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Category: News

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
Control Google’s Use of Your Content in AI Searches
I recently came across some intriguing news that Google might soon allow us to prevent our content from being used in their AI search features. Imagine having the power to opt out of AI Overviews and AI Mode!

Google is looking at ways to enable site owners to stop Google from using site content for Search AI’s generative features, like AI Mode and AI Overviews. They’re doing this in response to new guidelines from the UK’s Competition and Markets Authority (CMA). However, Google wants to ensure any new tools don’t disrupt the core functionality of Google Search.

What Google Has Shared. Google mentioned in a recent blog post:
- “We’re now exploring updates to our controls to let sites specifically opt out of Search generative AI features.”
They clearly state that these options shouldn’t compromise Google Search, saying:
- “Any new controls need to avoid breaking Search in a way that leads to a fragmented or confusing experience for people.”
Anticipated Timeline. It’s uncertain when these new controls will be introduced, but the idea of having more control excites many of us! Many of us—publishers, content creators, site owners—desire control over whether Google can use our content for AI features such as AI Overviews and AI Mode. These forthcoming controls, whenever they appear, will afford us the ability to better manage how Google utilizes our content.

Full Insights. Here’s the full message from Google’s blog this morning:

User behavior is evolving rapidly, and features like AI Overviews help people discover new content and explore more topics. Today, the UK’s Competition and Markets Authority (CMA) initiated a consultation on potential new requirements for Google Search, including the controls we offer websites to manage their content in Search AI features. This matter is complex, as it impacts how people find information and how websites get discovered in Search.

We’ve long provided publishers with a variety of controls, based on standards like robots.txt, to dictate how their content appears in Search. As tech evolves, so do our tools. We’ve added controls for Featured Snippets and image previews (relevant to AI Overviews). Recently, we unveiled Google-Extended, a new tool allowing sites to dictate how their content helps train our Gemini models.

Building on this framework and working alongside the web ecosystem, we’re exploring updates to our controls that specifically allow sites to opt out of Search generative AI features. Our mission is to protect Search’s helpfulness while giving websites the right tools to manage their content. We anticipate engaging in the CMA’s process and continuing our discussions with stakeholders.

New controls need to prevent fragmentation or confusion in Search. As AI becomes central to information discovery, new controls must remain simple and scalable for website owners.

We remain hopeful that we can provide more choice to content creators while ensuring a top-tier and innovative Search experience for users.

Why This Matters. It’s clear that more control is better than less. SEOs, publishers, and site owners have long called on Google to provide controls over how our content is used in AI features. These anticipated controls could arrive soon, so stay tuned for updates!

Inspired by this post on Search Engine Land.
January 28, 2026
Discover Bing’s New AI Performance Insights: A Sneak Peek

I recently discovered that Bing is testing a new AI Performance report within their Webmaster Tools. This has piqued my interest, especially since Microsoft has been teasing the idea of providing better insights into website performance in AI-driven Bing and Copilot searches for months.

It all started back in February 2023, and then in April 2023, Microsoft hinted at delivering data on Bing Chat and AI search impressions. Sadly, our hopes were dashed when they lumped this data together with regular web queries, leaving us still in the dark about our sites’ performance in Bing’s AI experiences. I can’t help but feel a bit let down.

Now, it seems Bing is experimenting with a new report within Bing Webmaster Tools, known as the AI Performance report. This report is in a super limited beta phase, and Microsoft hasn’t officially announced anything yet. A source shared that it showcases citation data from both Microsoft Copilot and its partners, detailing the number of citations and cited pages per day.

With this report, I can see how often Copilot cites my website and across how many pages. However, it still doesn’t reveal how many people clicked through from those citations to my site. The report also presents data categorized by “grounding queries” and “pages.” While “grounding queries” might not represent the exact query entered in Copilot, it shows how Bing interprets them, including insights into the intent behind such queries, like whether they are navigational or informational.

This new report lets me identify the specific pages Copilot cites. While there’s excitement in seeing more AI performance-related data pop up in Bing Webmaster Tools, I can’t shake the feeling of wanting click-through data. Knowing the click-through rate from AI interactions compared to regular web searches is something I, and I’m sure many other publishers and site owners, have been eagerly anticipating.

It feels like all search engines are intentionally keeping this data under wraps, and it’s frustrating not having full transparency.

Inspired by this post on Search Engine Land.

January 27, 2026
EU Focuses on Google’s AI and Search Data: What It Means for Competition

I’ve noticed the European Union is turning its gaze towards Google once more, scrutinizing how it handles its AI and search data. This could lead to changes that might open up its Android features and search data, ultimately reshaping the competitive landscape.

The European Commission is now formally outlining the ways Google must share specific Android functionalities and its search data with competitors, in line with the Digital Markets Act.

Tuesday marked the start of two official proceedings by the Commission, aimed at establishing a structured approach for Google to meet key obligations under the DMA. It’s fascinating to see these regulatory dialogues become more concrete.

Why I care. This move by the European Commission could alter the dynamics in mobile AI and search. With Google potentially needing to share its search data and Android AI capabilities, it could boost the competition from other search engines and AI services. Such changes might impact where advertisers allocate budgets, alter the availability of advertising inventory, and shift campaign dependencies away from Google’s platforms.

First focus — Android and AI interoperability. The regulators are delving into how Google must enable third-party developers to access Android hardware and software features as freely as Google’s own AI services, like Gemini.

– The objective is to allow rival AI providers the same level of integration with Android devices as Google’s native tools.

Second focus — search data sharing. The Commission aims to define how Google should provide anonymized search data including ranking, queries, clicks, and views to rival search engines under fair, reasonable, and non-discriminatory conditions.

– This includes specifying the types of data to be shared, how it will be anonymized, eligibility for access, and whether AI chatbot providers can use this dataset.

Between the lines. It’s not just about ticking off compliance boxes. The Commission is making it clear that AI services are under the DMA’s watchful eye, especially where data and device control could influence emerging markets.

What’s next: Within three months, the Commission plans to send Google its initial findings and recommended actions. The full proceedings should wrap up within six months, accompanied by non-confidential summaries for public input.

The backdrop. Since March 2024, Google has been required to comply with DMA obligations, having been identified as a gatekeeper in services like Search, Android, and YouTube.

Bottom line. The EU is moving from planning to action with the DMA, testing how strongly it will influence competition by overseeing Google’s AI functions and search data management.

Inspired by this post on Search Engine Land.

January 27, 2026
Explore ChatGPT’s Costly Ads: Visibility at a Premium

I’ve noticed that OpenAI is introducing premium-priced ads on ChatGPT, but here’s something interesting: the data provided to advertisers is significantly limited compared to what we’re used to.

What’s happening. Reports indicate that OpenAI is offering ChatGPT ads at around $60 per 1,000 impressions. That’s about three times the rate of standard Meta advertisements! Yet, even with this higher cost, advertisers only receive basic metrics like total impressions or clicks, without insight into actions like purchases.

Why we care. ChatGPT is becoming a fresh, highly engaging ad space, but it’s not without its challenges. The hefty CPMs and limited insights mean that early advertising efforts will lean more toward enhancing brand presence and gathering learnings than achieving performance-driven efficiency.

For marketers who are open to trying new avenues, this presents a unique chance to gain insights into how ads function within AI-driven conversations before the format becomes more widespread or measurable.

The tradeoff. OpenAI is contemplating expanding its measurement capabilities in the future, yet it remains committed to user privacy. It has pledged not to sell user data or invade the confidentiality of conversations, which limits traditional targeting and attribution possibilities that platforms like Google and Meta offer.

Who will see ads. Initially, these ads will be available to those using ChatGPT’s free and lower-cost Go tiers, but won’t be shown to users under 18 or in conversations concerning sensitive topics like mental health or politics.

Between the lines. OpenAI is branding ChatGPT ads as a top-tier, trustworthy product, banking on the idea that context, focus, and brand safety can validate the higher pricing, despite the lack of detailed performance data.

Bottom line. Brands eager for prominent visibility in a cutting-edge AI-driven environment may find ChatGPT ads appealing, but those focused on performance metrics might hesitate due to the absence of detailed measurement.

Dig deeper. OpenAI Seeks Premium Prices in Early Ads Push (Subscription needed)

Inspired by this post on Search Engine Land.

January 26, 2026
Google’s Vision: Decoding Intent Before You Type

Have you ever wondered what it would be like if Google knew exactly what you wanted to search for even before you started typing? Well, that’s the future Google is aiming for.

Currently, Google is pushing this innovation onto our devices with small AI models that rival much larger ones in performance.

What’s happening. In a recent research paper presented at EMNLP 2025, Google researchers have introduced a groundbreaking approach. By dividing “intent understanding” into smaller, manageable steps, they have enabled small multimodal LLMs (MLLMs) to deliver results comparable to more powerful systems like Gemini 1.5 Pro. These models operate faster, at a lower cost, and crucially, they keep data processing on the device.

The paper, “Small Models, Big Results: Achieving Superior Intent Extraction through Decomposition,” details how Google deduces user intent based on their interactions with apps and websites, such as clicks, scrolling, and screen changes over time.

The future is intent extraction. Presently, most large AI models infer intent from user behavior via the cloud, leading to speed, cost, and privacy issues. By dividing the process into two straightforward steps, Google addresses these concerns effectively with on-device models.

Step one: Each interaction is individually summarized. The model records what appeared on the screen, what action the user took, and a preliminary guess of their intent.

Step two: Another model reviews these summaries, focusing solely on factual information. It dismisses guesses and formulates a concise statement outlining the user’s overall goal for their session. This targeted approach prevents the common pitfalls when smaller models are asked to process long chains of actions at once.

How the researchers measure success. Success is determined with Bi-Fact, where small models employing the step-by-step strategy consistently outperform other small-model methods, as evidenced by their F1 scores.

Models like Gemini 1.5 Flash, despite being only 8B, match the performance of the Gemini 1.5 Pro on mobile data. Errors diminish since unfounded guesses are removed, speeding up operation and reducing costs compared to large cloud-based models.

How it works. Intent is analyzed by breaking it down into distinct facts, identifying missing or fabricated details. This process reveals how and where understanding fails, offering insights into how systems misinterpret meaning and miss crucial information.

The research further shows that noisy training data impacts large end-to-end models more significantly than this structured approach. The decomposed system remains robust against the unpredictability of real user behavior.

Why we care. For Google to develop tools that suggest actions or answers before a query is entered, understanding user intent from behavioral patterns across apps, browsers, and screens is essential. This research is a major step towards that vision. Although keywords will remain important, optimizing for clear, logical user paths will take precedence over mere query inputs.

The Google Research blog post. Small models, big results: Achieving superior intent extraction through decomposition

Inspired by this post on Search Engine Land.

January 26, 2026
Apple’s App Store Ads Expansion: What You Need to Know

I’ve got some exciting news for those of us tracking changes in the digital advertising space. Apple’s expanding ad opportunities within the App Store search results, offering advertisers new chances to connect with users right at the moment they’re ready to download apps.

Starting March 3rd, there will be more ad slots in the UK, and following closely, Japan will see these changes too. By the end of March, this rollout is expected to reach all Apple Ads markets.

Why is this important? With more ad slots in the App Store, we have more chances to capture installs. But, this also means heightened competition for those high-intent queries, which could drive costs upward. Since we can’t pick our ad placements, ensuring creative relevance, refining keyword strategies, and monitoring conversion tracking have never been more crucial.

What’s changing? Previously, there was just a single sponsored ad spot at the top of the search results. Now, multiple ads can show up for a search query, not only in the top spot but also further down the page.

Devices running iOS and iPadOS 26.2 or later will support these additional placements.

How eligibility works: There’s no need for us advertisers to tweak anything to tap into the new ad slots. Our existing search results campaigns are automatically eligible for these new positions.

While we can’t choose our placement or bid for a specific spot, Apple determines where our ads appear within search results.

Ad formats and pricing remain constant. Ads look the same, relying either on a standard product page or a custom one. If we want, we can even direct users to specific in-app destinations via optional deep links.

Billing remains unchanged, continuing on a cost-per-tap or cost-per-install basis.

Matching ads to searches: Ads in search results still hinge on keywords—those we choose or those suggested by Apple. According to Apple, their relevance-based matching achieves an average conversion rate exceeding 60% for top-of-search ads.

Placement is a mix of relevance and bid, but relevance remains non-negotiable. Even a high bid won’t allow an ad into auctions if it’s not a strong match for the user’s query.

What should we keep an eye on? More ad slots could lead to greater opportunities, albeit with increased competition on the same search results page. It’s prudent to keep a close eye on performance metrics, query alignment, and conversion rates as the global rollout of this feature proceeds.

Looking ahead: As March progresses, more App Store search ads will be seen in all Apple Ads markets. For those of us in app marketing, this shift represents a significant transformation in how search visibility and competition will play out within the App Store.

Inspired by this post on Search Engine Land.

January 23, 2026

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