Tag: AI Search

How Google’s AI Mode Threatens Web Traffic: Insights from Yahoo CEO

As I delve into the evolving landscape of web traffic, I find Yahoo CEO Jim Lanzone’s insights on AI-powered search engines, particularly Google’s AI Mode, incredibly fascinating. He believes this technological evolution poses a significant threat to the web’s traditional traffic model.
Jim highlights a major concern: “I think that the LLMs are one big reason they’re under threat, with AI Mode in Google being the biggest challenge.” This makes me ponder the impact on publishers who rely heavily on these traffic flows.
I resonate with Jim’s view that publishers truly deserve this traffic. He articulates a fundamental truth: “Those publishers deserve [traffic], and we’re not going to have the content to consume to give great answers if publishers aren’t healthy.” This reflects the delicate balance required in the digital content ecosystem.
Why I care. Many websites, mine included, are noticing a dip in traffic coming from answer engines such as Google and OpenAI. It feels like a looming concern that could worsen. Yahoo’s dedication to maintaining the “search sends traffic” model is reassuring, as Jim passionately explains: “We have very purposefully highlighted and linked very explicitly and bent over backwards to try to send more traffic downstream to the people who created the content.”
Yahoo’s unique AI approach. Listening to Jim on the Decoder podcast, I learn that Yahoo is carving its own path with AI. Unlike the more conversational chatbot models, Yahoo isn’t pursuing to be an AI assistant: “Ours looks a lot more like traditional search and it is more paragraph-driven. It’s not a chatbot that’s trying to act like it’s a person and be your friend.” I see this as a move towards emphasizing informative search experiences.
Moreover, “We’re not a large language model. We’re not going to be the place you come to code. We’ve really launched Scout as an answer engine.” This strategy, I believe, could provide a clearer, more reliable information source online.
What’s next: Embracing personalization. In observing Yahoo’s strategy, I’m excited to see their efforts to evolve. They’re embedding AI across platforms: “You are very shortly going to see us get into very personalized results. You’re going to see us get into very agentic actions that you can take.” This indicates a future where user-specific solutions take precedence.
For instance, Jim notes, “There’s a button in Yahoo Finance that does analysis of a given stock on the fly… It is in Yahoo Mail to help summarize and process emails.” Such tools could transform how I interact with content on various platforms.
Yahoo vs. Google: A non-competition. Interestingly, Yahoo isn’t trying to directly outplay Google. Instead, as Jim points out, the focus is on existing users and enhancing their experience: “Nobody chooses, you will not be surprised, Yahoo over Google or somewhere else to search. The way that we get our search volume is because we have 250 million US users and 700 million global users in the Yahoo network at any given time. There’s a search box there. And infrequently, they use it.” It’s more about nurturing the loyalties of existing users.
A word of caution. The conversation also shines a light on the potential pitfalls of heavily relying on AI platforms. Jim references past experiences with Google: “You are tempting fate by opening up a way for consumers to access your product within a large language model.” This analogy resonates with me deeply, remembering the cautionary tales in tech history.
Yet, he warns: “The big bad wolf will come to your door and say everything’s cool.” It’s a timely reminder of the ever-competitive and unpredictable nature of tech alliances.
The interview. For those intrigued by Yahoo’s journey, check out Yahoo CEO Jim Lanzone’s full interview on reviving the web’s homepage.

Inspired by this post on Search Engine Land.

March 17, 2026
Boost Your AI Search Visibility with Entity Authority

I realized that the traditional webpage is no longer the center of digital visibility. We’ve been relying on URLs and keywords, a structure made for a journey that AI now bypasses entirely.

In this era where search is everywhere, the entity—a precise, machine-readable concept of a product, organization, or individual—has become the core unit of power.

Brands that dominate now in the AI landscape are those creating strong entity authority. The key to surviving the shift to generative discovery is not merely about the page anymore. It’s about developing entity linkages to build the foundation of AI visibility.

We need to acknowledge a profound transformation in how the web is indexed. We’ve moved beyond just retrieving information to a new three-stage evolutionary process.

Phase 1 (Strings): We focused on optimizing keyword strings in traditional SEO. The goal was to align queries with text on a page.

Phase 2 (Things): With modern search, we understand entities. Knowledge graphs now recognize brands, founders, and products as distinct entities.

Phase 3 (Entities): AI systems use structured entity ecosystems today. The aim is to become a verified authority within this interconnected network of entities and capabilities.

In this current phase, search engines evolve into reasoning engines, analyzing content and your brand’s ecosystem role.

Dig deeper: The enterprise blueprint for winning visibility in AI search

The evolution is powered by economic necessity: the comprehension budget. AI systems are resource-intensive, processing content and calculating interpretations.

Whenever an engine clarifies a brand or assumes a relationship, it exhausts valuable resources. Unstructured or inconsistent data increases this computational load.

To optimize performance, I use a comprehension subsidy, employing Schema.org to make data more accessible to machines, reducing the inference needs for AI systems.

Dig deeper: From search to answer engines: How to optimize for the next era of discovery

Shifting from traditional SEO to generative engine optimization (GEO), I focus on relevance engineering, structuring content to be part of AI-generated answers.

GEO is about making your brand’s information easily interpretable, verifiable, and useful in AI-generated responses across platforms like ChatGPT and Google’s AI Overviews.

Dig deeper: Chunk, cite, clarify, build: A content framework for AI search

Most enterprise sites have some structured data, but for AI, basic and fragmented schema is insufficient. It creates separate data islands and complicates the AI’s effort to form connections.

The correct approach is implementing a content knowledge graph, mapping entities hierarchically and ensuring they’re machine-readable through Schema.org and JSON-LD.

Dig deeper: Why entity search is your competitive advantage

To be globally recognized, properties such as @id for consistency and sameAs for linking to reputable sources help in entity disambiguation, boosting credibility.

To maintain a strong AI relationship, move beyond simple tagging to entity governance—establishing verifiable sources of truth for AI platforms at scale.

As the AI experience evolves toward active agents managing user actions, I focus on schema actions that make my entity callable and ready to support AI-driven interactions.

If my entity isn’t clearly defined, AI may overlook it, turning to competitors prepared with actionable data pathways for users and AI systems.

Schema drift is a risk: inconsistencies between human-visible content and machine-readable formats can lead to lower confidence scores, reducing citations.

Monitoring and continually updating schema with real-time signals ensure I remain present and operationally capable in the agentic web ecosystem.

Dig deeper: From search to AI agents: The future of digital experiences

The new key performance indicators in AI environments go beyond traffic metrics, emphasizing model share and citation value, ensuring AI reflects my brand accurately.

Maintaining AI trust requires precise alignment of schema with declared business specifics, preventing entity drift and supporting positive AI interactions.

Embracing entity-first strategies allows me to build credibility and presence in AI searches, where content knowledge graphs enhance my brand’s visibility.

Ultimately, it’s not just about being on the page — it’s about the confidence AI places in my entity, ensuring it remains a powerful tool for discovery.

Key Takeaways:

From strings to things to systems: Transition from keyword targeting to entity authority, focusing on overall concept dominance.

Efficiency is currency: Streamlined, structured data helps AI access your information more efficiently, enhancing citation potential.

Citations are the new clicks: Achieving top visibility now involves influencing AI recommendations rather than just page visits.

Governance is revenue protection: Avoid schema drift to maintain AI confidence and brand presence.

Callability = survival: Ensure your brand’s entities are ready for AI agent interactions with actionable schema.

Inspired by this post on Search Engine Land.

March 16, 2026
Why Basic SEO Tactics Falter in AI-Driven Search Visibility

As I delve into the world of AI-driven search, it’s clear that advice around AI is becoming way too simple. What really sets you apart are knowledge graphs, expert entities, and how you influence trusted datasets.

Recently, I came across a Harvard Business Review article that resonates with the shifts we’re noticing in SEO. AI Overviews and Google’s AI-enhanced search features are not only creating what’s known as a zero-click environment but they’re also redefining user journeys and behaviors.

User journeys that were once multi-touch are now compressed into a single, synthesized answer. The metaphor of the “Search” monolith crumbling visually captures this transformation.

In this dramatic shift, brands like mine lose many traditional touchpoints, requiring a change in marketing strategy. HBR brilliantly highlights how algorithms are reshaping first impressions. However, while pointing in the right direction, the article’s tactical advice feels too generic and superficial.

Much of the advice sounds strategic yet lacks deep operational insight. This gap is crucial for sustainable visibility and long-term success.

The challenge is deeper than what appears as simple advice to navigate at an executive level. Real structural change is essential to adapt to the evolving search landscape.

The Problem with Flock Tactics

The HBR article brings forward schema, authorship signals, and branded concepts but these suggestions risk becoming “flock tactics.” They spread because they’re easy to grasp, yet they lose their edge once widely adopted.

Schema

Schema is highly debated in LLM and AI optimization. Although Microsoft Bing uses schema for its LLMs, Google’s models have a more complex relationship with third-party LLMs.

Incorporating schema in AI and SEO activities is useful, but presenting it as a fundamental tactic neglects its diminishing returns when everyone implements it.

Another oversight is the importance of external knowledge systems such as Wikidata. LLMs often rely on these authorities more than on any single website.

There’s a significant gap in understanding how models process structured versus unstructured data signals.

E-E-A-T — Shallow Authorship Signals

Using real experts’ credentials aligns with E-E-A-T but often becomes superficial, focusing on bios and headshots without actually strengthening expertise.

There’s a profound difference between mere display of bios and nurturing an expert entity recognized in academia or industry.

Only genuine expertise creates the signals that AI models trust.

Vanity Concepts

Creating branded concepts like “The Acme Index” sounds appealing but is difficult to successfully execute. External adoption is key for them to gain traction.

These concepts must be embraced by reputable sources, which is a hurdle many brands fail to overcome.

The Structural Blind Spots

Beyond tactics, there are deeper structural issues in perceiving AI solely as an external shift rather than an opportunity to innovate internally.

Internalizing AI Infrastructure

The potential to integrate AI deeply into operations, through AI assistants or domain-specific agents, is often overlooked.

In controlled environments, fundamentals like site architecture and data structures remain crucial for success, even if they need to be reimagined for AI.

It’s Not Just SEO

SEO as a ranking problem is an incomplete perspective. It’s moving towards entity-level knowledge management.

Visibility now hinges on solid entity definitions and connections to external data sources.

Effective SEO requires understanding these complex relationships.

LLM Model Heterogeneity

Different AI systems use unique datasets and processes, implying a single strategy may not work for all AI platforms.

Essential is an awareness of these risks to prevent reputational damage due to missteps in optimization strategies.

Surface-Level Tactics Won’t Build AI Visibility

HBR article usefully outlines how marketing is changing with AI, emphasizing that traditional SEO is no longer sufficient.

Practical advice is thinner, filled with tactics quickly replicated by others.

The challenge lies in doing the harder, unglamorous work that leads to real, long-term visibility.

Inspired by this post on Search Engine Land.

March 13, 2026
Why Most ChatGPT Sources Aren’t Cited: Key Findings Revealed

When I think about how ChatGPT retrieves information, I find it fascinating that most sources it pulls in don’t make it to the final answers. According to a report by AirOps, a whopping 85% of the sources identified by ChatGPT never appear in its final response.

Why this matters to me. If I’m aiming to have my content mentioned in AI-generated answers, it’s clear that simply being discovered by the AI isn’t sufficient. Most pages that get retrieved ultimately don’t get the exposure I’m hoping for.

Key insight. It’s interesting to note that just because a page ranks and is retrieved doesn’t mean it gets cited. My content has to align closely with the prompt or the context it supports to be chosen.

Per the report: the focus shifts to how well I can optimize my content for selection in the AI synthesis process, beyond just showing up in the search results.

By the numbers:

82,108 citations appeared in final responses, but only 15% of the retrieved pages were mentioned. That means 85% of the pages that surfaced during research didn’t make it into the answers.

Citation rates also varied based on query type:

18.3% for product discovery queries, 16.9% for how-to queries, and 11.3% for validation searches.

Fan-out queries. I noticed that when ChatGPT generates an answer, it often triggers additional internal searches, resulting in a “second citation surface.” This stood out in the dataset findings:

89.6% of prompts prompted two or more follow-up searches. Fan-out searches expanded 15,000 prompts into 43,233 queries. Interestingly, 32.9% of the cited pages were results from these fan-outs and not the original prompt.

95% of fan-out queries had zero traditional search volume.

Google ranking correlation. I’ve learned that high rankings in Google significantly improve chances of citation:

55.8% of cited pages ranked within Google’s top 20. Pages in Position 1 were cited 3.5 times more often than those outside the top 20.

About the data. AirOps examined 548,534 pages from 15,000 prompts to understand how ChatGPT expands queries and selects which citations to include.

The study. For those interested in diving deeper, check out The Influence of Retrieval, Fan-out, and Google SERPs on ChatGPT Citations.

Inspired by this post on Search Engine Land.

March 13, 2026
Transforming SEO: The Shift from Keywords to Infinite Prompts

AI search expands the long tail into a multitude of prompt variations. Let me guide you through how fan-out queries, grounding, and task completion are reshaping SEO.

When I speak naturally, my language flows. It’s often messy, incomplete, and not always coherent. In contrast, the Google search bar made me condense my needs into short-tail or long-tail queries.

To navigate this, I would stack queries along a journey, refining them from A to B by stripping out personal nuances to suit what I thought the search engine could grasp. SEO experts built strategies around this, organizing queries by search volume and intent.

That’s evolving now. With Google promoting Gemini and companies like Samsung highlight AI features as key selling points, the landscape is shifting. I’m encouraged to be more expressive and detailed with my searches.

Moving from Keyword Research to Prompt Research

We need to transition from keyword research to prompt research. Traditionally, keyword research involved quantifying demand and optimizing at a phrase level. The new AI-driven search environment calls for understanding demand as generative concepts, preserving needs across numerous prompt formats.

This shift doesn’t render keyword research obsolete, but changes its scope. I’m learning to model user journeys, considering decision stages and user uncertainty, rather than just relying on search volume.

What I get from this isn’t merely a keyword map, but a task map reflecting real audience constraints. This signifies a shift from short and long-tail keywords to an infinite tail of prompt research.

Dig deeper: Why AI optimization is just long-tail SEO done right

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The Infinite Tail as a Behavioral Shift

The infinite tail is more than just an expansion of the long tail. It’s about personalization at each request. Users, like me, are layering contexts and preferences, creating unique prompt combinations.

As Ai systems evaluate these prompts, they predict responses probabilistically, shifting away from exact-match keywords. Now, it’s not just about ranking for specific phrases but ensuring my content solves the user’s problems.

In this journey, finding what users truly seek is as crucial as completing a task. With divergent user paths, flexibility replaces rigid step-by-step processes.

Dig deeper: From search to answer engines: How to optimize for the next era of discovery

Fan-out and Grounding Queries

Query fan-out is crucial in AI search. It breaks complex prompts into subquestions, enabling a deeper evaluation framework.

Content now needs to satisfy clusters of queries instead of single matches. Covering multiple dimensions of a task creates resilience in this network-centric world.

Grounding queries ensure AI answers are validated against the broader web, checking consistency and reputability across sources. For my content to be part of AI responses, it must seamlessly fit this network.

This evolution redefines authority in how corroborated content appears over technically manipulated content. It emphasizes structure, data consistency, and external validation, significantly easing an AI system’s decision-making process by reducing uncertainty.

Dig deeper: The authority era: How AI is reshaping what ranks in search

Designing for Hybrid Search

Organic search remains integral. It still dictates discovery and influences crawlability. However, AI now layers on top, impacting which brands feature in conversational responses. It’s a blend where organic visibility and AI selection coexist.

In this hybrid mode, the infinite tail favors genuine audience understanding, where my content should be designed to satisfy users’ situations instead of merely matching keywords.

This isn’t just a process renamed from keyword research to prompt research. It’s about understanding search motivations, decision-making, uncertainties, and evidential needs, fostering the infinite tail by prioritizing task completion over string matching.

Dig deeper: How to use AI response patterns to build better content

Inspired by this post on Search Engine Land.

March 10, 2026
Why Content Needs Strategic Distribution in Today’s SEO
“Content is king” has long been the mantra in the world of SEO. I’ve always leaned into content creation, though I know some focus on backlinks or technical SEO.

While I still believe content is crucial for search visibility, I’ve realized that it’s now essential to amplify its reach through effective distribution strategies.

With AI search evolving, asking, “What should I write next?” might not cut it. The game-changing question is, “Where should I distribute this content next?”

AI tools are further fragmenting search

Content distribution hasn’t always been our focus as SEOs. It was often a task for social media managers, PR specialists, and community managers.

But with AI search revolutionizing the landscape, distribution has become integral to achieving SEO success.

Here’s why:
- AI tools draw from broader sources.
- They operate under shifting logic.
- The visibility strategies for AI differ from traditional methods.
If that sounds abstract, let me break down the evidence behind these changes.

Different tools have different sourcing logic

As search tools diversify, a one-size-fits-all strategy is no longer viable. AI tools cite different sources, often with less overlap with traditional SERPs.

Users are more adaptable, shifting from tools like ChatGPT to Gemini quickly, challenging us to rethink our strategy.

Instead of focusing solely on one tool, I need a distribution strategy that considers a variety of AI systems.

AI search uses different logic from traditional search

AI models generally have low overlap with Google searches. This variance highlights the need for a diverse strategy to ensure visibility across platforms.

AI searches tap into a wider array of resources, sometimes prioritizing lesser-known sites, complicating the path to dominance.

The sourcing logic is changeable

This shifting logic, marked by phenomena like citation drift, further complicates our reach. Over time, AI tools significantly alter their source domains, up to 90% in just six months.

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Focus on broad, multi-channel distribution

The fragmentation of search demands a comprehensive distribution strategy. But how can we really make it work for us?

The key is not just in predicting where our content might appear but in expanding our reach across a variety of channels.

Our approach must adapt, embracing multi-channel distribution to reveal our brand in AI’s broad digital landscape. It involves targeting diverse platforms and collaborating with others, as third-party sources often overshadow personal domains.

1. Get good at collaborating

Winning in this fragmented environment requires teamwork. By integrating efforts across PR, social media, and community management, I leverage skills beyond traditional SEO.

I have to trust others with my projects and accept the shared accountability necessary for broader visibility.

2. Broaden your skillset

Understanding fields like digital PR and thought leadership is now part of my expanded role. I still focus on SEO, but I’m prepared to pivot where necessary.

While I may not master every skill, enhancing my knowledge of these interconnected fields enhances distribution capabilities.

3. Shift your mindset from ranking to presence

Google ranks remain important, but it’s equally crucial to populate as many platforms as possible. My goal is to plant hooks in the digital ecosystem that draw AI searches to my content.

I focus on presence rather than mere ranking, creating broader visibility to capture AI-driven searches.

4. Redesign your workflow

Integrating distribution into my workflow involves clear strategies from the outset. By planning post-launch phases and periodic content refreshes, I ensure a consistent distribution cycle.

Clear responsibilities and reusable elements prevent my distribution strategy from becoming an afterthought.

5. Start with these easy-to-implement best practices

Immediate actions help streamline this transformation, such as partnering with fellow businesses and adapting content for third-party sites like Quora or LinkedIn.

Keeping tabs on AI’s preferred sources and redistributing older content expands my reach and mitigates citation drift’s impact.

By prioritizing these initiatives, I boost my visibility in a world where distribution stands equal to creation.

Rethinking SEO processes for fragmented AI search

The landscape has shifted, urging me to adapt my SEO approach. As AI tools proliferate, navigating this fragmented terrain requires new methodologies.

SEO now demands more collaboration, intersecting with other teams like never before. The challenges are significant, but manageable strides toward cross-team coordination will set the foundation for future success.

Starting small allows me to slowly leverage these changes into formidable strategies, one step at a time.

Categories: AI SEO, SEO, Opinion

Inspired by this post on Search Engine Land.
March 10, 2026
Unlocking LinkedIn’s Dominance in AI Search Queries

In the past three months, I’ve noticed LinkedIn emerging as a key authority in AI-driven discovery. It’s fascinating to see how rapidly it’s progressed, skyrocketing from outside the top 20 to claim the top spot as the most-cited source for professional queries on AI platforms like ChatGPT.

This shift occurred between November 2025 and February 2026, a time of remarkable growth for LinkedIn. For me, these stats underline the platform’s potential for both companies and individuals eager to enhance their influence in the AI sector.

Inspired by this post on Try Profound Blog.

March 9, 2026
AI Assistants Dominate 56% of Global Search: New Study Unveils

I recently came across an intriguing study that shows AI tools are now responsible for generating 45 billion monthly sessions globally. This accounts for an impressive 56% of all search engine activity, according to Graphite.io CEO Ethan Smith.

The analysis combines web and mobile app usage across leading AI platforms and suggests that AI activity matches 56% of global search use and 34% in the U.S.

The surge is particularly evident in mobile applications like ChatGPT, Gemini, Perplexity, Grok, and Claude.

Why it matters: AI is broadening the horizons of discovery, rather than limiting the demand for search. Since 2023, combined usage across search engines and AI assistants has increased by 26% globally. It’s clear that having visibility in both LLMs and traditional rankings is crucial.

Key insights: The report dives into the performance of the top five LLM products—ChatGPT, Gemini, Perplexity, Grok, and Claude—and compares them to the biggest search engines. Here are some standout insights:

AI platforms generate 45 billion monthly sessions worldwide.

Within the U.S., AI accounts for roughly 5.4 billion monthly sessions.

An astounding 83% of global AI usage takes place within mobile apps (75% in the U.S.).

ChatGPT is leading the charge, representing 89% of AI sessions globally.

When looking at search-like prompts, AI usage constitutes 28% of the global search and 17% within the U.S.

The report leaves out prompts in the “doing” or “expressing” categories. According to OpenAI, around 52% of prompts focus on seeking information, akin to traditional search queries.

Reading between the lines: Most forecasts comparing AI and search focus only on website traffic, often just Google.com and ChatGPT site visits. This approach overlooks much of AI’s impact.

The research suggests these comparisons undervalue AI activity by a factor of 4-5 times because a significant chunk occurs on mobile apps.

The analysis takes into account various LLMs and search engines, rather than only comparing Google and ChatGPT.

What to keep an eye on: Google remains a dominant force in discovery, but the report estimates its share of search-related activity has declined from 89% in 2023 to 71% by the fourth quarter of 2025.

While global AI usage seems stabilized since July 2025, the U.S. usage is still on a rapid climb—up about 300% year over year by December 2025.

The full report. For more depth, you can read the analysis titled AI Is Much Bigger Than You Think.

Inspired by this post on Search Engine Land.

March 9, 2026

Why AI Search Challenges Persist Across Industries: Insights and Solutions

For two decades, I’ve witnessed the web operate on a simple transaction: create content to fulfill needs, secure a high search ranking, attract traffic, and then monetize through various channels like products, services, or ads.

However, zero-click answers and AI search are redefining this dynamic. The key question now is whether AI acknowledges you as a source and if that recognition translates into revenue.

In my quest to understand this shift, I conducted over 200 AI visibility audits spanning ten industries.

What I discovered was a pattern: most websites are easily scanned but rarely referenced. Surprisingly, those industries that depend most on organic traffic inadvertently make themselves the hardest to access.

How I Conducted the Audit

I executed 201 audits using a consistent rubric, generating an overall AI visibility score plus four detailed subscores:

Freshness.
Structure.
Authority and evidence.
Extractability.

Spanning ten industries:

Coupons.
Affiliate reviews.
Travel booking.
Local directories.
Personal finance comparison.
Health information.
Legal directories.
Online courses.
Job boards.
Recipes.

The dataset leaned heavily toward homepages, which are often more marketing-driven and less substantiated by concrete evidence.

I also monitored access issues, finding that 38 of the 201 audits (18.9%) returned errors, indicating AI systems were obstructed or couldn’t reliably retrieve content.

Eight more audits scored zero due to missing subscores, pointing to poor content extraction or problematic rendering styles that hinder accessibility.

When analyzing score distributions, I focused on successful audits (163 sites) to differentiate between “unreachable” and “low quality.” Each industry’s error rate acted as a signal of whether AI systems could consistently use a site as a source.

Where Industries Stand in AI Visibility

The table below displays industry performance based on the audits conducted:

Rank	Industry	Error rate	Median overall	Median authority	Median extractability	At risk
1	Travel booking and trip planning	33.3%	45.5	31.0	52.0	High
2	Job boards and career marketplaces	40.0%	64.0	44.0	74.0	High
3	Legal directories and lead gen	35.0%	63.0	44.0	74.0	High
4	Coupons and deals	20.0%	62.0	36.0	74.0	High
5	Local directories and lead gen	5.3%	64.0	38.0	74.0	Medium
6	Online courses and learning marketplaces	30.0%	67.5	46.5	80.0	Medium
7	Health info and symptom lookups	15.0%	69.0	52.0	80.0	Low
8	Personal finance comparison	5.0%	67.0	52.0	78.0	Low
9	Affiliate product reviews	0.0%	69.5	54.0	74.0	Low
10	Recipes and cooking content	5.0%	75.0	55.5	81.5	Low

What the Audits Actually Revealed

The findings illuminated that very few websites were consistently citation-friendly. Here are the critical insights:

Access Issues Are Bigger Than Most Teams Realize

A significant 18.9% of websites experienced access errors. In certain sectors, the issue intensified markedly: job boards (40%), legal directories (35%), travel booking (33%), and course marketplaces (30%).

Therefore, a substantial section of these markets is essentially inaccessible to AI by default.

Most Sites Are Caught in the Middle

Looking at the 163 successful audits:

Average overall score: 61.6
Median overall score: 66
70.6% fell into “Inconsistent visibility” (60 to 79)
Only 4.9% achieved “Strong foundation” (80 to 94)
0% reached “Exceptional” (95 plus)

Conclusion: Most brands aren’t constructed for predictable use and citation.

The Gap Lies in Proof, Not Formatting

Median sub-scores across the audits revealed:

Structure: 92
Extractability: 74
Authority and evidence: 48
Freshness: 45

While pages are easily parsed, fewer justify citation. Key issues included:

114 instances lacked a “last modified header,” demonstrating missing freshness.
Citations or outbound links were rare, appearing only 13 times.

Rather than fearing traffic loss, the larger risk is exclusion from AI’s consideration set.

Explore further: What AI Search Experiments Reveal About Attribution

3 Ways an Industry Can Vanish from AI Search

Industries disappear for specific reasons, fitting three failure modes:

1. Access Failure: AI Can’t Reliably Reach Your Content

If AI agents can’t consistently access your material, they may bypass you, compensating with data from alternative sources.

What access failure entails:

Strict bot protections or WAF rules treating agents as hostile entities.
App-like rendering prevents critical information from loading with initial HTML.
Barriers like popups or scripts impede content access.

How this causes vanishing:

AI’s inability to extract makes citation impossible.
Other sources or AI-native solutions satisfy the user’s query instead.

2. Trust Failure: AI Can Read You, But Can’t Justify Citing You

Trust failure is subtle: your page is understandable, yet lacks authoritative proof for AI to source it.

This was a common trend. In simple terms, the content reads well, but lacks defensibility.

A telling observation compares page types:

Articles’ median authority score: 76
Homepages’ median authority score: 45

A crisp homepage isn’t proof of authority. Citable proof resides in articles, policy pages, and similar in-depth resources.

3. Utility Failure: Even If You’re Visible, the Click May Not Happen

Utility failure is frustrating. You’re visible, potentially cited, but if your value is purely informative, AI creates an answer and the user never visits.

Visibility dictates your role in discussions, but utility affects revenue realization.

An applicable perception:

If your page answers the question, AI can replace it.
Where your product or service completes a user’s need, AI still requires you.

Access issues leave you ignored, trust issues mean you’re bypassed, and utility failures get your content summarized.

Why Certain Industries Are Vulnerable

Examining access, trust, and utility together reveals why some industries appear particularly exposed.

Categories repeatedly showing high risk in my findings shared three characteristics:

Inconsistent access due to blocking and extraction issues.
Content easily condensed into a single-answer format.
Limited business progression after the user obtains an answer.

This is why travel booking, job boards, legal directories, and coupons emerged as the most exposed in my analysis.

The larger implication is that while your business might thrive, your website might inadvertently be structured for exclusion.

Explore deeper: Each AI Search Study Tells a Unique Story

The Critical Point You Shouldn’t Overlook

This transformation impacts some industries more than others. Websites sustained by high-volume searches face heightened zero-click risks. However, even in these realms, a singular focus on information is perilous.

The misstep lies in equating AI search changes with ranking shifts; it’s truly an economic shift. From the audits, I realized:

Many industries render themselves inaccessible, ensuring models circumvent them.
Even when models interpret a page, lacking proof often prevents mentioning it.

The danger is becoming invisible. Triumph doesn’t come from concealment; it comes from proving your worth and offering something indispensable post-answer.

Trust combined with utility forms the new moat. Anything else remains outdated strategy.

Inspired by this post on Search Engine Land.

March 5, 2026

Mastering Content Chunking: Boost Readability and SEO
I’ve discovered that structuring content with a clear layout not only aids readers in scanning effectively but also helps AI systems in identifying precise answers. Let me guide you on how to break down ideas into concise, self-contained sections.

At first glance, structuring content might seem straightforward, but there’s more to it than meets the eye. Despite Google’s suggestion to avoid creating bite-sized chunks exclusively for AI benefits, the practice of chunking plays a crucial role in both enhancing online readability and catching the eye of AI models.

Chunking doesn’t just make content easier to find or cite in AI search; it naturally enhances content flow, making concepts more digestible for human readers like us. Let me walk you through the chunking process and its best applications.

What is chunking?

Chunking involves organizing text into clear, self-contained units of meaning. Each paragraph should focus on one idea, ensuring that readers grasp each concept quickly and thoroughly, without needing background context from surrounding text.

Does chunking help AI or people?

Recently, Google criticized chunking as being overly optimized for AI queries, implying it might not serve human readers well. However, based on my experience, chunking enhances content understanding for both readers and AI systems, providing a structured way to communicate ideas effectively.

When content is well-organized, it aligns with how we naturally read online, making it easier to scan. It benefits AI as well, since these systems process text by passages. A concise paragraph following a relevant heading offers a clear solution to AI searches, like identifying ‘how to measure keyword cannibalization.’

When to chunk content

I suggest integrating chunking from the beginning when creating new content. While it may not always be necessary to revise old content just for chunking, consider prioritizing high-traffic articles with low engagement for updates.
- Articles with significant traffic but high bounce rates.
- Content that ranks well but isn’t being cited effectively.
- Complex topics where clarity is needed for quick understanding.
How to chunk content

I find a chunk should succinctly cover a singular idea. Clear headings prepare readers for what’s next, and the corresponding paragraph fulfills that expectation. Here’s a simple approach to effective content chunking:

Build chunking into your content outline

Begin with a clear outline where each H2 or H3 represents a key concept with comprehensive explanation in the chunks below. This way, both writers and readers can see the content flow naturally.

How to edit existing content into chunks

Start by focusing on high-value pages, especially those with good traffic but poor engagement. Revise your headings to reflect their section’s content and break apart any paragraphs with multiple ideas to keep each thought independent and clear.

To chunk or not to chunk?

Don’t be swayed by the notion that chunking is just a trick. For me, chunking improves content for everyone—from readers hunting for specific answers to AI systems striving to connect queries to results.

Inspired by this post on Search Engine Land.
March 5, 2026

Tag: AI Search

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What is chunking?

Does chunking help AI or people?

When to chunk content

How to chunk content

Build chunking into your content outline

How to edit existing content into chunks

To chunk or not to chunk?