Google Lost Two Antitrust Cases, But Stock Rose 65% – Here’s Why via @sejournal, @MattGSouthern

In January, Alphabet passed Apple in market capitalization to become the second most valuable company in the world. Alphabet was worth $3.885 trillion. Apple sat at $3.846 trillion. Only Nvidia, at $4.595 trillion, was ahead.

That alone would be news. But the context makes it something else entirely. Courts had found that Google violated antitrust law in both general search services and general search text advertising. The Department of Justice asked judges to break the company apart, sell off Chrome, divest the Android operating system, and force the sale of its ad exchange. In the search case, the court rejected those proposed divestitures. In the ad-tech case, the government is still asking the judge to order a sale of Google’s ad exchange, and remedies are pending.

In this article, I’ll walk through every active Google antitrust thread, what courts have ordered, what’s still pending, and what the timelines mean. The gap between Google’s legal exposure and its market performance tells a story that matters for everyone working in search.

How We Got Here

When the DOJ’s search monopoly trial opened in 2023, the government argued that Google spent billions on exclusive deals with Apple, Samsung, and browser makers to lock in its position as the default search engine. The case centered on whether those deals maintained a monopoly or reflected a better product.

In 2024, Judge Amit Mehta ruled that Google had maintained an illegal monopoly in general search services. It was the first time a federal court found a tech company had maintained an illegal monopoly since the Microsoft case in 2001.

Then came the remedies phase, where the real fight began. The DOJ wanted dramatic structural changes. Prosecutors laid out four options, including forcing Google to sell Chrome and potentially divesting Android. That was the peak fear moment for investors. It was also the point at which the case stopped being abstract legal theory and started having direct implications for how search distribution works.

What happened next surprised the industry.

The Search Case: Where It Stands

On Sept. 2, 2025, Judge Mehta issued his remedies opinion. He declined to order any divestitures. No Chrome sale. No Android breakup. No forced separation of search from the broader Alphabet structure.

His reasoning centered on AI. Mehta wrote that generative AI had changed the course of the case. He pointed to the competitive threat that AI chatbots posed to Google’s search business and concluded that the market was too dynamic for the kind of structural remedy the DOJ wanted.

Instead, Mehta ordered behavioral remedies. The final judgment, entered on Dec. 5, 2025, limits how Google can structure search distribution deals. Agreements are capped at one year and cannot be used to lock partners into defaults across multiple access points. The judgment includes provisions that require partners to have more flexibility to surface rival search options and, in some cases, third-party generative AI products.

The order also sets out data-licensing obligations for qualified rivals, including access to a portion of Google’s web index and certain user-side data. An oversight process oversees how the implementation is carried out and ensures everything stays in line during the remedy period.

Google filed its Notice of Appeal on Jan. 16, 2026. The company is specifically challenging the data-sharing requirements and the technical committee oversight. The DOJ had until Feb. 3, 2026, to decide whether to file a cross-appeal seeking stronger remedies than what Mehta ordered.

The search case landed in a unique place. Google keeps Chrome and Android. The default search deals that delivered Google the majority of mobile search activity get restructured with shorter terms and fewer restrictions on partners.

Data-sharing could enable competitors to build better search products, but the timeline for that playing out is years, not months.

The Ad-Tech Case: What’s Coming

The second federal case against Google involves digital advertising technology. This one operates on a different track with a different judge and a different set of remedies at stake.

In April 2025, Judge Leonie Brinkema ruled that Google had willfully monopolized parts of the digital ad market. Where the search case focused on consumer-facing search defaults, this case targeted Google’s ad server, ad exchange (AdX), and the connections between them.

The DOJ’s post-trial brief requested the divestiture of Google’s Ad Manager suite, including the AdX exchange. That would mean separating the tool publishers use to sell ads from the marketplace where those ads get bought and sold.

During closing arguments in November, Brinkema expressed skepticism. She noted that a potential buyer for the ad exchange hadn’t been identified and called the divestiture proposal “fairly abstract.” The court, she said, needed to be “far more down to earth and concrete.”

Brinkema said she plans to issue a decision early in 2026. That ruling could arrive at any point in Q1.

The practical stakes here are different from the search case. The search remedies affect how people find Google. The ad-tech remedies affect how publishers make money through Google.

Any forced separation of AdX would directly change the monetization stack that millions of websites rely on. Even if Brinkema follows the same pattern as Mehta and declines structural remedies, the behavioral changes she orders could reshape how programmatic advertising flows through Google’s systems.

The Epic/Play Store Settlement Question

In late January 2026, Judge James Donato held a hearing in San Francisco on a proposed settlement between Google and Epic Games. The case, which centered on Google’s Play Store practices, appeared headed for resolution. But Donato threw the terms into question.

Donato described the settlement as overly favorable to the two companies and questioned whether it came at the expense of the broader class of developers affected by Google’s Play Store policies.

The settlement terms include Epic spending $800 million over six years on Google services, plus a marketing and exploratory partnership. Reports described the partnership as involving Epic’s technology, including Unreal Engine, alongside marketing and other commercial terms.

This case matters because it touches a different part of Google’s ecosystem. The search and ad-tech cases are about how Google dominates web search and digital advertising. The Play Store case is about how Google controls app distribution on Android. Together, these cases cover the three main ways Google generates revenue and the three main ways practitioners interact with Google’s platforms.

The EU Front

European regulators are pursuing their own path, and in some areas, they’re moving faster than U.S. courts.

In September 2025, the European Commission fined Google €2.95 billion for abusing its dominance in ad tech. Google said it would appeal the decision.

Reports from December indicate the EU is preparing a non-compliance fine against Google related to Play Store anti-steering rules. That fine is expected as early as Q1 2026, which would put it on roughly the same timeline as Brinkema’s ad-tech ruling in the U.S.

But the most consequential EU action may be the newest one. On January 26, the Commission opened specification proceedings under the Digital Markets Act focused on online search data sharing and interoperability for Android AI features. The process is framed around access for rivals, including AI developers and search competitors, and is expected to conclude within six months.

That goes beyond what the U.S. search case requires. Mehta’s order mandates data-sharing with search competitors. The EU proceedings ask whether Google must open access to a broader set of rivals, including those building AI-powered products that don’t fit neatly into the traditional search category.

For those watching how AI search develops, this EU proceeding could have bigger long-term implications than anything in the U.S. cases. The question of whether Google’s search index data feeds into competing AI products affects the entire ecosystem of AI-generated answers, citations, and traffic referrals.

Why The Stock Rose Anyway

Google’s stock rose 65% in 2025, CNBC reported, which made it the best performer among the big tech stocks. Apple, by comparison, rose 8.6%. The gap between Google’s legal losses and its market gains points to a pattern that has repeated at every stage of these cases.

When we covered the original verdict in October 2024 and looked at what it could mean for SEO, the range of possible outcomes was wide. Chrome divestiture, Android breakup, elimination of default deals, forced data sharing, and structural separation of search from advertising all sat on the table.

What investors watched play out was a narrowing of that range at every step. Google offered to loosen its search engine deals in December 2024, signaling that behavioral concessions were coming. The DOJ pushed for breakups. The court landed closer to Google’s position than the government’s.

A Financial Times analysis from January 2026 placed Google’s outcome in a broader context. Across multiple Big Tech antitrust cases, judges have shown reluctance to order structural remedies. Meta won outright in November when Judge James Boasberg ruled the company doesn’t hold an illegal monopoly. In the Google ad-tech case, Brinkema expressed discomfort with divestiture. Former DOJ antitrust chief Jonathan Kanter, who helped bring these cases, acknowledged to the FT that the rulings showed the U.S. was too slow to act.

The pattern across cases is consistent. Courts are willing to find that tech companies violated antitrust law. They’re reluctant to order the kind of structural changes that would break the companies apart. And they’re citing AI competition as a central reason for that restraint.

For Google specifically, the combination of light remedies, a strong AI narrative (signs that Google had caught up to OpenAI reinforced investor confidence, according to a Fortune report), and continued dominance in search revenue removed the threat that investors feared most. The breakup scenario didn’t happen, and the stock reflected that.

What This Means For Search Professionals

The antitrust cases resolved in a way that preserves Google’s structure while introducing new requirements around data access and distribution agreements. The impact will unfold over years, not weeks. Here’s what to track.

Search distribution could diversify gradually. The one-year cap on distribution agreements and the restrictions on tying defaults across access points give Apple and Samsung more room to offer users alternatives or to negotiate different terms. Whether they will is a separate question.

Apple’s search-default deal with Google has been widely reported to be worth tens of billions annually. Without that kind of long-term lock-in, Apple has financial incentive to build or license an alternative.

Data-sharing mandates could create new competitors. The judgment requires Google to license a portion of its web index and certain user-side data to qualified rivals, with an oversight process governing the details. The scope matters enormously. Providing limited index access is different from sharing the ranking signals and full index depth that would let a competitor build a viable alternative. Google is appealing this requirement, which tells you where the company sees the real threat.

The ad-tech ruling will directly affect publisher revenue. Brinkema’s decision, expected in early 2026, determines whether Google must separate the tools publishers use to sell ads from the exchange where those ads trade. Even if she orders behavioral remedies instead of a full divestiture, changes to how Google’s ad stack operates will ripple through programmatic advertising. Publishers using Google Ad Manager should pay close attention to the timeline.

The EU’s DMA proceedings open a different front. The January proceedings cover online search data sharing and Android AI interoperability, framed around access for rivals, including AI developers. The outcome would affect how AI search products source their information and, by extension, how content gets cited in AI-generated answers.

Looking Ahead

The next 12 months will determine whether the antitrust cases produce real changes to search markets or settle into a compliance exercise that preserves the status quo.

Key dates and events to watch include Brinkema’s ad-tech remedies ruling, expected in Q1 2026. The DOJ’s decision on whether to cross-appeal Mehta’s rejection of stronger search remedies was due by early February.

Google’s search case appeal will move through the D.C. Circuit, likely taking a year or more. The EU’s DMA specification proceedings on search data sharing and Android AI interoperability are expected to conclude within six months. And the Epic/Play Store settlement faces scrutiny after Judge Donato’s criticism.

Meanwhile, the Amazon and Apple antitrust cases are pending, with trials expected in 2027. Those cases will test whether courts continue the pattern of finding violations but declining breakups, or whether the legal environment changes.

In Summary

Google was found to have maintained illegal monopolies in two separate markets. It’s appealing one case and awaiting remedies in another. Regulators on two continents are pressing forward, and yet the company just became the second most valuable in the world.

Whether the courts ultimately deliver continuity or disruption will play out over the years ahead. Either way, what gets decided in these cases shapes the infrastructure that every search professional works within.

More Resources:

Featured Image: Collagery/Shutterstock

Bing Adds AI Visibility Reporting

Unlike traditional search engine optimization, AI search lacks native performance reporting to help businesses develop organic visibility strategies.

Google’s Search Console combines AI Overviews and organic listings in its “Performance” section, leaving optimizers to guess which channel drove visibility and traffic. ChatGPT shares metrics only with publishers that have licensed their content to OpenAI.

Bing is the first platform to offer some transparency. A few weeks after publishing its “guide to AEO and GEO,” Bing launched an “AI Performance Report” in Webmaster Tools.

AI Performance

The new report tracks citations in Microsoft Copilot, AI-generated summaries in Bing, and select AI partner integrations. But there’s no option to filter by a single surface, and no way to identify the integration partners or their purpose.

The report shows users’ “Total Citations” for the chosen period and “Avg. Cited Pages.” It then lists:

• “Grounding Queries,” which are “the key phrases the AI used when retrieving content that was cited in its answer.” In other words, the queries are the “fan-out” terms that Bing’s AI agents use to search for and find answers, though we don’t know which search engines or platforms they access.

• “Pages,” the URLs mentioned in AI answers.

The new Webmaster Tools section lists citations by “Grounding Queries” and “Pages.” Click image to enlarge.

Each tab includes additional visibility data:

• For every grounding query, Webmaster Tools reports on the average number of unique pages cited per day in AI answers.

• For each cited URL, the report includes its frequency — how often it appears in an answer — not its importance, ranking, or role within a response.

The report provides no traffic or click-through data and no clarity into which Grounding Queries triggered which citations.

Using the Data

The report is a good first step, but it offers little actionable data. Perhaps it will force other players to do more.

According to Bing, the new report:

… shows how your site’s content is used in AI‑generated answers across Microsoft Copilot and partner experiences by highlighting which pages are cited, how visibility trends change over time, and the grounding queries associated with your content.

I’m making the report more useful by:

• Researching organic keywords on Bing and Google that drive traffic to the cited URLs,
• Prompting ChatGPT or Gemini to turn the keywords into prompts,
• Evaluating whether the cited pages address those prompts or need better structure or clarity.

Also, I identify common modifiers in the grounding queries to understand how AI agents find the pages.

Identify common modifiers, such as “virus” in this example, to understand how AI agents find your pages.

Webmaster Tools

Setting up Bing Webmaster Tools takes only a couple of minutes with Search Console enabled.

Log in to Webmaster Tools with your Microsoft account, click “Add site,” and choose the “Import your sites from GSC” option. Allow roughly 24 hours for Bing to collect and report the data.

The Classifier Layer: Spam, Safety, Intent, Trust Stand Between You And The Answer via @sejournal, @DuaneForrester

Most people still think visibility is a ranking problem. That worked when discovery lived in 10 blue links. It breaks down when discovery happens inside an answer layer.

Answer engines have to filter aggressively. They are assembling responses, not returning a list. They are also carrying more risk. A bad result can become harmful advice, a scam recommendation, or a confident lie delivered in a friendly tone. So the systems that power search and LLM experiences rely on classification gates long before they decide what to rank or what to cite.

If you want to be visible in the answer layer, you need to clear those gates.

SSIT is a simple way to name what’s happening. Spam, Safety, Intent, Trust. Four classifier jobs sit between your content and the output a user sees. They sort, route, and filter long before retrieval, ranking, or citation.

Spam: The Manipulation Gate

Spam classifiers exist to catch scaled manipulation. They are upstream and unforgiving, and if you trip them, you can be suppressed before relevance even enters the conversation.

Google is explicit that it uses automated systems to detect spam and keep it out of search results. It also describes how those systems evolve over time and how manual review can complement automation.

Google has also named a system directly in its spam update documentation. SpamBrain is described as an AI-based spam prevention system that it continually improves to catch new spam patterns.

For SEOs, spam detection behaves like pattern recognition at scale. Your site gets judged as a population of pages, not a set of one-offs. Templates, footprints, link patterns, duplication, and scaling behavior all become signals. That’s why spam hits often feel unfair. Single pages look fine; the aggregate looks engineered.

If you publish a hundred pages that share the same structure, phrasing, internal links, and thin promise, classifiers see the pattern.

Google’s spam policies are a useful map of what the spam gate tries to prevent. Read them like a spec for failure modes, then connect each policy category to a real pattern on your site that you can remove.

Manual actions remain part of this ecosystem. Google documents that manual actions can be applied when a human reviewer determines a site violates its spam policies.

There is an uncomfortable SEO truth hiding in this. If your growth play relies on behaviors that resemble manipulation, you are betting your business on a classifier not noticing, not learning, and not adapting. That is not a stable bet.

Safety: The Harm And Fraud Gate

Safety classifiers are about user protection. They focus on harm, deception, and fraud. They do not care if your keyword targeting is perfect, but they do care if your experience looks risky.

Google has made public claims about major improvements in scam detection using AI, including catching more scam pages and reducing specific forms of impersonation scams.

Even if you ignore the exact numbers, the direction is clear. Safety classification is a core product priority, and it shapes visibility hardest where users can be harmed financially, medically, or emotionally.

This is where many legitimate sites accidentally look suspicious. Safety classifiers are conservative, and they work at the level of pattern and context. Monetization-heavy layouts, thin lead gen pages, confusing ownership, aggressive outbound pushes, and inflated claims can all resemble common scam patterns when they show up at scale.

If you operate in affiliate, lead gen, local services, finance, health, or any category where scams are common, you should assume the safety gate is strict. Then build your site so it reads as legitimate without effort.

That comes down to basic trust hygiene.

Make ownership obvious. Use consistent brand identifiers across the site. Provide clear contact paths. Be transparent about monetization. Avoid claims that cannot be defended. Include constraints and caveats in the content itself, not hidden in a footer.

If your site has ever been compromised, or if you operate in a neighborhood of risky outbound links, you also inherit risk. Safety classifiers treat proximity as a signal because threat actors cluster. Cleaning up your link ecosystem and site security is no longer only a technical responsibility; it’s visibility defense.

Intent: The Routing Gate

Intent classification determines what the system believes the user is trying to accomplish. That decision shapes the retrieval path, the ranking behavior, the format of the answer, and which sources get pulled into the response.

This matters more as search shifts from browsing sessions to decision sessions. In a list-based system, the user can correct the system by clicking a different result. In an answer system, the system makes more choices on the user’s behalf.

Intent classification is also broader than the old SEO debates about informational versus transactional. Modern systems try to identify local intent, freshness intent, comparative intent, procedural intent, and high-stakes intent. These intent classes change what the system considers helpful and safe. In fact, if you deep-dive into “intents,” you’ll find that so many more don’t even fit into our crisply defined, marketing-designed boxes. Most marketers build for maybe three to four intents. The systems you’re trying to win in often operate with more, and research taxonomies already show how intent explodes into dozens of types when you measure real tasks instead of neat categories.

If you want consistent visibility, make intent alignment obvious and commit each page to a primary task.

• If a page is a “how to,” make it procedural. Lead with the outcome. Present steps. Include requirements and failure modes early.
• If a page is a “best options” piece, make it comparative. Define your criteria. Explain who each option fits and who it does not.
• If a page is local, behave like a local result. Include real local proof and service boundaries. Remove generic filler that makes the page look like a template.
• If a page is high-stakes, be disciplined. Avoid sweeping guarantees. Include evidence trails. Use precise language. Make boundaries explicit.

Intent clarity also helps across classic ranking systems, and it can help reduce pogo behavior and improve satisfaction signals. More importantly for the answer layer, it gives the system clean blocks to retrieve and use.

Trust: The “Should We Use This” Gate

Trust is the gate that decides whether content is used, how much it is used, and whether it is cited. You can be retrieved and still not make the cut. You can be used and still not be cited. You can show up one day and disappear the next because the system saw slightly different context and made different selections.

Trust sits at the intersection of source reputation, content quality, and risk.

At the source level, trust is shaped by history. Domain behavior over time, link graph context, brand footprint, author identity, consistency, and how often the site is associated with reliable information.

At the content level, trust is shaped by how safe it is to quote. Specificity matters. Internal consistency matters. Clear definitions matter. Evidence trails matter. So does writing that makes it hard to misinterpret.

LLM products also make classification gates explicit in their developer tooling. OpenAI’s moderation guide documents classification of text and images for safety purposes, so developers can filter or intervene.

Even if you are not building with APIs, the existence of this tooling reflects the reality of modern systems. Classification happens before output, and policy compliance influences what can be surfaced. For SEOs, the trust gate is where most AI optimization advice gets exposed. Sounding authoritative is easy, but being safe to use takes precision, boundaries, evidence, and plain language.

It also comes in blocks that can stand alone.

Answer engines extract. They reassemble, and they summarize. That means your best asset is a self-contained unit that still makes sense when it is pulled out of the page and placed into a response.

A good self-contained block typically includes a clear statement, a short explanation, a boundary condition, and either an example or a source reference. When your content has those blocks, it becomes easier for the system to use it without introducing risk.

How SSIT Flows Together In The Real World

In practice, the gates stack.

First, the system evaluates whether a site and its pages look spammy or manipulative. This can affect crawl frequency, indexing behavior, and ranking potential. Next, it evaluates whether the content or experience looks risky. In some categories, safety checks can suppress visibility even when relevance is high. Then it evaluates intent. It decides what the user wants and routes retrieval accordingly. If your page does not match the intent class cleanly, it becomes less likely to be selected.

Finally, it evaluates trust for usage. That is where decisions get made about quoting, citing, summarizing, or ignoring. The key point for AI optimization is not that you should try to game these gates. The point is that you should avoid failing them.

Most brands lose visibility in the answer layer for boring reasons. They look like scaled templates. They hide important legitimacy signals. They publish vague content that is hard to quote safely. They try to cover five intents in one page and satisfy none of them fully.

If you address those issues, you are doing better “AI optimization” than most teams chasing prompt hacks.

Where “Classifiers Inside The Model” Fit, Without Turning This Into A Computer Science Lecture

Some classification happens inside model architectures as routing decisions. Mixture of Experts approaches are a common example, where a routing mechanism selects which experts process a given input to improve efficiency and capability. NVIDIA also provides a plain-language overview of Mixture of Experts as a concept.

This matters because it reinforces the broader mental model. Modern AI systems rely on routing and gating at multiple layers. Not every gate is directly actionable for SEO, but the presence of gates is the point. If you want predictable visibility, you build for the gates you can influence.

What To Do With This, Practical Moves For SEOs

Start by treating SSIT like a diagnostic framework. When visibility drops in an answer engine, do not jump straight to “ranking.” Ask where you might have failed in the chain.

Spam Hygiene Improvements

Audit at the template level. Look for scaled patterns that resemble manipulation when aggregated. Remove doorway clusters and near-duplicate pages that do not add unique value. Reduce internal link patterns that exist only to sculpt anchors. Identify pages that exist only to rank and cannot defend their existence as a user outcome.

Use Google’s spam policy categories as the baseline for this audit, because they map to common classifier objectives.

Safety Hygiene Improvements

Assume conservative filtering in categories where scams are common. Strengthen legitimacy signals on every page that asks for money, personal data, a phone call, or a lead. Make ownership and contact information easy to find. Use transparent disclosures. Avoid inflated claims. Include constraints inside the content.

If you publish in YMYL-adjacent categories, tighten your editorial standards. Add sourcing. Track updates. Remove stale advice. Safety gates punish stale content because it can become harmful.

Intent Hygiene Improvements

Choose the primary job of the page and make it obvious in the first screen. Align the structure to the task. A procedural page should read like a procedure. A comparison page should read like a comparison. A local page should prove locality.

Do not rely on headers and keywords to communicate this. Make it obvious in sentences that a system can extract.

Trust Hygiene Improvements

Build citeable blocks that stand on their own. Use tight definitions. Provide evidence trails. Include boundaries and constraints. Avoid vague, sweeping statements that cannot be defended. If your content is opinion-led, label it as such and support it with rationale. If your content is claim-led, cite sources or provide measurable examples.

This is also where authorship and brand footprint matter. Trust is not only on-page. It is the broader set of signals that tell systems you exist in the world as a real entity.

SSIT As A Measurement Mindset

If you are building or buying “AI visibility” reporting, SSIT changes how you interpret what you see.

• A drop in presence can mean a spam classifier dampened you.
• A drop in citations can mean a trust classifier avoided quoting you.
• A mismatch between retrieval and usage can mean intent misalignment.
• A category-level invisibility can mean safety gating.

That diagnostic framing matters because it leads to fixes you can execute. It also stops teams from thrashing, rewriting everything, and hoping the next version sticks.

SSIT also keeps you grounded. It is tempting to treat AI optimization as a new discipline with new hacks. Most of it is not hacks. It is hygiene, clarity, and trust-building, applied to systems that filter harder than the old web did. That’s the real shift.

The answer layer is not only ranking content, but it’s also selecting content. That selection happens through classifiers that are trained to reduce risk and improve usefulness. When you plan for Spam, Safety, Intent, and Trust, you stop guessing. You start designing content and experiences that survive the gates.

That is how you earn a place in the answer layer, and keep it.

More Resources:

This post was originally published on Duane Forrester Decodes.

Featured Image: Olga_TG/Shutterstock

Ask an Expert: Should Merchants Block AI Bots?

“Ask an Expert” is an occasional series where we pose questions to seasoned ecommerce pros. For this installment, we’ve turned to Scot Wingo, a serial ecommerce entrepreneur most recently of ReFiBuy, a generative engine optimization platform, and before that, ChannelAdvisor, the marketplace management firm.

He addresses tactics for managing genAI bots.

Practical Ecommerce: Should ecommerce merchants monitor and even block AI agents that crawl their sites?

Scot Wingo: It’s a nuanced and strategic decision essential to every merchant.

Scot Wingo

The four agentic commerce experiences — ChatGPT (Instant Checkout, Agentic Commerce Protocol), Google Gemini (Universal Commerce Protocol), Microsoft Copilot (Copilot Checkout, ACP), and Perplexity (PayPal, Instant Buy) — have nearly 1 billion combined monthly active users. With Google transitioning from traditional search to AI Mode, that number will dramatically increase.

For merchants, the opportunity is as big or bigger than Amazon or any other marketplace.

Merchants should embrace AI agents and ensure access to the entire product catalog.

But genAI models need more than access. Agentic commerce thrives not just on extensive attributes but also on the products’ applications and use cases. Merchants should expand attributes beyond what’s shown on product detail pages and provide essential context via a deep and wide question-and-answer section that includes common shopper queries. It enables the models to match consumer prompts with relevant recommendations, driving sales to those merchants.

The time for action is now. Gemini’s shift to AI Mode means zero-click searches will increase, likely producing 20-30% fewer clicks (and revenue) in 2026.