I’ve noticed something remarkable about how we, as Americans, are searching for information these days. Pew Research Center recently reported that 60% of us are now reading AI-generated summaries at the top of our search results, while approximately 40% have turned to chatbots for finding information.
It’s fascinating to see that AI-generated answers are appearing more and more, whether in traditional search results or dedicated chatbot platforms like ChatGPT, Gemini, and Copilot, as Pew discovered.
AI summaries reach most searchers. According to Pew, six out of ten American adults have read AI summaries at the top of search results. Surprisingly, three out of ten haven’t, which suggests room for growth.
Interestingly, another 10% are unsure if they’ve read AI summaries. It seems some of us may not clearly recognize them when they pop up in our search results. The research also found that men are slightly more inclined than women to read these summaries, with 63% versus 57%. Those of us aged 65 and older are less likely to engage with them.
Chatbots are search tools. Chatbots are increasingly becoming popular search tools. About half of American adults have used AI chatbots, which is a jump from one-third back in 2024. What’s more, about one in four of us make use of them daily.
The most common reason we use chatbots? Searching for information. Around 40% of adults turn to chatbots for this purpose, more than for entertainment, media creation, or even advice on fitness and medical matters. Interestingly, work-related tasks follow closely behind, with 38% of employed adults utilizing chatbots at their jobs.
ChatGPT dominates. ChatGPT remains the most popular chatbot by a significant margin. Pew indicates that 44% of U.S. adults have now engaged with ChatGPT, which is up from 34% last year and over twice the number reported in 2023.
Gemini takes second place, with about a quarter of us using it, followed by Copilot and Meta AI. Tools like Grok, Claude, and Character.ai have a much smaller audience, with only about one in ten of us using them, if at all.
Why we care. In today’s world, finding information doesn’t just mean looking at traditional search results. We now also find answers through AI summaries and chatbot responses, which is a fact worth noting, especially when it comes to understanding where people are sourcing their information.
Dig deeper. For more insights on AI search adoption and consumer trust, check out the study.
About the data. Pew Research Center gathered this data by surveying 5,119 American adults from February 17-23, 2026, via its American Trends Panel. The margin of error for this study is plus or minus 1.6 percentage points.
Hey there! Have you ever wondered what GEO is and how it can supercharge your content’s visibility and engagement in AI-based search engines like ChatGPT and Gemini?
I’m excited to share my insights on optimizing your content specifically for these AI platforms. Think of GEO as the key to getting noticed in the digital realm where AI engines are becoming the norm.
By mastering Generative Engine Optimization (GEO), you can pivot your strategy to cater to AI Overviews, boosting your reach by ensuring your content is relevant and easily discoverable. Let’s dive into this transformative journey together!
I’m excited to share how Adobe’s latest tool is changing the game for businesses eager to boost their brand visibility in AI-driven searches.
With the backing of 300 million AI prompts and the comprehensive data of Semrush, this platform is adept at tracking mentions, gauging share of voice, and identifying content gaps across prominent AI platforms.
Adobe introduced a pioneering solution for brands aiming to bolster their visibility and trustworthiness across AI interfaces. As part of the Adobe CX Enterprise, this tool offers an agentic AI system to streamline customer lifecycle management, covering everything from initial acquisition to fostering long-term loyalty.
AI traffic is skyrocketing. The way LLMs are utilized for product and service research represents a major pivot for both marketers and consumers. Recently, Adobe revealed data underlining this massive surge in AI traffic to U.S. retail sites—up by an impressive 1,324% from October 2024 to May 2026. The travel industry saw an even greater increase of 2,215% in the same timeframe.
As Vice President of strategy and product, Loni Stark, remarked to MarTech, “We used to get back the same thing—a SERP page with links. Now results seem random, but aren’t when scaled, and companies lack tools for this.”
Understanding brand visibility in AI search. Adobe Brand Visibility marks Adobe’s first venture into generative engine optimization (GEO), following its acquisition of Semrush. By integrating Adobe LLM Optimizer with Semrush’s AI Optimization tool, it provides unmatched insights.
Drawing from a staggering database of 300 million real-world AI search prompts, Adobe Brand Visibility helps teams pinpoint which prompts lead to brand exposure or loss.
Additionally, utilizing Adobe’s first-party data from owned channels, marketers gain a holistic view of how their brands appear on platforms like ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. Metrics encompass mention frequency, reach, competitive share of voice, and content gaps, allowing AI agents to offer prioritized recommendations that teams can rapidly implement and evaluate results.
Competitive intelligence unleashed. Adobe Brand Visibility offers tools for competitive brand analysis, comparison, and trend tracking, enabling marketers to effectively benchmark against competitors.
Featuring advanced SEO intelligence driven by Semrush’s extensive data of 28.5 billion keywords and 43 trillion backlinks, this platform underscores the continued importance of SEO fundamentals for AI search visibility. It shows the potential for existing search authority to yield AI citations and identifies opportunities for content investments across channels.
While there’s still much to learn about leveraging LLMs for brand visibility, Stark is confident in Adobe’s leadership position in this emerging space.
As Stark stated, “Adobe had proprietary data while Semrush offered data and trends. Though we may not have all answers, we possess unrivaled data.”
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.
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.
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.
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.
I’ve got some exciting news to share! I’m thrilled to introduce the revamped Profound Index, your go-to leaderboard for AI Search. This update marks a new era in search, providing both clarity and authority in the rapidly evolving world of AI-driven solutions.
In this rebuild, we focused on enhancing the user experience and performance metrics. Whether you’re an AI enthusiast or a professional seeking the latest insights, the improved Profound Index is designed with you in mind. Its comprehensive data sets and intuitive interface make it an indispensable tool for anyone looking to stay ahead in the realm of AI Search.
I realized that many web pages effectively address initial search queries, but often fall short when it comes to guiding the user toward their final decision. This is where the concept of next-question intent becomes crucial. It’s a tool that not only aids users but also aligns with AI systems for enhanced content utility and visibility.
In the world of GEO, much of the discussion revolves around how AI systems discover, extract, and suggest content. While these aspects are essential, I’ve learned that what truly determines visibility is the substantive content these systems find once they’ve reached my pages.
Next-question intent isn’t just about answering the initial query. It’s about whether my page provides enough depth for the user to take their next step, be it selecting a product or making a decision.
Often, a user’s first search is just a starting point. Key decisions hinge on follow-up questions and considerations that must be addressed.
By crafting content that anticipates these subsequent inquiries, I equip AI systems with rich materials to synthesize, compare, and recommend.
From Results to Narratives: Traditional Search vs. AI Search
Traditional search was once about offering a suite of links for users to peruse and decipher. Now, AI search focuses on delivering synthesized responses, pulling information from multiple sources.
This shift emphasizes the need for my content to provide comprehensive information that can help build AI-generated answers. Next-question intent is vital here.
While search intent asks what the user wants to do, next-question intent goes further. It asks what the user will need to know next to trust, compare, or decide.
In this AI-driven environment, content must support a complete answer pathway, far beyond the initial query.
The initial search often serves as just the beginning, an entry point. True decision-making occurs through follow-ups and specific concerns that arise thereafter.
Take the query “best CRM software for small business” as an example. It opens the door, but the true selection journey starts with follow-up questions.
Which platform is easiest for a two-person team?
Which integrates best with QuickBooks?
Which one works for a business without a formal sales department?
Which one is best for a local service company rather than a software startup?
Which one won’t frustrate owners or interns with tech complexity?
These aren’t ancillary. They define the decision-making path.
Otherwise well-structured content may falter if it fails to engage at this level, leaving AI systems with less context to assemble an answer, thereby reducing visibility.
Next-Question Intent is Not Just a Writing Exercise
As I’ve delved into content creation, it’s clear that next-question intent goes beyond simply writing better content—it ensures my pages support the next steps in a user’s decision-making process.
Practically speaking, it means crafting answer-ready content that addresses initial user needs, foresees additional decision layers, and provides concrete, verifiable information.
Visibility in AI search isn’t just about where I rank. It’s about citations and whether my brand becomes a trusted source in context-rich settings.
To achieve this, my content must offer enough substance for systems to understand what my brand does, whom it serves, when it’s useful, why it’s trustworthy, and how it fares against alternatives.
Where Good Content Goes Thin
While I often find that brands have content that’s accurate and keyword-optimized, it still might not suffice in the AI search environment.
AI systems require clarity and context to determine what I offer, who benefits from it, when it’s applicable, and why claims are valid.
This depth is where many pages fall short.
A service claim like “customized marketing strategies” begs the question: customized how?
A product claim like “safe for families” prompts: safe for which family members?
A software claim like “built for small businesses” asks: which type of business?
General claims offer little for people and even less for AI systems to utilize. Specific, structured, evidence-backed content serves a far better purpose.
I often find myself pondering how AI is changing the landscape of content strategy, especially in the realm of SEO and citations. It’s fascinating to see this shift from merely retrieving information to creating engaging and citation-worthy content.
As I delve deeper into the evolving AI search mechanisms, it’s clear that content needs to provide a stellar user experience to earn citations from LLMs like Claude and ChatGPT. The focus should be on understanding where our readers and potential customers are in their journey.
My strategy now includes considering how third-party platforms perceive our brand. It’s all about consistent messaging, ensuring that AI systems like Google’s understand our brand identity, target audience, and the right moments to highlight our offerings.
Transitioning from traditional SEO to what I call “experience-based GEO” offers exciting opportunities. Instead of prioritizing SEO, I focus on creating content that speaks directly to our desired audience, ensuring our brand emerges in relevant queries.
I’ve learned that while some SEO fundamentals remain, LLMs emphasize customized user experiences. This means our content marketing should aim to resonate with individual preferences, not just optimize for search engines.
Consider this: although the client’s CEO and I share similar demographics, our wine preferences differ, indicating how personalized AI interactions have become. When I’m seeking wine suggestions from an LLM, the results are tuned precisely to my tastes, showing how AI can truly understand consumer desires.
Google is shifting too, leaning towards AI-driven personalized results. This means that I need to adapt my content, both on my site and on external platforms, to align with these new AI paradigms.
Creating a content strategy extending beyond just our website is crucial. RAG (retrieval-augmented generation) depends on authoritative sources, which means featuring our brand in trusted platforms is key.
For instance, ensuring our wine retailer clients get mentioned in niche articles with relevant talking points can help them stand out in this AI-driven content realm. I emphasize using media buys or PR for placements that matter to our buyer personas.
As an individual brand, focusing on listicles and strategic mentions where our unique selling points are highlighted is vital. This ensures our brand is noticed for the solutions we provide.
AI systems crave expertise. By continually positioning ourselves as thought leaders and reliable retailers, we enhance our reputation, allowing LLMs to recognize and trust our brand over time.
It’s clear that traditional SEO techniques aren’t obsolete; they’re evolving. Schema, server-side rendering, and appropriate content structure remain essential, helping AI systems fully grasp who we are and what we offer.
In essence, my focus is on making our site an easy-to-navigate space for both human visitors and AI systems. By surveying customers and understanding their needs, I can tailor content to align with what they truly seek.
Creating a seamless customer experience ensures that our offerings are clear to both users and search engines, potentially improving our conversions.
I’m committed to keeping up with the evolving landscape of LLMs and SEO. By maintaining consistency and adapting our strategies, we can ensure our brand remains relevant and ready for whatever technological advancements come our way.
