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.
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’ve always been fascinated by the evolving nature of SEO, especially in an era dominated by artificial intelligence. For over twenty years, SEO heavily relied on keywords. But with the rise of generative AI and conversational tools like ChatGPT, we’re now seeing a shift toward prompts as the backbone of search visibility.
Understanding the prompts my audience uses with large language models is crucial. Otherwise, my content might never see the light of day in search results. Let’s explore how prompts vary by industry and their impact on search visibility.
How Prompts Differ by Vertical
It’s clear to me that the context holds paramount importance in the responses generated by large language models (LLMs). Different industries have specific patterns that dictate how users construct their prompts. I need to tailor my content to these unique frameworks to ensure maximum relevance.
Healthcare: Symptom-driven and Cautious Language
In the healthcare sector, I’ve observed users leveraging AI as an initial triage tool. Instead of a vague term like “chronic fatigue,” detailed prompts narrate specific symptoms.
The prompt pattern: These healthcare prompts are rich in personal context, symptom mapping, and cautious constraints. Questions often revolve around symptom lists and safety considerations linked to age or medication.
Anatomy of a healthcare prompt: Consider a prompt like: “I’m a 45-year-old female experiencing sudden joint pain and a rash after starting [Medication X]. What side effects should I monitor, and when is it critical to seek medical help?”
The content shift: To stand out here, my content cannot simply define medical terms. It must align with a patient’s decision-making process.
The action: I focus on structured FAQs, clear risk factors, and headers addressing specific symptoms combinations to engage effectively.
B2B: Comparison-heavy and ROI-driven
In B2B contexts, I see users turning to AI for detailed comparisons and ROI evaluations, bypassing traditional marketing materials.
The prompt pattern: B2B prompts are analytical, featuring deep dives into financial justifications. Requests often include data for presentation-ready tables or matrices.
Anatomy of a B2B prompt: Typical requests might be like: “Compare CRM ‘Brand A’ and ‘Brand B’ for a 500-user company, with implementation timelines and ROI over three years formatted in a table.”
The content shift: Without transparent, data-rich content, my B2B efforts remain invisible to LLMs.
The action: I need to publish open comparison pages with hard data, ensuring technical details are structured in an easily extractable format for AI systems.
Ecommerce: Intentional Clusters of ‘Best,’ ‘Cheap,’ and ‘Reviews’
The ecommerce landscape, as I see it, is an interactive shopping experience with AI-driven, personalized recommendations.
The prompt pattern: Queries often combine quality markers like “best reviewed” with budget constraints like “under $150” within specific contexts.
Anatomy of an ecommerce prompt: An example might be: “What are the best-reviewed running shoes for overpronators under $150, excluding brands with poor durability?”
The content shift: Beyond simple keyword targeting, I must infuse my content with the semantic depth necessary for LLM validation.
The action: I optimize my merchant feeds with conversational attributes, ensure crawlable user reviews, and connect product specs to consumer value.
Why Prompt Structure Impacts Your Search Visibility
Understanding why prompt structures matter is key for me. They shape whether my site appears in LLM responses, based on how a user constructs their inquiry.
The Power of ‘Reasoning Lift’ and Direct Citations
By optimizing for direct citations and structured data, I could boost the visibility of my content by up to 40%, according to research from Princeton and the Allen Institute for AI.
It’s intriguing how more than 80% of links in AI-driven searches come from domains not ranking in traditional top searches. This emphasizes the importance of content quality and structure over legacy backlinks.
Operationalizing Prompt Research
Shifting my focus from keywords to prompts is crucial. I need to revamp my content strategy to align with conversational search trends, ensuring my brand stays visible.
Stop tracking isolated keywords: Instead, I’ll search for conversational data within search logs and consumer interactions.
Audit for LLM readability: My content must be easily parseable by AI, underpinned by modern standards and structured data.
Write for the follow-up: Rather than focusing solely on initial queries, I’ll anticipate and address follow-up questions within the same content.
To stay ahead, aligning my content with AI interaction patterns is non-negotiable.
When I dive into platform reports, I realize they tell only part of the story. It’s the incrementality, CRM data, and broader measurement insights that truly reveal the impact of our marketing efforts.
I recall a time when PPC attribution was never flawless. Now, with AI widening the gap, it’s even trickier to pinpoint what truly influences a conversion and what ends up receiving credit.
Imagine someone discovering a product on social media, watching a YouTube review, diving into Reddit opinions, using an AI tool to compare options, and then returning through a branded Google search ad days later.
While the PPC report might show a single conversion from branded search, I see a more complex journey that needs recognition beyond the final click.
AI is reshaping brand discovery, how purchases are researched, and how ad platforms decide who sees which ads. As a marketer, I find there’s now less visibility into these platform-driven decisions.
It’s clear to me that relying solely on platform attribution data doesn’t tell the whole story of my business’s truth.
AI is changing where the journey begins
Traditionally, the search journey starts well before an advertiser sees a measurable click. Recently, findings like those from Responsive’s 2025 research indicate that a significant portion of B2B buyers favor generative AI over traditional search when exploring vendor options.
For someone entrenched in the tech sector, I can’t ignore how 80% of tech buyers are now using generative AI at least as much as traditional search.
If AI-derived lists are excluding my brand from their answers, I’m instantly out of the buyer’s consideration set, which is disconcerting.
Google’s announcements about AI advancements reaching billions of users show how rapidly the landscape is evolving. This shift means that brands like mine need a strategy to ensure we’ll still be visible.
I can’t help but notice how Pew Research Center’s findings about declining clicks when AI summaries are present have personal and business implications for me.
I also realize the importance of brand recognition, even if initial interactions don’t result in a direct click-through.
The discovery phase deeply influences the eventual conversion, yet often, only the final touchpoint gets the credit.
Branded search often receives credit for demand generated elsewhere
Observing branded search, I frequently note it’s a classic case where attribution is mistaken for actual impact.
The efficiency portrayed by a branded search campaign can be misleading. Although such campaigns often perform well on metrics, primarily because they target users already familiar with the brand, they don’t always generate the initial demand.
A user might only search my brand due to exposure from other channels, such as social media, YouTube, or even an AI-generated suggestion.
Thus, distinguishing between demand capture and creation is vital. The real test is understanding whether certain conversions would have occurred absent of these campaigns.
AI-driven discovery creates a measurement blind spot
In client data, I’ve observed that direct traffic from AI platforms boasts a higher conversion rate compared to organic search, which piques my curiosity.
With these findings, I’m reminded of how much goes unmeasured. AI introduces complexities that create attribution challenges, as visible AI traffic might be just a small fraction of the journey.
Recognizing this, I understand the importance of viewing these interactions as part of a larger conversion narrative.
Ads are becoming part of AI-generated search journeys
With ads now interwoven in AI results, I face an added layer of complexity in correlating AI search with paid media.
Google’s policy of serving ads based on the commercial intent inferred from AI responses means my ads could surface earlier in the buyer’s research journey—a fact that fascinates me.
Despite these placements, I’m aware of the limited visibility and reporting challenges they present, which is both frustrating and intriguing to navigate.
Platform automation can make attribution look better while making analysis harder
Within account platforms, the allure of automation promises efficiency, yet it can blur analytical clarity.
I reflect on how broader targeting can deliver impressive surface-level results, but the lack of granular insights into why certain ads perform complicates future decisions.
This dilemma emphasizes for me the critical balance between leveraging automation and maintaining rigorous scrutiny.
I see the trap of prioritizing metrics like reach and click-through rate over genuine business outcomes.
The challenges extend to future optimizations and highlight the importance of qualifying lead quality over sheer volume.
Bringing CRM data into PPC reporting brings everything full circle, ensuring the focus isn’t lost in translation between metrics and actual business value.
Assessing upper-funnel activities, I note that they can have sustained, profound impacts on lower-funnel metrics— a sentiment validated by research indicating significant long-term returns on initial media investments.
This insight reassures me of the need to invest in awareness and video campaigns that extend beyond immediate ROAS measurements.
A single ROAS figure no longer suffices. PPC reporting, in my view, must integrate platform attribution with broader business metrics and strategic experiments.
1. Separate demand creation from demand capture
I ensure campaigns are assessed by their unique objectives—demand creation versus demand capture.
2. Review attribution paths, not just final clicks
Using GA4’s paths report, I analyze the customer journey comprehensively to understand how channels influence conversions from start to finish.
3. Import deeper CRM outcomes
For me, importing qualified leads and sales data enriches platform optimization and aids strategic alignment.
4. Monitor the metrics sitting outside the PPC dashboard
I track various metrics—branded searches, AI-referred sessions, and lead quality, which together form a holistic view of the customer journey.
5. Test incrementality rather than assuming
Incrementality testing, such as Google’s Conversion Lift, helps me understand the genuine impact of my ads beyond the dashboard numbers.
6. Add regular human checks to automated accounts
Despite automation, I regularly review and ensure account settings and outcomes align with my overall business objectives.
I’ve learned there isn’t a single PPC attribution model to explain the fragmented, AI-influenced customer journey we see today.
Rather than abandoning attribution, I see the value in treating it as just one piece of the puzzle alongside analytics and CRM outcomes.
The most insightful question isn’t, “Which channel received the conversion credit?” but instead, “What would be different if this activity never happened?”
I’m witnessing a fascinating shift in the search industry, something I hadn’t anticipated witnessing in my career.
The supply of search expertise now outweighs the demand.
We can point fingers at artificial intelligence, the economy, or the increasing commonality of checkbox SEO.
Whatever the cause, the outcome remains unchanged.
SEO job cuts are rising. Openings are dwindling. I’ve never seen the market as competitive in my 15+ years.
The hard truth is many SEO skills that were once invaluable are becoming easier to automate or outsource.
Grab a seat.
I’d love to explore why this is occurring, which skills are now expected, and what SEO talent employers should really be seeking as we move towards 2026.
If I were hiring an SEO in 2026, I would focus less on technical details and more on how candidates handle complex situations.
I’d ask for a disagreement experience.
For example, I suspected H1 tags didn’t significantly impact rankings. Initially, people laughed, and opinions varied until further confirmed by experts.
I care more about their resolve than their correctness.
I’d ask about a failed test.
Experienced SEOs know projects often stall. The key is their follow-through post-failure.
I’d inquire about AI mishaps.
I aim to find candidates who turn knowledge into tangible outcomes.
The hard part has always been delivering results, not knowing what to do.
AI won’t substitute SEOs, but those unwilling to adapt may face challenges.
This article initially appeared on my personal site, shared here with permission.
When I think about how much time I used to spend manually creating ads for each product, the introduction of OpenAI’s latest feature feels like a game-changer. OpenAI’s Ads Manager beta now allows retail advertisers like us to upload our product feeds, automatically generating ads from individual catalog items.
This update opens the door for brands to scale their advertising efforts within ChatGPT, seamlessly serving up products that truly matter to users during purchase-focused conversations. It’s an exciting development, as it aims to enhance ad relevance and impact.
What’s happening? Now, we can upload our entire product catalogs and generate dynamic ads using feed data, bypassing the need to create campaigns item by item. It’s a major efficiency boost, and so far, feed-based ads have demonstrated strong performance in the Ads Beta phase.
Why does this matter to us? With OpenAI’s product feed ads, retailers gain a scalable method to align catalog inventory with high-intent conversations, promising improved ad performance. This new functionality mirrors tried-and-true strategies from giants like Google and Meta.
Getting started. For those of us participating in the beta, it’s time to review feed requirements and start creating campaigns directly from our uploaded product catalogs. This could be the beginning of a new era in how we manage ad setup.
The bottom line. By expanding its advertising capabilities, OpenAI is offering a more scalable and automated advertising solution in ChatGPT, specifically tailored for retailers like us aiming to enhance ad performance.
Inside scoop. The announcement of this update was shared by Menachem Ani, the Founder of JXT Group. He posted about it on X, sharing the email he received from OpenAI.
I’ve often wondered how people are truly interacting with AI technology and what those interactions mean for our digital strategies. As I dive into recent survey data, it’s clear that real-world users are blending short queries with personal context, altering how brands achieve visibility in AI-driven searches.
Initially, I was surprised to learn that most people don’t use AI in the manner many Generative Engine Optimization (GEO) discussions suggest. Through surveys conducted by Stella Rising, where I’m the VP of SEO, we discovered that many AI prompts closely resemble traditional search engine queries.
For instance, in a beauty-focused study from August 2025 and a general study from January 2026, most prompts were succinct and keyword-driven, much like a Google search. However, many users are now providing AI systems with personal details, such as location and preferences, creating a deeper level of personalization.
Based on these findings, it’s evident that GEO strategies need to embrace this dual approach: accommodating classic keyword searches while optimizing for prompts enriched with personal context. This challenge presents a significant opportunity for brands willing to navigate this new landscape.
A lot of people are still typing like it’s 2008
A significant revelation from the surveys is that typical AI users still submit minimal inputs, hoping for optimal results.
Notably, our January general-audience study indicated:
Two-thirds of users wrote prompts with 15 words or less.
Only a small faction, about 12%, crafted what might be considered a comprehensive AI prompt.
Most framed their questions while very few issued direct commands.
When I replicated a basic scenario — asking for a shoe recommendation — the average response consisted of eight words. Real entries included queries like:
“Shoes nearby”
“Tennis shoes”
“Nike”
“Ladies tennis shoes size 7 near me”
“Best price for hiking shoes”
These align closely with findings from Semrush’s clickstream data, showing that the average prompt ranges between 4.2 and 8.7 words, paralleling standard Google queries. Structured, detailed prompts often surface in tasks beyond simple searches, like coding or content creation.
The shift between the two surveys
In the beauty-focused August 2025 survey, nearly half the prompts were firm, SEO-keyword-shaped. However, by January 2026, such prompts reduced to about 30%, with richer context becoming more prevalent.
Key observations included:
Nearly a quarter incorporated the term “best,” highlighting an opportunity in “best [category]” visibility.
A noticeable percentage mentioned budget or price, pointing to financially mindful consumers.
“Near me” remained a common phrase, adapted from Google to AI interactions.
A notable share included personal attributes, reinforcing the importance of personal context in queries.
However, the varying audiences surveyed offer caution. The 2025 beauty panel represented a unique demographic, while the 2026 group was more general and transactional, showcasing more complex query evolution.
The user embedding layer is where this gets interesting
The data revealing that 32% of users incorporate personal context into their prompts is significant. This includes details like job roles or life scenarios that traditional search queries do not capture. Real-world queries from users might include:
“What shoes are ideal for standing all day at work?”
“Find affordable running shoes on Amazon; size men’s 10.”
“Suggest trendy, comfy women’s shoes, size 8 wide, under $120.”
The last example incorporates several layers of identity and specifics, which typical search engines never explicitly addressed. The embedding layer fuels AI’s ability to ‘know’ its user, leveraging past interactions to tailor responses, and it’s a game-changer for brand visibility.
Brands need to recognize that purchase-driving prompts often diverge from those seen in search engine results pages (SERPs). Real prompts hold significant buying influence and highlight the importance of context-rich brand mentions within AI interactions.
Where synthetic prompts fit — and where they don’t
Constructing synthetic personas helps test AI models’ representation of different user traits. However, synthetic prompts frequently miss the nuanced, ongoing dialogue a real user shares with AI tools. These personas can illuminate potential brand-user interactions but shouldn’t be the sole basis for measuring success in AI visibility.
Instead, complement synthetic prompts with insights from real user interactions for a holistic view. Pull real-world data from customer inquiries, support tickets, and search patterns to gauge true user engagement with your brand.
What to actually track
The current dynamics in AI search query patterns prompt us to reconsider our tracking strategies. With retrieval rates soaring, traditional SEO keywords are far from obsolete in AI contexts.
Yet, it’s crucial to focus tracking efforts wisely. Generic terms or single-brand queries may not yield insightful visibility information. Here’s how I recommend setting up an effective tracking framework:
Use synthetic-persona prompts to cater to user embedding layers.
Gather a set of real prompts from various data inputs for short, retrieval-invoking prompts.
Maintain a qualitative set of context-heavy prompts to ensure content relevance and thoroughness.
What the broader data tells us about AI search
Further insights from January 2026 underscore why these prompt configurations matter in AI search:
Users increasingly trust AI recommendations
Approximately 68% of respondents trust AI recommendations more than Google’s, highlighting a trust transition driven by personalization and a lack of advertising clutter.
AI search is becoming a daily habit
Half of active AI users engage with these tools daily, gradually shifting dependency from Google to AI for common tasks. This shift signifies a change in how search habits are being shaped by AI convenience.
Citations still drive traffic
A substantial number of users still click on citations, validating that mentions within an AI context act as a gateway rather than an endpoint, showing the importance of monitoring and optimizing referral traffic through AI channels.
Voice may finally be having its moment
Voice interactions are finally seeing substantial usage, suggesting the long-predicted rise in voice-activated search is materializing, reinforced by the data from Ahrefs indicating visible shifts in clickthrough patterns.
In summary, AI search is taking form as a more personalized, interactive endeavor. It blends traditional intent with modern layers of user context, posing new demands and opportunities for content optimization. SEO and GEO strategies need to align closely with these evolving practices to maintain competitive edge.
What changes — and what doesn’t
As an SEO strategist, here are my top three recommendations for leveraging these insights:
Revamp Your Prompt-Tracking Strategy: Blend synthetic prompts with real user inputs for a fuller understanding of AI visibility.
Align Content with User Embeddings: Identify key user personas and ensure your content addresses their specific needs.
Continue SEO-Keyword Optimizations: Traditional searches still play a crucial role, especially with high retrieval rates in play.
It’s vital to recognize that while AI evolves, many users still engage reminiscent of Google’s era, albeit within a platform more attuned to their specific contexts. This understanding guides where our optimization efforts must focus, staying attuned to changing user interactions and preferences.
The August 2025 study surveyed 178 members of Stella’s community specializing in beauty, while the January 2026 survey covered a broader user base of 524 active users with some margin of error. These insights offer a directional lens into the broader adoption and interaction patterns within the AI space.
Could AI be losing a crucial source of its training data? As a major shift looms, significant publishers are urging Common Crawl to pause its collection and distribution of their content for AI training.
Digital Content Next (DCN) has sent a cease-and-desist letter to the Common Crawl Foundation, asking them to stop scraping and sharing protected publisher content.
Representing leading digital publishers like the AP, the New York Times, NBC Universal, Bloomberg, NPR, and Fox, DCN is also insisting that Common Crawl remove its members’ content, including paywalled and subscriber-only news articles, from its datasets.
Concerns Over Opt-Outs: Questions arise regarding Common Crawl’s adherence to publisher opt-out requests. Specifically, DCN’s lawyers are scrutinizing whether previous statements about compliance—often citing technical costs and delays—were perhaps misleading.
The registry maintained by Common Crawl does list sites opting out, including several prominent news organizations.
Claims of Infringement: DCN firmly holds that copyright isn’t an opt-out system. They allege Common Crawl has been “flagrantly infringing” on publisher copyrights by distributing protected content without authorization or compensation.
The group further critiques how Common Crawl shares this content with AI developers.
DCN’s CEO, Jason Kint, signifies this legal action is a stance against the notion that online content is available for unrestricted collection, storage, and reuse.
Common Crawl’s Defense: Rich Skrenta, the Executive Director, denies allegations of bypassing paywalls and misleading publishers. He references a prompt and technical response to remove previously crawled content upon request.
“Our removal process aligns with our dataset’s technical framework,” Skrenta explains.
Importance of This Battle: The outcome of this dispute could drastically influence the scope of publisher content that AI search engines use without explicit permission. Should there be heightened consent requirements, licensed sources may prevail, reducing reliance on openly available web content.
The High Stakes of AI Training: Established in 2008, Common Crawl has amassed billions of webpages to form a free public repository, a vital tool for training AI models. Notably, The New York Times’ lawsuit against OpenAI in 2023 cited that Common Crawl comprised 60% of GPT-3’s training data, as reported by Press Gazette.
A 2024 Mozilla Foundation paper found generative AI would scarcely exist today without Common Crawl.
Common Crawl’s ongoing efforts to create AI crawling standards indicate a willingness to adapt, yet DCN calls for decisive action—fully halting the scraping of protected content.
In early 2026, a significant shift unfolded in the world of search engines—68.01% of Google searches ended without a click. I discovered this intriguing fact through a study by SparkToro, which utilized Similarweb clickstream data. This percentage marks a noticeable rise from 60.45% in 2024, a 7.56-point increase over two years.
Fewer searches are resulting in clicks. Between 2024 and 2026, the share of searches generating at least one click fell by 9.51 percentage points, representing a decline of 22.9%. This includes clicks to organic results, paid ads, and Google-owned platforms like Maps and YouTube, excluding follow-up searches within Google.
During this period, I noticed that the share of searches leading to another Google search increased by 7.2 percentage points. This trend demonstrates Google’s growing proficiency in providing direct answers within its search results, encouraging us to refine or continue our searches without leaving the platform.
AI Overviews and the zero-click phenomenon. SparkToro suggests that AI Overviews might be contributing to the rise in zero-click searches, though the study doesn’t pinpoint how much of the rise from 2024 to 2026 can be specifically attributed to these overviews.
According to the research, I’ve observed that AI Overviews now appear in over 20% of Google searches, causing click-through rates to plummet by nearly 60% when they do.
AI Mode and zero-click growth. While AI Mode seemed to play a minor role during the study period from January to April 2026, SparkToro noted that only 0.34% of searches transitioned into AI Mode. However, Google announced during I/O 2026 that AI Mode had attracted over 1 billion monthly users, with query volume more than doubling each quarter, indicating a future increase in influence on search behavior.
Historical perspective on zero-click searches. SparkToro’s long-standing tracking of zero-click searches reveals an upward trend, although constantly changing data sources mean that long-term comparisons might lack precision. Nonetheless, available data consistently indicates an increase in zero-click behavior over time.
Here are some historical insights: In 2019, 49% of Google searches ended without a click, based on Jumpshot clickstream data. By 2020, SimilarWeb data showed that the figure had risen to 64.82%. And in 2024, 58.5% of U.S. searches (59.7% in the EU) ended without clicks, according to Datos data.
Why this matters to us. These findings imply that Google is increasingly meeting user needs internally, which might reduce traffic to external websites. However, direct year-to-year comparisons should be approached with caution due to differing methodologies in SparkToro’s analyses.
The evolving role of SEO. SEO remains crucial, but it’s not the sole solution for regaining traditional levels of Google-referred traffic. Rand Fishkin, SparkToro’s co-founder, advised us to focus on building brand awareness and engagement on platforms where our audience is active, irrespective of the impact on direct site visits.
SEO is still valuable for certain categories, such as branded searches, local business inquiries, and high-intent transactional searches, according to Fishkin.
About the study data. The research utilized Similarweb desktop and mobile web panel data on U.S. Google searches from January through April 2026. SparkToro estimated two-thirds of searches occurred on mobile devices, with the remainder on desktops. Searches within Google’s mobile search app, where zero-click behavior might be higher, were excluded.
Building a strong digital footprint is essential for helping AI understand my expertise, recognize my credibility, and recommend my brand to potential customers.
AI forms opinions about my brand from my online presence—my digital footprint. The challenge? AI often captures only pieces of my business: the website, content, reviews, and mentions. Unfortunately, much of the expertise and customer insight I offer doesn’t always make it into that footprint.
To address this, I’ve learned to surface that hidden knowledge, organize it into a single source of truth, and convert it into machine-readable signals. Here’s my strategy for collecting, organizing, and distributing this knowledge across the platforms AI uses to understand and recommend brands.
What You Feed the Machines: Understandability, Credibility, and Deliverability (UCD)
Everything I contribute to my digital footprint feeds into three key aspects for AI: understandability, credibility, and deliverability, which together form the whole funnel.
Does AI know who I am, what I do, and whom I serve? My about page, product pages, and structured data contribute to this understanding, but the operational details that highlight my business’s value are often overlooked.
Credibility: Building Trust with AI
Does AI trust I’m proficient in what I do? This is about N-E-E-A-T-T credibility—Notability, Experience, Expertise, Authoritativeness, Trustworthiness, and Transparency. It’s an extension based on Google’s E-E-A-T.
I am aware of the credibility signals I currently utilize: case studies, credentials, and testimonials. However, many businesses, including mine, often underestimate how much of this credibility is already woven into daily operations.
Deliverability: Reaching My Audience
Is my content available to the AI engine for delivering to my target audience? I recognize that my deliverability roots lie in topical content, marketing strategies, and authority pieces. Deliverability often hides within the content my business operations generate.
With AI viewing every brand in my category impartially, my task is to build a clearer and more trustworthy picture of who I am and what I represent. By showcasing my strengths more effectively than competitors and being transparent with AI, I position myself as the top recommendation for my target audience.
