources.
IBM defines hallucination as an AI “perceiving patterns or creating outputs that are inaccurate or entirely fabricated.” In other words, it makes things up that feel true. Studies, including one from arXiv (2024), argue these errors are inevitable in large language models due to how they synthesize probabilities instead of verifying facts.
Google has already faced public scrutiny for this. In one now-famous case, an AI Overview suggested adding non-toxic glue to pizza sauce to make cheese stick better– advice traced back to a Reddit joke post.
In a follow-up statement, Google admitted that its AI Overviews “sometimes produce unhelpful or inaccurate results,” but emphasized that such cases were rare. The company noted that most hallucinations came from “uncommon queries” or satirical web content that the model failed to interpret correctly.
How Google is tackling hallucinations
Google says it’s working on three major safeguards to reduce hallucinations inside AI Overviews:
Grounding in high-quality sources
Gemini models now use what Google calls “multi-stage grounding” — cross-checking generated statements against verified pages from Google’s search index before displaying them. This helps confirm that every claim has a traceable citation.Downranking unreliable content
AI Overviews prioritize sources with high authority and domain trust. Pages with poor E-E-A-T signals, unverified claims, or thin content are less likely to be included in summaries.Tight query controls and user feedback loops
Google has limited the types of queries that can trigger AI Overviews — particularly those involving health, safety, or news. It also added a feedback button so users can report inaccurate results directly, helping retrain the model.
In a June 2024 blog update, Google wrote:
“We’ve already made over a dozen technical improvements to reduce instances of AI Overviews displaying inaccurate or unhelpful information… grounding responses in high-quality sources and better detecting nonsensical prompts.”
The future of search
Search will definitely become more dynamic and sensory.
Users can now upload images, ask questions about videos, or use Google Lens to initiate AI-driven queries.
AI Overviews increasingly combine text, visuals, and structured cards to present information like a living encyclopedia, not a list of links.
This evolution will reshape content formats. Static blogs and keyword-heavy pages will give way to interactive, multimedia-rich assets.
In mid-2024, Google announced that ads would begin appearing inside AI Overviews, alongside the summaries themselves.
These ad placements appear above, below, and even within the generated content block — a move designed to “connect businesses with users in moments of exploration,” according to Google’s Ads & Commerce division.
It’s the clearest sign yet that AI search monetization is here.
For marketers, that means paid and organic visibility will start merging — and brands that once relied solely on ranking will need to compete for space inside the AI summary itself.
Search isn’t dying.
It’s just learning to think.
AI Overview FAQs
1. How do Google AI Overviews work with large language models (LLMs)?
AI Overviews are powered by Google’s Gemini LLM, which blends traditional search ranking with generative reasoning.
Here’s how it works:
Google retrieves relevant pages from its search index (based on the same ranking signals used for normal results).
Gemini analyzes those pages to identify consistent facts, definitions, and context.
It then synthesizes a short, conversational summary that cites trusted sources directly in the overview.
In simple terms: Search finds the data; Gemini writes the summary. This makes AI Overviews less about keywords and more about structured, fact-rich, and verifiable content that Gemini can confidently cite.
2. How does Google decide which pages to include or cite in an AI Overview?
Inclusion depends on three main signals:
Topical relevance: The content must directly and comprehensively answer the user’s question.
E-E-A-T strength: Pages with clear author expertise, organizational credibility, and accurate sourcing are prioritized.
Technical clarity: Structured markup, schema, and scannable formatting help Google’s LLM understand and extract information cleanly.
High-ranking pages are often candidates for inclusion, but ranking alone doesn’t guarantee citation. Even mid-ranking content can appear if it demonstrates better clarity, neutrality, or structured expertise.
3. Why do AI Overviews sometimes get facts wrong or “hallucinate”?
Because LLMs like Gemini generate language through probabilistic prediction, not direct fact retrieval.
When source data is sparse, inconsistent, or ambiguous, the model may fill gaps with plausible but inaccurate statements.
This is known as an AI hallucination.
Google has acknowledged this limitation and introduced measures like grounding (cross-verifying summaries against high-quality pages) and query restrictions (limiting AI Overviews for health, financial, and breaking-news topics).
Still, hallucinations remain possible because LLMs are designed to generate meaning, not verify it.
4. How do ads appear inside AI Overviews?
For now, they don’t.
While Google has publicly confirmed plans to include ads within AI Overviews, these placements are still in testing and have not yet rolled out widely. You won’t currently see sponsored results inside the generative summary itself on live searches. You’ll see them above and below the AIOs.
5. What’s the best way to monitor all of this in one place?
Build an ‘AI visibility dashboard’ using Looker Studio or BigQuery.
Combine data from:
Google Search Console (for CTR and query-level visibility)
Profound or AthenaHQ (for AI citations)
Manual audits from Perplexity, ChatGPT, and Gemini
This gives you a unified view of how often your content is seen, cited, and trusted — across the entire AI search ecosystem.
6. Are there tools that show when my content appears inside AI-generated answers?
Yes. A few platforms are already tracking citations inside generative search results. Examples include: AthenaHQ, Trakkr, Profound, Ahrefs Brand Radar, Otterly, etc.
