I’ve noticed a significant shift in how enterprises are adapting to the era of AI search. It’s fascinating to see businesses harnessing this powerful technology to enhance their upstream visibility and overall digital presence.
The image I see before me captures this technological evolution beautifully, depicting how AI is revolutionizing search capabilities. It’s driving home the idea that those who adapt quickly can gain a competitive edge.
Inspired by this post on Conductor Blog.
When I think about Google’s journey with artificial intelligence, Sergey Brin’s admission strikes a chord. He candidly revealed that Google ‘for sure messed up’ by not prioritizing AI investments at the right time. Looking back, it seems clear that we released groundbreaking research but didn’t capitalize on it to ride the current wave of generative AI.
Google’s cautious approach always intrigued me. According to Brin, there was hesitation and fear about potential missteps, especially since chatbots could ‘say dumb things.’ It’s fascinating to hear him acknowledge that Google didn’t move quickly enough after publishing the Transformer paper.
The hesitation seemed to stifle opportunities. Brin admitted that while Google was reluctant, companies like OpenAI leapt forward with brilliant foresight. They seized the moment, leveraging insights and even talent, such as Ilya Sutskever, to drive AI innovation.
Reflecting on the past, Brin shared, “In some ways, we for sure messed up in that we underinvested… eight years ago when we published the transformer paper. We didn’t take it all that seriously and didn’t necessarily invest in scaling the compute. And also we were too scared to bring it to people because chatbots say dumb things. And you know, OpenAI ran with it, good for them.” He graciously acknowledged the value of the history we’ve built.
The current landscape still favors Google, as Brin points out. Years of AI research and development, deep learning, and our robust infrastructure continue to provide a competitive edge. This bedrock underlines the control we maintain over key technologies driving AI today.
Why does this matter? Brin’s insights shed light on why Google’s AI-driven changes in search sometimes seem sudden and erratic. Our earlier caution means we now find ourselves speeding ahead, possibly too quickly, to bridge the gap. The fluctuating nature of Google Search is a byproduct of this rapid adjustment process.
When Brin spoke about AI’s future, he characterized the field as highly competitive and ever-evolving. He mentioned, “If you skip AI news for a month, you’re way behind.” It’s uncertain what the ultimate potential of AI is, or if there’s a limit to its intelligence.
On a personal note, Brin shared that he often uses Gemini Live for engaging conversations while driving. Interestingly, he noted that the public version is outdated, with a “way better version” on the horizon.
Looking back and forward, Brin’s remarks at Stanford were part of an event celebrating the School of Engineering’s century-long legacy. The discussion touched on Google’s early days, its culture of innovation, and the present AI ecosystem. You can watch the full video here.
Inspired by this post on Search Engine Land.
As I dive into the evolving world of SEO, I’ve noticed one thing: the industry is entering its most unpredictable phase yet. With traffic on the decline and AI increasingly handling informational queries, it’s clear that the landscape is shifting beneath our feet.
It’s fascinating to observe how social platforms are now serving as search engines, and Google is transforming from a gateway to a comprehensive answer engine. This transformation leaves many of us in the industry uncertain about what metrics matter, what we should optimize, and essentially, what SEO’s role truly is in this new digital era.
Despite the chaos, I’ve found clarity in one specific marketing metric that cuts through the noise: share of search. This metric offers a straightforward insight into brand health and potential future demand, aligning marketers and SEOs with confidence.
Share of search becomes particularly important as we notice a significant shift in how discovery and measurement need to adapt. The days of accidental discovery through traditional search behavior are dwindling.
AI and platforms like Meta are increasingly providing direct answers without directing traffic elsewhere, shifting the focus towards metrics that provide a clearer indication of consumer interest, like share of search.
Interestingly, share of search, a concept developed by James Hankins and Les Binet, calculates a brand’s search volume against the total search volume for its category. This simple yet powerful metric correlates strongly with market share and future buying behavior.
In our rapidly changing environment, share of search provides a critical signal for marketers, showing whether a brand is being searched for more or less compared to competitors. This insight offers a palpable reflection of underlying consumer interest and demand.
While traffic as a metric is losing its significance because of AI pre-answering queries, share of search cannot be manipulated easily. It stands resilient as a reflection of authentic consumer desire.
Moreover, this metric crosses platforms effortlessly, as people now search across various digital spaces such as Amazon, TikTok, YouTube, and potentially even LinkedIn. Share of search adapts to fragmented discovering behavior precisely.
It’s exciting to see how, even if AI-driven systems like ChatGPT rarely generate clicks, they often trigger brand searches, emphasizing the importance of this metric as a measure of marketing effectiveness.
For SEOs like me, adopting share of search means transforming our roles from content producers into strategic partners, providing deeper insights into consumer behavior and brand demand.
Ultimately, embracing share of search elevates our value within an organization, offering a fresh narrative around brand visibility and performance. As AI continues to reshape the digital landscape, this metric is becoming indispensable for those of us in SEO and marketing. I encourage everyone to learn more about this compelling metric and explore its potential to transform how we measure success in the AI era.
Inspired by this post on Search Engine Land.
On December 11th, I was excited to learn about OpenAI’s announcement of GPT-5.2, their most advanced frontier model yet. Knowing how transformative AI can be, I was thrilled to see that Profound is now tracking GPT-5.2 responses in ChatGPT. This upgrade is integrated across our entire product suite, including Answer Engine Insights, Prompt Volumes, and Agent Analytics.
Starting today, every ChatGPT response on my dashboards reflects this cutting-edge model. It’s exhilarating to think about the enhanced capabilities and strategic insights that I can now access thanks to this update.
Inspired by this post on Try Profound Blog.
I’m thrilled to share that OpenAI has announced its latest advanced model, GPT-5.2, on December 11th. This groundbreaking development offers incredible capabilities, and starting December 14th, I’m excited to let you know that Profound is now tracking GPT-5.2 responses across all our products, enhancing your experience with ChatGPT.
From Answer Engine Insights to Prompt Volumes and Agent Analytics, every ChatGPT response in your dashboard now reflects the prowess of GPT-5.2. This update signifies a leap forward in the responsiveness and accuracy of interactions, providing you with richer insights and more refined outputs.
Let’s dive into this new era of AI on Profound, where the boundaries of what’s possible continue to expand daily. Whether you’re using it for research, business insights, or simply exploring the model’s capabilities, you’ll notice a distinct enhancement in all ChatGPT interactions.
Stay tuned as we continue to innovate and incorporate these advancements into your everyday tools. I can’t wait for you to experience the phenomenal benefits that GPT-5.2 brings to the table.
Inspired by this post on Try Profound Blog.
I recently discovered that Google’s AI made several damaging claims about me, alleging that I had been suspended for selling sick notes. As a UK doctor and YouTuber, I was stunned to find out that these false allegations could severely impact my career.
In a recent video, I explained how Google’s AI created a complete fiction about my professional life, despite my spotless record over the past decade. This alarming revelation raises questions about defamation and the responsibility of AI-generated content.
Why this matters. When AI-generated narratives start presenting false and damaging claims as facts, the implications for defamation and accountability are significant. It’s crucial to understand whether these AI statements are shielded by Section 230 protections.
What Google’s AI claimed: The AI falsely stated that:
‘None of this is true.’ With nearly 500,000 followers, I have no idea how many people might have seen this false information before I discovered it. Replicating this erroneous overview revealed even more fabrications, including misleading insurers and content theft.
How did this happen? I believe that Google’s AI pieced together a narrative from random bits of data, wrongly associating my identity with unrelated events:
Why this is more than just a mistake. The AI didn’t question its fabricated claims; it presented them as undeniable truths. This poses issues because:
The big legal question. Is Google’s AI guilty of defamation? Is it protected under Section 230? Ultimately, courts may decide. However, some legal experts suggest:
Resolved? Searching for my supposed suspension now returns a different, unclear explanation:
“Dr. Ed Hope (of the ‘Dr. Hope’s Sick Notes’ YouTube channel) faced scrutiny and suspension…” This narrative erroneously conflates events and imagines contexts.
A more recent search presents even vaguer details, blending potential fictional characters with real drama.
The video. Watch my video discussing these false claims: “SUSPENDED as a DOCTOR – Thanks Google!”
Inspired by this post on Search Engine Land.
For the last two years, I’ve been swept up in the AI gold rush era. It’s reminiscent of what Taylor Swift would call the “Lover” phase—everything was shiny, fresh, and filled with potential.
My approach? I tried to buy it all.
But now, I’m shifting gears to a “Reputation” phase, which feels darker, edgier, and all about the receipts.
Noticing headlines like Microsoft’s decision to lower AI sales targets got me thinking. People framed it as a disappointment, but what I see is a market maturing.
As we’re evolving, I’m realizing that we’re leaving behind the AI gold rush era. Microsoft’s recalibration is just one sign that we’re stepping into AI’s Production Phase era.
Conversations are changing: I’m more focused on whether these tools actually work within my business, connect to our stack, and drive revenue.
There’s a shift happening as the AI market remains a bit unstable. With almost 40% of U.S. consumers having tried generative AI, regular use isn’t quite there yet, as shown by moves in platform loyalty.
This instability means that for me, orchestration is key to staying future-proof in a fragmented ecosystem.
The martech scene has exploded with over 15,384 solutions available, yet I see only 33% of tech being fully utilized. We were paying for a full suite, but truly benefiting from just a third of it.
During the rush, we bought point solutions to address specific problems, but lacked a conductor to bring everything together harmoniously.
This results in what I’d call Pilot Theater—demos that impress but fail to deliver ROI because they’re trapped in isolated silos.
Imagine your P&L hit by these issues: budget disconnects, experience breaks, and content gaps. These gaps are a signal, but what’s missing is coordination, and the pressure is mounting with CEOs keen for AI ROI.
Moving forward, I have to go beyond automation, to embrace agentic orchestration—this is where systems don’t just automate, but adapt and integrate.
Orchestration becomes the nervous system of my marketing operations. It’s my survival strategy in a rapidly evolving AI space.
Real orchestration happens now, with intelligent feedback loops replacing manual processes. Here’s how it’s working for me:
I’ve seen how orchestration aligns efforts, such as in budget fluidity, buying group alignment, and closing content loops to meet real buyer needs.
As a leader, I’m now part of what’s known as the “Builder” generation. Marketing teams, including mine, are becoming more like product teams, building custom platforms to meet our unique needs.
Integration is key, and it’s becoming clear: Orchestrators are now the leaders. This isn’t the end of AI, but the end of tourist AI. Growth now requires intelligence, not volume.
My advantage lies in developing an AI nervous system that is effective across channels, capitalizing on opportunities before they slip away. The orchestration era in AI is here to stay and it’s time for orchestrators, like myself, to lead.
Inspired by this post on Search Engine Land.
Every year, Black Friday offers a unique glimpse into how consumers search, compare, and decide. This year, it added another layer: it became a real-world arena to see how AI models comprehend commerce amidst genuine demand.
I embarked on a journey to test major large language models (LLMs), analyzing 10,000 responses to understand how these systems perceive the retail landscape and the signals that shape their responses.
As I dissected the dataset, a pattern was unmistakable: Black Friday acts as a genuine stress test for AI-driven discovery.
The sheer number of queries and the diversity of categories reveal the sources, structures, and behaviors LLMs rely on for reasoning about products, retailers, and consumer intent.
The outcomes offer a sneak peek into how AI search is transforming—and how this will impact the broader commerce ecosystem.
TLDR; LLMs lean heavily on a limited range of external domains with YouTube, large retailers, and U.S. review media leading the charge.
Generalist retailers dominantly capture nearly half of all retail citations, serving as the recurring funnel LLMs use to address shopping queries.
Social and user-generated content see an 8.1% surge during Black Friday, as conventional retail and media sites experience a decline.
Off-page signals like Reddit, YouTube, Amazon, and Consumer Reports are vital, equally important as on-page content for shaping LLM comparisons and recommendations.
Structured comparison content wields significant influence, far surpassing branded assets.
The behavior of LLMs differs not only from Google but also from each other, with each platform like Gemini, OpenAI, and Perplexity offering unique formats, lengths, and reasoning patterns.
Unlike traditional search, where the process begins with a query leading to a list of ranked results, AI search reverses this. It starts with a model’s internal web of relationships, sources, and signals to construct a response.
In our review of the top 50 most-cited domains across 10,000 LLM responses—all centered around deals, reviews, and product recommendations—the distribution was notably skewed:
YouTube led with 1,509 citations, followed by Best Buy with 950, Walmart with 885, Target with 477, TechRadar with 355, RTings with 342, and Consumer Reports with 325.
This cluster shapes much of the commercial “knowledge” from which LLMs draw. It gravitates towards large retailers, global media outlets, and platforms specializing in comparisons and reviews.
In analyzing 10,000 responses, I compared the week leading up to Black Friday with the event itself. Pre-Black Friday, responses reins focused on planning behavior.
Retail and brand domains: 59.6%
Media: 23.4%
Social and user-generated content: 17%
When Black Friday commenced, the mix rapidly evolved. Social and UGC content jumped to 25.1%, gaining significant share, while retail and media slightly retreated.
This shift within the models mirrors consumer behavior but also highlights the models’ reliance on conversation-driven content for in-the-moment decision cues.
One of the most transparent insights is the weight third-party domains carry on AI reasoning. Today’s LLMs thrive by absorbing as much human interest in products as possible. Huge volumes of consumer insights, reviews, product demos, sentiment, and structured data guide how models reason and decide.
An analysis revealed key off-page signals LLMs depend on:
Reddit: 34%
YouTube: 19.5%
Amazon: 15.5%
Business Insider: 9.2%
Walmart: 8.9%
Each domain influences different aspects of the model’s decision-making. Across the board, LLMs lean on content that captures human interest, organizes consumer options, and mitigates uncertainty through verifiable data.
While third-party domains reign supreme, brand websites still hold measurable sway. They are vital for any consumer brand aiming to excel in AI discovery.
A site’s architecture plays a crucial role in how a model interprets a brand. Homepages account for 40% and serve as the primary identity layer—establishing tone, positioning, and offering quick semantic signals to models.
Blogs and product pages clarify brand definitions and long-tail context, providing the factual details models need.
Brands that rely too heavily on promotional copy, weak hierarchies, or thin product content risk sacrificing major visibility.
Across the entire dataset, certain retailer categories led the charge in model responses.
Generalist retailers hold 48% of the conversation. Walmart, Target, and Best Buy capture almost half of all retail citations. Their range, familiarity, and content depth make them central figures in LLM commerce reasoning.
Electronics specialists grasp 23% of the share. Best Buy leads, trailed by Newegg and Micro Center, with tech-focused queries often directing models toward these sources.
Other verticals lag behind. Despite strong category leaders, sectors like fashion, beauty, and home capture smaller portions due to the content volume disparity compared with generalist retailers.
Reviewing the platforms uncovered another pattern: major LLMs not only offer different answers but exhibit distinct thinking styles. Each platform has its own rhythm, structures, and styles for presenting commercial information.
Gemini provides the most detailed responses, with essays averaging 606 words, using lists and headings extensively.
OpenAI stands in the middle, averaging 401 words per response, with high list usage and balanced headings.
Perplexity shifts towards brevity with an average of 288 words, favoring short summaries akin to executive briefs.
These differences define unique retrieval and reasoning methods, shaping how each platform interprets brands, categories, and commercial intent.
The data presents a clear direction: AI search is forging its ecosystem, driven by familiar SEO inputs, source quality, content structure, and off-page signals, all interpreted to deliver precise answers.
If your content isn’t well-structured and present across the web, it risks becoming invisible to AI platforms delivering answers or product suggestions.
As this new environment evolves, it’s crucial for retailers and brands to rethink their communication strategies across the entire digital landscape.
On-page actions that matter:
Develop semantically coherent homepages that convey the brand, product categories, and relevance to core queries. LLMs prioritize clarity over cleverness.
Strengthen product pages with factual content, clear specifications, and Q&A sections aligned with user research intents.
Establish educational content clusters tied to core product themes, serving as reusable frameworks for AI models.
Off-page actions that matter:
Foster comprehensive review ecosystems and discussion forums to validate trust signals LLMs recognize with product quality.
Ensure visibility in media driven by comparisons and recommendations. Regularly appear in “best of” lists, product roundups, and influencer content.
Invest in rich media showcasing product value, particularly on YouTube and TikTok. Video content helps train LLMs on product use cases, reflecting sentiment, and experiential value.
Maintain accurate, indexable product data in marketplaces like Amazon, Walmart, and Etsy to enhance AI discovery pathways.
OpenAI’s Shopping Research announcement escalates the stakes. With ChatGPT, OpenAI tracks real-time consumer research behavior, turning preferences into a user-trained targeting engine for commerce.
This isn’t just AI learning about your product. It’s AI absorbing consumer shopping behavior, revolutionizing discovery through an active AI participation model.
Brands not infused into these AI systems risk invisibility during AI-driven consumer journeys.
What Black Friday revealed was more than top-selling products; it showed how LLMs operate under real demand, revealing their reasoning, referencing, and prioritizing patterns.
The advent of AI-native visibility requires structured, semantically rich content, adequately represented across the right off-page ecosystems to align with major AI models’ reasoning.
Black Friday might be the stress test, but the real transformation is only just beginning.
Inspired by this post on Search Engine Land.
Today, I am thrilled to share that Search Engine Land is celebrating its 19th anniversary!
Nineteen years is an incredible milestone. For almost two decades, we have been diving deep into the ever-evolving world of search engines, always striving to make sense of the changes and challenges Google and the search industry present.
This year, 2025, has been one of the most transformative since our launch in 2006. The rapid pace of change has been exhilarating.
Through it all, our mission remains steadfast: to provide clear news, insightful analysis, and practical guidance to help you navigate the world of search.
Before we look to the future, I want to express my heartfelt thanks for your support and reflect on the past year with you.
Sincerely, thank you for being with us.
Every day, we focus on you: what you need to know, what really matters, and what changes will impact your work today or your strategy months down the line.
Our goals include:
If you haven’t yet, I encourage you to subscribe to our daily newsletter for a curated summary of all things search, helping you stay updated without feeling overwhelmed.
Our team’s passion is what has driven our success for almost two decades.
Though small, our team accomplishes significant and impactful work because we are mission-driven and dedicated to search.
I extend my greatest thanks to:
And to the entire team at Third Door Media within Semrush, whether or not your name appears here, your contributions are invaluable.
Despite the uncertainties of this year, Search Engine Land thrived, thanks to the trust of our community.
This was arguably the highlight of the year. SMX Advanced’s return in person after six years was electrifying.
With attendance surpassing expectations, the sessions were dynamic, and conversations felt like reunions for the search marketing community. It was clear that we all missed these face-to-face exchanges about AI, Google’s updates, and more.
We learned again that when great minds gather, extraordinary things happen. We eagerly await our next gathering in Boston, June 3-5.
This year, more than ever, transformed the search landscape. We’ve provided the clarity and reporting needed in this evolving environment.
Our readers rely on us for insights during times of change, and we take pride in shaping the industry’s future understanding of search.
This year saw a surge of new and returning readers turning to us for insight into SEO and PPC shifts, from AI to SERP experiments.
Our growth owes much to our fantastic contributors, and I extend my gratitude for their impactful work.
As we embrace our 19th year, our resolution is steadfast: to offer unparalleled coverage of search-related topics.
This year, you can anticipate:
Mark your calendars for:
We have much in store for you, with the aim of equipping you with the insights necessary for your best work.
Launched on Dec. 11, 2006, Search Engine Land began with a vision of search as a vast community. A place of exploration, connection, and evolution. Over these years, it’s grown beyond our expectations.
The mission remains the same:
Search Engine Land is your destination to remain informed, educated, and connected within the world of modern search engines.
From everyone here at Search Engine Land and Semrush, thank you for your readership, engagement, and passion for the evolving world of search.
Here’s to a promising rest of 2025 and a remarkable 2026.
Inspired by this post on Search Engine Land.
As someone who deeply values efficiency in my digital marketing strategies, I’m excited to introduce Profound Workflows—a revolutionary automation layer designed specifically for the AI search era. This innovative tool is set to reshape how we manage content operations, offering a significant leap in productivity.
With Profound Workflows, I can now audit, analyze, and optimize content on a large scale with ease. Thanks to its automated processes, it takes the heavy lifting out of content management, enabling me to focus on strategic decisions rather than getting bogged down by manual tasks.
The integration of research-backed insights ensures that every piece of content I work with is not only optimized for search but also tailored to meet user needs. This streamlined approach reduces my workload while enhancing our growth trajectory.
For marketers like me, using Profound Workflows means embracing a seamless transition into the future of AI-enhanced content management—where manual effort is minimized, and operational growth is expedited.
Inspired by this post on Try Profound Blog.