Ads in Google's AI Mode: Navigating GenAI Discovery, Privacy and Monetization

Google has quietly begun testing advertisements inside its AI-powered Search interface and related Gemini-driven experiences — and the move is already prompting a broad re-think of how discovery, commerce and privacy will work when answers are generated instead of links being listed.

Background / Overview​

Google’s AI Mode (the conversational, Gemini-powered tab in Search) and the company’s AI Overviews have been part of a deliberate shift away from a pure list-of-links model toward a multimodal, conversational retrieval surface. Google published the initial framing of AI Mode and the expansion of AI Overviews earlier in 2025, positioning these features as ways to help with complex, multimodal questions and follow‑up dialogues. On May 21, 2025, reporting confirmed Google would begin experimenting with ads inside both AI Overviews and the AI Mode interface, showing examples such as sponsored product recommendations embedded alongside synthesized answers. The Verge’s coverage described tests that include clearly labeled “sponsored” product recommendations and expanded desktop placements for AI Overview ads. Separately, reporting indicates OpenAI is also exploring advertising options inside ChatGPT — specifically the idea of showing ads informed by ChatGPT’s memory feature (the personal information the system retains to improve conversations). The reporting surfaced through outlets that cited internal discussions and focus-group work, and those reports are now being widely circulated by mainstream tech press.

---

What Google has confirmed (and what the company says it is testing)​

AI Mode and AI Overviews: product context​

AI Mode is an experimental, opt‑in Search Labs feature powered by the Gemini family of models. It synthesizes results, supports follow‑up questions and increasingly accepts multimodal inputs such as images (via Google Lens) to produce context-aware answers. Google’s official blog and product posts describe AI Overviews as already reaching large audiences and being upgraded with new Gemini model improvements. These features are being rolled out incrementally through Google Labs and staged server-side gating, so availability and behavior can vary per account and region. The Windows desktop experiment that folds AI Mode and Lens into a summonable overlay (Alt+Space) is another vector by which Google is bringing conversational answers to where people work. Early hands‑on coverage highlights the unified local + cloud search experience and notes the experimental nature of the client.

The ad tests Robby Stein described​

In a recent podcast appearance, Robby Stein, Vice President of Product for Google Search, acknowledged that Google is running early experiments with ads inside AI Mode and other AI experiences. He framed the tests as exploratory and emphasized Google’s continuing role as a utility for everyday needs — from insurance quotes to local business discovery — even as search habits change. Multiple reports paraphrasing the podcast quote note Google’s stance that traditional Ads are not going away, and that AI surfaces may create new opportunities for advertisers.

---

How ads might be integrated into conversational answers​

The shift from link lists to synthesized answers presents a design challenge for advertising: conversational responses leave fewer obvious “slots” for banner-like placements. Reported test examples and product commentary suggest several potential formats Google (and other vendors) are exploring:

• Inline sponsored recommendations: brief, labeled product suggestions embedded directly in the AI response (for example, a “helpful ad” for a website builder when a user asks how to build a website).
• Shoppable image results: AI Mode’s visual responses already surface images with retailer links; these could be augmented with paid product placements or prioritized shopping entries.
• Sponsored cards and callouts inside AI Overviews: lists of recommended products, local businesses or services that carry a “sponsored” label and purchase/click-through links.
• Eligible campaign migration: existing Search/Shopping/Performance Max campaigns may be made eligible to appear inside AI responses, giving advertisers a route to bid for placements in generative surfaces.

These formats aim to preserve the conversational flow while giving advertisers a transparent label and users a clear way to click through when they want to act. Early signals show Google intends the label to be visible, but execution details (placement prominence, frequency caps, and measurement) remain in flux.

---

Why ad tests are happening: economics and incentives​

Running large language models and providing free AI experiences at scale is expensive. The major AI platforms have converged on a tiered monetization model — free access with limits, and premium subscriptions for power features — but the long-run economics of free tiers push companies to seek alternative revenue streams. Advertising is the most familiar, immediately scalable lever. For Google specifically, ads inside AI surfaces let the company:

• Maintain a low‑friction path for advertisers to reach intent-rich users inside a new discovery surface.
• Leverage its existing ad infrastructure (Search Ads, Shopping, Performance Max) to monetize attention that previously produced referral clicks to publishers.
• Offer advertisers new creative formats tied to multimodal queries (visual shopping, local bookings, recommendation cards).

OpenAI’s reported exploration of ad placements is framed similarly: with memory and personalization, there is theoretical value in offering advertisers highly relevant placements — though OpenAI leadership has repeatedly expressed ambivalence about the aesthetic and trust trade-offs of ads. The proposals reported internally would, if adopted, use memory signals to increase relevance but would also raise privacy and opt‑in questions.

---

UX, labeling and the risk of “conversation-first” monetization​

Integrating ads into conversational answers tests foundational assumptions about user trust and transparency. The core UX risks include:

• Confusion about what is paid vs. organic: if a synthesized sentence recommends a product, users must be able to tell whether that recommendation is sponsored. Early tests emphasize labeling, but conversational language complicates clear separation.
• Reduced referral traffic to publishers: when an AI answer resolves a user’s question directly, the likelihood of clicking out to the web shrinks. Industry analysis has shown AI overviews reduce click-through rates substantially in some contexts, a trend that can squeeze publisher ad inventory. This dynamic is part of the broader debate over “zero‑click” search and platform concentration.
• Perception of manipulation: when assistants begin to nudge users toward purchases, the line between helpfulness and bias can be thin. This is especially fraught if ranking or revenue incentives influence product suggestions. Editorial and product governance will matter greatly.

Design mitigation will need more than labels; it will require explicit separation of monetized content, user controls to prefer non-sponsored answers, and independent auditing to ensure recommendations are not unduly influenced by payment. Early product messaging from Google emphasizes clear “sponsored” labels and consumer-first design, but independent verification will be essential as tests scale.

---

Privacy, memory and regulatory angles​

ChatGPT memory — opportunity and liability​

OpenAI’s memory feature is designed to make conversations more useful by retaining user preferences, context and other signals across sessions. The product page explains how users can control memory and delete or disable it. But the very existence of memory also creates a potential data reservoir that could be used to increase ad relevance — if an operator chooses to do so. Recent reporting says OpenAI is considering that path; OpenAI has not announced a commercial pivot to advertising as policy, and the idea remains a reported exploration rather than a confirmed roadmap.

Data protection and compliance​

The combination of local context (for instance, a Google desktop app that indexes local files or enables screen capture for Lens) and cloud AI processing raises multiple compliance questions for enterprises and privacy-conscious users:

• Where is local content indexed and processed — purely on device or routed to cloud services for analysis?
• What retention policies apply to captured screen content or local-file snippets surfaced into AI Mode?
• Does opting out of memory or local indexing guarantee no personalization signals are used for ad targeting?

Early hands‑on reporting of Google’s Windows experiment shows the app is opt‑in through Search Labs and requires sign‑in, Lens permissions and Drive linking — but Google has left several operational details (encryption at rest for local indexes, enterprise admin controls) under-specified in initial announcements. Administrators should treat the client as experimental and cautious pending a formal enterprise FAQ.

Regulatory scrutiny​

Ads inside generative responses are likely to attract regulatory attention on multiple fronts: consumer protection (labeling and transparency), data protection (use of memory or local content for ad targeting), and competition (platforms that control both discovery and monetization). Antitrust scrutiny and publisher pushback are plausible downstream consequences if platform owners are seen to tilt discovery in ways that favor their ad inventories.

---

Impact on publishers, advertisers and the open web​

For publishers​

Generative search surfaces that deliver answers instead of links threaten referral traffic patterns that many publishers rely on. Multiple independent analyses and industry data have shown that AI Overviews and synthesized answers can reduce click-through rates substantially for queries that would previously have sent users to the open web. Publishers should accelerate diversification of direct audiences (apps, newsletters, subscriptions) and negotiate clearer terms with platforms about content usage and compensation.

For advertisers​

Conversational AI presents both opportunity and complexity for advertisers:

• Opportunity: high-intent, context-rich placements within AI responses could generate strong performance when shown to users ready to act.
• Complexity: new ad formats require new measurement practices, and advertisers will need clarity on placement transparency and conversion attribution inside generative surfaces.

Advertisers should demand testing controls and holdout experiments (A/B tests) so performance claims made for AI placements can be validated against existing channel outcomes before wholesale budget migration.

---

Windows users and IT teams: immediate practical guidance​

Google’s Windows overlay experiment and AI Mode integration bring these questions directly to Windows desktops. The Windows client is opt‑in via Search Labs, brings Lens and AI Mode to a summonable overlay, and can surface local files, Drive documents and web results in a single pane. Early reporting suggests the experiment is limited to personal accounts and U.S./English users at launch, and lacks enterprise admin controls initially.
Practical steps for IT and power users:

• Treat the client as an experimental consumer app until Google publishes enterprise controls and a privacy/technical FAQ.
• Block or restrict installation on managed devices if the device stores sensitive data or if your compliance regime forbids unvetted outbound processing.
• If you pilot it, run network monitoring and log where AI Mode and Lens traffic goes, and require opt‑in only on non‑production workstations.
• Educate end users that conversational answers may synthesize content and to verify citations before acting on non-trivial recommendations.

These are conservative but prudent measures while product documentation and admin tooling are still in early stages.

---

Product and policy questions Google (and others) must answer​

• Ad labeling clarity: How will “sponsored” be shown in natural language responses to avoid subtle bias?
• Opt‑outs and preferences: Can users opt out of receiving monetized recommendations while keeping AI Mode functionality?
• Measurement and auditing: Will platforms provide transparent placement-level reporting (impressions, clicks, conversions) for AI surfaces that advertisers can reconcile with other channels?
• Enterprise readiness: When will admin controls, data residency guarantees, and audit logs be available for managed accounts?
• Third‑party content use: How will content licensing and compensation for publishers be handled when AI summaries are built from multiple sources?

Answers to these questions will shape whether stakeholders view AI‑surface ads as acceptable and market‑supporting or as a threat to the open web and user privacy. Early product statements emphasize consumer-first design and visible labeling, but independent verification and regulatory engagement will be required to ensure good outcomes.

---

How to watch the experiments and what signals matter​

• Product rollouts: watch Google Labs release notes and the Google Search blog for changes to AI Mode eligibility, ad formats and labeling protocols.
• Publisher traffic trends: sustained, measurable declines in referral traffic for particular verticals will indicate how often AI overviews are replacing clicks.
• Advertiser migration: observe whether major ad budgets move into AI placements and whether publishers receive compensation or new ad share.
• Regulatory statements: regulators may issue guidance or enforcement actions related to labeling, competition, or data use; track statements from competition and privacy authorities.
• OpenAI decisions: because OpenAI has publicly rolled out memory and control features, any official pivot toward advertising using memory signals would be a major industry inflection point. For now this remains reported internal exploration rather than a confirmed roll‑out.

---

Strengths, risks and a balanced assessment​

Strengths (why platforms want this)​

• New commerce pathways: AI surfaces can convert discovery into action more quickly, potentially improving advertiser ROI for certain queries.
• Improved relevance: Multimodal inputs and memory/personalization can make promotional content more relevant and useful when handled transparently.
• Sustainable free tiers: Advertising is a proven way to subsidize free access at scale, which platforms argue preserves broad user access to AI tools.

Risks (what could go wrong)​

• Trust erosion: Monetized suggestions inside a conversation risk undermining perceived impartiality of the assistant.
• Publisher revenue loss: Fewer clicks to the open web can materially reduce publisher ad inventory and diminish the open web’s economic health.
• Privacy and compliance danger: Using personal memories or local content for ad targeting raises consent, data-minimization and regulatory risks.
• Concentration of ad dollars: If discovery and monetization consolidate inside a handful of generative platforms, independent ad tech players and smaller publishers could be squeezed.

Balanced takeaway​

If executed with clear labels, strong user controls, independent auditing and fair publisher deals, ads inside AI answers could be a pragmatic way to fund free access to powerful assistants and to create new commerce integrations for users. If executed poorly — opaque labeling, aggressive personalization without consent, or preferential treatment of paid results — the result will be erosion of trust and a deeper consolidation of the web under a few platform owners. The early testing phase is the moment to set the guardrails.

---

Recommendations for WindowsForum readers (practical checklist)​

• If you’re a Windows power user: try AI Mode and the Google Windows overlay in a personal, sandboxed environment to evaluate utility and to learn how the interface surfaces sponsored content. Disable Lens or local-file indexing if you want to avoid cloud analysis of local content.
• If you’re an IT admin: treat early deployments as consumer experiments. Block or restrict the client until Google publishes enterprise controls, and update BYOD and acceptable‑use policies to explicitly address experimental AI apps. Monitor outbound traffic from endpoints used in pilots.
• If you’re a publisher: expand direct audience channels and pursue licensing conversations with platforms that use your content to generate AI summaries. Run analytics tests to quantify query-level traffic changes attributable to AI Overviews.
• If you’re an advertiser: insist on transparent placement metrics and run controlled holdout experiments before moving meaningful spend into AI placements. Ask for clarity on when campaign types (Search, Shopping, Performance Max) are eligible to appear in AI surfaces.

---

Conclusion​

Google’s confirmed experiments with ads inside AI Mode — and parallel reporting that OpenAI is exploring ad placements tied to ChatGPT’s memory — represent the next logical battleground for monetization of generative search. These tests will shape how discovery, commerce and privacy coexist when answers are synthesized rather than linked. The outcomes will depend less on technology than on product governance: labeling clarity, user controls, publisher agreements and regulatory scrutiny.
For Windows users and IT teams, the immediate posture should be cautious curiosity: evaluate functionality and productivity benefits in controlled settings, but wait for enterprise-grade controls and clear privacy documentation before broad adoption. The desktop is becoming another front where assistants and ad tech meet — and how the industry builds the guardrails during these early experiments will determine whether generative search becomes a net benefit for users, publishers and advertisers, or a source of concentration and distrust.

---

Source: Storyboard18 Google experiments with ads in AI search mode