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Category: Opinion

How AI User Prompts Impact SEO Strategies Today
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.
Methodology
The studies referenced were spearheaded by Stella Rising. You can delve into them further in the report titled, “New Data: How Consumers Use LLMs for Search in 2026 (And What It Means for GEO).”
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.

Inspired by this post on Search Engine Land.
June 10, 2026
Transform SEO Reports into Actionable Insights

When I work on SEO reports, I know they often include comprehensive research. This might involve keyword data, technical analysis, competitor insights, content gaps, and actionable recommendations. Yet, the challenge arises when stakeholders finish reviewing the report but remain unsure about the immediate next steps.

Take, for example, a report suggesting improvements in internal linking. It typically fails to pinpoint which specific pages need links, the team responsible for these updates, the timeline for execution, or the expected outcomes. Similarly, identifying a crawl issue without outlining its priority compared to fixing existing content gaps can leave teams confused.

This is a critical juncture where many SEO reports lose their impact. The analysis may be accurate, but the path forward often lacks clarity.

What I strive for in a strong SEO report is to guide readers into understanding the present priorities, their importance to business objectives, and the immediate actions needed. This reduces the need for further interpretation before implementation can commence.

Research Is Useful, But It’s Not the Final Output

The SEO activities I engage in, such as keyword research, SERP analysis, technical crawls, competitor reviews, and content audits unearth many hidden opportunities and risks. However, it’s crucial that these inputs don’t overshadow the final report.

What stakeholders truly need from me are the conclusions derived from this research. They need clarity on which findings are impactful, which improvements can be deferred, and which actions should be prioritized.

As an illustration, while identifying 300 pages with missing meta descriptions, the report should clarify the significance of those pages. If the descriptions are of low-value archive pages, they might not require immediate attention. However, missing descriptions on high-intent service pages demand prompt action.

The same principle applies to keyword gaps; a useful report pinpoints high-opportunity keywords aligned with commercial intent and informs stakeholders why certain issues deserve immediate action.

Tailor Reports to the Stakeholder

In my experience, SEO reports often fail to incite action because they treat all stakeholders the same. Each stakeholder, whether a CEO, marketing lead, developer, or content manager, requires different levels of detail, and presenting information in their context is critical.

For executives, I focus on business opportunities, risks, resources, and expected impacts, while marketing leads need to understand how SEO efforts tie into demand generation and campaign strategy.

Developers require a clear technical path, and content teams need page-specific action plans. My goal is to present findings in a way that each stakeholder can easily act upon.

What a Decision-Ready SEO Report Should Show

In crafting a useful SEO report, I aim to address a concise set of questions that, while varying across stakeholders, consistently serve the purpose of guiding the next steps.

By starting with clearly identifying where SEO can create business value, pinpointing constraints, and defining prioritized actions, I ensure the report supports effective decision-making.

Finally, outlining how progress will be measured ensures stakeholders remain aligned and motivated as the project unfolds.

Turn Every Finding into a Clear Next Step

All significant findings in an SEO report should be immediately actionable. By answering what was found, why it matters, and what action should follow, I enable stakeholders to move forward confidently.

For instance, a discovery of high-traffic pages lacking links to commercial pages should lead to specific steps involving content updates and measurement, ensuring progress is tracked and objectives are met.

What to Cut from SEO Reports

To make SEO reports concise and effective, I exclude unnecessary data such as tool screenshots and extensive keyword exports. While supporting materials are valuable, the main report should focus on clarity and priority.

I also shorten methodologies unless essential for building trust or understanding. Keeping the report streamlined ensures stakeholders are not overwhelmed with information that doesn’t aid in decision-making.

The Best SEO Reports Make the Next Step Obvious

Ultimately, my objective with SEO reporting is to minimize uncertainty. After reviewing the report, stakeholders should clearly understand what requires attention and the direction to proceed.

Although SEO lacks absolute prediction, each recommendation should outline expected impacts and the signals used to measure progress, turning findings into active projects that propel the business forward.

Inspired by this post on Search Engine Land.

June 10, 2026
Mastering Prompt Tracking: Strategies for Accurate AI Insights

I’ve come to realize that prompt tracking is often misunderstood as mere noise, but it’s actually a golden opportunity to refine AI interactions through a structured approach.

AI responses can be unpredictable. However, by utilizing repeated runs, establishing fixed sampling rules, and calculating confidence intervals, we can transform variance into a trustworthy metric.

By embarking on this journey with me, you’ll soon be equipped to create a reliable AI tracking system.

You’re already ahead if you’ve embraced persona-based prompt design as discussed in Synthetic Personas for Better Prompt Tracking.

For those immersed in AI SEO strategies, understanding the true trajectory of your efforts over the noise is crucial. Explore more with How Much Can We Influence AI Responses.

While many have dismissed prompt tracking due to its variability, I’ve discovered that it mirrors the unpredictability seen in weather forecasts and credit scoring, which are still meticulously tracked.

Reflecting on keyword tracking’s evolution, I see a parallel path for prompt tracking, which requires adapting its methodology to account for the numerous platforms now at play.

At pivotal industry events, experts speak of a shift from single search queries to a conversational model, emphasizing the changing landscape we must adapt to.

The shortcomings of current prompt-tracking tools are evident in their lack of innovation, yet I believe we can rise above with a more strategic approach.

Although single-turn prompts provide limited insight, constructing full conversational sequences reveals persistence, a vital metric often overlooked.

Imagine tracking a B2B SaaS CRM journey through defined stages, extending prompts to capture decision-making across multiple touchpoints to truly gauge influence.

HubSpot’s visibility across platforms like ChatGPT and Perplexity illustrates the nuanced understanding needed to strategize investments in brand-centric content.

The future of prompt tracking resembles opinion polling, employing systematic and repeatable methodologies to extract meaningful data amidst variability.

This piece first appeared on the author’s website and is shared with permission here.

Inspired by this post on Search Engine Land.

June 10, 2026
Top 11 Common PPC Mistakes and How to Avoid Them

As I delve deeper into the world of ad platforms, it’s fascinating to see how algorithms are transforming the landscape—putting a spotlight on marketers and their expertise.

Managing ad accounts today is a whole new ball game compared to just a few years back. With automation taking center stage through tools like Meta’s Advantage+ and Google’s Performance Max, the skills needed for effective account management have evolved significantly.

Recently, I’ve conducted numerous B2B account audits, for companies both in-house and those transitioning from agencies. It’s striking how rare it is to find operators who truly grasp these systems.

Even with advanced technology at our fingertips, many proficient B2B marketers continue to make costly errors. Here are the frequent mistakes I’ve identified recently on LinkedIn and Google.

Mistakes Advertisers Are Still Making on LinkedIn

LinkedIn is indispensable for B2B outreach, yet some advertisers persist with avoidable blunders like these.

1. Ignoring Audience Targeting

Shockingly, I still see this rookie error. Ads end up targeting entry-level individuals, students, and irrelevant businesses instead of the intended audience.

2. Neglecting to Adjust Targeting Over Time

LinkedIn’s professional targeting can be excellent, but not infallible. It often lets unrelated roles and titles slip through, wasting budget on irrelevant audiences.

3. Over-relying on Automated Settings

This is more common than a mere oversight. Keeping options like audience expansion active leads to ads appearing in less relevant, low-quality spots.

4. Stagnating Creative Content

Veteran LinkedIn marketers know that LinkedIn’s ad structure complicates creative testing, emphasizing the need for creative refreshes and theme variations.
Dig deeper: 5 B2B LinkedIn Ads tests to run in 2026
Mistakes Advertisers Still Making on Google

Here are the top pitfalls I’ve discovered in Google Ads accounts recently.

5. Using Conversion Strategies Without Adequate Data

Ad accounts employing conversion-based strategies without any conversion data often mislead algorithms, targeting the cheapest and least effective actions.

6. Defaulting to Display and Search Partners

This blunder reallocates budget from high-intent searches to less effective, lower-quality placements, wasting resources.

7. Forgetting Sitelinks

Omitting sitelinks diminishes the potential to occupy more space in search results, reducing appeal to potential customers.

8. Ignoring Non-Converting Keywords

Failure to scrutinize and modify ads for non-converting keywords results in wasted investment.

9. Overusing Broad Match

Exclusively using broad match without employing phrase or exact match can lead to unnecessary expenditure.

10. Unrestricted PMax Utilization

While PMax for B2B has evolved, neglecting enhanced conversions and educational setup is a recipe for low-quality leads.

11. Discounting AI Max Testing

With Google constantly innovating ad serving and optimization through AI, early adoption can offer a competitive advantage.
Dig deeper: Top 10 Google Ads mistakes to avoid in 2026
Automation Still Needs Skilled Oversight

Critical gaps often occur post-data collection, where follow-up sequences and accurate data linking are necessary to optimize Return on Ad Spend (ROAS).

Focusing solely on past practices like granular negative keywords without adapting to newer automation capabilities highlights inefficiencies.

Mistakes might appear minor, but they signal deeper mismanagement when left unattended, stressing the need for skilled supervision even in an automated environment.

Remember, effective ad management still necessitates quality inputs, accurate measurement, and vigilant oversight.

Inspired by this post on Search Engine Land.

June 10, 2026
How AI Shapes Your Brand’s Digital Presence

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.

Inspired by this post on Search Engine Land.

June 9, 2026
The Real Impact of AI on Brand Visibility: Beyond Metrics

Recently, I’ve noticed that many AI visibility platforms base their insights on a limited set of prompts. It’s time we explore more suitable metrics for our ever-evolving query landscape.

Traditional share of voice (SOV) has become outdated. But what concerns me even more is how organizations are embracing AI share of voice, an equally flawed metric.

Software vendors are now attempting to quantify brand visibility across platforms like ChatGPT, Gemini, Claude, and Perplexity with a single percentage score. This approach relies on a denominator none of us can see.

Unlike the traditional search with a fixed set of keywords, AI prompts are limitless, making these metrics often unreliable.

Though traditional SOV had its drawbacks, its assumptions were clear. We marketers would define a keyword list, observe our visibility against competitors, and use a stable denominator.

This methodology is no longer valid. With dynamic and personalized search results taking over, it’s vital that AI visibility platforms stop presenting precise percentages that lack auditing or validation.

For this reason, we must redefine how we measure visibility in AI searches to avoid misleading leadership teams with fictional metrics.

Why Traditional SOV Metrics Now Fail

The core principles of SEO and digital brand tracking have been disrupted by two significant trends: the end of static result pages and the rise of personalized interfaces.

Search engines have become dynamic and change constantly based on real-time data.

With AI-generated summaries, localized results, and continuous scrolling, one person’s search experience will never be identical to another’s.

Given this, gauging an accurate ‘share’ of screen space is now mathematically impossible.

In today’s landscape, being ranked first might still mean sitting beneath several higher-priority elements like sponsored listings or AI-generated content.

Search engines now tailor layouts dynamically based on immediate user intent and past interactions, resulting in hourly ranking fluctuations.

Attempting to gauge share of voice on these terms is as inefficient as measuring ocean tides with a ruler.

The Modern AI Share of Voice

As traditional rank tracking became less relevant, vendors provided new metrics like LLM Visibility or AI share of voice, promising polished and reliable percentage scores.

These metrics claim to chart a brand’s footprint across various platforms, yet they obscure key methodological weaknesses that demand attention.

Legacy Tracking vs. LLM visibility: Legacy methods allowed for fixed keyword lists and auditable ranks on SERP, whereas LLM relies on random subsets and subjective denoting.

Beyond AI Share of Voice: 3 Key Metrics

The need to transition from pure search volume metrics to evaluating how well a brand is integrated in digital dialogues is evident. Rather than focusing solely on keywords, evaluation should revolve around a brand’s prominence in AI’s conceptual frameworks.

1. Share of Mentions: AI models build connections rather than simply recording pages. Thus, a brand needs to be part of the training dataset or real-time retrieval sources used by AI to ensure visibility.

2. Share of Recommendations: This measures how frequently your product is advised when buyers consult AI engines. A precise and well-documented position in the market is crucial for prominence.

3. Share of Narrative: Monitoring the qualitative nature of mentions is essential, as being depicted negatively despite frequent mentions can be detrimental to the brand.

Inspired by this post on Search Engine Land.

June 8, 2026
Master AI Search Visibility: Track Influence Beyond Clicks

The journey from discovery to decision is becoming increasingly obscure. I’ve discovered how to merge traditional attribution methods with new, subtle signals of influence.

Most traditional attribution models were designed for a world where clicks were king. Someone would search for something, click on a result, visit a page, and eventually convert. Simple, right?

Analytics platforms used to connect these actions seamlessly, painting a fairly accurate picture of success. While not perfect, at least the process was visible. Now, AI-generated search experiences have made this path much harder to trace.

Imagine a scenario where a prospective buyer consults ChatGPT about the best project management software or leans on Google’s AI Overview for cybersecurity advice before compiling a list of potential vendors. My company might make it into those discussions without a single click to show for it. This discrepancy between influence and traffic is precisely why I need to rethink attribution.

Search trends have been gravitating towards zero-click experiences for years now. Features like snippets, knowledge panels, and local packs have effectively reduced click-through rates by providing answers directly in the SERP.

Generative search takes this even further by compressing what used to be a multi-click research journey into one pivotal interaction. Users can now compare vendors, appraise recommendations, and gather data without ever leaving the SERP.

For brands, this translates to lost visibility in certain parts of the buyer journey. But it also opens up new avenues for influencing decisions before a website visit even takes place.

Dig deeper: What 4 AI search experiments reveal about attribution and buying decisions

Even though we’ve traditionally relied on website visits as the primary indicator that marketing has made an impact, AI is changing the game by disconnecting discovery from measurable traffic.

A prospect might come across my brand several times through AI-generated answers before ever arriving on my site. By the trip they make to my site, their journey can look deceptively simple in analytics: Direct visit, branded search, conversion.

Those early interactions that introduced my brand or influenced a buying decision can remain invisible in reporting.

As more initial discovery and evaluation happens within AI frameworks, traditional attribution captures less of the decision-making landscape. While it still records visits, much of what occurs before that remains unseen.

These harder-to-measure interactions are still crucial, creating fresh chances to influence how buyers discover, evaluate, and compare choices.

A potential buyer might first hear about my company through one of these AI channels, then go on to use AI to weigh options, explore alternatives, and make a shortlist—all before visiting my site. During this process, they might encounter my brand through various touches such as recommendations, comparisons, citations, and AI-generated responses that foster familiarity and build credibility.

These interactions, despite not generating a click, can play a critical role in shaping buyer decisions and determining which brands make it to the final evaluation stage.

Dig deeper: Why AI visibility starts before search and ends with citations

While traditional attribution is still valuable, it now provides a less comprehensive description of how decisions are made. As AI becomes a bigger part of how buyers research and scrutinize options, a broader view of influence is essential. This involves going beyond the conversion path to incorporate signals that outline how awareness and consideration develop over time. Here’s where I begin.

1. Assisted conversions: AI-generated recommendations frequently shape decisions well before entering a measurable funnel. Assisted conversion reports can highlight which channels influence conversions, even if they’re not the final touchpoint.

2. Branded search growth: An observable rise in branded search activities can indicate that AI visibility is growing brand awareness. More searches for my company following AI-generated mentions are a promising sign.

3. Direct traffic trends: While direct traffic shouldn’t solely represent AI’s influence, unexplained increases can be telling. They may suggest that people are learning about my business from AI sources before returning directly or via branded searches later.

4. Brand visibility within AI systems: Observing how often my brand appears in AI prompts and recommendations provides valuable insight. It reflects whether AI frameworks consider my brand a credible option within a given category.

The ultimate goal is to integrate traditional attribution data with these new visibility and influence signals to create a fuller understanding of decision-making dynamics.

Dig deeper: The micro-macro shift: How to measure AI visibility now that precision is gone

The takeaway here is to build a more comprehensive view of influence. My understanding of market influence starts with the realization that the consumer journey extends well beyond visible interactions and analytics.

As AI continues to grow in prominence for discovery and evaluation, adapting strategies to account for this broader scope of influence will be crucial for staying competitive.

Inspired by this post on Search Engine Land.

June 5, 2026
Navigating Google’s Evolution: The New Era of AI and SEO

As I delve into Google’s expanded candidate set, I can’t help but sense a transformative shift in how search systems evaluate content. It’s fascinating to see how AI systems now approach broader pools of information, with visibility increasingly relying on verification, semantic relationships, and trust signals rather than just keywords.

This evolution pushes SEO from simply focusing on retrieval and ranking mechanics to something akin to forensic architecture. This approach gears systems to help machines verify and trust information on a larger scale.

Recently, I read an article on Google’s expanded candidate set, and it felt like the culmination of my five-year journey through the depths of AI and digital ecosystems. It’s reassuring to see the industry moving towards what I’ve been passionate about.

Throughout my 30-year career, I’ve always strived to meet current demands while anticipating future trends. This experience has honed my ability to identify emerging patterns and make proactive decisions aimed at where the industry is heading.

To grasp why this "selection crisis" is happening, it’s important to differentiate between a crawler and an AI agent. When Googlebot first emerged, it acted like a mechanical fetcher, following simple, rules-based logic to record, not understand, content.

Over time, this mechanical clerk has transformed into a forensic investigator, with advances like RankBrain, BERT, and the recent Gemini AI enhancing its capabilities immensely. These technologies herald a new age where AI systems synthesize broad content pools to deliver unique answers effortlessly.

The advent of ChatGPT in 2022 was a catalyst for shifting towards answer engines. This change, which I term the "selection crisis," now requires AI to selectively curate information, democratizing access to high-quality information regardless of user familiarity with search processes.

Those of us immersed in this transition quickly realized that AI systems now value information gain and atomic facts as primary currencies. In essence, succinct and precise information now carries greater weight than verbose content.

This understanding didn’t come overnight but from decades of dealing with problematic zombie facts and constant trial and error in high-stake industries like online pharmacies. Trust is fundamental here; it’s not just a catchy phrase but the backbone of sustained business.

In these industries, I learned early on the need for systems that not only find but also understand our digital presence. This realization led me to develop tools that address gaps in content credibility and reliability.

One significant hurdle I faced was the "commodity crisis." Managing multiple ecommerce sites selling identical products taught me the necessity of presenting unique, verified information that distinguishes us from the competition.

While building solutions like the E-E-A-T engine, atomic sandwich architecture, and forensic IG evaluator, I realized the tools must integrate seamlessly to address larger systemic issues like context debt and trust gaps.

In conducting a recent forensic audit across 28 digital entities, I confirmed this crisis of selection has infiltrated the general web. Now more than ever, systems evaluate not just keyword proficiency but verify the trustworthiness of sources at an unprecedented scale.

To tackle this, I’ve employed three pillars of forensic engineering: cryptographic authority using JSON Web Signature standards, semantic graphs that optimize relationship reading, and regulatory alignment mapping to the EU AI Act.

These pillars demonstrate the evolving landscape of answer engines, demanding that entities not only rank but also build credible and intelligible systems for AI to depend upon.

The SEO landscape is drastically changing, requiring us to go beyond retrieval to support machines in understanding and trusting your content’s credibility. It’s time to embrace this new frontier, assembling public domain frameworks into reliable AI-friendly structures.

Inspired by this post on Search Engine Land.

June 4, 2026
AI Adventures: When Confidence Meets Costly Errors

Have you ever found yourself immersed in the SEO world, only to be told by an AI that everything you know is wrong? That’s exactly what happened to me, and not just once, but three times in a single week with Gemini.

It’s not the mistakes that rattled me—it was how credible they sounded. The answers from Gemini were polished and convincing, enough so that most would accept them without question.

When it comes to topics you’re not deeply versed in, how do you even begin to challenge such confident wrongness?

Laughably, I caught two, but the third one hit me where it hurts—my wallet. All this unfolded within a week.

Here’s a closer look at what went down.

In one scenario, Gemini misguidedly walked me through technical SEO for a client. During a site migration task on Shopify, where canonical tags were misbehaving, I turned to Gemini for solutions.

The advice was not just misleading but used terms that would raise red flags with leadership—talk about penalties!

Semantic clarity is crucial here; an internal misstep with jargon can make stakeholders halt essential projects.

Gemini further compounded the issue with incorrect guidance on URL parameters hosting.

The experience echoes another incident where Gemini’s mechanical advice almost led me to make a $3,000 error on my Jeep SRT. The AI’s confident proclamation of a rear differential issue had me nearly misappropriating my resources.

After sharing more data, Gemini pivoted, claiming it had leapt to conclusions without sufficient evidence.

In yet another amusing episode, my Madden game finance strategy, courtesy of Gemini, resulted in a fictional $20 million oversight. Although the stakes were virtual, it was a stark reminder of why critical thinking remains indispensable.

These anecdotes underline that it’s not AI replacing experts but rather pushing out those who stop questioning.

The real skill remains in smelling the bull and asking deeper, more insightful questions.

Inspired by this post on Search Engine Land.

June 4, 2026
Mastering SEO: Strategies for Sustained Growth in 2026

Keyword research and on-page optimization still matter, but authority, distribution, and brand visibility now drive more organic growth.

Over the past 18 months, I’ve watched a shift in what drives SEO success. What worked in 2022 isn’t as effective today, yet many are stuck in the old ways.

One major realization emerged: Teams feel busy but ineffective because the old model doesn’t encompass all that’s needed to succeed now.

This isn’t about AI replacing SEO; it’s about evolving practices to keep pace with industry changes.

The list of SEO priorities has shifted, with an old emphasis on standalone keyword research no longer holding its former value.

High-volume content production and simple on-page optimization aren’t enough. They’re the foundation, but not the entire building.

Today’s success builds on the basics but requires efforts in entity work, original research, and distribution.

To make headway, we need to prioritize brand building, nurturing its presence across platforms.

For growth, focusing on unique research and proprietary data can set you apart.

Effective distribution and PR work are necessary for visibility, no longer relying on the content to naturally earn links.

In-house SEO leaders should consider reshaping teams to match the evolving needs.

Agency-side practitioners must adjust their offerings to stay relevant, emphasizing strategic activities over standardized deliverables.

The future of SEO still has robust potential for those willing to adapt and innovate.

Inspired by this post on Search Engine Land.

June 4, 2026

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