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.”
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.
LinkedIn’s Off-Platform event ads now empower me to promote external events effectively in-feed, driving registrations directly to my site by May 6.
LinkedIn has unveiled Off-Platform Event Ads, providing me with a novel way to promote events without the need for a native LinkedIn Event Page.
What’s happening. This innovative format lets me craft Event Ads that link directly to external destinations. These can be webinar platforms, landing pages, or livestream sites, allowing me to guide traffic away from LinkedIn for a more tailored experience.
This transition signifies a move from experiences contained on a single platform to more adaptable, marketer-directed journeys.
How it works. I can now create an Event Ad using a third-party URL, add essential event details like date and format, and select objectives such as awareness, engagement, traffic, or lead generation.
Every click takes users directly to the external event page, while I can still track performance metrics with Campaign Manager.
Why we care. Previously, promoting events on LinkedIn often meant staying within platform-imposed limits, complicating the user experience and restricting control over registrations.
With Off-Platform Event Ads, I can leverage LinkedIn’s targeting features while retaining traffic, data, and conversions on my own platform, which simplifies scaling campaigns and preserving consistency for participants.
What to watch:
Whether these ads result in higher registration rates compared to native Event Pages
How I can balance LinkedIn’s precise targeting with off-platform conversion tracking
Possibilities of LinkedIn extending similar versatility to other ad formats
Availability. Off-Platform Event Ads are being gradually introduced globally and should be available to all marketers, like myself, by May 6.
Bottom line. By allowing Event Ads to target off-platform destinations, LinkedIn provides an opportunity to elevate event promotion without the need to operate solely within its ecosystem, which is a game-changer for my marketing strategies.
I’ve recently learned that YouTube is testing an innovative search feature called “Ask YouTube”. This aims to make searching on YouTube more conversational and interactive, just like Dave from YouTube explained. It deepens our interaction with content, allowing us to explore topics with more depth.
What it looks like. I had the chance to see it in action through a captivating GIF:
How can I try it? If, like me, you’re curious to test this feature, visit youtube.com/new. There, you can opt-in to experience this new way of interacting with YouTube.
Currently, this experiment is only open to Premium users in the US who are 18 and older. However, Google has plans to expand access soon, which is promising for non-Premium users.
What it does. Here’s an example shared by Dave from YouTube:
“If you’re in the experiment, you can try it out by selecting “Ask YouTube” in the search bar. For instance, you might ask for help planning a 3-day road trip from San Francisco to Santa Barbara. Instead of just a list of videos, you’d receive a detailed, step-by-step itinerary. The response incorporates a mix of long-form videos, Shorts, and informative text, featuring local tips and must-see stops. You can even ask follow-up questions, like “where can I find good coffee?” to discover local gems along your journey. This approach surfaces various videos and video segments, complete with titles and channel details, making it easier to find new creators and content that matches your search.”
Why we care. The integration of AI search is becoming prevalent in all Google platforms, and YouTube is joining this transformation. We should anticipate more AI-enhanced search experiences across various Google services as they evolve over time.
For more insights and updates, you can check out detailed coverage on Techmeme.
On PPC Live The Podcast, I had the opportunity to chat with Pete Bowen, a seasoned Google Ads expert with a keen focus on B2B lead generation.
Pete shared that throughout his career, he learned two pivotal lessons: never neglect the fundamentals, and don’t assume everything around your ads is functioning perfectly just because the campaign appears fine.
The Currency Mistake That Cost 10 Times the Budget
In our discussion, Pete recounted an incident where a South African client’s account was mistakenly set to the UK currency, leading to a spend ten times higher than planned. Initial results looked impressive, but the oversight eventually set unrealistic expectations and cost the client relationship.
Why Checklists Protect PPC Teams
The lesson here is to incorporate learning into a formal process. For instance, implementing a currency check in initial setups can transform frustrating mistakes into reliable, repeatable safeguards.
The Bigger Problem: System Decay
Beyond errors in setup, Pete discussed a more insidious issue: “system decay.” This involves the gradual breakdown of the infrastructure linking ads, tracking, CRM, and sales processes, often without detection.
Why Conversion Data Failures Hurt Performance
If conversion data flow is disrupted, Google’s algorithms miss out on critical optimization feedback, resulting in reduced spending, declining performance, or campaigns that seem to halt unexpectedly.
PPC Managers Need to Look Beyond the Interface
A common error among advertisers is focusing solely on Google Ads. Optimal performance involves the whole journey, from click to conversion to revenue, and any disruption can diminish results.
What to Do When Conversion Tracking Breaks
Priority number one is identifying and fixing the root of tracking failure quickly. Leveraging data exclusions to prevent poor data from affecting optimization is crucial, as is implementing monitoring systems to catch recurring issues early.
The Danger of Optimising for Clicks
Pete highlighted another frequent mistake: prioritizing clicks over outcomes. Without effective conversion tracking, advertisers might end up with significant traffic that yields few leads or sales.
Why Performance Max Needs Strong Tracking
Automation tools like Performance Max can exacerbate this issue if they receive misleading signals. Accurate conversion data is essential before making the most of automated tools.
Why Bid Strategies Need Guardrails
Google’s powerful bidding systems optimize based on the success criteria provided by advertisers. Clear objectives, reliable data, and sensible constraints like CPC limits are needed to prevent extreme results.
Testing AI Features Carefully
With new AI tools, the risk isn’t of premature testing, but of testing without clearly defined success metrics. Beyond just impressions and clicks, the focus should be on impacting qualified leads, sales, and overall revenue.
The Problem with “Always Be Testing”
Pete also challenged the constant testing philosophy. Many accounts lack the data volume to effectively run small tests, so energies are often better directed towards strengthening core practices than chasing minor improvements.
The Key Takeaway
The overarching lesson is that mistakes are valuable if they lead to robust systems. Each error should translate into a checklist, a monitoring strategy, or a preventive measure to ensure it doesn’t recur.
When I learned about Adthena’s new Google Ads-to-ChatGPT conversion tool, I was immediately intrigued. This innovation allows advertisers to seamlessly repurpose their existing search campaigns for ChatGPT, simplifying budget shifts and campaign setup.
What’s happening? Adthena has introduced AdBridge, designed to translate Google Ads campaigns into formats suitable for ChatGPT advertising. The concept is straightforward: leverage what already works instead of starting from scratch.
The tool evaluates advertisers’ search campaigns to compile keyword lists, identify negative keywords, and gain competitive insights, ready for direct application in ChatGPT campaigns. It identifies which brands dominate certain auctions, their frequency, and the prompts triggering these placements, offering more than just a simple copy-and-paste solution.
Why it matters to me. Adbridge has significantly reduced the effort needed to reallocate my advertising budget from Google Ads to ChatGPT. By reusing existing keywords and insights, I can test and scale ChatGPT ads with minimal risk. As the platform expands, tools like this lower entry barriers, potentially speeding up ChatGPT’s adoption as a viable performance channel.
As Adthena’s CMO, Ashley Fletcher, mentioned, the goal is to prepare campaigns to run directly, mimicking the CSV-based workflows familiar across major platforms.
Early testing feedback. Adthena has already conducted numerous sessions with large enterprises experimenting with the tool, highlighting growing demand from advertisers eager to expand their presence in ChatGPT’s nascent ad environment.
Reading between the lines. This goes beyond just convenience—it’s building momentum. Advertisers testing ChatGPT ads face challenges like restricted inventory and scale. By easing campaign deployment, Adthena is positioning itself to facilitate quicker adoption as these challenges diminish.
A closer look. AdBridge is part of Adthena’s broader strategy, accompanied by Arlo, an AI assistant that lets advertisers query performance data and compare results across ChatGPT and search campaigns. Together, they indicate a future where AI-driven ad management mirrors existing search workflows.
The backdrop. OpenAI rapidly evolves its ad offerings—quietly launching an ads manager, lowering minimum spend limits, and introducing flexible pricing models. Collaborations with firms like Criteo and Smartly point to a burgeoning ecosystem.
The bottom line. As ChatGPT ads race to compete for search budgets, the ease of transition facilitated by tools like Adthena may determine the winners. Adthena aims to lead that charge.
During a recent presentation, I was thrilled to learn about Microsoft’s latest tease regarding new AI reporting features in Bing Webmaster Tools. These updates aim to enhance the existing AI performance reports, offering fascinating insights into citation share, query intent grounding, and GEO-focused recommendations.
I stumbled upon shared screenshots from this intriguing presentation delivered by Krishna Madhavan at SEO Week in the bustling city of New York. Azeem Ahmad captured the essence of this moment, highlighting the growing transparency gap between Bing and Google.
Intriguing Details: The presentation shared several slides showcasing these promising new features. One can feel the excitement building within the SEO community as these innovations hint at a more insightful way to track AI interactions.
Bing Webmaster Tools just dropped some VERY COOL stuff at #SEOWeek 2026
Stay Tuned: While these features aren’t live just yet, catching a glimpse of them was very promising. It seems Microsoft is ramping up to offer more ways to navigate AI-driven search results.
Why This Matters: Gaining more transparency on how our content performs in AI search results is invaluable. I eagerly anticipate the day when these tools go live, promising greater clarity and control over AI interactions.
At the moment, details on the exact functionality and release timeline remain vague. I will certainly keep my eyes peeled for further updates to better understand their full potential.
I’ve recently experienced frustrations with Google Ads as there’s a known issue causing Demand Gen ads to face review delays of over a week. Google acknowledges this problem and assures us that they’re working on a solution.
Some of us advertising on Google have noticed our ads are lingering in review, taking more than seven days—something that deviates from normal review timelines.
What’s happening. Matthew Skelton, a senior PPC specialist I follow, has pointed out a trending issue: Demand Gen campaigns stuck in review for an unexpectedly long time. This delay is noticeable across various accounts and industries, seemingly without any policy breaches causing it.
Interestingly, other campaign types, like Search and Performance Max, aren’t affected and continue processing as usual, which suggests the problem is isolated to Demand Gen ads.
Why we care. For those of us using Demand Gen to test creatives and drive top-of-funnel results, speed is crucial. Long review times hinder our ability to iterate swiftly, delay launches, and make it challenging to respond to seasonal trends or time-sensitive opportunities.
A delay lasting a week can disrupt our pacing and diminish the effectiveness of campaigns relying on rapid optimization.
The response. Ginny Marvin, a Google Ads Liaison, acknowledged this issue specifically impacting Demand Gen image ads, admitting reviews are taking longer than anticipated. She assured us that Google’s team is actively seeking a solution, but no clear timeline has been provided yet.
Bottom line. If you’re experiencing delays with your Demand Gen ads, know that it’s a widespread issue acknowledged by Google rather than something you can directly address.
First seen. This situation was first reported by Matthew Skelton, who shared his insights on LinkedIn.
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.
Recently, I’ve noticed that ChatGPT is rolling out ads to users who aren’t logged in. This change could dramatically boost the ad inventory as advertiser interest surges.
What’s happening. According to early reports, ads are seamlessly appearing within conversations for those not logged in, although OpenAI hasn’t made a formal announcement. Interestingly, these ads fit into the chat responses rather than looking like traditional banners.
Why we care. For me, the expansion to logged-out users means more inventory, allowing budgets to stretch further and reach audiences with intent. If this trend continues, I believe ChatGPT could become a powerhouse in the performance marketing arena.
Zoom in. I’ve noticed that advertisers in the pilot phase struggle to spend due to limited inventory, despite lowered financial barriers (from $200,000 to $50,000). Expanding the potential audience seems like a logical step to overcome this hurdle.
User experience. Personally, I find the ads relatively unobtrusive and well-integrated into conversations, though some minor UX issues persist.
Between the lines. It’s clear to me that this is an inventory issue, not a demand one. Advertisers are eager, and OpenAI is diligently working to scale up.
What to watch. I’ll be keeping an eye on whether OpenAI formalizes this rollout and expands further, which will indicate how rapidly ChatGPT can evolve into a competitive ad channel.
Bottom line. I think opening ads to logged-out users is the key that could convert advertiser interest into substantial spending power for ChatGPT.
