I recently discovered how Meta is revolutionizing online shopping on Facebook and Instagram. Their new features aim to simplify the purchase process and enhance how advertisers turn casual browsing into actual sales.
Exploring New Possibilities. Meta is making a significant move by spreading Live Video Ads globally on Facebook, and now they’re introducing these to Instagram. This expansion allows businesses to reach more people during live shopping events, potentially increasing sales directly from these experiences.
In the U.S., Meta is partnering with several live commerce providers such as CommentSold and TalkShopLive to help sellers transform live streams into ads that can connect with untapped audiences.
Thanks to Facebook’s Live Shopping Tools, users can now browse and purchase products without leaving the livestream, making shopping more seamless than ever before.
Introducing a New Checkout Experience. Starting this summer, Meta will be offering a virtual card payment feature on both Facebook and Instagram through a collaboration with Mastercard and Visa.
What excites me about this feature is that it generates temporary, one-time card numbers linked to my existing cards. This means I can shop without sharing my real card details, enhancing both security and trust among users.
Benefits for Advertisers. Meta is integrating product data as a core aspect of all Sales campaigns. This streamlines the advertising process by allowing advertisers to combine product feeds with creative assets, all while Meta’s AI assembles the most engaging ads tailored to individual users.
By using product details like pricing and availability, advertisers can craft detailed and high-performance shopping campaigns.
Why This Matters. Meta’s innovations offer brands more ways to convert browsing into purchases without shoppers leaving the app. With these new features, advertisers can potentially reach larger audiences through live shopping events and AI-driven ads, optimizing their approach to sales.
The introduction of virtual card checkout aims to reduce barriers in the purchase process and build consumer trust, possibly boosting conversion rates.
A Glimpse into the Future. Meta sees AI as a game-changer in product discovery, emphasizing how recommendations now organically appear in content feeds and creator videos over traditional searches.
By leveraging product catalogs as vital data points, Meta empowers these discoveries across various platforms like creator content and business recommendations.
I’m thrilled to introduce the latest addition to Profound: the External MCP Connectors. With these, I’ve found it incredibly easy to link my favorite CMS tools, project trackers, and team communication platforms directly to Profound via MCP.
This seamless integration has transformed the way I manage projects, allowing me to streamline workflows and enhance team collaboration. Now, all my critical tools are accessible from one central hub, boosting my productivity like never before.
Try it out and see how Profound can help you connect everything you need in one cohesive system. It’s a game-changer for efficiency and team synergy.
Starting in August 2026, Google will begin to automatically categorize customer types in conversion-based lists, removing some of the control we advertisers once had. I must now provide Google’s systems with clearer signals on where audiences are in their customer journey.
As someone deeply involved in advertising, I know the importance of precise audience targeting. With these changes, I’m urged to review and update my classifications in the Google Audience Manager before they kick in.
What’s Changing? From August 2026, Google Ads will automatically classify customer lists into categories like:
Existing customers
New customers
Other customer segments
Why Google’s Making This Shift. It appears that Google aims to enhance audience consistency across its tools for customer acquisition and retention. This standardization allows for better optimization decisions in Google’s automated bidding and targeting systems by clearly defining prospecting from retention audiences.
Why This Matters to Us. As an advertiser utilizing customer acquisition strategies, the precise classification of these lists is crucial. Any misclassification could impact Google’s optimization of users throughout their lifecycle, affecting campaign performance.
What We Should Do. It’s vital for us to audit our Customer Match lists—based on conversion data—before August. Consider these questions:
Are my customer lists categorized correctly?
Do they represent existing customers versus acquisition targets?
Will Google’s automatic classification align with my internal definitions?
Reviewing these settings now could prevent unexpected changes when Google enforces these classifications.
The Bottom Line. Google is taking an active role in managing audiences, further streamlining the signals powering their automated advertising systems by assigning lifecycle labels to conversion-based lists.
First Spotted. This update was noticed by Google Ads expert Bia Camargo, who shared the alert on LinkedIn.
AI search is reshaping the marketing landscape faster than anything I’ve seen before.
During my time at Zero Click NY, I witnessed how significantly AI search has evolved over the last six months and identified emerging features that might define its future.
Among all the discussions, these seven trends were the most compelling.
From the emergence of marketing engineers, to the way Claude and ChatGPT differ in results, and Claude’s rapid ascent in the business world over the past year, here are the key insights I gathered.
1. Every AI relies on different content
According to Profound data, only 8% of citations are shared between ChatGPT and Claude. This means 92% of the sources that ChatGPT refers to would not be recognized by Claude for the same inquiry. Essentially, a brand may have high visibility in one AI and not exist in another.
Moreover, each AI favors different types of content.
ChatGPT frequently indexes community content: Reddit, Quora, and forums make up around 16% of its citations.
In contrast, Claude cites listicles 36% and opinion content 13.2% of the time, compared to ChatGPT’s ~20% and 7.2%, respectively.
The disparity also applies to traditional search. A significant 64% of websites Claude cites appear in Google’s top 50 for equivalent queries, whereas it’s only 37% with ChatGPT.
Takeaway: It’s vital to inform stakeholders that AI visibility differs between LLMs, and strategic prioritization is necessary to reach your audience.
Track your visibility by engine because effective strategies in one platform may not translate to another. UGC helps drive ChatGPT while listicles boost presence on Claude.
2. Claude is quietly winning B2B — so sequence your optimization by audience
Claude may appear insubstantial in AI traffic-share charts, but it’s a different story when it comes to enterprise usage.
Web traffic doesn’t tell the whole tale. Anthropic derives about 85% of its revenue from enterprise and API usage, not visible in consumer data.
A critical chart from Ramp’s AI Index reveals the true penetration of Anthropic in the business sector. A year ago, only a small number of businesses used Anthropic. Now, it’s at 34.4%, surpassing OpenAI at 32.3%.
This insight led me to reconsider: if more business users are engaging with Claude and consumers are on ChatGPT, shouldn’t our optimization priorities reflect audience preferences?
Should B2B entities focus on Claude first, while B2C aim for ChatGPT visibility?
Currently, few distinguish between ChatGPT, Gemini, or Claude usage. This distinction is bound to grow.
3. ChatGPT ads are here, and this is what we’re seeing
The game has changed: competitors are securing visibility through ChatGPT ads. These ads are now live and available for self-serve directly within the chat interface.
Recent weeks also saw the debut of GPT 5.5, citation chips morphing into clickable links (leading to a 60% spike in referral traffic overnight), and Google integrating AI Mode into its main search functionality.
This wasn’t incidental. The hyperlinks are crucial for an ads business. Analyzing over 100,000 ad placements highlighted three essential revelations.
ChatGPT Ads match on topic
Ads align with topic similarity, not intent. Only 14% of real user prompts express commercial intent, yet 20% show ads, even if the prompt involves a math problem.
Embedding analysis indicates that ad titles and descriptions significantly influence which conversations you appear in, transforming them into tactical targeting tools.
Paying for ads
We have entered a “pay-to-play” era. Approximately one-fifth of ad placements appear when a direct competitor is mentioned, but only 8% of organic references belong to the associated brand.
Competitors are twice as likely to advertise around your brand’s organic mentions than you are.
For instance, Startup CRM Adia is targeting prompts mentioning Salesforce, with Salesforce responding by showing paid ads 40% of the time, defending their position even when organically mentioned.
Ad inventory is scarce and expensive
Currently, ChatGPT presents about one ad per conversation, with the median exchange spanning three turns. Only 30% of eligible users ever see ads, and CPMs/CPCs are about four times Meta’s rates.
Expect future changes: additional ad slots per reply, ads woven deeper into conversations, and engineered suggestions to prolong interactions, ultimately increasing inventory.
The insight: Understanding both organic AEO and paid defense strategies is essential. Monitoring your brand’s organic citations without tracking who advertises against them offers a partial view.
4. Claude is the most directly optimizable AI right now
Claude sources web content directly from Brave searches, not merely influenced by it, as discussed in the presentation I attended.
In recent testing by Profound, 79.2% of Claude’s citations were directly aligned with Brave’s top 10 search results for equivalent queries.
Reshuffling is minimal; no other AI model trusts its search provider so extensively.
This transparency makes Claude the most straightforward AI to optimize for: a visible index, checkable rankings, and, as we’ll explore next, predictable retrieval.
If I’ve convinced you of the importance of Claude for B2B, here’s your approach: identify where you stand on Brave for key prompts and use that as your roadmap for Claude visibility.
This level of transparency won’t last forever. Take advantage now while it’s possible.
5. Claude only performs web searches a third of the time
There’s a significant caveat: ChatGPT initiates web searches for nearly 95% of prompts, but Claude does so only a third of the time, likely due to cost ($5 per thousand searches via Brave’s API).
You can optimize Claude effectively only when it conducts a search.
The encouraging part is its predictable search habits. Prompts framed around recent events (“best X in 2026”) initiate searches about 81% of the time.
Ranking-related prompts lead to 67% search initiation, location-specific prompts 55%, and comparisons 51%.
Prompts concerning definitions and procedures rarely trigger searches, making them poor targets for Claude optimization.
The lesson: Before investing to enhance Claude visibility for a prompt category, determine if Claude actually conducts searches for it.
Focus on recent events, rankings, locations, and comparisons for effective Claude optimization using Brave rankings as a guide.
Other areas rely on internal memory beyond our reach.
6. Query fan-out: A raffle on one platform, near-deterministic on another
Two speakers offered perspectives on query fan-out, presenting a contrast worth exploring.
Query fan-out entails background synthetic queries to collect content prior to providing an AI-generated response.
Mike King of iPullRank viewed it as a raffle: The task is to gain more tickets through a wider content range across owned, earned, and shared channels, and the right content formats make all the difference.
Even if you rank for a fanned-out query, the wrong format renders you ineligible.
According to his research, content-to-query cosine similarity and information gain strongly correlate with success in AI search.
Conversely, Josh Blyskal from Profound notes that Claude’s fan-outs are highly predictable; the same prompt results in consistent fan-out strings 65% of the time. Interestingly, 94% of Claude’s fan-outs are current-year stamped, unlike ChatGPT’s 17%
Where ChatGPT’s fan-outs constantly evolve, Claude’s remain relatively stable. Thus, both perspectives may hold true for different engines.
With stable fan-outs like in Claude, content creation can directly focus on them. The year-stamping trend suggests using the current year in titles.
For volatile fan-outs as in ChatGPT, King’s approach applies: maximize exposure through format variety.
One mechanism demands two strategies, tailored by engine, potentially requiring prioritization between them.
7. The marketing engineer is here, and agents are the new workforce
The role of a “marketing engineer” might sound like a buzzword, but the hiring trends prove otherwise.
Google’s recently hired its first marketing engineer, Figma has an opening at a $295,000 salary, and both RBC and Autodesk have placed hires.
It’s a rapidly growing search term, and Google’s AI marketing lead dubbed it “the hire for 2026.”
What makes someone ideal for this role? Is the priority given to an engineer learning marketing or vice-versa?
The emerging profile emphasizes marketing experiences such as someone with channel expertise who builds and runs AI systems, reports to the marketing head, and supports the team by removing obstacles. They are marketers advancing the state-of-the-art.
The underlying concept is that marketing functions decompose into pipelines: data extraction, transformations, and loading into useful formats. Agents can now automate these pipelines.
Monitoring competitor pricing and auto-generating sales content.
Scheduling and assessing AEO presence and landing page efficiency.
Analyzing sales call objections and drafting relevant content solutions.
What previously were backlogged tasks now become brief agent-building exercises. Creativity replaces headcount as the limiting factor.
If marketing engineering isn’t a role in your team yet, it’s likely only a matter of time before it is.
The job now: Figuring out how this all works
There remains no definitive roadmap for AI search. When a guidebook emerges, the key step will be prioritizing one LLM contingent upon who you wish to reach.
In many instances, that “who” will now be agents, simultaneously assisting us in our endeavors and highlighting the rising need for professionals adept at engineering such systems.
I’ve noticed a fascinating trend recently: AI referrals to U.S. travel sites have surged significantly in May. According to Adobe, travelers coming from AI sources tend to spend more time on these sites and are less likely to leave immediately compared to those from traditional referral sources.
By the numbers: This remarkable growth is backed by data showing a 194% increase in AI-driven traffic year-over-year for May 2026. Since Adobe started monitoring AI traffic in October 2024, there’s been an astounding 2,215% rise.
AI-assisted travel planning has moved beyond initial stages. Now, it’s common for travelers to utilize large language models for comparing destinations, examining hotel features, creating itineraries, discovering promotions, and making bookings.
AI visitors showed stronger engagement: Although AI-referred visitors currently convert 28% less than non-AI visitors, the gap is closing. Adobe reports that the difference has narrowed by nearly 70% since October 2024.
Engagement metrics reveal that AI-referred travelers are 21% more engaged than their non-AI counterparts, spending 70% more time per visit and having a 41% lower bounce rate.
Adobe suggests that such patterns indicate more deliberate and high-intent behavior, even though AI-referred traffic still lags slightly in conversion rates.
Travel pages and AI readability: Adobe has also been assessing the readability of travel websites by AI systems. They developed an AI Content Visibility Checker to evaluate how much page content AI can process.
Within the travel sector, hotels and car rentals are ahead. Hotel homepages scored 63% readability, while car rental homepages reached 59%. Individual product pages performed even better, with hotels at 73% and car rentals at 71%.
Nonetheless, Adobe reports that over a third of content on leading travel pages is still unreadable by AI systems.
Where travel sites scored best: Hotels seem to excel in several page categories, including destination guides, activity pages, search results, customer service, and promotions.
Car rentals excelled on FAQ pages, while cruises led in blogs and news content. Conversely, airlines lagged behind other major travel sectors across all page types analyzed by Adobe.
This trend illustrates how well-structured, information-rich pages allow AI systems to better interpret content, thanks to detailed property descriptions, amenities, and core offerings.
Retail’s conversion advantage: AI-driven traffic to U.S. retail sites also set a new record in May, surging 138% year-over-year and an impressive 1,324% since October 2024.
Unlike in the travel sector, AI-referred retail visitors had a 54% higher conversion rate than non-AI traffic, overturning last year’s trend where AI conversion rates were nearly half.
Cosmetics and electronics shine in retail readability due to detailed content like ingredient lists, tutorials, product specs, and how-to guides, while grocery and furniture lagged.
Why we care: Adobe’s insights suggest AI referrals are increasingly valuable commercially, particularly in retail. However, many sites miss the mark by having significant content inaccessible to AI systems. If key content is hidden, poorly structured, or blocked, you could lose visibility before users reach your site.
About the data: Adobe’s research draws on over 8 million visits to U.S. travel sites, over 1 trillion visits to U.S. retail sites, and more than 100 million SKUs. Additionally, they surveyed more than 5,000 U.S. consumers in March regarding their use of AI in shopping and travel planning.
Recently, I delved into an intriguing study exploring how enabling search impacts ChatGPT’s product recommendations. Remarkably, these changes affect a vast 80.2% of responses, as observed from an extensive analysis of 20,000 interactions conducted by Jeff Oxford, the founder and CEO of Visibility Labs.
In Oxford’s experiment, he executed 1,000 product-recommendation prompts, running each ten times with search enabled and ten times with it disabled.
Surprisingly, a mere 19.8% of products recommended without search were repeated in the results with search activated.
Search reshapes top suggestions. Even the products that ChatGPT frequently recommended without search seldom appeared once search was turned on. Among those consistently recommended in search-disabled responses, only 15.8% showed up when search was activated.
Oxford anticipated that highly recommended products would still dominate with search, but they turned out to have the least overlap.
Source mentions and visibility. This study also scrutinized whether products cited in ChatGPT’s sources appeared more frequently in recommendations, showing a modest correlation of 0.4 Pearson between source mentions and recommendation frequency.
Products mentioned more often in cited sources had higher Visibility Scores, based on the percentage of instances a product appeared for a given prompt.
The analysis didn’t prove that source mentions directly caused these recommendations.
Search refines the list. With search enabled, ChatGPT’s responses averaged 5.2 products compared to 6.2 without search.
On average, across ten runs for each prompt, there were 19 unique products returned with search enabled, versus 21.8 with it disabled.
Why it matters to us. These findings are crucial because they show how search significantly changes ChatGPT’s product recommendations, even for staple products. Also, products cited in sources may achieve greater visibility when search is enabled, though this study doesn’t conclusively show that source visibility is more influential than web visibility as a whole.
About the study. The analysis covered 1,000 product-recommendation prompts, with each run ten times with search enabled and ten times without. Product names were standardized for consistency. As an observational study, it didn’t establish a direct cause between source mentions and recommendation frequency.
The detailed report. For more insights, see the full study here.
When it comes to enhancing AI’s view of our content, understanding which topics influence those third-party authority signals is vital. Through personal insights, I’ve learned that AI doesn’t always rely on the same sources for every query. Instead, identifying and engaging with those voices that shape answers in your niche can make a huge difference.
I’ve often been advised to build authority outside my site using digital PR, mentions, and gaining links from high-authority sources to boost AI visibility. While the instinct is right, I’ve realized that these efforts need to be specific to the topic because AI trusts different sources depending on the subject matter.
This week, I’ve been exploring why AI leans on various source sets based on different topics, wasting resources on scattered authority efforts, and how to pinpoint exactly where AI derives its citations for your focus area. By doing so, you can carve out your place among those trusted sources.
AI’s approach is fascinating. It relies on a distinct set of sources for each topic. Through a sample analysis, it became evident that AI citations follow a topical pattern, affecting where I focus my authority-building efforts.
In invoicing-related queries, competitor domains account for a significant portion, whereas for starting-a-business inquiries, those numbers drastically decrease. This shift highlights the importance of a topic-focused backlink strategy, where I aim for links with authority in my target topics.
Interestingly, video and social platforms behave differently, affecting visibility. Entities like YouTube are exceptions, especially across larger language models, creating unique pathways to building authority.
My goal is to strategize where and how I build authority, ensuring it’s topic-driven to resonate accurately within the AI’s trusted data pool. Copying PR strategies from adjacent topics won’t yield effective results; instead, I tailor my approach to align with AI’s preferences.
AI’s genuine trust extends from entities it already acknowledges. This means our brand’s perception by AI doesn’t start fresh every time. Instead, it’s shaped by existing trust in associated authoritative sources and documents.
Therefore, my work extends beyond my blog. While it’s crucial, it’s just one part of the broader picture. Publications and experts that mention me streamline my standing with AI models.
Authority developments don’t spread evenly; they progress in leaps. A quality mention can dramatically elevate your citations, especially within high-authority domains. From my experience, investing in in-depth relationships with top-tier sources pays off significantly more than spreading efforts thinly across lesser-authority sites.
This journey to building authority is strategic and measurable. It’s about making informed choices about who we engage with and understanding that authority-building is not just about quantity but also about the lasting relationships we foster in our professional spaces.
How I’m Building Authority in AI’s Trusted Sources
I realized that not all third-party signals weigh the same. While research picked up across reputed blogs can enhance citation frequencies, executive podcasts may not. Here are some steps I’ve taken:
Firstly, I’ve identified a few willing subject matter experts (SMEs) within our team who have a strong understanding of our brand and are eager to publish. Empowering them to create sharp, relevant content fosters credibility.
Through mapping exercises, I’ve pinpointed entities already respected by AI and targeted collaborations with the people behind them. This targeted approach ensures my SME’s work is recognized by the AI and appreciated by audiences.
3. Enhancing Authority Tier Concentration
By ranking potential partners by their authority tier, we refine our investment strategy, concentrating efforts where they’ll generate the most return. This tactic has consistently improved our citation metrics.
Finally, embracing the power of LinkedIn, I leverage influencer partnerships in my domain. This platform acts as a fast lane, allowing us to penetrate AI responses with reputable voices rapidly.
For a long time, “ultimate guides” were my go-to for SEO dominance. They were carefully crafted to meet Google’s algorithm standards for high-value content.
Incorporating the “skyscraper technique” further solidified the idea that length equates to depth.
Yet, as the web evolved, so did search intent. Users’ desire for quick answers and AI’s rise diminished the importance of lengthy content. Google’s system now frowns upon content that offers zero informational gain.
So, what are my next steps?
Extractability is the new content challenge, affecting every stage from briefing to publication.
AI platforms like Gemini limit approximately 380 words for query grounding, making it crucial for me to adapt.
The extraction data reveals:
Pages under 5,000 characters: 66% AI extraction rate.
Pages over 20,000 characters: 12% AI extraction rate.
The once high-traffic “ultimate guides” now stand in the way of effective AI visibility.
What steps into this void is a new, challenging form of content—where every sentence must pull its own weight by clearly stating entities, relationships, conditions, or citable claims.
The “padlock principle” is now my guide, turning search from keyword chasing to addressing specific problems for specific people. My content became more like solutions than broad categories.
For instance, a car insurance page now targets new drivers under 25, declined by standard insurers, turning from general to particular needs.
Breaking from tradition, each content piece now aims to solve a defined user problem. With AI’s impact on SEO, I’ve embraced strategic shifts to make my content more credible and logically structured.
Here are the three strategic rewrites I apply for effective problem-first positioning:
Replace categorical identity with problem identity
Before: “We are an insurance provider.”
After: “We solve the underwriting problem for first-time drivers under 25 who are declined by standard insurers.”
Rewrite titles as outcomes, not labels
Before: “Car Insurance | BrandName”
After: “Car insurance for new drivers under 25 declined by most providers”
Lean into constraints rather than suppressing them
Recognizing target limitations adds credibility to my service offerings, contrasting the generalized advice typically available for free.
The content landscape has radically shifted from information archives to pieces serving individual, extraction-friendly sentences. My approach leverages structured, meaning-rich content that AI systems can confidently source.
Building an LLM-friendly foundation involves familiarizing myself with semantic triples, because AI judges content with a retrieval efficiency that applies across various format types.
So, whether I’m crafting a blog or a product description, explicit headings signal relevance, boosting my content’s retrieval likelihood by 17.54%.
Adopting the citation-bait formula, I begin each paragraph with a direct declarative opening, followed by trimmed-down contextualization and structured evidence—ensuring the content is both extractable and engaging.
In pursuing content harmony between machine readability and human interest, I capitalize on the AI inverted pyramid approach. By positioning narrative transitions after structured answers, I balance AI efficiency with engaging storytelling.
Every part of my content creation—from heading formulation to section structuring—serves a dual purpose: making content AI-retrievable while nurturing human trust and engagement. I constantly refine this synergy, ensuring each piece of content wholly aligns with emerging AI standards.
Ultimately, I strive for a content strategy that doesn’t yet exist, one that will meet evolving needs by balancing the semantic precision AI demands with the rich narratives only human creativity can offer.
I am excited to share that Meta has rolled out the revolutionary AI Mode in Facebook Search, designed to enhance the user experience by providing AI-generated answers directly gleaned from public Facebook content such as Groups, Reels, and other Meta platforms.
Instead of the usual list of search results, Facebook now offers direct responses crafted by Meta AI. These answers are rooted in actual conversations and experiences shared publicly across Facebook’s apps, providing real-life advice and insights.
AI answers in search. With AI Mode, I can explore both broad topics and specific queries. As I navigate Facebook, the Meta AI surfaces relevant public content right in my feed, transforming how information is discovered and shared.
For instance, engaging with Groups and Reels offers a novel method to gather information about products, places, hobbies, and everyday tips.
Source selection is unclear. Although Meta promises “real answers from real people,” how AI Mode selects which public posts, Groups, or Reels get featured remains a mystery. Additionally, it’s not yet clear if brands, creators, or publishers will be informed when their content is utilized.
Why we care. This evolution signals a shift in Facebook’s search landscape, relying heavily on AI-generated responses from public social interactions. Consequently, the discovery process for recommendations, local news, and brand discussions is undergoing transformation within Meta’s universe.
A familiar name. Interestingly, Meta’s new feature shares its name with Google’s AI Mode, which raises some eyebrows about creativity.
What Meta is saying. This new AI Mode harnesses the power of both Meta AI and Muse Spark. However, Meta hasn’t divulged how Muse Spark affects search rankings, or the selection and generation of answers.
This search enhancement is just a piece of a larger Facebook AI update introducing new creative features for Photos, Videos, Profile Pictures, and Stories.
When I first heard about the Profound Index, it intrigued me as the ultimate leaderboard for AI search. Its reputation precedes it, setting a benchmark for excellence in AI-driven search solutions.
The image above perfectly encapsulates what the Profound Index represents—a fusion of innovation and performance in AI search technology. This impressive leaderboard not only showcases top contenders but also encourages competitive enhancement within the AI community.
For anyone deeply invested or casually interested in AI advancements, understanding the Profound Index provides insights into where AI search is headed. It’s a journey worth exploring for its potential to revolutionize how we interact with and leverage AI search capabilities.
