Google is leaving the door open to advertising in its Gemini AI app, with a senior executive telling WIRED the company is “not ruling them out” — a notable shift from the flat denials made just months ago.
What’s changed: In January, Google DeepMind CEO Demis Hassabis told reporters at Davos that Google had no plans to put ads in Gemini. Now, SVP Nick Fox is saying otherwise — noting that learnings from ads in AI Mode will “likely carry over” to Gemini down the road.
The current strategy. Rather than rushing into Gemini, Google is using AI Mode — its Gemini-powered Search product — as a testing ground for ad formats in AI experiences.
Why we care. Google’s entire business is built on advertising. How and if they bring ads into AI products will shape the future of the industry — and set the tone for every AI company trying to figure out how to monetize free users. The brands that figure out how to show up relevantly in conversational AI environments now — before the auction gets competitive — will have a significant first-mover advantage.
The bigger picture. Google is in a stronger position than its rivals to take its time. The company crossed $400 billion in revenue in 2025, giving it the luxury of patience. OpenAI, by contrast, is under pressure to more than double its $30 billion in revenue this year — and has already started testing ads in ChatGPT’s free tier.
Between the lines: Fox’s framing is careful but revealing. By positioning Gemini ads as a “prioritization question” rather than a values question, Google is signaling it’s a matter of when — not if.
What to watch: Personal Intelligence — Gemini’s feature that pulls from a user’s Gmail, Photos, and Calendar — is the sleeper story here. Fox called personalization his “holy grail” for Search, and hinted it could eventually roll into the broader Search experience. If it does, advertisers would gain access to an entirely new layer of contextual targeting — though Fox was quick to add that user data will not be sold or shared.
What’s next. Advertisers should start preparing now. As Google refines its AI ad formats in AI Mode, those learnings will eventually migrate to Gemini. Brands that understand how to show up relevantly in conversational, context-rich AI environments will have a significant head start when the floodgates open.
Dig deeper. Google Is Not Ruling Out Ads in Gemini (registration needed)
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Microsoft have launched their first preview of DirectStorage 1.4 At GDC 2026, Microsoft released the public preview of DirectStorage 1.4 and the Game Asset Conditioning Library. These tools arrive as part of Microsoft’s next-generation feature set for “Project Helix“, Microsoft’s next-generation Xbox. With DirectStorage 1.4, Microsoft has officially added support for Zstandard compression. This popular […]
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Google’s AI Overviews may be reducing traditional search clicks, but publishers still have meaningful growth opportunities in breaking news and Google Discover, according to new data from Define Media Group.
Why we care. AI-generated answers are reshaping search traffic. Evergreen content is losing clicks, while real-time news coverage and Discover distribution are emerging as stronger traffic channels for publishers.
By the numbers. Across Google Search, Discover, and Google News, breaking news traffic grew 103% from November 2024 through early 2026 in the company’s dataset. Losses were concentrated in informational and evergreen content:
Discover’s role: Google Discover, which grew 30% across the portfolio, is now the main growth engine for breaking news distribution. Discover traffic rose steadily as web search traffic fell. For the first time in the dataset, Discover and web search now drive roughly equal traffic.
Why is this happening? AI Overviews appear less often for news queries than for other topics. AI Overviews appeared for about 15% of news queries — nearly three times less often than in categories such as health and science — according to Ahrefs data cited in the report.
The report. BREAKING! News Thrives in the Age of AI
Google is launching Ask Maps, a conversational AI feature powered by Gemini that lets you ask Google Maps complex, real-world questions and get personalized, actionable answers.
What’s new. You can now ask Maps questions like “Is there a public tennis court with lights where I can play tonight?” or “My phone is dying — where can I charge it without a long wait?” and get a conversational answer with a customized map view.
Key capabilities:
On ads. Ask Maps doesn’t include ads yet, but Google isn’t ruling them out, the Gemini team told SEO consultant Glenn Gabe. Because ads are already common in local search, it wouldn’t be surprising to see them appear here eventually.
Why we care. Ask Maps changes how you find places, shifting discovery from keyword searches to AI-generated recommendations. The businesses that get picked will have rich, accurate, up-to-date Maps profiles and strong community engagement, because that’s the data Google’s AI uses to make its picks.
Availability. Ask Maps is rolling out now in the U.S. and India on Android and iOS, with desktop coming soon.
What’s next. Advertisers and local businesses should pay close attention. When AI mediates how people discover places, visibility in Maps becomes more critical than ever. Keep your business listings accurate, complete, and review-rich as Gemini draws from that data to power recommendations.
The announcement. How we’re reimagining Maps with Gemini
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Google redesigned the Asset Optimization section in Google Ads for Demand Gen campaigns, consolidating AI-powered creative controls into a single, cleaner interface.
Why we care. Advertisers managing creative at scale now have a centralized panel to toggle automated features on or off — making the process less manual and time consuming.
What’s new. The redesigned layout groups three key automation capabilities together:
How it works. The new panel surfaces simple toggles for features like Resized videos and Image assets, letting advertisers quickly enable or disable each automation without digging through multiple menus.
Bottom line. Advertisers running Demand Gen campaigns should head into the Asset Optimization panel now and audit which automations are enabled. Turn on video resizing and landing page image pulls if you haven’t already — these are low-effort wins that can meaningfully expand reach without additional creative production.
Also make sure your landing pages are clean and visually strong, since Google will be pulling from them directly. And as Google continues rolling out more AI-driven creative tools, start shifting your workflow toward providing high-quality source assets and letting the platform handle format and placement optimization from there.
MoveAlerts surfaces market-moving news from across the web, analyzes it with AI, and delivers real-time alerts. Use the live dashboard to see sentiment, importance, and bullish/bearish scores for 10,000+ tickers, with stock-level summaries. Get Discord alerts with summaries and market sentiment, build watchlists, and track trending stocks from news and Reddit.
An analysis of ChatGPT conversations found the default and premium models cite almost entirely different sources for the same queries.
The post ChatGPT’s Default & Premium Models Search The Web Differently appeared first on Search Engine Journal.
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With AI-driven search and hyper-fragmented media channels reshaping how people discover brands, the “set it and forget it” approach to marketing measurement is officially dead.
Measuring impact isn’t a static check of dashboard data. Used strategically, measurement is a virtuous cycle where data informs your ad platform settings and those settings, in turn, generate better data (and business outcomes).
Here’s how to build a measurement flywheel that keeps your growth efficient.
Imagine a Bay Area SaaS company, PowerLoop, selling an AI-powered analytics platform. They’re investing heavily in Google Search, LinkedIn, and some emerging AI publication sponsorships.
Their problem? Google Ads is reporting fantastic ROAS, but their internal CRM shows a significant number of leads and opportunities that can’t be directly attributed to any specific ad campaign, making it hard to prove marketing’s true impact to the board.
This is your in-engine reality. Whether it’s Google Ads or Meta, platform ROAS uses pixel and conversion API data to tell you what the platform thinks happened. This might go without saying, but platforms don’t have a habit of underestimating their own impact.
The ideal: Use this for real-time optimization.
The limitation: These signals feed your tCPA (target cost per acquisition) or tROAS (target return on ad spend) bidding strategies. It’s the fastest feedback loop you have, but it’s rarely the full truth. This leads us to…
What it looks like in practice (example): PowerLoop’s Google Ads account is configured with a tCPA bid strategy for “Free trial sign-ups.”
Google Ads reports a healthy $50 CPA, well within their target. LinkedIn also shows strong engagement and click-through rates. This looks great on paper, but the unattributed leads are a nagging concern.
Dig deeper: How to avoid marketing mix modeling mistakes that derail results
Platform data is optimistic. Your bank account is realistic.
Back-end ROAS, coming from your CRM of choice (Salesforce, Shopify, HubSpot, etc.), connects your ad spend to your actual CRM or internal database. It’ll likely require some data engineering work to properly map back-end performance against ad platform spend, but the effort is well worth it.
The ideal: Clean out the “noise” (refunds, fake leads, or credit card declines), and evaluate marketing efficiency based on your own first-party data.
The benefit: You can use back-end ROAS to validate your account structure. If the platform says a campaign is winning but the back end shows low-quality leads, it’s time to restructure your targeting or creative.
What it looks like in practice (example): When PowerLoop connects their ad spend to Salesforce, they find that many of the “Free trial sign-ups” from Google Ads are either incomplete profiles or come from IP addresses outside their target market and never convert to qualified sales opportunities.
LinkedIn, while showing engagement, has a lower conversion rate than expected. This insight leads them to refine their Google Ads audience targeting and adjust LinkedIn campaign objectives to focus more on high-intent lead forms.
This is the “So what?” metric. iROAS answers the question: How many of these sales would have happened even if we didn’t show the ad? This is where marketing mix modeling (MMM) and incrementality testing (geo-lift tests or holdout tests) come into play.
The goal: Identify true value and “halo effects” across channels.
The action: MMM insights tell you where to double down and where you’re just paying for customers who would have converted anyway. Use these insights to prioritize your next round of incrementality tests.
What it looks like in practice (example): PowerLoop conducts a geo-lift test by pausing Google Ads in select non-core markets for a few weeks and measuring the difference in sign-ups between dark areas and similar areas where ads are still running. They discover that while Google Ads drives some incremental sign-ups, a significant portion of those attributed by Google would have signed up organically anyway, through direct traffic or referrals.
Conversely, their MMM suggests that the AI publication sponsorships, while not driving direct “last-click” conversions, are significantly contributing to brand awareness and reducing the overall CPA across all digital channels by driving more organic searches for their brand. This reveals that the sponsorships have a higher iROAS than initially thought.
Here’s an example of overvalued and undervalued channels:
The greater the incrementality factor, the more undervalued this channel has been, such as YouTube and podcasts in this example. The lower the incrementality factor, the more overvalued these channels have been, such as paid review sites in this case.
Dig deeper: Why incrementality is the only metric that proves marketing’s real impact
The final frontier is understanding where to spend the next dollar. Every channel eventually hits a plateau where efficiency craters. This truism is called the law of diminishing returns. Understanding when you hit that mark is key to efficient budgeting.
The goal: Estimate the “room for growth” before hitting a performance ceiling.
The benefit: By monitoring mROAS, you know when to pull back on a saturated channel and reallocate that budget into emerging spaces.
What it looks like in practice (example): PowerLoop’s analysis shows that after spending $100,000/month on Google Ads, another $10,000 yields a marginal return of $0.80 for every dollar spent – meaning they’re essentially breaking even or losing money on additional spend.
However, for their AI publication sponsorships, every additional dollar spent is still returning $2.50 in incremental value, indicating significant room for growth. They decide to reallocate 15% of their Google Ads budget to expand their sponsorship program.
Marketing measurement is a work in progress because the landscape is constantly shifting. Today, you might be perfecting your Google Search strategy. Tomorrow, you’re figuring out how to measure the impact of a mention in a ChatGPT or Perplexity response.
The hypothetical PowerLoop team understands this. They’re constantly evaluating new AI-driven channels and planning how to integrate them into their measurement cycle. They know that what worked last quarter might not work this quarter and that relying solely on platform data is a recipe for wasted spend.
The goal isn’t to find a “perfect” number that stays set in stone. The goal is to use this cycle to stay agile. When your iROAS reveals that a channel is more incremental than you thought, you push your tROAS targets in the platform (Step 1) more aggressively. When mROAS shows you’re hitting a plateau, you start testing new, unproven channels to find different audiences.
Dig deeper: Break down data silos: How integrated analytics reveals marketing impact
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Google Maps launches Ask Maps, a Gemini-powered conversational feature for local discovery in the U.S. and India, plus a navigation overhaul in the U.S.
The post Google Maps Launches AI Conversational Search With Ask Maps appeared first on Search Engine Journal.
V-COLOR has started selling single DIMM DRAM kits with an extra “filler” module V-Color has officially released new “1 +1 Value Pack” DDR5 memory kits for AMD Ryzen systems. These kits include a single DDR5 DRAM module and a second “RGB Filler NON-DRAM solution”. This gives users the appearance of a dual-channel memory system with […]
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A growing share of search interactions now begins inside generative systems. Users open AI tools and ask questions the same way they’d ask a colleague: in full sentences, with context, and often across multiple follow-up prompts.
Generative systems synthesize answers from sources they interpret as credible and relevant to the prompt. Visibility increasingly depends on whether a brand’s content aligns with the questions people ask AI systems, not just the keywords they type into search engines.
Traditional search results haven’t disappeared. Today’s discovery environment blends ranked results, AI-generated summaries, and conversational assistants.
This shift introduces a new research layer: prompt research. It’s quickly becoming a foundational practice for SEO and generative engine optimization (GEO).
Here’s how prompt research works, why it matters, and how to incorporate it into content planning.
Search queries are becoming more context-rich as generative AI platforms encourage users to ask questions in natural language and refine them through follow-up prompts.
Many searches now unfold as a sequence rather than a single query. A user asks an initial question, reviews the generated response, then adds clarifying prompts with new constraints, comparisons, or context.
In these environments, search behaves more like a conversation than a lookup. Each prompt builds on the previous response, creating a chain that gradually clarifies intent.
Several shifts reinforce this pattern:
As a result, the unit of search interaction is shifting. Instead of optimizing for isolated queries, you increasingly need to understand how prompts are phrased, sequenced and refined within AI-driven search sessions.
Understanding those prompt patterns is the goal of prompt research.
Dig deeper: A smarter way to approach AI prompting
Prompt research analyzes the questions people ask generative AI systems and how those prompts shape the answers those systems produce.
In practice, it functions as the AI-era extension of keyword research:
This changes the research process. Instead of mapping keyword variations alone, teams need to:
For example, someone researching email marketing software might begin with a prompt like:
Follow-up prompts extend the conversation:
Prompt research identifies these patterns so you can structure content around how users explore topics through AI search.
Prompt research expands the scope of content strategy beyond ranking individual pages to clusters of related questions.
For SEO, that means ensuring content covers the full topic landscape rather than a single query. For GEO, it means ensuring content provides the context generative systems need to synthesize answers.
Several strategic priorities follow.
Prompt clusters reveal the full range of questions users ask about a topic. Content that addresses those related questions is more likely to rank in traditional search and surface in AI-generated answers.
Search engines and generative systems rely on entities to understand context. Clearly referencing relevant companies, products, technologies, and concepts helps them interpret how information fits together.
Well-organized content is easier for systems to work with. Clear headings, concise explanations, and logical sections help search engines index pages and help generative systems extract key points.
Prompt research often shows that users ask questions in natural language. Content that answers those questions directly — through explanations, comparisons, and FAQs — aligns better with search queries and AI prompts.
Together, these practices help content perform across the modern search environment.
Dig deeper: How generative engines define and rank trustworthy content
Organizations can integrate prompt research into their SEO and GEO workflows through four stages.
Prompt discovery focuses on identifying the questions users ask across generative platforms and AI-assisted search.
Useful sources include:
The goal is to surface prompts with clear intent — especially questions that require explanations, comparisons, or recommendations.
Once prompts are collected, they can be grouped into intent-based clusters. These clusters reveal how users explore a topic across multiple questions.
Common prompt clusters include:
Informational prompts
Comparative prompts
Transactional prompts
Strategic or multi-step prompts
Prompt clustering helps identify patterns and prioritize content topics.
Prompt mapping connects prompt clusters to content strategy.
This typically involves:
For SEO, this helps expand coverage across related queries. For GEO, it helps ensure content addresses the types of prompts that trigger AI-generated answers.
The final step focuses on structuring content so search engines and generative systems can interpret it clearly.
Effective response optimization often includes:
Clear, structured answers improve reader usability while increasing the likelihood that content surfaces in search results and AI-generated responses.
Dig deeper: How to use AI response patterns to build better content
Prompt research introduces new complexities for teams working across SEO and GEO:
Despite these challenges, the underlying opportunity remains clear: understanding prompt patterns helps you anticipate how AI systems assemble answers.
The example below illustrates how that process can shape a content strategy.
Consider a hypothetical SaaS analytics company looking to expand its visibility across AI-generated answers and traditional search.
Initial prompt research reveals several clusters around predictive analytics:
Rather than targeting these prompts with isolated pages, the company builds a content structure around the broader topic.
Each article includes structured explanations, FAQs that mirror common prompts, and citations from industry research.
This structure supports SEO and GEO. The foundational guide captures informational search demand, while supporting and comparison content addresses follow-up prompts users ask as they explore the topic.
Over time, the content appears in both traditional search results and AI-generated answers, expanding visibility in the new search environment.
Dig deeper: Advanced AI prompt engineering strategies for SEO
Brands that begin analyzing prompt patterns today will gain insight into emerging discovery behaviors. A practical starting point involves auditing existing content through a new lens:
Search visibility increasingly depends on how well content participates in AI-generated knowledge systems.
Prompt research helps ensure that participation happens by design rather than by chance.
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Groundsource is a new AI-powered methodology from Google Research that transforms millions of public records into actionable data.
Google Maps has two new AI features: Ask Maps and Immersive Navigation.
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Imagine your ideal customer going to ChatGPT and asking, “Is [BRAND] worth it?”
They’re not getting a vetted list of links in response. They’re getting a synthesized answer, most likely summarizing who you are, what you’re known for, and whether you’re credible. They’ll get a confident answer to the nebulous question of assigning worth.
You don’t control that summary. But it will shape their decision before they convert, possibly before they ever visit your site.
This is the new reality of search. SEO has traditionally been a discovery channel: higher rankings led to more traffic, which led to more conversions. But AI-powered search experiences, from AI Overviews to ChatGPT, Gemini, and beyond, are changing the game.
Narrative is now the goal. Brands have to actively monitor and shape how they’re described, evaluated, and synthesized in AI-powered search experiences.
SEO has officially entered its defensive era. Protecting brand narrative in the new search landscape is quickly becoming table stakes.
You’re probably asking: Isn’t this just reputation management? Or isn’t this what good SEO has always done? Not exactly.
Traditional SEO has focused on visibility: earning rankings, driving traffic, and increasing conversions. Defensive SEO focuses on something slightly different: how your brand is perceived once it’s visible.
Today, perception matters as much as placement. Defensive SEO is the practice of shaping that narrative. It means paying close attention to how AI tools describe your brand and where evaluation-based queries influence buying decisions.
In practice, defensive SEO is:
Just as importantly, defensive SEO is not:
It’s not about hiding weaknesses. It’s about reducing ambiguity.
When your positioning is unclear, AI fills in the gaps with whatever signals are readily available: reviews, old content, aggregator summaries, and competitor comparisons. Defensive SEO ensures the strongest and most accurate version of your brand gets reinforced.
At its core, defensive SEO is structured, proactive brand narrative management across the modern search landscape.
Dig deeper: Why SEO is your best defense against declining organic traffic
Several forces are converging to make defensive SEO necessary today.
Traditional search results allowed users to explore multiple perspectives. Someone researching a brand could read reviews, scan articles, and evaluate different viewpoints before forming an opinion.
AI-generated answers compress that process. Nuanced positioning, evolving messaging, and subtle differentiation can all be condensed into just a few sentences. Those sentences become a prospect’s first impression of your brand — a simplified version of your reputation.
Search behavior is shifting toward evaluation-driven questions. Users are increasingly searching for things like “Is [BRAND] worth it?” or “[BRAND] reviews and complaints.”
These are high-intent, high-impact queries. They signal real conversion consideration.
If brands avoid these topics, outside sources step in to answer them. Review sites, forums, and aggregator pages become the dominant narrative. Ignoring these evaluation queries doesn’t prevent them from shaping perception. It simply removes your voice from the conversation.
Generative engines don’t invent brand reputations. They amplify patterns that already exist.
They rely heavily on reviews and ratings, authoritative third-party mentions, and frequently cited claims or descriptions. Over time, this creates a feedback loop. The most commonly cited narrative gains weight and visibility, while alternative or evolving positioning becomes less prominent.
Dig deeper: Is SEO a brand channel or a performance channel? Now it’s both
Defensive SEO isn’t a single tactic. Like all SEO efforts, it’s an ongoing process focused on understanding and shaping how search engines interpret your brand.
The first step in your defensive SEO tactical plan should be an AI visibility audit.
Auditing AI-generated responses for brand consistency helps ensure that LLMs accurately and positively reflect your brand.
Start by querying AI tools the way real users would. Identify a standard set of questions that someone may realistically ask about your brand.
The goal is to test how the AI agents describe your company across different themes, such as brand overview, services, culture, reputation, and positioning.
Use the same question set across multiple AI tools and LLMs — ChatGPT, Gemini, Copilot, and Claude. Don’t forget to ask for citations, especially if the response is unexpected.
Now that you have all of this data, it’s time to analyze the responses for consistency, accuracy, and opportunity. Look for patterns.
This audit should be done regularly. These patterns reveal how your brand narrative exists within AI-driven search, and how it evolves.
Dig deeper: 200+ AI audits reveal why some industries struggle in AI search
The next step in your defensive SEO tactical plan: update the source material these LLMs are drawing from. While you may not be able to log into ChatGPT and “fix” an answer, you can influence how your brand is portrayed.
Many brands avoid creating content that acknowledges trade-offs or criticisms. In the past, that instinct may have made sense. But today, avoidance can often backfire.
AI systems tend to trust content that provides balanced explanations and transparent comparisons. Ultimately, this type of comparison content is an age-old SEO tactic.
If you’re not creating content that addresses it, chances are your competition is. Clear answers to common concerns signal credibility to your audience and search engines alike.
Instead of ignoring evaluation queries, we should be addressing them head on. The goal isn’t to eliminate criticism, it’s to ensure the context around it is accurate and fair.
We know that generative AI relies heavily on independent sources such as indexed content in traditional search engines, media mentions, reviews, and forum commentary. These third-party sources are influencing how your brand is described just as much, if not more than, owned content.
This means defensive SEO can’t exist in isolation. It requires alignment across multiple disciplines, including PR, social media, and customer experience.
SEO can influence visibility, but SEO alone can’t fix narrative gaps.
Leverage PR in coordination with off-page SEO to earn media coverage and mentions from authoritative third-party sources. Consider Reddit to engage with your audience and share content. Monitor and update social profiles, review aggregators, directory listings, and partner sites.
Many brands evolve faster than their content does. Pricing models change, product offerings expand, and messaging shifts to reflect new positioning. Yet older pages with outdated information often remain.
AI systems pull from everything available and fill ambiguity with whatever is most prominent. That’s why outdated content can shape a brand’s AI output long after it’s relevant.
Regularly reviewing and updating legacy content on your website ensures the signals being used by generative AI reflect the brand you are today.
Use structured data and schema markup to clarify information. Ensure your About pages, service pages, and leadership bios are up to date and comprehensive. Publish well-optimized blog posts and press releases that reinforce your positioning.
If the web is your brand’s resume, make sure it reflects your strongest work, not an outdated version of who you used to be.
Dig deeper: How to use AI response patterns to build better content
Traditional SEO metrics like rankings and sessions still matter, but they’re no longer sufficient on their own.
Defensive SEO introduces a new set of signals to monitor:
Taken together, these indicators help reveal something traditional SEO dashboards rarely capture: how your brand is being interpreted across the search landscape.
Organic share of voice measures how often your brand appears, but in AI-powered search, presence alone no longer tells the whole story. What matters just as much is how your brand is described once it shows up.
This is where the broader idea of “description share of voice” becomes useful. Instead of measuring pure visibility, description share of voice looks at the language and framing associated with your brand relative to competitors.
For example, imagine two companies appearing equally often across AI-generated summaries and search results. One is consistently described as “innovative,” “trusted,” or “customer-focused.” The other is described as “affordable,” “basic,” or “consistent.” Both brands may technically have the same share of voice. However, the narrative attached to that visibility is completely different.
Description share of voice captures that distinction. It reflects the themes and positioning that AI is repeatedly associating with your brand relative to others in the category. And over time, patterns will emerge. Certain descriptors get reinforced, while others may disappear from the conversation entirely.
Tracking these patterns and adjectives provides a clearer understanding of how your brand is being framed and characterized when it does appear.
Despite the name, defensive SEO isn’t about reacting to threats. It’s about strengthening clarity and trust.
When brands actively manage their narrative across the modern search landscape, they reduce misinformation, support informed decision-making, and create a more consistent brand experience. Ultimately, defensive SEO ensures that when someone asks AI about your brand, the answer reflects who you actually are.
This shift isn’t just an evolution for SEO. It’s an organizational one.
Shaping how a brand is understood in AI-driven search queries forces collaboration between teams that too often operate in silos. PR influences the narratives circulating in the media. Customer experience teams hold the signals that shape reviews and sentiment. Social media can surface emerging perceptions long before they appear in search results.
All of those signals increasingly feed the systems that summarize and interpret brands for users.
Most SEOs agree that search has evolved beyond just a discovery channel. It’s now a reputation and perception engine, and often the first filter through which customers understand your brand.
In this multimodal, multichannel world shaped by AI, visibility alone isn’t enough.
Ranking without narrative alignment is fragile. Ranking without context leaves interpretation to systems you don’t control.
The brands that succeed will rank well, shape how they’re understood, and make sure the right story is told.
We’ve tested Google AI Max over the past nine months, analyzing 23 individual tests across 16 already mature advertisers operating within a range of verticals. This article reveals what we did to maximize success with this campaign type.
Your experiments and observations may vary. If so, we’d welcome the debate.
This is intended to be just one voice among many in the conversation around AI Max. All the analyses we discuss are replicable within your own accounts, so you can ratify or dispute the findings based on your own data.
Before launching an AI Max test, consider several factors. Two are particularly significant:
With those prerequisites satisfied, we can now cover some of the juicier findings we’ve uncovered from our AI Max tests.
AI Max performs best when you enable all three core features simultaneously:
Overall, we saw a 40% higher uplift in test success rates for campaigns that used all three features compared to those that opted in only to the baseline search term matching functionality.
Google has been pushing the text customization concept in various guises for a few years. However, earlier versions, like auto-applied recommendations, have had limited uptake. So, we were keen to finally assess the impact this would have.
Using the Added by segment in the assets report, you can compare how text customization performs compared to standard advertiser-provided assets.
We found that AI-edited assets delivered an improved return on ad spend (ROAS) and helped extract more value per impression. Put simply, clients were better off when text customization was activated than when it wasn’t.
This trend was consistent across both headline and description assets, even though we found that text customization modified headlines far more often than descriptions.
Strong performance is the ultimate objective for AI Max campaigns. But from a search geek’s perspective, the arguably more tantalizing result is that text customization demonstrably improved Quality Score.
We assessed historical Quality Scores for clients who activated text customization before and after the test launch. This analysis is valid because the Google Ads interface reports Quality Score only when the search query syntax exactly matches the keyword. This methodology provides a like-for-like comparison across a group of queries that were targeted both before and after switching on AI Max.
We saw a topline improvement in weighted Quality Score, from 6.8 to 7.3. This upward trend repeated across the three components of the Quality Score, with ad relevance showing the most notable uplift.
Logically, this shouldn’t be a surprise. After all, the premise of text customization is that Google shows the best possible ad to each individual user. Nonetheless, it’s satisfying to see this story unfold in our analysis.
At the same time, this finding is noteworthy because advertisers have generally been reluctant to use the full AI Max suite. Across all our test cases, only 50% used text customization, and even fewer (44%) enabled URL optimization.
Some brands will need to adhere to compliance guidelines that outright prohibit the use of these features. But our results suggest that if you have any wiggle room at all, you’d be well served by running a test with all three features.
Google is constantly rolling out additional guardrail features to clarify what is and isn’t off-limits from a brand messaging perspective. Marketers in more risk-averse organizations would be well-advised to keep a close eye on these releases.
Dig deeper: Google expands AI Max text guidelines globally
This next suggestion might seem counterintuitive, but hear me out.
If you’re testing out AI Max for the first time, you might be better off enabling the feature across your entire account right from the start, rather than following a step-by-step approach. There are a few reasons for this.
With AI Max enabled, you can target more queries and users than before. And of those queries, many will genuinely be net-new to your account.
However, it’s also common for queries that another campaign in your account once reached to get pulled into your AI Max campaign.
When we assessed performance at the campaign level, we saw an average +7% increase in conversion value, directly generated by queries the campaign had never targeted before.
When we zoomed out to an account-level view, however, only 46% of those queries were actually new to the account. The remaining 54% had previously been captured elsewhere in the account.
That still isn’t a bad result. An approximately 3% incremental uplift in conversion value, especially for accounts that were already running with a high broad match adoption, is great.
But this finding does have two key implications:
Don’t rely on a cost per acquisition (CPA) by match type analysis to assess AI Max’s efficacy. This approach reveals attribution data within your campaign. But what you really want to know is whether AI Max has improved your overall ability to generate returns at an incremental investment that you’re comfortable with.
There are examples of advertisers trialing AI Max and achieving account-wide efficiency improvements. But you should identify those cases by reflecting on macro, account-wide performance — not by looking at your match type CPAs.
Consider how AI Max interacts with your other campaign types and targeting methods. Let’s call out one particularly glaring example: Dynamic Search Ads (DSA). In our own analysis, every successful AI Max test occurred in an account with low-to-no adoption of DSA campaigns.
This is understandable. Almost every single capability of DSA campaigns is now available in AI Max. So, it shouldn’t be surprising that having both campaign types running in parallel doesn’t improve performance.
It’s plausible that we may not be that far away from Google announcing another round of campaign streamlining initiatives, similar to those for Smart Shopping and Discovery campaigns in previous years. But until then, it’s on marketers to put some thought into the role you intend each campaign type to play within your overall account plan.
Dig deeper: AI Max in action: What early case studies and a new analysis script reveal
If you’re already comfortable with AI Max and you’re ready to push onto the next step, there’s a wealth of new testing opportunities to think about.
Search Bidding Exploration (SBE) was and still is the first major user-facing change to Google’s bidding technology in the last five years. Yet there’s been remarkably little industry chatter so far about this feature. SBE feels like a natural partner for AI Max, given that both tools are designed to reach incremental and previously inaccessible customers.
AI Max also gives you the chance to evolve your thinking around account structure. In an AI Max world, the optimal balance between segmentation and consolidation may lie elsewhere than before.
We’re already starting to see some green shoots of successful hyper-consolidation approaches. But it’s still too early to decisively comment one way or another.
Dig deeper: AI Max increases revenue 13% but drives higher CPA: Study
It’s an intriguing time to be working in paid search, and AI Max has already sparked significant debate and experimentation within the industry. If you’re a later adopter or if you’re looking to improve on a previously unsuccessful foray into AI Max, then consider the following:
HackAIGC is an all-in-one NSFW AI platform for uncensored chat, image creation, and video generation. It lets you converse freely with an adult chatbot, turn text into images, edit and transform images, and create videos from text or images without content filters. Powered by private AI running on your device, it keeps prompts and data local and secure. Free and premium plans provide daily credits and advanced features for high-volume use.
Session Sense connects to your calendars and payroll to show the real cost of every meeting. It centralizes attendance, time, and compensation data in an intuitive dashboard so you can see where time and money go. Its AI finds costly patterns, flags bloated invites, and recommends changes to optimize cadence, duration, and participants. Use reporting to align teams, trim waste, and ensure the right people are in the room.
Xbox Mode is coming to Windows 11 Microsoft has confirmed that its “Xbox Full Screen Experience” is rolling out to Windows users (in select markets) next month under the new “Xbox Mode” name. Xbox mode will let PC gamers switch between the standard Windows user interface and Microsoft’s controller-friendly “Xbox Mode” user interface. This UI […]
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Git layer that preserves the why behind AI-written code
Personal AI Assistant that operates your entire computer
Interactive product tours powered by AI
Google’s newest London building, Platform 37, is named to honor Google DeepMind’s AlphaGo.
We analyze over 650 PC games to compare DLSS, FSR, and XeSS support to reveal how far Nvidia, AMD, and Intel really are from each other in upscaling and frame generation adoption.
Web3Trackers delivers Web3 attribution and crypto marketing analytics that connect Web2 campaigns to on-chain conversions across Ethereum, Solana, Base, and TON. Add one script tag to detect wallet connections, build UTM-style links, and view CAC, LTV, and ROI by channel in real time. It scores wallet quality, filters bots, and tracks swaps, mints, and transfers without an SDK. Start free.
Google announced that Search Console's brand queries filter is open to all eligible sites, spurring questions about the feature.
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AMD is co-engineering its next-generation FSR tech with Xbox for Project Helix AMD’s Jach Huynh has confirmed that AMD is working on its next-generation FSR technologies as part of a “deep co-engineering partnership” with Xbox. Xbox will support “FSR Diamond”, which will be “natively optimised” for Project Helix (Xbox’s next-generation console) and “deeply integrated into […]
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Xbox promises huge CPU and GPU performance uplifts with its next-generation Xbox console At GDC 2026, Microsoft has been shedding some light on its next-generation Xbox console, codenamed Project Helix. Microsoft’s Jason Ronald has confirmed that Project Helix will have an “order of magnitude increase in ray tracing performance and capability”. This places Microsoft’s next […]
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Browser Agents that communicate using ASCII wireframes
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Turn user drop-offs into Pull Requests
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Manage all your browser tabs in one place
Turn any web component into pixel-perfect AI Prompt
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Platform that turns multi-agent AI into a game-like visual
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Superset, Ghostty and tmux coding workspace but for Windows
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Turn messy voice notes into ready-to-publish content.
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Rank in search. Get cited in AI.
Design App Store screenshots in your IDE
Your marketing agent for organic, paid, and AI search
This is Ava, a different kind of ChatGPT.
Agents that experiment together to 100x any outcome
Vibe-edit videos locally using Codex
Create and edit CAD models into production-ready prototypes
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Vibe-automate workflows and earn passive income
The video AI demo agent for B2B SaaS for immediate demos
Open source IDE where AI agents coordinate as a team
Give your agent a card, email and phone in one command
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Open protocol for AI agents to share memory, not tokens
Build, optimize, and scale your AI-native store
Curb is a community-powered parking network that lets you find, reserve, and share parking spots in the city. Use the app to locate available spaces for any vehicle, book in seconds, and help fellow drivers by listing your own spot when you don’t need it. Join a growing community that reduces circling and makes urban trips smoother. No AI, just humans.
Mailwarm is an automated email warmup and deliverability platform that helps businesses keep their emails out of the spam folder and consistently reach the inbox. It improves sender reputation by warming up domains, inboxes, and SMTP servers through natural interactions between real email accounts across providers like Gmail, Outlook, and Microsoft 365.
Mailwarm includes multi-provider warmup, spam score tracking, bounce prevention, and deliverability monitoring to help founders, sales teams, and agencies safely scale cold email outreach and improve inbox placement.
AgentDesk equips AI agents to resolve engineering tickets end to end. They read incoming issues, clone the right repo in a secure sandbox, understand the code, write a fix, run tests, and open a pull request for your review.
It integrates with GitHub and JIRA, supports CLI workflows like Vercel or Azure, and stores secrets in an encrypted vault. You bring your Anthropic or OpenAI API key to control costs, with full audit trails and optional human approvals.
ADot is a platform for solo founders who are tired of building alone. It uses AI to match you with another founder at your exact stage—pre-launch, first users, or trying to monetize—for structured weekly check-ins. You set goals together, review progress, and hold each other accountable when things slip. There is also a community feed where wins get noticed, AI tools that recommend next steps based on similar products, and a skill exchange where founders trade expertise. Free to join, with paid tools coming later.
EVERYTHING Studios lets you create 3D models and deploy augmented reality experiences from plain text or 2–4 reference photos. It automatically builds geometry and textures, optimizes for WebXR, and provides a one-line embed or downloads in GLB, FBX, OBJ, and USDZ formats. Use it to preview products, boost conversions, and launch AR on the web or mobile without code, with plans for individuals, teams, and enterprises.
Primatomic offers managed event sourcing with high-performance materialized views. Append events to immutable logs, deploy WebAssembly views to precompute state, and get consistent low-millisecond reads with automatic snapshots for faster restores and S3-backed archives. You avoid managing Kafka or Kubernetes while gaining durable storage, compute isolation via WASM sandboxes, and a simple API that accepts raw bytes. Start on a free tier and scale with usage-based pricing.
A new Google AI initiative aims to improve heart health outcomes for people living in remote Australian communities.
Updates to Google Play include a new You tab, plus new features in the Apps tab and in Google Play Games.
The new features, which also include a voiceover teleprompter and personalized sound effects, are part of a swiftly expanding suite of creative options in the app.
The video platform, in partnership with Google.org and The Centre for Public Impact, will also work with creators to bring the initiative to audiences.
The guide, created in partnership with SurveyMonkey, highlighted ways in which brands can gain traction with the platform’s human-curated communities.
The company also disabled more than 150,000 accounts associated with scam center networks following this year’s Joint Disruption Week.
The new feature is designed to help parents monitor pre-teen behavior in the app and has features similar parental oversight tools on other platforms.
The new feature allows users to save played tracks Your Music and add them directly to playlists.
The company announced location-based increases ranging from 2% to 5% and said it would no longer be covering the cost of digital service taxes and other fees.
ATMOS is a visual execution system that turns tasks into focused time blocks so you can capture, plan, and execute with clarity. Dump tasks into one inbox, drag them onto your calendar, and work one block at a time while everything else disappears. Connect multiple calendars, use built-in 40Hz binaural beats and brown noise to enter flow, and choose Focus Companions that match your energy, from Pomodoro-style sprints to deep-work sessions.
UnAIMyText transforms AI-generated text into natural, human-sounding writing that evades AI detectors like GPTZero, Copyleaks, and Turnitin while preserving your original meaning. Its engine focuses on flow and stylometry, not just synonyms, to remove detectable patterns. The platform offers unlimited usage, sentence-level rephrasing, multilingual support, saved history, and PDF/Doc uploads, with a privacy-first approach and a generous free tier. You can choose modes including Ultra for tougher detectors, adjust tone and style, and manage work via a simple three-step workflow and an active Discord community.
Gutenberg 22.7 adds features that lay the groundwork for a role as an AI publishing platform and improve the editing experience.
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AI Staff Kit provides seven pre-configured Claude agents for marketing, sales, support, content, data, operations, and executive assistance. Run them in Claude Desktop with Cowork Scheduled Tasks so deliverables ship on schedule while you focus on priorities.
This kit includes over 50 config files, senior-level system prompts, memory and safety frameworks, industry starter packs, three dashboard templates, a 70-page guide, troubleshooting docs, and Iris, a kit manager that diagnoses issues and helps you customize roles. Buy once and receive future updates at no extra cost.
Xbox details Project Helix at GDC 2026 At their “building for the future with Xbox” event at GDC 2026, Microsoft detailed some of the “innovation” inside their next-generation “Project Helix” console. As mentioned before, Microsoft has confirmed that this new system will play both Xbox Console and PC games. Furthermore, Microsoft has confirmed that this […]
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The Galaxy S26 Ultra is Samsung refining rather than reinventing its flagship formula. Reviewers are positive about the excellent cameras, a Snapdragon processor that benchmarks neck-and-neck with Apple's latest, and the standout Privacy Display. Faster charging, improved low-light photography, and Super Steady video round out an update that moves the needle without raising the price.
Clriti turns your strategy docs, notes, or bullet points into stakeholder-ready roadmaps in seconds. Generate tailored views for execs, engineering, delivery, and sales, and switch between timeline, Gantt, Now/Next/Later, swimlane, and other formats from the same data. Use presentation mode, drag-and-drop timelines, and branded themes to share polished plans. Collaborate with your team, upload documents to auto-structure initiatives, export to PNG, PDF, or PowerPoint, and control everything with a natural-language AI command bar.
WordPress issues second security release 6.9.4 to address vulnerabilities that version 6.9.2 apparently failed to patch.
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Eight in ten Performance Max advertisers are receiving connected TV (CTV) impressions via YouTube, as reported by Smarter Ecoommerce’s Mike Ryan. Google has expanded the channel’s reach over the past year — and the trajectory is only accelerating.
The timeline of how we got here:
Why we care. CTV is no longer a specialist buy. If you’re running PMax, you’re almost certainly already on the big screen — and Google has been steadily upgrading what that means for commerce. Google is automatically turning your product feed images into TV ads and allocating budget to CTV impressions, with no action required on your part.
Without actively checking your channel performance breakdown, you have no visibility into where your spend is going or whether auto-generated creative is actually fit for a 65-inch screen.
What advertisers should do right now:
The big picture: YouTube CEO Neal Mohan confirmed that TV has surpassed mobile as the primary device for YouTube viewing in the U.S. by watch time, and YouTube has been the #1 streaming platform in the U.S. for two consecutive years. PMax advertisers are already there — the question is whether they’re managing it intentionally or just along for the ride.
Dig Deeper. YouTube Viewing on TV Now Surpasses Mobile, Desktop in U.S.
Yahoo today introduced MyScout, a customizable homepage inside Yahoo Scout, its beta AI answer engine.
How MyScout works. Logged-in users can customize the homepage with tiles that pull information from Yahoo properties (e.g., Mail, News, Sports, Finance, Games). Examples include:
Users can add, remove, reorder, or create tiles based on topics or queries they want to follow.
New publisher features. Yahoo says Scout supports the open web by linking users directly to original sources used in its AI answers. To support that goal, Yahoo News is also launching new publisher features designed to help you grow recurring audiences on its platform:
Availability: Yahoo Scout — including MyScout — is available in beta for U.S. users at Scout.com and through the Yahoo Search app on iOS and Android.
Yahoo’s announcement. Yahoo Introduces MyScout, the First Personalized Homepage for AI Answers
QuickWise turns your documents, website, and FAQs into a data-grounded AI support chatbot that answers customer questions instantly. It uses a RAG pipeline with source citations, a corrections system to override mistakes, and an FAQ priority layer to enforce authoritative answers. QuickWise also includes built-in support ticketing with automatic bot-to-human handoff, analytics, an embeddable widget, a documentation portal, and API/webhooks. Deploy in minutes, manage multiple chatbots, and track deflection and satisfaction from one dashboard.
Most people have no idea what they're actually worth. Money is scattered across bank accounts, investments, property, and debt, with no single view tying it together. Finsory fixes that with one elegant dashboard and six dedicated modules: real-time net worth tracking, a monthly ledger, property and vehicle valuations, investment portfolio performance, and private lending management. Every asset and liability visible in one place. You wouldn't run a business without knowing your numbers, so why run your financial life without clarity? Finsory gives you the same insight that CEOs demand, applied to your personal wealth.
Fort tracks strength for people who care about longevity.
Adapted remarks from Google VP Christy Abizaid’s keynote at the "Growing Up in the Digital Age" Summit at Google Dublin.
Google’s branded queries filter in Search Console is now available to all eligible sites. Google’s new branded queries filter, announced Nov. 20, lets you separate branded and non-branded search traffic in the Performance report.
Why we care. Separating branded and non-branded queries has long required manual regex filters or keyword lists. This update gives you native segmentation in Search Console, making it easier to measure brand demand versus discovery traffic.
Google’s announcement. Google confirmed the broader availability in a LinkedIn post today:
The details. The branded queries filter appears in the Search results Performance report. It lets you segment queries into two groups:
When applied, Search Console limits metrics — impressions, clicks, CTR, and average position — to the selected group. The filter works across all search types (Web, Image, Video, News) in the report.
Insights report. Google also added a new card to the Search Console Insights report that shows a click breakdown between branded and non-branded traffic.
Google’s brand classification. Google uses an internal AI-assisted system to determine whether queries are branded. The system can recognize:
Some queries may be misclassified due to the contextual nature of brand detection, Google said. The filter is strictly a reporting feature and doesn’t affect search rankings.
What to watch. Today’s announcement indicates it has reached all eligible sites, though some properties may still not qualify due to query and impression volume requirements.
We joke every time we hear Google’s John Mueller answer a question with “it depends.” But actually, it’s true.
There are few definitive answers or universally established facts in SEO. Do meta titles matter? Yes. Is internal linking a good practice? Yes. Is duplicate content bad for SEO? Yes.
But if I tried to make a list of SEO questions with a single, clear, absolute answer, it wouldn’t be long.
That’s the real challenge: we operate in an industry where things almost always depend on context, intent, competition, your website’s situation, and the platform itself.
Yet over and over, we see questions framed as if there must be one right answer. SEO tips are often shared as universal truths — one-size-fits-all for websites, industries, and business models.
My purpose here is simple: to shift that mindset. Especially if you share SEO advice publicly, let’s move away from “this is the only way” and toward “this is one way, depending on your situation.”
The idea for this article came to me when I saw Mueller respond to a Reddit thread about the importance of schema markup. He replied, “This question will stick with us for the next year and longer, and the short answer is yes, no, and it depends…”
And he’s absolutely right.
Schema isn’t a special case of “it depends.” It’s just a familiar one. The same logic applies across almost every debate in our industry, including arguably the biggest one right now.
This has become one of the most debated topics going into 2025 and 2026. Is SEO the same as generative engine optimization (GEO)?
Well, it depends. If we’re talking about core tactics — content quality, structured information, entity relationships, internal linking, bot accessibility, and content discoverability — then yes, there is significant overlap.
But if we’re talking about platforms and how they operate, then no. SEO traditionally optimizes for search engines like Google. GEO aims to influence visibility within generative systems like those developed by OpenAI and others.
The mechanics differ:
That doesn’t mean one replaces the other. It means the context changes.
So, do you still think GEO is the same as SEO? (Yes, no, and it depends are all correct answers.)
This was another Mueller moment on Reddit, where he responded with: “I think I’m trying to say ‘it depends’?”
Is domain age a ranking factor? Not directly.
Can a newer website outrank an older one? Generally yes. Specifically, it depends on a lot of factors:
There are too many moving parts to give a universal answer, and that’s exactly the point.
While it’s tempting to say yes, the standard answer is no. 404s don’t automatically hurt your website’s performance in search.
Fixing 404s is on every technical SEO checklist. It’s a good practice and definitely reduces your website’s technical debt. They don’t naturally hurt your performance in search because Google understands that pages are retired naturally.
Products go out of stock. Articles get removed. Content evolves. A 404 status code, by itself, is not a penalty trigger.
Unless your website creates a large number of 404s in a short period, which can happen during website migrations, for example. If a significant percentage of previously indexed URLs start returning 404s, that can absolutely impact your search visibility for the whole website. Especially if the number of 404s is a noticeable percentage of your website’s pages.
But imagine this: a website with tens of thousands of pages, or even millions of pages, and they have 10 404s. These are definitely not a high-priority fix. Right?
Yes, I would ignore them, especially if your dev team has higher-priority items in their queue. They’re just 10 links. They don’t matter…
Unless they have valuable backlinks linking to them.
Or unless those URLs are heavily linked internally, meaning users and crawlers repeatedly encounter them.
Or you’re running a news website and content is timely, and these 404 pages are ranking in search for time-sensitive keywords instead of your status 200 working content pages.
See what happened? The answer changed based on context. For every rule, there seems to be an exception.
To be a great SEO, you cannot simply operate off a checklist:
You have to ask:
And once again, it depends.
The real skill in SEO isn’t memorizing best practices or having the best, most comprehensive checklist.
It’s knowing when different things apply and understanding:
Saying “it depends” means you understand the question well enough to know it has no single answer.
In an industry shaped by evolving algorithms, multiple platforms, and constantly shifting user behavior, knowing this is foundational.
So maybe instead of rolling our eyes every time we hear “it depends,” we should recognize it for what it is — the most honest answer in SEO.
The largest annual survey of PPC professionals finds the industry under growing pressure — more opaque platforms, weaker measurement, and AI tools that help but haven’t transformed the day-to-day.
Why we care. More than half of practitioners (53%) say PPC is harder than it was two years ago, up from 49%. The dominant reason isn’t competition — it’s that platforms are making more decisions advertisers can’t see or override, and that gap is only widening.
With 89% of digital spend flowing to just three companies, advertisers who don’t build measurement infrastructure independent of platform reporting are increasingly flying blind.
By the numbers:
What they’re saying. Exact match keywords remain the most trusted feature (75% use them often or always). AI Max for Search has the lowest adoption of any tracked feature — 34% have never used it (but then it’s the youngest of Google’s major updates). Auto-apply recommendations are firmly distrusted across the board.
Between the lines. Agency survival is the subtext of the whole report. Finding talent and growing revenue are both flagged as “very or often challenging” by 62% of agency respondents. And the threat isn’t defection to rival agencies — it’s clients cutting agencies out entirely by using AI in-house.
The big picture. Practitioners seem to have found a pragmatic relationship with AI — use it for copy and research, distrust it for autonomous decisions. The harder problem is one AI can’t solve: platforms are taking more control while giving advertisers less visibility. That gap is widening, and there’s no clear fix in sight.
Dig deeper. The State of PPC Global Report 2026
Remove Sora watermarks in 5 seconds. Lossless. It fetches the original clean video (no AI inpainting), so quality stays intact and the process is fast. How it works: paste a Sora share link, wait about 5 seconds, then download. It’s freemium.
Ads Decoded features a conversation on bidding and budgeting with product managers Kristina Park and Carlo Buchmann.
Google is making Merchant Center for Agencies generally available in the U.S. and Canada today — giving agency teams a single login to manage, monitor, and optimize merchant clients at scale.
What’s included:
Why we care. Managing multiple merchant accounts across Google’s ecosystem has historically meant jumping between logins and dashboards. Having it all surfaced in one place means problems get caught faster, before they quietly drain client revenue. And with merchandising opportunity tools built in, it’s not just a monitoring dashboard — it’s designed to actively surface ways to improve performance across your entire client portfolio.
Early results. Digital marketing agency Socium Media piloted the product ahead of the holiday season, using it to monitor client promotions, inventory, and feed diagnostics from one place — and reported 50% faster resolution on monitoring tasks as a result.
The big picture for agencies. Time spent on account monitoring and diagnostics is time not spent on strategy. Tools that compress that operational overhead — especially during high-stakes periods like Q4 — directly translate into capacity for higher-value client work. Agencies managing large retail portfolios should prioritize getting set up before the next peak season.
What’s next. Full details are available in Google’s Help Center, with the rollout live in the U.S. and Canada starting today.
Every few weeks, a new study drops declaring that Reddit (or YouTube, or Wikipedia) is the most important source for AI citations. Marketers share it. Clients ask about it. Someone starts drafting a Reddit strategy.
Because it does. These analyses often flatten the nuance of prompt intent, model differences, and vertical context into a single headline number, and brands jump to start building strategies and teams around benchmarks that have nothing to do with their actual category or customer journey.
The shiny object problem in AI search is real, and it’s getting more expensive.
Tinuiti’s Q1 2026 AI Citation Trends Report (Disclosure: I’m the senior director of AI SEO innovation at Tinuiti) tracked high commercial-intent prompts across nine verticals and seven major AI platforms, including ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Google Gemini, Microsoft Copilot, and Meta AI, over four months ending in January 2026.
The early finding is also the most important one: there’s no universal top source. There are only patterns shaped by intent, platform, and category.
The Reddit headline is real. Across all categories and platforms we tracked, Reddit’s citation share grew by at least 73% from October 2025 to January 2026 and more than doubled in some industries. For Perplexity specifically, 24% of all citations in January came from Reddit alone.
But a deeper look at ChatGPT social citations adds important context for brands: 99% of Reddit citations point to unique discussion threads, not subreddit pages, brand profiles, or corporate content, according to analysis from Profound. ChatGPT isn’t citing just anything from Reddit, so a Reddit presence simply isn’t going to cut it.
The citation opportunity lives in whether the authentic conversations happening in your category contain useful, self-contained answers and whether your brand has any presence in those conversations at all.
This means brands need to focus on driving authentic conversations, not simulating them. Fostering real community in spaces like Reddit — finding your brand ambassadors, participating genuinely, making it easy for satisfied customers to talk — is community building and reputation management. That’s the work that earns citations.
The vertical variance is dramatic.
A brand in OTC health looking at the aggregate Reddit growth number and assuming it applies to them is starting from the wrong baseline.
The platform layer makes it more complex still. Reddit’s share on ChatGPT was above 5% in January. On Google Gemini, it was 0.1%.
If your audience is primarily finding your category through Gemini, the Reddit conversation is almost irrelevant to your AI visibility right now.
Dig deeper: A smarter Reddit strategy for organic and AI search visibility
Here’s where the “it depends” argument gets uncomfortable even for brands that think they’ve done the work of platform segmentation.
Reddit accounted for 44% of all social media citations in Google AI Overviews in January. In Google Gemini, that number was 5%. That’s nearly a 9x difference in Reddit’s influence between two AI products built, maintained, and branded by the same company.
The divergence extends across every social platform we tracked.
A brand building its AI visibility strategy around Gemini performance data could draw nearly the opposite conclusions about Reddit than a brand tracking AI Overviews. They’d also reach different conclusions about Medium, YouTube, and LinkedIn. Same parent company. Same logo. Fundamentally different citation ecosystems.
This is also why the volume of unique domains cited diverged so sharply across Google’s surfaces over the same period.
By January, Google AI Mode was citing 143% more unique domains than AI Overviews – a gap that barely existed two months earlier. The surfaces are evolving at different speeds, in different directions, with different source preferences. Treating “Google” as a single AI channel is like treating “social media” as a single content strategy.
Another data point adds context: roughly 17% of AI Overview citations overlap with Page 1 organic rankings, according to BrightEdge, and that share varies significantly by industry.
Gemini, AI Mode, and AI Overviews cite different sources, and AI Overviews operate on logic that’s largely separate from the traditional Google rankings you’ve spent years optimizing. The surfaces diverge from each other and from organic at the same time.
Dig deeper: AI Overview citations: Why they don’t drive clicks and what to do
Consider what happened between Amazon and Walmart on ChatGPT over the past four months.
In October 2025, Amazon led all major multi-category retailers in ChatGPT citations. By November, its share had dropped sharply. The major cause: Amazon has been aggressively blocking AI crawlers, with nearly 50 specific user agents restricted in its robots.txt file by late January, including all three of OpenAI’s crawlers.
Walmart, which hasn’t taken the same approach, filled that gap and has seen its ChatGPT citation share rise steadily ever since.
Amazon’s strategy is deliberate, but complicated. By blocking the Google-Extended crawler that feeds Gemini while allowing Googlebot (which powers AI Mode and AI Overviews), Amazon is being very intentional about what information it allows direct access to, with the trade-off that it’s not included in the marketplace set in ChatGPT embedded ecommerce.
Amazon would clearly rather drive users directly to Amazon, where it controls the cross-sell, the upsell, and the recommendation via its own shopping agent, Rufus, than have its long history of product data and user-generated content fuel competitor platforms. Amazon sued Perplexity in late 2025 over exactly this kind of access dispute.
The result is that Amazon’s citation share looks completely different depending on which platform you’re analyzing. In Google AI Overviews, it still holds a commanding lead over every other ecommerce player. On ChatGPT, Walmart has taken over.
Dig deeper: How AI-driven shopping discovery changes product page optimization
Citation studies (including ours) should be used as directionals. When you see Reddit growing, check whether it’s actually part of your customers’ research journey in your category. Listen to the conversations, identify your brand ambassadors, and assess the level of effort versus the impact.
When a platform dominates headlines, weigh whether it matters for your vertical before building a brand-new strategy and team around it.
Value, effort, and impact look different for a beauty brand than a manufacturing company, even if they’re reading the same AI search news cycle.
Test with that context. Use aggregate benchmarks to generate hypotheses, then validate them with your own data. The brands building durable AI visibility are doing the less glamorous work of understanding their own category well enough to know where to show up and why.
For the full methodology and findings, see Tinuiti’s Q1 2026 AI Citation Trends Report (registration required), developed with Profound.
Optimizing your client’s TripAdvisor listing is an important part of the local SEO ecosystem, even though it’s often treated as a secondary channel. Done well, it can increase visibility, drive more qualified website traffic, and strengthen brand positioning and online reputation.
TripAdvisor frequently appears in search results for tourism and hospitality businesses and often serves as a key third-party discovery touchpoint. Treating it as a strategic SEO asset — not just a review site — can create meaningful advantages in visibility, trust, and conversions.
TripAdvisor is a travel booking and decision-making platform where users arrive with clear conversion intent, typically in the mid-to-lower funnel stages. It functions as both a comparison tool and a marketplace for hotel reservations, excursions and attractions, restaurants, and cruises.
Reviews on TripAdvisor matter, but they don’t operate in isolation. Their impact depends on the overall quality of the business profile, including the clarity of its unique value proposition and the strength of its brand image.
TripAdvisor offers less control than an owned website and doesn’t align with classic technical SEO frameworks the way platforms like Amazon or LinkedIn do. Still, Google continues to surface TripAdvisor prominently across tourism and local business searches, reinforcing its role as a trusted external source.
TripAdvisor is also known for operating one of the most powerful programmatic SEO architectures in the industry, with millions of URLs indexed by city, category, search intent, and experience type. The platform is estimated to receive around 490 million unique visits per month.
Dig deeper: Local SEO sprints: A 90-day plan for service businesses in 2026
Optimizing your business profile on Tripadvisor doesn’t just mean hoping users will leave positive reviews of your location or services. Follow these three strategies to strengthen your listing.
Strategically responding to reviews can strengthen semantic SEO by enriching the contextual signals around your business and increasing the likelihood of being referenced in AI-powered search experiences and LLMs.
For example, if a guest mentions enjoying the hotel pool during a family trip but provides little detail, your response can thank them for their stay while highlighting the range of water-based activities available for children and older guests. This builds richer context around the hotel’s leisure facilities beyond the original review.
It’s also important to guide guests when possible. For example, when using QR cards to encourage reviews, briefly explain the value of writing a detailed, descriptive review rather than a one-line comment.
Images on TripAdvisor act as immediate scroll stoppers. They must be visually strong, eye-catching, and vibrant, clearly conveying a positive emotion and a high-quality experience within seconds.
If you’re unsure which images to place in cover or top positions, review performance data from platforms like Instagram or TikTok to identify visuals that generated the highest engagement.
Ideally, refresh images every 4 to 6 weeks. Each caption should include a clear description, written in natural, fluent language, that provides context for the dish or service, the target audience, and the type of experience offered.
For example: “Grilled salmon served on our sea-view terrace, a popular choice for solo travelers during the summer.”
Dig deeper: The local SEO gatekeeper: How Google defines your entity
Proper tagging and categorization is one of the most poorly handled aspects of TripAdvisor. Incorrect categorization or missing relevant tags directly affects internal visibility, influencing rankings, filters, and curated lists.
As part of TripAdvisor’s programmatic SEO architecture, these signals also influence how pages are structured and surfaced in Google when users search for local businesses.
For example, to appear in TripAdvisor rankings like [The 20 Best Restaurants for a Romantic Valentine’s Day Dinner in New York], your categories and tags must cover the full range of real experiences your business offers.
It’s surprising how many businesses still have duplicate listings on TripAdvisor in 2026. This usually happens because creating a listing is relatively easy and doesn’t require official business verification—something the platform could improve.
However, claiming and merging duplicate listings does require official documentation to verify ownership, such as a business registration certificate or a recent utility bill linked to the business address.
Make sure your business description, menu item names or services, opening hours—especially public holiday hours—and any other sensitive business information match exactly what appears on your Google Business Profile.
From a brand SERP perspective, particularly in tourism and hospitality, TripAdvisor is often the main third-party channel where users discover your brand.
In many cases, a TripAdvisor listing appears above your own website. An incomplete, outdated, or poorly optimized profile can weaken trust before users reach your site. Optimizing TripAdvisor means owning a critical part of your brand’s search footprint.
Dig deeper: Want to win at local SEO? Focus on reviews and customer sentiment
One advantage of TripAdvisor business profile SEO, which receives relatively little attention, is that when you execute it properly and quickly, it can become a clear competitive advantage and a strong strategic position against competitors.
Just keep the following in mind for TripAdvisor rankings:
TripAdvisor SEO depends on consistency, attention to detail, and understanding how reviews, content, and engagement signals work together to influence rankings and user decisions.
When you do it well, your customers become your strongest marketing asset.
Nvidia confirms DLSS 4.5 upgrade for War Thunder Nvidia has confirmed that War Thunder will be getting upgraded with support for DLSS 4.5, improving the fidelity of Nvidia’s AI upscaling solution. With this change, Nvidia users should be able to enjoy clearer visuals in War Thunder, especially when upscaling from lower resolutions. Nvidia has boasted […]
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Dutch Web Services delivers production-ready backend infrastructure with AI-powered generation, enterprise authentication, and fully managed deployment. Define your data model with a visual schema builder, generate APIs automatically, and ship to production with monitoring and environment controls. Use real-time CRUD, advanced entity management, OpenAPI specs, and third-party integrations. Secure apps with multi-tenant isolation, machine-to-machine auth, SSO and SAML, and role-based access. Scale with team collaboration, custom domains, SLA-backed support, and optional on-premise deployment.
LinkedIn made some good moves last year that I’ve seen pay off for our suite of B2B clients. Now that we’re into 2026, with yearly marketing goals in focus, I’ve got some recommendations based on our 2025 learnings for you to test and leverage in the coming months. Those include:
Let’s put a magnifying glass on each and explain the benefits you stand to gain.
Even though Meta and TikTok are more natural fits for video, LinkedIn isn’t immune to the video movement — particularly short-form video (between 7-15 seconds). While having video content is an important line item on your marketing strategy plan, the right content is even more important.
There are plenty of ways to leverage video, including new-ish placements like First Impression Ads. What I recommend is that you try video ads in the feed first to compare performance and engagement with other types of in-feed ads you’ve been running.
The usual caveats apply here:
Dig deeper: LinkedIn study reveals how B2B video ads can gain +129% engagement lift
One of the toughest parts of B2B advertising is engaging potential customers on behalf of a business or corporate entity. Thought Leader Ads (where companies can essentially boost content from employee accounts) have actually been around for a couple of years, but we got serious about testing them in 2025 and earned much higher engagement than with typical ads from business profiles.
TLAs open up some creativity, too. Humor-focused posts, for one, are a lot more natural a fit from a personal account.
As with other boosted content, be judicious about where you invest. If a post already has traction organically (and that’s become harder over the last year as LinkedIn has throttled back reach) and makes a good business case for working with your company, that’s a good candidate for a TLA.
A couple of caveats here, too:
Dig deeper: LinkedIn Ads retargeting: How to reach prospects at every funnel stage
In the latter half of 2025, we ran a significant number of tests with personalized LinkedIn ads across different geos and using different campaign types.
In our global campaigns, we saw an average of >20% improvement in cost per lead, with higher CTR and lower CPC. U.S. campaigns were even more successful. CPLs dropped 33%.
Per our LinkedIn rep, European users in particular value privacy more than U.S. users, so it makes sense that personalization was more effective stateside. Either way, and even in U.S. campaigns, personalized ads began to show signs of fatigue after about a month.
We responded by combining personalized and non-personalized ads into one campaign to lower the frequency of the personalized ads — and also allow for side-by-side comparisons in the same environment.
Dig deeper: LinkedIn’s new playbook taps creators as the future of B2B marketing
If you’ve run Conversions API (CAPI) and enhanced conversions in Meta and Google, you’re familiar with the idea of Qualified Lead Optimization. Essentially, this is LinkedIn’s way of letting you integrate your first-party data into the platform’s back end to help its algorithm find higher-quality users.
Now, this isn’t quite as effective as its Meta and Google counterparts yet, but we’ve seen an increase in the proportion of qualified leads.
To test it:
This one is tactical, but it’s saved me a ton of time in our accounts, so it’s worth making sure you’re aware of it.
In March 2025, LinkedIn launched a few updates to Campaign Manager, including a new feature that makes it easier to duplicate ads across campaigns and accounts. This has greatly improved our time to launch new campaigns – there’s no downside to getting your hands around it.
We haven’t yet aggressively tested LinkedIn’s new CTV capability, but we’re keeping an eye on industry perspectives. This can be a great medium to gauge the messaging and positioning that works for your brand with niche targeting options before rolling out big-screen campaigns.
In the scheme of things, LinkedIn provided some quality-enough updates last year for us to shift more client budget there. As always, you need to carry the right expectations for the platform and make sure you have a strong methodology for measuring its value in your pipeline.
With those in place, and with a rock-solid understanding of your ICP that lets you fully leverage LinkedIn’s targeting levers, I’m betting LinkedIn can be a pleasantly surprising source of growth in the coming months.
Pearl Abyss released detailed hardware requirements for Crimson Desert Crimson Desert is due to be released on PC and consoles next week, and Pearl Abyss have released detailed PC and console specifications for their game. This includes detailed resolution, settings, and framerate targets for their game. On PC, Crimson Desert appears to be well optimised, […]
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Nvidia’s now ready for Crimson Desert and Death Stranding 2 NVIDIA has officially released their new GeForce 595.79 WHQL driver for Death Stranding 2: On The Beach and Crimson Desert. With this driver comes crash fixes for Crimson Desert, graphical fixes for Resident Evil Requiem, and crash fixes for Star Citizen. This new driver contains […]
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