Tag: AI

How AI Interprets Your Brand Through Mathematical Insights

As I observe the evolving landscape, I realize that the transition from traditional search to AI requires brands like mine to present information in a way that AI can effectively read, verify, and rank it.

Scott Stouffer, the co-founder and CTO at Market Brew, recently shared that AI perceives brands differently than we might expect.

Despite our efforts to publish content, optimize pages, and adhere to SEO best practices, the game has changed. It’s no longer just about keywords and links; it’s about understanding meaning and intent within AI systems.

Whereas legacy SEO allowed for lower ranking visibility, AI-driven methods prioritize retrieval first, determining if your content even makes it into the search results.

Stouffer emphasizes, “If you’re not retrieved, you do not exist to AI.”

I find it fascinating that in AI systems, our brand becomes a mathematical object. Although we might intend our brand to be one thing, AI interprets it based on the content we’ve published.

The version of our brand computed by AI might significantly differ from what we originally intended.

Retrieval precedes ranking in the AI world. Traditional SEO emphasizes ranking positions, but AI first filters which content is even eligible for consideration.

This initial step is called retrieval, and if my content isn’t part of it, I receive no impressions or clicks.

Shifting from exclusion to inclusion is crucial, as Stouffer puts it, “You don’t lose. You just never entered the game.”

AI does not view web pages as a single unit. Instead, it dissects them into smaller sections, evaluating each chunk separately. This means even a single sentence can stand out if it aligns closely with a user’s query.

Meaning is translated into math by converting each chunk into a vector. This vector captures context and intent, showing that AI measures how close the content’s meaning is to a query, rather than just keyword overlap.

I learned that content naturally forms clusters in this vector space. Similar ideas group together, which reflects how AI systems understand topics beyond mere website layout.

Our brand’s positioning in these clusters is represented by a centroid, the average position of all related content. This centroid is what AI uses to understand our brand, not our carefully crafted homepage or brand guidelines.

Stouffer mentions that it’s not just about optimizing individual pages; it’s about ensuring consistency across our entire content portfolio to maintain a clear, stable centroid.

When queries are entered, AI searches for the closest matches in meaning space, first assessing if content is close enough before applying traditional ranking factors.

Many brands look nearly identical to AI due to similar strategies and content, leading to what Stouffer describes as cluster collision. To stand out, we need to create distinct content that occupies a unique position in the meaning space.

SEO is evolving into a continuous process where each new piece of content shifts the centroid, requiring ongoing alignment monitoring and adjustment to avoid drift.

Most teams struggle with visibility into these AI processes, often resorting to trial and error. Understanding these dynamics can help us better control our brand visibility.

In summary, our brand exists as a mathematical object in AI systems. By controlling our centroid, we can effectively manage our AI visibility. Stouffer succinctly concludes, “If you control your centroid, you control your visibility.”

Inspired by this post on Search Engine Land.

April 30, 2026
Can A Fictional Brand Outsmart AI? Our Surprising Experiment!
In late 2024, I embarked on an eye-opening 16-month journey with SE Ranking’s research team to test the performance of AI-generated content in organic search. We launched 20 diverse websites, eagerly tracking their progress.
But my curiosity didn’t end there. I was driven to comprehend how AI systems find, process, and use information. This inspired me to expand our project and delve deeper into AI search and LLM visibility experiments.
In our next phase, we boldly created a fictional brand and inserted it into a real, competitive niche. Our aim? To see how fast AI would catch on and if our make-believe brand could stand toe-to-toe with industry giants and governmental sources.
After just one month, enlightening patterns began to emerge.
Methodology behind the experiment
I crafted a fictional brand and dispersed content across various platforms:
- A fresh website exclusively for the brand, registered specifically for this daring experiment.
- 11 seasoned domains, each over a year old with a solid history and existing rankings.
I experimented with seven different content formats:
- Comprehensive guides.
- “Alternatives” listicles.
- “Best of” listicles.
- Review articles.
- Comparative (“vs”) pages.
- How-to/tutorial content.
- Clickbait-style articles.
Kicking off in March 2026, I monitored five AI systems: ChatGPT, Google’s AI Overviews, Google’s AI Mode, Perplexity, and Gemini, tracking 825 prompts and generating 15,835 AI answers during the initial month.
For every prompt, I considered:
- Our brand’s appearance in AI responses.
- Its recognition as a source.
- Frequency of being the main cited source (position 1).
This ongoing experiment was initially designed to observe AI systems’ reactions to freshly created, fictitiously branded information.
Key experiment insights
- 96% of our brand’s AI visibility stemmed from branded searches. Even in a low-competition niche, a new domain struggled to compete on non-branded topics.
- For niche-specific queries, our brand outshined well-established competitors by up to 32 times, achieving dominant visibility in under 30 days.
- Despite lacking authority, clearly articulated identity pages, like “[Brand Name] Complete Guide” and “About Us”, became frequently cited, highlighting the importance of brand positioning in AI.
- Perplexity surfaced new content swiftly, often citing additional domains over the main site.
- Google’s AI Mode offered stability on branded queries.
- Gemini struggled with brand identification, resulting in 60% of responses without our brand’s citation for uniquely branded queries.
- Deep guides, review articles, and comparison pages gained the most citations, while generic content saw minimal impact.
- A hub page with 10 supporting articles yielded no citations, whereas shorter, repetitive pages garnered over 1,800 citations, emphasizing the power of high-volume content publishing.
Insight 1: New domains may not beat market leaders right away, but they can define their brand narrative in AI search
A new site struggles to compete broadly initially. However, our fictional brand quickly gained traction through branded queries, largely because these were the focus points.
Of all AI answers, a staggering 96% came from branded searches alone, reiterating the crucial role of brand-specific queries in early visibility.
This mirrors traditional SEO patterns where new brands must first build trust and recognition.
My key takeaway for marketers was clear: AI systems are inclined to use your site as a primary information source during your brand’s formative years.
This insight was reinforced as pages consolidating brand information, such as the “Complete Guide” and “About Us”, became the primary sources cited from our main domain.
Therefore, shaping the brand narrative early on AI platforms is crucial, even for emerging brands.
Insight 2: AI engines behave very differently
Our experiment shed light on the unique behaviors of five AI systems in indexing and presenting our fictional brand.
Google’s AI Mode: The most stable for branded visibility
Google’s AI Mode proved to be a reliable ally, consistently putting our brand at the top for around 90% of branded queries.
It was the bastion of predictable brand visibility in our experiment.
Google’s AI Overviews: High visibility, lower consistency
Though less consistent, Google’s AI Overviews provided notable brand visibility. Yet, fluctuations and temporary drops were observed during our test period.
Whenever links were absent, visibility suffered, highlighting the need for sustained link presence.
Perplexity: The fastest to pick up new content, but not always brand-first
Perplexity swiftly indexed new content, quickly boosting early visibility.
However, its affinity for additional domains over the main brand site complicated content attribution in AI responses.
ChatGPT: Slower to react, stronger over time
ChatGPT gradually improved recognition of our brand, with a notable increase in visibility over March.
Notable growth occurred in unique claims and comparisons (“vs”), showcasing ChatGPT’s potential for longer-term brand assimilation.
Gemini: Weakest performance and most inconsistent behavior
Gemini presented challenges with niche recognition, improving only when framing prompts appropriately.
Despite effort, results remained inconsistent, with significant citation gaps on brand-specific queries.
Insight 3: Content format matters, but so does the volume
Through diverse content experimentation, we found in-depth articles earn the most AI citations.
Comprehensive guides, reviews, and comparisons outperformed simpler formats, reinforcing the power of detailed content presentation.
The volume of content also played a role. Although the individual performance was low, 30 shorter pages collectively generated impressive AI visibility.
This doesn’t diminish the value of quality but indicates a large amount of content can boost overall reach.
Insight 4: Topical clustering alone doesn’t produce AI visibility
Our structural tests revealed that topical clustering, without substantial content, didn’t boost AI visibility.
It challenges the notion that clustering inherently strengthens authority, stressing the importance of standalone content value.
Though structured linking offers insight into site understanding, AI systems prioritize the need for direct and valuable information retrieval.
So, do AI engines reward entity coherence more than truth verification?
Our first month’s results point to a significant insight: AI systems value availability and consistency over strict truth verification.
Though not all-reaching, well-structured, repeated, and available content can be surfed with surprising ease.
This phenomenon was observed during manual checks where even a fictional brand received favorable recommendations due to consistent narratives.
It’s not simply LLMs favoring new brands, but where gaps exist, even limited information may be built up positively.
Final thoughts
The true revelation isn’t the visibility of a fictional brand. Rather, it’s how visibility aligns with brand-centric inputs like unique claims and varied content.
This leads to pivotal conclusions:
- AI search isn’t arbitrary. It responds to discernible and influenceable signals.
- AI remains vulnerable to manipulation. Without inherent truth-checking, strategies used by legitimate brands can simulate credibility.
Illuminating the need for active narrative shaping, our experiment urges businesses not to rely on AI systems to innately capture accurate brand representation.
We’re committed to expanding and monitoring these insights over time, as we collect ongoing data.

Inspired by this post on Search Engine Land.
April 29, 2026
Transform Your SEO: From Being Seen to Being Chosen

I’ve learned that SEO is not just about getting noticed — it’s about earning trust and becoming the top choice.

Wil Reynolds, founder and CEO of Seer Interactive, really got me thinking about how artificial intelligence is changing the game for us SEOs.

In his SEO Week session, “SEO is a performance channel, GEO isn’t. How do you pivot?” he emphasized that too many of us are chasing the wrong goals and crafting content that people simply don’t buy into.

Marketing isn’t just about being seen

Reynolds challenged us to look beyond visibility to what truly drives success — belief in our brand.

“Marketing was never just to be seen or be visible,” he said. “It’s about transforming that visibility into brand belief… and ultimately, being chosen.”

He outlined a crucial journey for marketers: being seen, being believed, and then being chosen.

Even when we hit that number one ranking, the job isn’t done. As Reynolds put it, “Job’s not finished.”

Low-quality marketing is everywhere

Reynolds made me rethink some of the standard marketing tactics we use that don’t actually provide value.

He criticized methods like automated outreach, saying, “That’s not marketing.”

I found myself questioning my past work habits — was it really marketing?

The industry is producing ‘zombie content’

Reynolds shed light on our tendency to churn out templated content just to rank, equating it to “zombie content.”

Lists like “best restaurants in Minnesota” when such searches aren’t even realistic? It truly made me think about content creation differently.

Short-term tactics vs. long-term brand building

Reynolds pointed out the stark contrast between short-term wins and the sustained success of building a powerful brand.

“Some focus on winning now, others play the long game,” he explained.

He made it clear that chasing immediate results often leads to producing work nobody wants.

SEO success doesn’t translate to AI visibility

Reynolds illustrated this with an example about “ethical jeans,” showing how AI results can diverge significantly from SEO.

A brand could rank highly on Google yet fail to gain traction in AI models due to a lack of genuine credibility.

Visibility without belief doesn’t lead to outcomes

Just having visibility doesn’t guarantee anything if people don’t trust or believe in us. A reality check I needed.

This visibility is merely a stepping stone, not the end goal.

What people say matters

Reynolds encouraged us to listen actively to how people discuss brands, especially on platforms like Reddit.

Despite how brands might try to show themselves as leaders, user sentiment can reveal a drastically different picture.

The wrong metrics are being measured

Many of us fall into the trap of focusing on easy-to-track metrics instead of those that tell the real story.

Reynolds suggested that if our visibility isn’t driving results, we’re looking at the wrong data points.

Watching real users changes the picture

He emphasized the breakthroughs that come from observing actual users interact with AI tools. It’s eye-opening and transformative.

Start with your brand

Understanding exactly how our brand is perceived in AI-generated content is vital.

If we’re not ensuring our brand is accurately represented, all our marketing efforts might be in vain.

AI can shape your brand narrative

Reynolds shared a personal experience where AI misrepresented his company, prompting him to take action by publishing clear, corrective content.

There is too much content

With all this content flooding the digital space, I’ve realized the importance of stepping back and curating high-quality material instead.

Rethinking performance

Reynolds drew attention to the varying effectiveness of different traffic sources, reminding me to focus on the ones that truly convert.

A final question for marketers

He left us pondering: Are we prepared to give up a fraction of visibility for the sake of being more credible?

Inspired by this post on Search Engine Land.

April 28, 2026
Embracing AI in PPC: Ginny Marvin’s Evolution in Search

I find it quite fascinating how the world of search has transformed over the years from manual PPC efforts to AI-driven systems. Reflecting on Ginny Marvin’s journey offers a glimpse into these dynamic changes and underscores the importance of staying curious and adaptable as marketers.

My journey into PPC wasn’t fueled by a master plan but rather by a desire to reinvent myself professionally. Transitioning from print publishing and advertising sales, I found myself at a crossroads when the startup magazine I had helped establish ceased operations. That pivotal moment pushed me towards digital marketing, starting from entry level.

Starting fresh meant embracing the unknown. As Marvin put it, she didn’t know what she was doing initially, which makes her story relatable for anyone starting anew. This fresh start paved her path into search marketing, eventually leading her to significant roles at Search Engine Land and Google as the Google Ads Liaison.

During our interview, Marvin shared insights into the evolution of paid search, highlighting common misconceptions marketers still hold, and emphasized how the next era of search will value curiosity over control.

Interestingly, PPC clicked for me faster than SEO. My initial foray into the industry was through SEO at a small agency, but I quickly discovered my passion when the paid search manager took a vacation, and I temporarily managed the campaigns. This experience showed me the power of PPC’s speed and measurability, especially coming from a print background where results were slow and uncertain.

Marvin observed that Google’s clear focus and rapid iteration were key to outpacing competitors like Yahoo and Microsoft. Google’s relentless enhancement of its offerings to align with advertiser needs set it apart and solidified its leadership in the industry.

I remember the early days of PPC being a manual slog full of exhaustive keyword lists and precision-targeted campaign strategies. We spent hours meticulously crafting keyword combinations, but today’s campaigns are more sophisticated and goal-oriented, aligning more naturally with business objectives rather than conforming to platform constraints.

When Search Engine Land was in its infancy, Marvin was also establishing her footprint in the search field. The platform quickly became essential for industry news, insights, and expert analyses, fostering professional growth by making information accessible.

One standout characteristic of the search community, as Marvin noted, is its openness to sharing and collaboration. People have always been generous about sharing their experiments, successes, and failures, recognizing that ongoing learning benefits everyone. This spirit of community has been a cornerstone in my own career development.

Regarding AI, Marvin asserts that it’s not as novel as many perceive. Although the rapid advancements fueled by large language models seem sudden, machine learning has been embedded in systems like Google Ads for years, refining aspects like Smart Bidding and close variants.

The real shift lies in consumer behavior, where search patterns have become increasingly complex and diverse. With people using images, voice, and multimodal inputs, modern search engines understand intent beyond simple keywords, necessitating a comprehensive view of the customer journey.

Despite all these changes, the essence of search success remains tied to business results. What’s different now is the enhanced ability to accurately measure outcomes and align campaign activities with strategic business goals, highlighting the critical role of data and first-party signals.

Looking ahead, Marvin champions curiosity as the trait that will define successful marketers over the next two decades. Adaptability, understanding customer behavior, and proactively learning new technologies like AI will keep marketers ahead of the curve.

Marvin candidly remarks that while PPC marketers often claim to embrace change, they can be resistant when major shifts occur. Her advice is to adopt a long-term perspective because seemingly abrupt changes often have deep-seated, gradual developments.

Experimentation is key, according to Marvin. Even if a new feature doesn’t yield immediate success, dismissing it entirely could be shortsighted. As platforms and capabilities evolve rapidly, what didn’t work before might succeed now, and clinging to outdated methods could hinder progress in the evolving search landscape.

Reflecting on her career, Marvin expressed pride in the resilient and collaborative nature of the search community. Her contributions at Search Engine Land and Google have always been geared towards fostering an informed and empowered marketing community. To her, “by marketers, for marketers” is more than a motto; it’s a driving mission.

Inspired by this post on Search Engine Land.

April 27, 2026
Mastering Paid Search: What to Optimize When Keywords Matter Less

In today’s digital landscape, I’ve noticed that paid search platforms are evolving to prioritize who sees my ads, often without depending solely on my chosen keywords.

This shift means I need to focus on optimization strategies beyond just keywords, such as leveraging audience data, enhancing landing page context, and understanding conversion behaviors. Recognizing this shift is crucial for me to know where to focus my efforts now.

A decade ago, keywords gave me a sense of control. Back then, hypersegmentation and single keyword ad groups were the norm.

We’d meticulously create unique landing pages for each keyword in every ad group, reveling in the manual process, convinced that we controlled the machine.

Times have changed, and the forecast of Google and Microsoft phasing out keywords feels more real than ever.

With tools like Performance Max and emerging AI Max solutions, along with contextual LLM-driven searches such as ChatGPT, I see the industry leaning towards a keywordless future.

Still, keywords remain vital as they reveal user intent and indicate where users stand in their journey:

If these signals are now managed behind a black box, my role as a marketer is evolving. So, what am I optimizing for?

Dig deeper: Beyond keywords: Mastering AI-driven campaigns

Intent is now inferred from a web of signals, relegating individual keywords to the background. My optimization focus should now be on three main pillars in 2026.

Google now emphasizes customer match and first-party data over mere queries. With Data Manager API integration, it identifies users in auctions matching my key deals.

No longer do I bid on “cloud security.” Instead, I target IT directors (sharing first-party data) investigating SOC 2 compliance, even if they search for something vague like “scaling infrastructure.”

B2B match rates can be challenging, but this is where I must innovate my strategy, broadening one-to-one list matching and collaborating with integration partners.

Clustering individuals by shared pain points and offering on-site experiences help me understand their verified intent before reaching the remarketing list.

My landing page serves as a vital data source. Google’s AI examines it to grasp the nuances of my offerings, making creative assets crucial signals that align with my target themes and keywords.

If my landing page effectively communicates “mid-market manufacturing,” AI identifies relevant users regardless of specific keyword use, transforming my “keyword strategy” into a content strategy.

Opting for a creative approach similar to Meta’s, where Andromeda elevates the creative as a primary targeting signal, is beneficial. These creative inputs define my audience, demanding a balance between creative and technical input.

Journey-aware bidding and value-based bidding mean algorithms now analyze a user’s journey beyond the final click.

Optimization now targets “high-value need states,” feeding the system data about mid-funnel behaviors that result in significant contracts.

Dig deeper: Why better signals drive paid search performance

The most profound change for digital marketers, including myself, is shifting focus from query-level to user-level intent.

While the previously ignored query “how to manage payroll” might not have targeted enterprise SaaS companies, AI now understands if that user is a financial VP at a large firm, indicating commercial intent.

If it’s the right user, the right signals should prompt AI to act on their purchasing stage.

As AI handles matching, my role shifts towards becoming a data architect.

Data quality determines my success. I must feed AI with valuable leads to optimize for value-based bidding effectively.

Assessing the health of my signal, from landing pages optimized for AI readability to correct technical content, ensures Google accurately targets my audience.

I now focus less on micromanaging search terms and more on managing brand exclusions and negative themes.

The future of search is about being the best solution for the right individual at their evolving need state.

Keywords served as training wheels, but it’s time to see how quickly my data can propel me forward.

Dig deeper: Why PPC teams are becoming data teams

Inspired by this post on Search Engine Land.

April 27, 2026
Unlocking Personalized Marketing: Why Brands Struggle and How to Succeed

When I think about the last time I got hooked on those true crime documentaries, I remember how my streaming app seemed to know exactly what to suggest next. Suddenly, investigative series filled my homepage, and I even got alerts for new releases. The marketing was flawless, and I never saw the behind-the-scenes magic that made it happen—I just dove into the next compelling story.

This is the expectation now. A recent Adobe report reveals that 71% of consumers desire personalized deals and content, with 78% expecting a seamless experience across different channels. Surprisingly, fewer than half of brands meet these expectations consistently.

The root problem lies in the structure of customer data. When it’s scattered across various systems, it becomes difficult for teams to sync insights, timing, and execution effectively. AI cannot magically fix these issues alone. As per the Adobe 2026 report, only a minority of organizations have a data foundation robust enough to support AI at scale.

Starting on the path to modernize and personalize marketing efforts can seem overwhelming. However, by laying a strong foundation for a unified customer experience, progress becomes achievable.

Most brands have ample data, yet it often lacks coherence. If your marketing efforts span across email, web, mobile, paid media, support, and in-person channels, it’s crucial these signals communicate swiftly to shape the next customer interaction.

If alignment isn’t there, the consequences are immediate. Imagine a customer browsing a product online but receiving a different price via email, or having to repeatedly explain their issue to customer support. These inconsistencies slowly erode the trust you’ve built.

Delivering a cohesive customer experience means continuously updating the understanding of the customer and sharing this insight across all teams and touchpoints without delay.

To make this happen, here are a few critical steps:

A unified customer experience begins with a consolidated and dynamic customer profile. Rather than maintaining separate records per channel, build a real-time profile that captures behavior, preferences, and interactions throughout all departments.

With this comprehensive data, customer segmentation becomes more insightful, and messaging more relevant. Customers will no longer face conflicting or redundant communication.

Enhance your data by linking insights directly to actions quickly. For instance, if a customer leaves a cart abandoned, a subtle follow-up can kindle action without delay. Engage with real-time product recommendations and remove offers that no longer resonate.

Real-time relevance is crucial. Our eyes interpret digital ads in under 400 milliseconds, meaning interaction timing is everything. If your systems don’t react swiftly, you miss valuable chances to connect.

AI accelerates these interactions at scale, discerning patterns, predicting intent, and suggesting best actions within milliseconds. Accurate and unified data is essential for AI to function effectively.

In this age of rising privacy standards, protecting customer data is paramount. As more signals are unified and activated in real time, it’s crucial to integrate governance from the ground up.

To maintain a unified experience at scale, companies need a modern cloud foundation to process and activate data effectively, ensuring swift response times, minimal data movement, and stronger security.

Personalization becomes second nature when brands anticipate not just the right message, but the right moment. Unified data, activated in real time with secure infrastructure, elevates personalization from trial-based to operational, making relevance repeatable.

Adobe Experience Platform, powered by AWS, integrates these components, easing execution for your teams. It creates real-time customer profiles that support segmentation and journey orchestration across touchpoints, leveraging AWS’s scalable infrastructure.

Explore our eBook, Capturing Attention in the Age of AI, to discover how Adobe and AWS provide marketers with a complete customer view that optimizes personalization and enhances customer value.

Ready to see how Adobe and AWS can streamline your journey to unified experiences? Reach out to start the conversation today.

Inspired by this post on Search Engine Land.

April 27, 2026
Google AI CTR Rebound: Promising 85% Increase in Two Months
I’ve been following the shift in Google’s AI Overviews, and it’s exciting to see the organic click-through rate on these searches finally on the rise. After a year-long slump, the CTR is showing promising signs of recovery. But could this mean the end of click losses?

Back in December 2025, the CTR had hit a low of 1.3%, but by February 2026, it had climbed to 2.4%. That’s an impressive 85% jump in just two months, according to the latest data from Seer Interactive.

Understanding CTR Movement. When AI Overviews are part of a search, pages that are cited see a significant increase in clicks compared to pages that aren’t cited, yet they still garner fewer clicks than searches without any AI Overviews.

Here’s a breakdown of the CTR percentages:
- No AI Overview: ~3.3% CTR
- AI Overview with citation: ~2.1% CTR
- AI Overview without citation: ~0.9% CTR
Where are the clicks going?. Interestingly, searches that don’t include AI Overviews are seeing an increase in value. Their CTR rose from 2.8% at the start of 2025 to 3.8% by February 2026.
- One factor: AI Overviews are handling quick answers, leaving users with more complex questions to search deeper.
AI Overviews Depend on Query Intent. The presence of AI Overviews varies greatly depending on the type of query:
- Informational: ~36% feature AIOs
- Transactional: ~5%
- Comparison: ~95%
- Question: ~86%
A nuanced perspective. It’s important to note that a lower CTR doesn’t always equate to poor results. In instances where clicks remained stable but impressions grew, brands may have appeared more frequently in AI Overviews even as CTR percentages dropped.

The stability of paid search. I noticed that when Google presents an AI Overview, the paid CTR increases slightly from 14.6% to 16.2%. Without AI Overviews, the CTR drops from 26% to 21.8%.

Why this matters. Google’s AI Overviews are not just reducing overall clicks; they’re shifting them. This means you need to aim for your site being cited in AI Overviews and focus on queries where users are more likely to click.

About the Research. Seer analyzed data from 53 brands, 5.47 million queries, and 2.43 billion impressions between January 2025 and February 2026.

See the full report here: AIO Impact on Google CTR: 2026 Update

Inspired by this post on Search Engine Land.
April 24, 2026
Google’s AI Search Evolution: Changes in Queries and Content

As someone deeply interested in how technology shapes our interactions, I found Google’s new AI developments in search particularly fascinating. Google’s VP of Search, Liz Reid, recently delved into how AI is transforming search intent, monetization, and content visibility. In a new Bloomberg podcast, she explained how these changes are reshaping our search behavior.

Reid assured us that AI is not diminishing Search but altering its usage. AI Overviews now help filter low-value clicks while encouraging more frequent searches. Reid highlighted how AI reduces “bounce” clicks, those quick visits to a page for a single fact. It’s an interesting evolution—sometimes we only have seconds to spare, while other times, we aim to immerse ourselves for longer periods.

People Want AI and the Web Together

Reid debunked the myth that users desire AI over the web. Instead, she notes, people want AI integrated into their web experience. I see this pattern in my own browsing habits, where I might search for a quick fact one moment and dive deeply into an article the next. She emphasized that people still crave human perspectives and diverse insights.

AI Overviews: Adapting to User Needs

Liz Reid explained that AI Overviews aren’t activated for every search. Google’s strategy is user-centric, providing AI support only when it’s beneficial. This selective approach ensures we get the best possible answer for our queries. The system evolves as user behaviors change, and Google continually refines which queries deserve an AI Overview.

Changing Search Habits

It’s intriguing to note the shift in how we query Google. Searches have become longer and more conversational, moving away from terse keywords. In my own searching, I now use full sentences to express my needs, which aligns with Reid’s insights. She reiterated that users now articulate their problems more clearly, allowing Google to provide comprehensive responses.

Ads and AI: A New Dynamic

Even with AI-enhanced answers, Google can still generate revenue from Search, assuring us that the commercialization of queries largely remains unaffected. When I’m on the hunt for products, such as buying shoes, I still rely on ads to guide my purchasing decisions. Reid also highlighted that detailed queries offer potential for more targeted ads.

Monitoring User Retention

Reid highlighted that a key metric for Google is whether users return to Search more frequently. This is more than just increased search volume; it’s about building a loyal user base that turns to Google consistently because it meets their needs effectively.

AI Slop: Addressing Content Quality

Interestingly, AI hasn’t introduced new content quality issues but rather increased its volume. Reid assured us that Google’s aim is to spotlight quality content while minimizing the visibility of “slop.” It’s a challenge, but one that Google is committed to tackling by continually enhancing its ranking systems.

Inspired by this post on Search Engine Land.

April 23, 2026
Discover How OpenAI is Revolutionizing Ads with ChatGPT CPC

Have you heard the news that OpenAI has introduced CPC ads to ChatGPT? This strategic shift has transformed it into a performance-driven channel, offering advertisers new avenues for engaging intent-driven audiences and tracking ROI.

OpenAI is moving away from a focus purely on impressions in ChatGPT to prioritize performance. This change places OpenAI in direct competition with giants like Google by adopting cost-per-click (CPC) ads, allowing advertisers to pay only when users click on their ads.

What’s happening? OpenAI has started testing CPC ads within ChatGPT, where advertisers only pay when their ads receive clicks. Initial reports highlight that these clicks are priced between $3 to $5. They’re rolling out this feature through a limited ads manager, alongside their existing CPM-based model.

Why now? The main catalyst seems to be pricing pressure. Since its launch, ChatGPT’s CPMs have significantly decreased from around $60 to approximately $25. Switching to CPC helps mitigate this decline by connecting revenue to tangible outcomes rather than mere impressions.

Why do we care? With its evolution into a performance channel, ChatGPT is now not just a branding space. The CPC pricing model makes it easier for us to connect budgets directly to measurable actions, test ROI, and compare these results with channels like Google Search.

I’m excited about the opportunity for advertisers to access what could be a high-intent audience in a new format. This presents a first-mover advantage before competition—and the associated costs—escalate.

The bigger picture: This isn’t just a pricing change; it’s a strategic pivot. By embracing CPC advertising, OpenAI challenges Google’s dominance in the market, thereby positioning ChatGPT as a contender for performance marketing budgets.

Reading between the lines: A major challenge lies in proving user intent. While search advertising is effective because it captures users actively searching for something, ChatGPT’s conversational context needs to generate clicks with equal value. Advertisers will likely compare these results directly with Google, setting a high standard for quality and conversion.

Zoom out: Advertising is becoming integral to OpenAI’s long-term revenue plan, supported by investments in ad infrastructure, measurement tools, and a wider self-serve platform.

Bottom line: By implementing CPC ads, OpenAI is vying for the performance-driven ad dollars that have long supported traditional search platforms.

Inspired by this post on Search Engine Land.

April 22, 2026
Evading AI’s ‘Bland Tax’: How to Maintain Brand Visibility
When I think about brand visibility today, it’s clear that being chosen by AI systems is crucial. Authority, unique insights, and consistent signals now determine if my brand makes the cut.

I’ve realized that AI isn’t just reshaping search; it’s deciding which brands are seen and which are ignored.

I learned from Andrew Warden, CMO of Semrush, at the Adobe Summit that visibility is evolving fundamentally, and our brands risk being systematically filtered out by AI systems.

“The idea of standing out is no longer optional. There’s a real risk of sameness,” he pointed out.

With AI systems deciding what to highlight and what to ignore, I know I must compete more fiercely for visibility in AI-generated answers.

AI is Changing How Discovery Works

The change is evident in the data: 60% of Google searches now end without a click to a website. People are still seeking information but aren’t always visiting websites. They’re getting their answers directly from AI systems like Google AI Overviews and ChatGPT.

These AI systems have become, as Warden described, the “new gatekeepers.”

This shift ushers us into the agentic era, where AI systems act as intermediaries, guiding users from inquiry to decision in one seamless interface.

Meanwhile, user behavior is evolving. People engage more in conversational environments, posing follow-up questions, refining queries, and surveying options within the interface, all resulting in fewer clicks but often attracting higher-intent users.

Warden noted that consumers using LLMs convert at least four times higher than those relying solely on search.

SEO is the Foundation

Despite some claims that AI could replace search, Warden reassured us that SEO is not dead.

SEO has become more foundational than ever. It’s essential to ensure my brand exists in the data layer AI systems rely on.

Warden emphasized, “SEO isn’t just for humans anymore. This is a training manual for AI right now.”

This involves ensuring:
- Crawlability
- Indexability
- Structured data
- Authority signals
Without these, my brand won’t appear at all.

Research backs this up: 94% of Google AI Overviews cite at least one top organic result, reaffirming that traditional search signals still support AI outcomes.

The Rise of the ‘Bland Tax’

One striking concept from the session was what Warden dubbed the “bland tax.”

AI conditions itself to overlook blandness, causing generic or repetitive content to vanish.

If I’m generic, Warden warned I’m perceived as average, and if I’m bland, I’m effectively invisible.

AI systems don’t reward sameness. Rather than highlighting my brand, they often condense similar content into a single, attribution-lacking response.

“This is an invisible penalty,” Warden noted.

The consequences manifest in several ways:
- My brand identity gets erased in AI-generated summaries
- My content is filtered out as low-value
- My work becomes training data for AI without offering visibility to my brand
“You also become a free training ground for LLMs,” he said.

What Visibility Depends On

Warden redefined brand visibility as a blend of:
- Discoverability: Can LLMs easily find me?
- Authority: Do they trust my brand enough to include it?
“You absolutely need both,” Warden asserted.

SEO ensures I’m discoverable. Authority determines whether my brand shows up in AI-generated responses.

Without authority, I risk turning into a “commodity that isn’t worth being mentioned.”

How to Win: Three Key Signals

Warden outlined three crucial areas determining whether my brand appears or gets filtered out:

1. Entity Authority

AI systems map entities and relationships, and they must recognize my brand as an authority on a topic.

One key signal is brand demand. If people aren’t seeking out my brand, neither will AI.

Strong brands emphasize their authority across various platforms—owned content, media exposure, and community discussions—demonstrating their niche.

2. Information Density and Originality

AI systems prioritize content that offers new insights. It’s vital to not just publish content but contribute something meaningful.

They emphasize new facts with proprietary data, original research, unique perspectives, and expert insights.

According to Warden, original insights can enhance visibility by 30 to 40%.

3. Signal Alignment

AI evaluates not just what I convey but also what others say about my brand.

This includes reviews, discussions on platforms like Reddit and YouTube, media mentions, and customer conversations.

Warden warned that conflicting signals could prompt AI to flag my brand as unreliable.

Consistency across these channels creates what he called a “consensus signal” that AI systems can trust.

Why Most Organizations Aren’t Ready

One of our biggest challenges is organizational, as visibility isn’t just a channel issue; it’s an organizational one.

Currently, responsibilities are fragmented. SEO teams focus solely on rankings, PR and brand teams manage messaging, and growth teams conduct experiments. This leaves no one clearly owning AI visibility.

This fragmentation leads to inconsistent signals and missed opportunities for us.

To truly compete, we need alignment across teams, working on a shared strategy about how my brand appears wherever LLMs gather data.

The Measurement Problem

Meanwhile, traditional performance metrics are unraveling.

Many marketers, including myself, notice a gap where rankings hold steady, but traffic declines. Meanwhile, leads might increase, yet attribution remains murky.

Warden explained that demand remains, but traffic no longer serves as its proxy. Our content is utilized, but not in ways directing users back to us.

This creates a growing disparity between impact and the ability to measure that impact accurately.

From Rankings to Relevance

The nature of competition has evolved. I’m no longer vying for a mere position; instead, I’m competing to be featured in a synthesized AI answer.

Authority, once easier to influence, now hinges on external validation—emphasizing what others say over what I publish.

Algorithms have shifted from being my allies to arbiters of meaning, marking a significant change in search dynamics since Google itself emerged.

The New Rules of Brand Visibility

AI has not altered what makes a brand strong but has transformed how that strength is measured and rewarded. The brands that win today will build real authority in a focused niche, publish original and high-value content, and ensure consistent messaging across every platform.

The need for consistent third-party validation across an ecosystem is paramount.

As Warden urged, I must make it impossible for LLMs to ignore my brand.

Inspired by this post on Search Engine Land.
April 21, 2026