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Understanding ChatGPT Ads: Behavior Over Targeting
Ads in ChatGPT signify a major transition from focusing on keyword intent to understanding user behavior. This evolution changes how we approach relevance, creativity, and performance measurement.

Currently, ads are being tested in ChatGPT in the U.S., appearing to various users across different account types. For the first time, we see advertising stepping into an AI environment designed for answering queries, which fundamentally changes the game for marketers like me.

AI has been an integral part of ad creation and planning across platforms like Google and LinkedIn for years. However, placing advertisements inside an AI that people trust to assist with thinking, decision-making, and actions is a completely new challenge. It’s not just another channel in our existing media strategy.

The primary concern for us isn’t targeting, but understanding psychology. Replicating strategies successful in search or social may lead to disappointing performance or even damage trust.

To thrive, brands must comprehend why users engage with ChatGPT, and what implications that has for capturing attention and enhancing the customer journey.

ChatGPT is a Task Environment, Not a Feed

When people use ChatGPT, they have a purpose. Whether it’s:
- Solving a specific problem.
- Refining a shortlist.
- Planning a trip.
- Writing something.
- Making sense of a complex decision.
Unlike feed-based platforms, where users passively scroll and consume content, ChatGPT users are goal-oriented.

In such a task-centered environment, behavior shifts:
- Goal shielding: Users focus narrowly on finishing tasks, filtering out distractions that don’t contribute.
- Interruption aversion: When focusing, unexpected distractions feel more annoying.
- Tunnel focus: Clarity and speed take priority over exploration.
This means gaining clicks will be more challenging than some advertisers might anticipate. If ads don’t assist users in progressing their tasks, they’ll seem irrelevant, no matter how topically aligned they might be.

Considering trust in AI is still being established, tolerance for distracting ads is particularly low.

Dig deeper: OpenAI moves on ChatGPT ads with impression-based launch

Behavior Over Search Volume: Designing a Strategy for ChatGPT

Traditionally, search volume has directed our planning.

Keywords informed us about what users sought, how often, and the level of demand competition. This framework informed both SEO and paid media strategies.

However, ChatGPT changes this model. Instead of searching for keywords, users describe situations, ask detailed questions, and pursue outcomes beyond mere information.

Without query data to optimize, our success depends on understanding:
- The task the user aims to complete.
- The journey stages they’re outsourcing to AI.
- The specific help they need at that moment.
This is where behavioral insights replace keyword demand as the foundational strategy.

Transitioning from Keyword Intent to Behavioral Targeting

Instead of centering our plans around queries, we should focus on behavior modes, representing the mindset of users when they turn to ChatGPT.

We can consider these modes as follows:
- Explore mode: Users seek inspiration or shape a perspective.
- Ads here should ignite ideas, offer options, or reframe the problem.
- Reduce mode: Users aim to narrow choices effectively.
- Ads should clarify differences, simplifying decisions.
- Confirm mode: When users want reassurance, trust trials such as reviews or guarantees matter most.
- Act mode: Users aim to complete the task, so ads that eliminate friction, like clear pricing, will succeed.
These modes correspond with recognized human drivers in search behavior: forming perspectives, informing, reassuring, and simplifying. ChatGPT condenses these moments into one interface.

Dig deeper: What AI means for paid media, user behavior, and brand visibility

In ChatGPT, Relevance is About Utility

The key shift is that relevance in ChatGPT is not merely about a match but about functionality.

An ad can align with a category but still fall short if it doesn’t help users with their tasks. Anything creating extra work or that distracts from goals feels frustrating in a task environment.

High-performing ads are likely to act less like traditional ads, and more like:
- Tools.
- Templates.
- Guides.
- Checklists.
- Shortcuts.
- Decision aids.
Such ads integrate seamlessly into user workflows.

Generic brand ads, mere awareness messages, and content serving as detours are likely to underperform.

Dig deeper: Your ads are dying: How to spot and stop creative fatigue before it tanks performance

Helpful Content Bridges Channels

The assets that create compelling ChatGPT ads—guides, frameworks, and reassurance-focused content—do more than boost paid performance. They enhance authority for SEO, earn media coverage for digital PR, and strengthen brand trust across social and owned channels.

Here, silos can break performance.

Paid media teams cannot create “helpful ads” in isolation while SEO focuses on authority, PR works on trust signals, and brand teams shape voice independently. AI-driven discovery blends these signals.

The best-performing ads may rely on:
- Brand voice for consistency.
- Trusted voice from reviews, experts, or validation.
- Amplified voice through media coverage and authority.
The line between advertising, content, and credibility is increasingly blurred.

Rethinking Measurement

Evaluating ChatGPT ads purely on click-through rates risks missing their broader influence. These ads might sway decisions without triggering immediate clicks, aiding in brand recall or re-entry through different channels.

More significant indicators might include:
- Shortlist inclusions.
- Brand recall.
- Assisted conversions.
- Branded search increases.
- Direct traffic improvements.
- Conversion boosts further down the line.
This underscores the need for cross-department collaboration. If performance spans the customer journey, so too must measurement and accountability.

Dig deeper: AI tools for PPC, AI search, and social campaigns: What’s worth using now

Winning Brands Master Behavior

This is not just a new ad format; it’s a shift in behavior. Brands that succeed will deeply understand:
- What people use ChatGPT for.
- Journey stages being shifted to AI.
- How to support these moments without losing trust.
We should revisit jobs-to-be-done thinking, mapping actions leading up to a purchase, inquiry, or commitment, and identify where AI reduces effort, uncertainty, or complexity.

This approach empowers us to ask, not simply, “how do we advertise here?” but “how can we be genuinely helpful when it counts most?”

Adopting this mindset will not only shape performance in ChatGPT but influence the broader future of AI-led discovery, where understanding behavioral intent will surpass the old focus on keywords.

Inspired by this post on Search Engine Land.
February 2, 2026
2 Million LLM Sessions: AI Discovery Insights Revealed
Analyzing nearly two million LLM sessions across nine industries throughout 2025 was a fascinating journey for me. I began with the assumption that ChatGPT would dominate and that AI usage patterns would be relatively uniform with minimal impact.

The findings, however, were surprising.

While ChatGPT does indeed control 84.1% of the trackable AI discovery traffic, it’s primarily serving as a broad-market tool. This discovery significantly impacts strategic approaches.

In today’s landscape, relying solely on a single discovery strategy is not viable. A multi-platform approach that aligns with how and where users find productivity is essential.

Brands must now discern which platforms are empowering productivity rather than merely supporting initial discovery phases.

Various LLMs are excelling in different sectors, often with stark differences. The key takeaway for 2026 is more complex than simply focusing on ChatGPT.

Here’s what I’ve discovered from the data.

The Growth Rate Divergence: ChatGPT vs. Competitors

Throughout 2025, major LLM platforms exhibited significant growth discrepancies:
- ChatGPT: 3x growth
- Copilot: 25x growth
- Claude: 13x growth
- Perplexity: 1x growth
- Gemini: 1x growth
Although ChatGPT grew, Copilot and Claude experienced much more rapid growth. Platforms like Perplexity and Gemini remained steady, reinforcing specific workflows.

These numbers highlight strategic priorities:
- Satya Nadella celebrated Copilot reaching 100 million monthly users.
- Dario Amodei revealed that Anthropic’s revenue grew from $100 million to $8–10 billion in under two years.
- Aravind Srinivas noted significant interest in Perplexity Finance.
The focus on growth is crucial because it signals true user value:
- Copilot excels in the Microsoft ecosystem.
- Claude appeals to developers.
- Perplexity thrives among finance professionals.
Different LLMs are thriving in various industries at markedly different rates.

Pattern 1: Copilot’s Striking Growth

Copilot’s remarkable 25x growth is indicative of its premier position in B2B environments reliant on Microsoft tools.

SaaS
- ChatGPT: 2x growth
- Copilot: 21x growth
- The rapid adoption mirrors modern SaaS practices, embedding LLMs directly into workflows.
Education
- ChatGPT: 6x growth
- Copilot: 27x growth
- Copilot benefits from educational settings fostering knowledge sharing and synthesis.
Finance
- ChatGPT: 4.2x growth
- Copilot: 23x growth
- Finance aligns with Copilot due to automation needs and context dependency.
Copilot’s growth is most pronounced in industries where professionals are deeply integrated with Microsoft tools.

Instruments like Excel transform into data interpretation powerhouses with Copilot, eliminating the need for external searches.

Implications

For work-centric audiences like SaaS, finance, and education specialists, AI discovery is shifting into LLMs embedded in workflows.

Pattern 2: Perplexity Shines in Finance

While Perplexity has flat growth overall, it stands strong in finance with a 24% market share, unlike in other sectors where it has diminished.
- SaaS: down to 7.3%
- E-commerce: down to 3.4%
- Education: down to 5.2%
- Publishers: down to 3.6%
Finance demands accuracy; thus, traceable sources make Perplexity vital in this sector.

Partnering with Benzinga, FactSet, and others, Perplexity offers in-depth data vital for financial decisions.

Trust and verifiability are crucial in finance, and that’s where Perplexity excels.

Implications

In finance, selection of platforms that integrate with licensed data and credible sources is critical. Success hinges on being part of these authoritative ecosystems.

Pattern 3: Claude’s Dominance in Analysis

With just a 0.6% share, Claude might appear to be an underdog, but it thrives in specialist sectors like publishing and finance.
- Publishers: 49x growth
- Education: 25x growth
- Finance: 38x growth
- SaaS: 10.3x growth
Claude’s strength lies in standalone, strategic thinking rather than integrated tools like Copilot.
- Publishing professionals and financial analysts use Claude for its substantial context window, enabling complex and strategic queries.
Implications

Target audiences that require in-depth analysis should focus on creating structured and detailed content. Claude’s user base is smaller but highly influential.

Pattern 4: Challenges in Tracking Gemini

The data concerning Gemini is puzzling, showing both growth and declines. This could be attributed to issues with attribution rather than an actual decline in users.
- Education: −67% tracked traffic
- SaaS: +1.4x growth
- Finance: +1.3x growth
- E-commerce: +2.7x growth
Gemini’s interaction model keeps users within its ecosystem, making measurement challenging.

The reality is that usage might still be robust, but the tracking systems need to catch up with user behaviors.

Implications

As AI-assisted conversions increasingly occur, traditional last-click attribution models need reconsideration.

Monitor brand search performance and invest in broader visibility strategies.

Strategizing Your LLM Approach

AI discovery is diversifying rather than converging. Tailoring strategies based on your audience’s preferences and behaviors is crucial.
- Enterprise Audiences: Focus on Copilot integration for SaaS and B2B environments.
- High-Stakes Decisions: Consider Perplexity’s reliability in providing traceable data.
- Technical Evaluations: Claude’s detailed analysis capabilities require rich, structured content.
- Emerging Sectors: Initiate with ChatGPT, monitor for evolving platform preferences.
- Measurement Challenges: Adjust strategies to accommodate for gaps in tracking.
Success in AI discovery is rooted in understanding your audience’s platform preferences and their specific needs.

Read the full study: 2025 State of AI Discovery Report: What 1.96 Million LLM Sessions Tell Us About the Future of Search

Inspired by this post on Search Engine Land.
January 30, 2026
Boost Your Google Ads in 2026 with v23 API Insights

As I delve into Google Ads API v23, I’m excited to share this update marks the beginning of a faster-paced release cycle in 2026. With this update, I’m now able to access improved Performance Max reporting, sophisticated AI-driven audience tools, and more detailed campaign controls.

What’s new:

Performance Max Transparency: I’ve discovered that PMax campaigns now offer ad network type breakdowns, making it easier for me to analyze performance.

More Detailed Invoices: Through InvoiceService, I can retrieve campaign-specific costs, regulatory fees, and adjustments, allowing for more precise financial tracking.

More Precise Scheduling: It’s a game-changer for me to now schedule campaigns using precise start and end date-times instead of limiting to date-only fields.

Local Data Access: I’m now able to access store location details via PerStoreView, which matches the data in the Stores report accurately.

New Audience Dimension: With life-event-based audience building through LIFE_EVENT_USER_INTEREST, my Insights tools are more powerful than ever.

Smarter Demand Gen Planning: The conversion rate forecasts I rely on now vary by surfaces such as Gmail and Shorts, enhancing my strategy planning.

Generative AI Audiences: I can efficiently translate free-text audience descriptions into structured attributes, simplifying audience target creation.

Expanded Shopping Metrics: The inclusion of new competitive and conversion metrics by conversion date helps me improve my shopping ads performance.

Why I care: A quicker update cycle means I can leverage new features faster. With Google’s shift towards automation and AI-driven insights, staying on top of these updates helps me optimize campaigns effectively.

Between the lines: These updates require my team to upgrade client libraries and code, so scheduling development time is crucial to benefit fully from v23.

Bottom line: The Google Ads API v23 is setting the stage for 2026. I’m ready to embrace these improvements that introduce faster releases coupled with enhanced AI insights, refined reporting, and better campaign control for large-scale advertisers.

Inspired by this post on Search Engine Land.

January 29, 2026
How AI Highlights the Vital Role of Human Connections in Agencies

Working as an office manager in my early 20s, I discovered Dale Carnegie’s “How to Win Friends and Influence People.”

The timeless principles in that book have been my guiding compass through various career shifts. I’ve realized that success in most professions hinges on how we interact with others—be they clients or colleagues.

For many years, combining human touch with technical skills has been a winning formula for digital marketers. It was this ability to demystify complex machines coupled with strong relationship-building that allowed agencies to retain clients.

But now, this model is under scrutiny as AI becomes integral to PPC platforms, raising a pertinent question: why shouldn’t clients dive into an entirely AI-driven approach?

What agencies have an edge on is their relational strength—their ability to communicate effectively and understand what business owners genuinely need.

1. Ask questions

I’ve learned that one of the most effective ways to understand people and what makes them tick is by asking questions. Though it seems straightforward, communication often becomes lost in translation or obscured by assumptions.

Whenever I walk into a sales call, I arm myself with a list of questions. How much can I uncover about this potential client in a brief half-hour conversation?

Similarly, during strategy discussions, I prepare a comprehensive set of queries—some for myself, and some for the client. What are they aiming to achieve? What aspects of their current strategy need refinement? How can we enhance it?

To this day, AI can’t fulfill this role—not yet, at least. Our exchanges with AI remain predominantly one-sided.

AI doesn’t actively seek to understand us as individuals or identify our unique challenges. These discoveries only come from asking questions and actively listening, which leads to the next point.

Dig deeper: 6 tips to build PPC client relationships

2. Talk less, listen more

How often do I find myself in conversations, impatiently waiting for a pause to insert my thoughts? I’m guilty of this, but I’ve found that clients crave the opportunity to be heard.

Allow them to express themselves fully, encourage them with more clarifying questions, and just keep listening. It’s remarkable what you can learn about someone when you enter a conversation with no other agenda but to understand the other person.

Fill the silences only if they become awkward, and if you have valuable agenda points to address based on what you’ve learned. This approach fosters collaboration and generates ideas more swiftly than dominating the conversation could. It solidifies agreement, which is foundational in building relationships.

Dig deeper: 8 questions to ask your new PPC clients

3. Find common ground

Whenever possible, I aim to discover commonalities between myself and new acquaintances. By doing so, I build rapport, enriching both personal and professional relationships.

Being personal and specific, whether dealing with a friend or a client, is key. I love recalling little details about people and bringing them up in future conversations. People appreciate being remembered and valued.

Though AI is beginning to develop memory, finding shared experiences with others is a uniquely human skill that, fortunately, remains beyond AI’s reach.

Dig deeper: When and how to fire PPC clients

4. Smile, be less serious (when it’s appropriate)

In the fast-paced marketing realm, it’s easy to succumb to the all-consuming cycle of data analysis and testing. Remember, though, not to take ourselves too seriously.

After all, this profession is relatively new, and its evolution is unpredictable. Let’s not forget why we ventured into marketing—to help and connect with people. Let’s embrace opportunities to be less serious and inject humor when it fits.

We’re human, and it’s vital for those we work for to recognize this humanity as an integral part of any relationship.

Dig deeper: How to set and manage PPC expectations for teams and stakeholders

What differentiates a partner from an algorithm

In a world increasingly dominated by AI, the focus is shifting from technical prowess to personal connection. AI excels at data and analysis, available at a moment’s notice, but knowledge alone isn’t sufficient anymore.

Empathy, shared experiences, and true rapport are beyond AI’s capability to replicate. These human principles, combined with expertise, are what enabled agencies to decode machines for clients and nurture enduring relationships.

By returning to relational basics—posing insightful questions, practicing active listening, and establishing common ground—agencies can affirm their indispensable value.

These relational skills are vital in distinguishing a partner from an algorithm, ensuring that the work of agencies remains not just relevant but essential.

Inspired by this post on Search Engine Land.

January 29, 2026
Maximizing AI Visibility: Why It’s 30x Tougher Than Google SEO

When it comes to AI assistants like ChatGPT, Gemini, and Perplexity, my experience shows that many brands excelling in local Google searches struggle to be visible. SOCi’s 2026 local visibility index highlights this gap, revealing that AI recommends a meager 1% to 11% of locations.

Interestingly, business profile accuracy varies significantly across platforms. While Gemini maintains 100% accuracy, ChatGPT and Perplexity lag behind at 68%.

AI limits local visibility. Examining data from almost 350,000 locations tied to 2,751 multi-location brands, it’s clear that AI platforms are incredibly selective compared to Google’s local outcomes:

Only 1.2% of locations get the nod from ChatGPT, 11% from Gemini, and 7.4% from Perplexity. In contrast, Google’s local 3-pack features brands 35.9% of the time, indicating that securing AI visibility is three to 30 times more challenging.

AI and Google visibility differ. Across different sectors, less than half of brands topping Google’s local visibility are also among the elite in AI search outcomes.

For instance, in retail, only 45% of the top 20 brands in local search visibility made it to the top in AI recommendations.

Why I care. Just because my brand does well on Google doesn’t mean it will shine in AI-driven results. It’s crucial to ensure that my brand’s information is consistent across all platforms AI systems draw from, such as Google Maps, Yelp, and Facebook. Strong, accurate data and a clear, positive presence are now fundamental.

AI favors higher-rated businesses. AI consistently promotes businesses with high customer sentiment, treating reviews more like a filter than a ranking tool.

For instance, ChatGPT recommends locations averaging 4.3 stars, whereas ratings on Gemini and Perplexity are slightly lower. Unlike traditional methods that prioritize proximity, AI platforms look for confidence.

Impact varies by industry. Different sectors experience varying levels of AI visibility:

Retail: A limited number of retail giants like Sam’s Club lead, while others like Target fall short. This gap illustrates AI’s focus on reliable signals across platforms.

Restaurants: Culver’s excels with up to 45.8% recommendation rates, driven by strong ratings and accurate profiles, highlighting how essential these elements have become.

Financial services: Liberty Tax improved its AI visibility by boosting data accuracy and ratings, achieving impressive figures compared to competitors lacking in these areas.

Failing to maintain top-notch fundamentals now means disappearing from AI recommendations altogether. SOCi observes this as a critical shift.

The report. For more detailed insights, I can check out the 2026 Local Visibility Index (registration required).

Inspired by this post on Search Engine Land.

January 28, 2026
Meta’s New Paid Subscriptions: A Game-Changer for Social Media?

Recently, I’ve noticed that Meta is testing paid subscriptions on Instagram, Facebook, and WhatsApp. Their goal is to unlock premium features and incorporate AI more prominently across these platforms, which could significantly shift how we create and interact with content.

What’s unfolding? Meta’s new subscription trials aim to bring exclusive features to each app, tailored to productivity, creativity, and enhanced AI capacities, while the core experiences remain free. It’s interesting to see how Meta plans to develop unique subscription offerings instead of just a single bundle, especially as they explore which combinations of features might work best.

Subscriptions will provide premium controls and tools that can benefit everyday users, creators, and businesses, distinct from Meta Verified. For instance, on Instagram, initial testing might include features like unlimited audience lists, insights into non-followers, and stealth story viewing.

Meta also aims to launch paid AI features, notably increasing access to its Vibes AI video generation tool through a freemium model. I’m curious about how this might change our interaction with content creation tools.

Why this matters to us. These paid subscriptions could transform user engagement on Meta’s platforms, potentially altering privacy settings and audience reach. Additionally, new AI-driven creation tools could shift the landscape of user-generated content that advertisers either compete against or harness for campaigns. Over time, these subscription tiers might redefine targeting strategies and the value of organic versus paid engagement on these platforms.

Reading between the lines: AI is central to this strategy. Meta plans to scale Manus, an AI agent they acquired for $2 billion, by embedding it within their apps and offering standalone subscriptions to businesses. Reports suggest that we’ll soon see Manus shortcuts directly in Instagram, creating tighter integration between social media engagement and AI-enhanced content creation.

Why the timing is right. While advertising is still at the core of Meta’s revenue model, diversifying into subscriptions can provide a new income stream. With users more open to paying for unique social features, as seen with Snapchat+ boasting over 16 million subscribers, Meta is betting on replicating that success without adding to the subscription overload many of us feel.

The takeaway. Meta’s experiment with premium social and AI enhancements could potentially open a significant new revenue stream beyond advertising. The real test will be whether these features provide enough value to justify another subscription in our already crowded monthly commitments.

Explore further. For more details, check out TechCrunch’s full article on Meta’s subscription test.

Inspired by this post on Search Engine Land.

January 28, 2026
Streamline Ad Reviews with Google’s Instant PMax Previews

I’ve noticed something pretty exciting in Google’s recent update to Performance Max. They have introduced one-click ad previews, making it incredibly easy to review creatives directly from the asset group table. This update feels like a breath of fresh air to anyone who’s ever been bogged down by the previous clunky process.

What’s new? Now, with just a click on any image or video within the Asset Groups table, I can instantly see how my ads will look across different Performance Max placements, without needing to navigate away from the page.

Why we care. Before this, checking ad previews meant jumping through various hoops into different views or settings. Now, everything is streamlined, keeping my workflow smooth and efficient, which makes creative quality assurance and iteration a lot less of a hassle.

Between the lines. There has been consistent feedback about the transparency limitations of Performance Max. So, even these small UI changes that bring creatives to the forefront are a big deal for me and many others in the field.

The bottom line. While one-click previews aren’t a game-changer in terms of strategy, they are a real time-saver. This especially helps when I’m handling large asset libraries or frequent creative updates.

First seen. This handy update was first spotted by Paid Search marketer Bia Camargo, adding another reason to appreciate these nuanced yet impactful changes.

Inspired by this post on Search Engine Land.

January 28, 2026
Study Reveals AI Recommendations Rarely Repeat: What It Means

I recently came across an intriguing study about AI recommendation lists that caught my attention. It revealed that AI systems like ChatGPT, Claude, and Google’s AI don’t often repeat the same recommendations when asked for brands or products. This means if I ask them the same question multiple times, I’ll likely get different lists each time.

This finding came from Rand Fishkin of SparkToro and Patrick O’Donnell of Gumshoe.ai. They investigated how consistent generative AI recommendations are, and their results were quite fascinating.

What They Tested. Over 600 volunteers used 12 identical prompts on ChatGPT, Claude, and Google’s AI nearly 3,000 times. What they found was quite revealing.

Each AI response was turned into an ordered list of brands or products, and the overlaps, order, and repetitions were compared to see how often the same answers appeared.

The short answer: almost never. Achieving identical lists twice was incredibly rare, with odds of under 1 in 100, and getting the same list in the same order was even less likely at 1 in 1,000.

Even the length of the lists varied. Some responses listed only two or three options, while others had more than ten. If I’m dissatisfied with the result, simply asking again might yield a better outcome.

Why This Matters. We often hear about personalization in AI answers, but this study is the first to provide real data to support that claim, showing a clear departure from traditional SEO.

Design and Randomness. This variability isn’t a flaw — it’s intentional. These systems are probability engines designed to create diverse outcomes, not stable ordered results like Google’s blue links.

One Consistent Metric. Despite fluctuating rankings, one metric that proved more stable than expected was visibility percentage. Some brands repeatedly appeared in a majority of responses.

Consistent presence in these lists carries more weight than exact ranking, especially across multiple runs and intent changes.

Context Size Counts. The consistency of AI answers improves in smaller, niche markets compared to larger categories, where results scatter significantly.

Real-World Prompts. Testing with actual human prompts showed varied results — as people phrased their queries differently, semantic similarity was low.

Yet, AI still returned similar brands for the same intent, proving that AI captures the underlying purpose behind the queries.

The Power of Intent. Even with hundreds of unique prompts for headphone recommendations, prominent brands like Bose, Sony, and Apple surfaced consistently.

When I change the purpose — say, to gaming or noise-canceling — the brand results shift accordingly, indicating that AI comprehends intent despite varied prompts.

What Doesn’t Help. Tracking exact positions in AI answers is unreliable because these rankings are too unstable to mean anything.

What Could Work. A more effective approach might be to track how frequently my brand appears over many prompts, even if it seems complex and imperfect.

Unanswered Questions. There are still gaps to explore, like determining how many attempts are needed for reliable visibility stats or whether API-based results align with real user behavior.

Conclusion. AI recommendation lists are inherently variable, but with large-scale, careful visibility measurement, I can derive actionable insights. Just don’t mistake this for traditional ranking metrics.

For more details, you can read the full report here.

Inspired by this post on Search Engine Land.

January 28, 2026
U.S. Google Searches Drop: The Impact of AI on User Behavior
I recently came across a fascinating Datos/SparkToro report revealing a significant change in our search habits. It’s no surprise that U.S. Google users are searching less than they did a year ago. While Google isn’t losing users, it’s clear they’re experiencing fewer repeat searches.
Why this matters to me. Google still reigns supreme in the search world, but fewer searches mean dwindling opportunities for clicks, ads, and traffic—even if the total search volume seems stable.
The numbers speak for themselves. The report showed a nearly 20% year-over-year decline in desktop searches per U.S. user, based on data from millions of users.
- This sharp decline is unlike the European trend, where searches only fell by 2-3%.
- Despite fewer searches per person, traditional search still constitutes about 10% of all U.S. desktop activity—a share that held steady throughout 2025.
Reasons behind the drop. The rise of AI-powered answers and instant results appears to be the main culprit:
- Users now get the information they need without conducting multiple follow-up searches.
- Zero-click searches remain high but have leveled off in the low-20% range by year-end.
- Little change is observed in repeat searches and clicks within Google-owned properties, hinting at a plateau in user behavior.
The reshaping of search by AI. AI isn’t pulling users away from search; rather, it’s enhancing it. Despite ongoing AI buzz, the report discovered:
- AI tools contribute to less than 1% of total U.S. desktop activity (0.77%), though they’ve seen remarkable growth.
- Google AI Mode remains small, accounting for about 0.06% of U.S. desktop events by December, with steady adoption increase.
Query evolution. One notable behavior change is how we phrase our searches:
- Mid-length queries of six to nine words are increasing rapidly in the U.S.
- Very long queries (15 words or more) are still rare but show significant experimentation and volatility.
- People seem to find it easier to express complex needs directly in their searches.
Discovery becomes a challenge. With concentrated search-driven discovery, breaking into post-search destinations is tougher:
- YouTube, Reddit, Amazon, Wikipedia, and Facebook remain dominant.
- ChatGPT soared to No. 7 among U.S. search destinations, a rare significant mover.
- Meanwhile, Quora has fallen out of the top 15.
AI’s few dominators. AI-driven traffic largely directs users to already established platforms like Google, YouTube, GitHub, and Wikipedia rather than new or independent publishers. When it comes to AI platforms:
- ChatGPT is the leading tool in the U.S., reaching around one-quarter to one-third of desktop AI users.
- Google’s Gemini emerged as a strong No. 2, consistently growing throughout 2025 and surpassing DeepSeek.
- Other tools like Claude, Perplexity, and Copilot stay niche with modest reach.
Industry insight. Rand Fishkin, co-founder and CEO of SparkToro, highlighted in the report:
“The big highlight here is the decline in # of Google searches/searcher from 2024–2025. It’s a nearly 20% decline in the US, though only 2–3% in the EU/UK. Other studies have shown that Google is sending less traffic than in years past, especially to the long-tail of the web, and I suspect that AI answers have dramatically altered the way many users engage with Google, answering their questions before they ever need to click on an organic result or perform a second/third/fourth search.”
The complete report. Discover more in the Q4 State of Search report

Inspired by this post on Search Engine Land.
January 28, 2026
Discover Meta’s AI: The Power of Andromeda and GEM

When I think about Meta’s advertising journey, it amazes me how far we’ve come from the manual days of targeting and account tweaking. Back then, I had to rely on finely tuned audience definitions and schedule constant tests to keep ad performance up.

But as privacy policies evolved and signal clarity dimmed, those methods began to lose their effectiveness. This change prompted Meta to harness the power of AI in reshaping its ad platform.

With Andromeda at the helm, Meta launched its first major AI initiative for personalized ad retrieval, soon followed by the expansive GEM, Meta’s Generative Ads Recommendation Model. These systems reinvent how ads are chosen and delivered across Meta’s ecosystem.

Our role as advertisers has transformed significantly. It’s crucial now to understand how Andromeda and GEM operate in unison and to align our strategies with this AI-first approach that’s defining ad success in 2026.

Let’s dive into the specifics—

Andromeda: Unveiling Meta’s AI Evolution

Andromeda, to me, feels like the beating heart of Meta’s AI transformation. By leveraging past user interactions, it flips traditional targeting on its head, going beyond pre-defined audiences to assess the most engaging ad elements.

Personally, the introduction of Andromeda in 2024 reshaped how I approached advertising. I noticed that broader target groups started to outperform detailed interest-based setups, signaling a shift towards creative-first strategies.

By 2025, it was clear that simplified structures and continuous creative refreshes were the keys to unlocking Andromeda’s potential.

The Shift with Andromeda

With Andromeda, a shift occurred from audience-centric to creative-centric matching, making the creative elements the primary indicators of relevance over traditional targeting metrics.

As I experimented, I found that broader campaigns offered more data for AI to optimize, proving highly effective in meeting diverse campaign objectives.

Source: Engineering at Meta

Enter GEM: The Brain Behind Ad Precision

GEM, the core intelligence engine of Meta’s advertising realm, brought with it a new era of predictive precision. It adds depth by analyzing wide interaction datasets to enhance ad selection and sequencing.

For me, the seamless integration of GEM with Andromeda led to noticeable improvements in campaign efficiency by late 2025, driving results more effortlessly than ever before.

Why GEM Transformed the Ads Landscape

GEM isn’t just about displaying an ad—it’s about the continuous learning and anticipation of what should come next. Imagine Andromeda as your ad’s gatekeeper and GEM as its storyteller, predicting the next successful narrative in real-time.

Source: Engineering at Meta

My approach has evolved to value long-term engagement patterns over short-lived peaks, requiring both patience and strategic creativity.
Dig deeper: Rethinking Meta Ads AI: Best practices for better results

Harnessing AI in Advertising: Strategies for 2026

This year, my focus is set on innovative creative strategies and stability, as simplicity in structure seems to generate superior results.

Creative Strategy: The Cornerstone

I’ve learned that providing a rich array of creative content enhances Meta’s AI learning. Tailor content to different personas and employ diverse media formats to keep engagement high.

Streamline for Impact

Simplifying campaign structures has shown remarkable improvements. Fewer campaigns with broader reach enable Andromeda and GEM to identify patterns swiftly.

Giving up granular control wasn’t easy, yet it has proven essential for the AI systems to optimize effectively.

The Power of Patience

I’ve discovered that patience, coupled with a stable strategy, is a game-changer. Avoid making hasty modifications; instead, monitor performance over broader time scales to truly grasp overall trends.

Budget as a Strategic Tool

Generally, larger budgets accelerate learning. Meta’s AI thrives on consistent data flow to optimize performance and develop effective solutions.

Redefining My Role

Today, I see myself less as a manual optimizer and more as a strategic architect, focusing on creative originality and brand fidelity while trusting the AI to handle optimization duties.
Dig deeper: 3 PPC myths you can’t afford to carry into 2026

Mastering Meta’s AI Ecosystem

From observation, AI is the cornerstone of Meta Ads now, transforming how I handle campaigns. Merging human-created strategies with AI insights unlocks immense potential.

By feeding diverse, quality inputs into the system, I’m able to align better with Meta’s AI, which is now the linchpin of ad success.

The rules may have changed, but the opportunity for creative success remains immense.

Inspired by this post on Search Engine Land.

January 28, 2026

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