In the past three months, I’ve noticed LinkedIn emerging as a key authority in AI-driven discovery. It’s fascinating to see how rapidly it’s progressed, skyrocketing from outside the top 20 to claim the top spot as the most-cited source for professional queries on AI platforms like ChatGPT.
This shift occurred between November 2025 and February 2026, a time of remarkable growth for LinkedIn. For me, these stats underline the platform’s potential for both companies and individuals eager to enhance their influence in the AI sector.
I recently came across an intriguing study that shows AI tools are now responsible for generating 45 billion monthly sessions globally. This accounts for an impressive 56% of all search engine activity, according to Graphite.io CEO Ethan Smith.
The analysis combines web and mobile app usage across leading AI platforms and suggests that AI activity matches 56% of global search use and 34% in the U.S.
The surge is particularly evident in mobile applications like ChatGPT, Gemini, Perplexity, Grok, and Claude.
Why it matters: AI is broadening the horizons of discovery, rather than limiting the demand for search. Since 2023, combined usage across search engines and AI assistants has increased by 26% globally. It’s clear that having visibility in both LLMs and traditional rankings is crucial.
Key insights: The report dives into the performance of the top five LLM products—ChatGPT, Gemini, Perplexity, Grok, and Claude—and compares them to the biggest search engines. Here are some standout insights:
AI platforms generate 45 billion monthly sessions worldwide.
Within the U.S., AI accounts for roughly 5.4 billion monthly sessions.
An astounding 83% of global AI usage takes place within mobile apps (75% in the U.S.).
ChatGPT is leading the charge, representing 89% of AI sessions globally.
When looking at search-like prompts, AI usage constitutes 28% of the global search and 17% within the U.S.
The report leaves out prompts in the “doing” or “expressing” categories. According to OpenAI, around 52% of prompts focus on seeking information, akin to traditional search queries.
Reading between the lines: Most forecasts comparing AI and search focus only on website traffic, often just Google.com and ChatGPT site visits. This approach overlooks much of AI’s impact.
The research suggests these comparisons undervalue AI activity by a factor of 4-5 times because a significant chunk occurs on mobile apps.
The analysis takes into account various LLMs and search engines, rather than only comparing Google and ChatGPT.
What to keep an eye on: Google remains a dominant force in discovery, but the report estimates its share of search-related activity has declined from 89% in 2023 to 71% by the fourth quarter of 2025.
While global AI usage seems stabilized since July 2025, the U.S. usage is still on a rapid climb—up about 300% year over year by December 2025.
I’ve been keeping an eye on the latest developments in AI advertising, and it’s time to prepare for something big: ChatGPT ads are on the horizon. As consumers shift towards shopping through AI prompts, ChatGPT could potentially rival search as a powerful demand-capture channel, leading to a redirection of ad budgets.
Recently, OpenAI began testing ads in ChatGPT for a limited group of U.S. users, clearly marking these placements as sponsored content. Based on the platform’s internal dynamics, it won’t be long before this feature becomes widely available.
As advertisers, we have a unique opportunity to tap into a fresh demand-capture channel. However, it’s crucial to approach this space with clear expectations and understanding.
For ChatGPT advertising to truly succeed, consumer behaviors will need to evolve. And even if they do, remember that ChatGPT won’t expand the market but rather, redistribute it.
Why ChatGPT is Embracing Ads
It’s no shock that ChatGPT is moving towards advertising. Running an LLM query is estimated to be ten times the cost of a simple search query. With users generating 2.5 billion prompts daily, expenses pile up swiftly.
The core difference here isn’t just a model shift; it’s the data landscape. Over the years, users have fed personal information into ChatGPT, giving it insights unmatched by traditional advertising tools. The burning question is how ChatGPT will use this data to target its users effectively.
Advertisements have traditionally relied on repetition to generate demand, whereas search meets buyers with intent. ChatGPT might forge a similar path, equipped with more user context.
Imagine this: asking which security camera works with a certain system and receiving an informed answer and purchase link because the platform already knows about your existing setup.
Should this happen, ChatGPT could be the first new demand-capture channel since Google’s PPC ads launched two decades ago. Yet, obstacles remain.
Today’s AI queries largely lack buying intent, serving more informational needs. When buying happens, the conversion tracking might fall short due to users completing purchases on platforms like Amazon or Google after doing their research on ChatGPT.
Don’t be discouraged; such challenges are surmountable. Google’s journey from a homework help tool to shopping powerhouse wasn’t overnight. Likewise, ChatGPT will need time to educate consumers about shopping through AI.
While a brand-new demand-capture platform is exciting, have realistic expectations about its potential.
Market Share Reality Check
Despite the capabilities of AI, it won’t expand the advertising marketplace. ChatGPT ads won’t magically bring a wave of new consumers.
Instead, it will capture pieces of the existing market shared by Google, Meta, and Amazon. It’s more about shifting budgets rather than expanding them.
Competition will be fierce, particularly with Google’s AI platform, Gemini, presenting a formidable challenge. Market consolidation seems inevitable as AI races towards profitability.
The Differentiator: Hyper-Personalization
AI’s true edge might be in hyper-personalization. With their vast knowledge of user preferences, these platforms can deliver perfectly tailored recommendations.
This feature could make AI incomparable, offering personalized results seamlessly. However, this comes with risk, as hyper-personalization might feel invasive to some users.
If AI can maintain trust and avoid crossing privacy boundaries, its personalized convenience will likely be favored by most.
Steps to Take Now
While widespread ChatGPT advertising is still on the horizon, preparation is key. Here’s how to get ahead:
Align on Measurement: Consider research-heavy metrics and assisted conversions.
Optimize Mobile UX: Ensure a smooth, fast purchasing experience to avoid loss in demand capture.
Plan Early Tests: Testing carries risks but can provide an early competitive edge.
Being strategic now will set the stage for success when ChatGPT advertising becomes fully operational.
I recently stumbled upon an intriguing revelation: ChatGPT sources a staggering 83% of its carousel products from Google Shopping via shopping query fan-outs. This prompted an investigation into how ChatGPT utilizes shopping query fan-outs and what implications arise from this dependency.
In November 2025, while delving into the depths of AI research, some colleagues and I unearthed an enigmatic piece of code within ChatGPT. The field called id_to_token_map, encoded in base64, ultimately revealed parameters linked to Google Shopping, such as productid and offerid.
To validate that this field pointed to Google Shopping, we attempted to reconstruct a shopping URL solely from these decoded parameters. Here’s an example from a ChatGPT carousel showcasing “best smartphones under $500,” showing how this process could replicate Google’s shopping links.
The question was whether this shopping link corresponded exactly to products shown in ChatGPT. As it turns out, it did! Yet, it raised more questions about the nature of ChatGPT’s sourcing process. Does this apply across various product categories? Does ChatGPT prefer higher-ranked Google Shopping products?
To deeply explore these queries, we investigated over 40,000 carousel products and analyzed the results. By examining the similarity between ChatGPT carousels and Google and Bing organic products, the study shed new light on ChatGPT’s reliance on Google Shopping for sourcing.
Diving into our findings, we see a stark difference between normal search and shopping query fan-outs. Notably, shopping fan-outs are typically shorter, aiming to fetch specific items rather than broader contextual information. This suggests ChatGPT optimizes these fan-outs specifically to compile its product carousels.
Further, the data indicates most ChatGPT carousels mirror Google’s organic shopping results. Almost 84% of similar products matched within Google’s top 20 positions, reinforcing a clear preference for Google’s top-performers.
Interestingly, ChatGPT’s sourcing from Bing was minimal, with a mere 0.16% exclusive matches, indicating a predominant preference for Google’s data. This stark contrast highlights ChatGPT’s systemic approach to product sourcing.
These findings are crucial for brands aiming to feature in ChatGPT’s carousels. Monitoring your Google Shopping rank is integral, yet understanding additional contextual factors—like product sentiment—could enhance visibility.
For the field of AI, this study underscores that ChatGPT employs a distinct, independent pipeline for its product carousel, separate from the standard search query fan-outs. Future changes in ChatGPT’s methods remain a possibility, but for now, a systematic reliance on Google Shopping has been firmly established.
After tracking an incredible 2 million ChatGPT prompts, I found a surprising trend: shopping appears in less than 10% of them. Diving deeply into the data over nine months, it was clear that a staggering 79% of prompts simply never activated a shopping response.
What intrigued me further was the persistence of those that did trigger shopping. There was an impressive 83% chance they would do so again the following day. However, this persistence isn’t indefinite. Model updates seem to wash away those triggers overnight.
In my quest to understand these patterns, I analyzed 26 million prompts across 13,000 categories. The goal was to pinpoint where shopping emerges, how reliable this occurrence is, and what insights this holds for brands shaping their strategies on a platform where responses are sparsely shopping-oriented.
As someone navigating the world of SEO and content marketing, I’ve noticed a looming problem: everything is starting to sound eerily similar. It’s the same phrases, the same structure, and a robotic tone that seems to dominate.
The web is overflowing with content that’s perfectly optimized yet fails to engage readers. That’s the real danger, not AI replacing SEOs or causing penalties. The biggest threat is losing our unique brand voice in the quest for efficiency.
Rather than flattening our content, AI should enhance our SEO efforts. It should make us faster and more adaptable, without stripping away what makes our brand stand out. Here’s how I ensure AI doesn’t turn my brand into a faceless entity.
To me, AI works best when it complements a clear strategy. It’s not a substitute for a marketing plan or brand direction. Just like tools such as Google Analytics or Semrush, AI is a support system, not a replacement.
In my experience, without a deep understanding of our audience, AI merely churns out content that lacks distinction. That’s why defining who you are as a brand is crucial before turning to AI as an assistant.
I’ve found AI shines when handling large data sets, spotting trends, or identifying content gaps. It accelerates my processes, allowing me to focus on the strategic aspects of SEO.
However, AI falls short in areas that depend on creativity and emotional engagement. It doesn’t truly understand brand values or ethical nuances. It can mimic, but not truly connect or empathize.
Therefore, I let AI handle data-driven tasks, while keeping the heart of my branding – its voice and soul – firmly within human hands.
Before using AI, I clarify my brand’s tone, language, and boundaries. A well-defined brand voice ensures AI assists without diluting our identity.
In practice, I use AI for research and framework creation, but ensure human inputs sculpt the final content. Editing and authenticity checks are critical steps I never skip.
The key takeaway is that AI amplifies whatever brand essence you feed it—it can’t create it from scratch. Maintaining clarity and a distinct brand voice is what sets successful SEO apart.
I’ve been diving deep into how AI is transforming the landscape of public relations. It’s amazing to witness how AI PR is reshaping the industry, and I’m excited to share some insights with you.
One of the fascinating aspects I’ve learned about is the importance of citations in AI-generated answers. They play a vital role in establishing credibility and authenticity, which is crucial in our digital age.
Another intriguing factor is how LLM visibility affects our processes. By understanding how AI models operate in the public domain, we can adapt and refine our strategies to enhance our PR efforts.
As PR teams, it’s essential for us to stay ahead of these changes and tweak our approaches accordingly. Embracing AI tools and strategies ensures we remain competitive and effective in our communication efforts.
Recently, while exploring the latest developments in web technology, I stumbled upon something groundbreaking: WebMCP, introduced in Chrome 146. Being a tech enthusiast, I was intrigued to learn how this emerging protocol could reshape the way AI agents interact with websites.
Chrome 146 has rolled out an exciting early preview of WebMCP, hidden behind a flag. This protocol, known as Web Model Context Protocol (WebMCP), is designed as a web standard to lay out website tools in a structured manner, guiding AI agents in executing tasks seamlessly.
So, what does this mean for us? Historically, the internet has been developed with humans in mind. Buttons, forms, and dropdowns are all elements we understand. But there’s an emerging user—AI agents. Soon, they will be completing registrations, purchasing tickets, and achieving other goals autonomously on websites.
Currently, AI agents face a daunting task. They navigate websites by crawling and attempting to decipher their functionalities. Imagine an AI agent trying to book a flight. It has to identify input fields, guess data formats, and pray nothing goes awry. It’s far from ideal.
The introduction of WebMCP is set to change this. By exposing the structure behind web tools, AI agents will be equipped to understand and execute tasks with ease.
Let’s dive a bit deeper to understand WebMCP. Picture yourself needing to book a flight.
Without WebMCP: An AI agent scrambles to find a relevant button like “Book a Flight” or “Search Flights.” It then interprets the on-screen information, hoping it inputs correctly.
With WebMCP: Forget searching for buttons. Instead, the agent calls a function, like bookFlight(), using well-defined parameters (such as date, origin/destination, and passengers), receiving a structured result in return. Much like developers interacting via APIs, AI agents will seamlessly call functions.
WebMCP empowers websites with JavaScript APIs and HTML form annotations, guiding AI agents on interacting with web tools in three steps:
Discovery: What tools does the page support? Examples include Checkout, BookFlight, or searchProducts.
JSON Schemas: They precisely define expected inputs and the kind of output that will be returned.
State: Tool availability alters based on the page’s state, allowing agents to only see actions relevant to the current context.
My website, for instance, could offer a list of actions each detailing its functionality, accepted inputs, returned outputs, and required permissions.
But why does this matter? AI agents are infiltrating our daily workflows rapidly. Soon, AI will handle our flight bookings, fill out forms, and publish content. But, as of now, AI agents struggle to interact seamlessly with websites due to two current approaches:
Automation (fragile): An AI acts by clicking buttons and inputting data like we do, but since websites frequently update, this can lead to failures.
APIs (limited): While APIs offer a structured approach for interaction, they’re not universally available or comprehensive.
WebMCP offers a middle ground, allowing websites to make tools accessible without the drawbacks of UI automation or needing separate APIs.
Like the early 2000s SEO race, WebMCP symbolizes a shift towards optimization for AI agents. Those who adopt this early could enjoy significant advantages as AI-centric search and commerce grow.
This opportunity is not merely about SEO anymore. It’s about realizing broader growth potential through WebMCP, which signifies not just being discoverable, but actionable by AI agents who’ll soon connect with future customers.
Practical applications of WebMCP in B2B and B2C scenarios are vast, from simplifying quote requests to expediting inventory checks, offering a seamless experience for business and everyday consumers alike.
To start experimenting with WebMCP, Chrome 146 lets you access it behind a feature flag. It’s still in its nascent stage but provides a valuable chance for developers and innovative teams to play around with the conceptual framework.
While getting acquainted with WebMCP, you’ll need Chrome version 146.0.7672.0 or later and a basic understanding of Chrome flags. Follow these steps to set up:
Navigate to chrome://flags/#enable-webmcp-testing in Chrome.
Set the “WebMCP for testing” flag to “Enabled”.
Relaunch Chrome.
Start experimenting with WebMCP today and perhaps even install the Model Context Tool Inspector Extension to witness WebMCP in action. It’s an exciting era we’re stepping into, enabling websites to be as understandable to AI as they are to us.
As someone keen on improving AI search visibility, I’ve delved into the world of schema markup. Let me share what I’ve learned about essential schema types, practical implementation tips, and how structured data enhances the understanding of content by Large Language Models (LLMs).
By incorporating schema markup, I’ve noticed significant improvements in how AI and search engines interpret my content. This not only boosts my content’s visibility but also ensures it reaches the right audience effectively.
The right schema types serve as a bridge, enabling AI systems to decipher and present content accurately. In my experience, selecting the appropriate schema type is crucial for optimizing how LLMs process information.
Moreover, implementing schema markup isn’t as daunting as it seems. With some practice, I’ve found that the structured data seamlessly fits into my workflow, enhancing the overall search optimization process.
When I first dove into the complexities of AI recommendations, the process seemed daunting. But understanding the AI engine pipeline and its 10 gates offers incredible opportunities to optimize brand visibility and gain a competitive edge.
AI engine pipelines, from discovery to the final winning moment, are intricate systems where small adjustments can yield significant results. By embracing the entire pipeline, from upstream disciplines to structural shifts, we can profoundly influence how AI recommends our content.
Every piece of digital content navigates through a 10-gate journey before becoming an AI recommendation. I refer to this progression as the AI engine pipeline, or DSCRI-ARGDW, encompassing these crucial stages:
Discovered: The bot becomes aware of your existence.
Selected: The bot opts to further investigate your content.
Crawled: The bot fetches your material.
Rendered: The bot comprehends the content it has gathered.
Indexed: Your content is committed to the algorithm’s memory.
Annotated: The algorithm classifies the meaning of your content.
Recruited: Your content is integrated for use by the algorithm.
Grounded: The system verifies your content’s credibility.
Displayed: The user is presented with your content.
Won: You’ve secured the prime spot in the AI decision-making process.
The journey through these gates determines the strength of your AI recommendation. After securing a ‘win,’ the eleventh gate, which focuses on how your brand serves post-decision, plays a crucial role in reinforcing or diminishing ongoing AI confidence.
It’s essential to create a seamless path that bots can easily navigate (DSCRI) and outperform your competitors during the stages of recruitment, grounding, and display (ARGDW).
As the AI engine progresses through each gate, it evaluates your content against checkpoints and standards. Skipping gates by using structured feeds or direct data pushes can give you a strategic advantage by circumventing traditional path constraints.
Ultimately, understanding and optimizing for each gate in the AI engine pipeline not only enhances your brand’s digital footprint but also helps secure long-term recommendations consistently. Join me as we unravel how to enhance our content throughout this AI landscape and ensure it stands out at every step.
