I’ve noticed that Bing is testing a double-rowed sponsored product carousel in its shopping results. As someone who keeps an eye on these updates, this change could offer substantial visibility boosts for Microsoft Shopping advertisers.
The test, first spotted by Digital Marketer Sachin Patel, caught my attention when he noticed the broader layout while searching for cushions on Bing. This new format combines a significant double-rowed sponsored carousel, prominently paired with organic results below.
Why this matters to me: If Bing decides to roll out this format broadly, I foresee a significant increase in screen space dedicated to sponsored products. This extra visibility typically translates to higher click-through rates, especially for those running Microsoft Shopping campaigns. The visually appealing double-row carousel puts Bing’s shopping ads on par with similar offerings by Google Shopping.
Here’s the catch: The test seems to be in its early stages, as not all users, including seasoned industry experts like Mordy Oberstein, are seeing this expanded format. When I checked myself, I noticed a more compact layout, hinting at Bing’s ongoing experimentation.
The takeaway: Bing often experiments with its search engine results pages without officially rolling them out. As a retailer using Microsoft Shopping, it’s crucial for me to stay alert for any increase in product impressions if the format becomes more widespread.
Initially discovered. This testing phase was initially spotted by Sachin Paten, who shared his insights and a screenshot on X.
During a recent study, I discovered that Reddit stands out as the most-cited domain in AI-generated answers. In fact, it’s ahead of heavyweights like YouTube and LinkedIn, thanks to an analysis of 30 million sources conducted by Peec AI, a tool specializing in AI search analytics.
The findings: I’ve learned that Reddit claims the top spot across various AI platforms including ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews. Top contenders YouTube, LinkedIn, Wikipedia, and Forbes are right behind. Platforms like Yelp and G2 frequently appear when searching for recommendations.
As I delved deeper into the research, it became clear which domains the AI models tend to lean on:
ChatGPT values Wikipedia, Reddit, and editorial sites like Forbes.
Google shows preference for platforms such as Facebook and Yelp.
Perplexity favors Reddit, LinkedIn, and G2 for queries within the B2B realm.
Why we care: The insight that resonated with me was the importance of having authority beyond just our own websites. Brands that consistently feature on reputable third-party platforms have a better chance of being cited by AI.
Why these sources? It’s fascinating to see how AI systems are wired to prioritize both authority and authentic user input:
I’ve found that Reddit excels because it mirrors genuine user discussions.
YouTube shines in video citations, owing to their comprehensive transcripts and descriptions.
Wikipedia not only serves real-time data but also acts as a foundation for training datasets.
About the data: The analysis spanned 30 million sources, providing a comprehensive look at how often domains are directly cited in AI answers, effectively revealing what shapes these responses.
During YouTube’s NewFront presentation, I discovered a groundbreaking update to their Creator Partnerships platform. This update introduces Gemini-powered creator matching, enhanced measurement tools, and innovative ad formats that leverage creator content. As a creator and marketer, this is incredibly exciting news!
Why I care. As someone invested in influencer marketing, I know how essential it is to find the right creators and showcase a solid return on investment. YouTube’s latest upgrades address these critical challenges, making influencer campaigns more efficient and measurable.
With Gemini-powered matching, I can now easily navigate through three million creators to find the perfect fit for my campaigns. Plus, the ability to run creator content as paid Shorts and in-stream ads helps me quantify success just like any other campaign, boasting a reported 30% conversion lift.
How it works. YouTube’s platform updates use Gemini to suggest creators from their extensive pool of over three million YouTube Partner Program members. This selection is tailored to align with my campaign goals, ensuring greater control and visibility of partnerships’ performance.
The big new feature. What truly excites me is the revamped Creator Partnerships boost. This feature allows me to run creator-made content directly as Shorts and in-stream ads – formats that reportedly deliver an impressive average 30% lift in conversions.
The big picture. This announcement builds on BrandConnect, YouTube’s existing infrastructure for creator monetization. It’s clear to me that YouTube is significantly enhancing the creator economy as a powerful growth strategy for advertisers.
According to a recent, though unverified, report, Google Gemini’s AI is designed to tailor its responses based on the user’s tone, intent, and emotional context. This fascinating development suggests that the AI aligns its answers with the emotional backdrop of each query.
Why This Matters. If this information holds true, it means that the responses generated by AI might vary significantly, depending on how we phrase our queries, rather than just on the data available. This could change the way we engage with search engines.
New Findings. At the heart of this revelation is a system called upcast_info. As reported by Elie Berreby, head of SEO and AI search at Adorama, this system seems to provide the blueprint for how Gemini processes user queries, aiming to:
Reflect the user’s tone, energy, and purpose.
Acknowledge emotions before formulating a response.
Deliver answers from the user’s perspective.
Implications. Instead of maintaining a neutral stance, the AI’s responses could:
Emphasize negative perspectives (“Why is X bad?”).
Highlight positive aspects (“Why is X great?”).
Should the public sentiment toward a topic be negative, the AI might intensify that sentiment. As the report indicates:
AI mirrors prevalent emotional signals.
It doesn’t offer the balancing act usually provided by traditional search result links.
The Role of Query Framing. The emotional tone of a query can impact:
The choice of sources cited.
The style of summaries presented.
The overall tone and substance of the answers.
Google’s AI Overviews already demonstrate shifts in tone that align with the intent of queries, providing potential insight into the mechanics behind these changes.
Unsubstantiated Information. Google has yet to confirm this leak. As Berreby mentions: “I’ve decided to share just a portion of the leaked internal system data publicly. It’s not a security exploit or major breach, just a minor leak.”
I’ve always been fascinated by how Google keeps improving its search capabilities. Recently, Gary Illyes from Google shared more about Googlebot’s operations, diving into its crawling ecosystem, fetching processes, and how it handles data.
Googlebot Reimagined. It’s intriguing to learn that Google uses multiple crawlers for diverse objectives. Referring to Googlebot as a singular entity might not capture this complexity anymore. You can find more details on the various crawlers and user agents here.
Understanding Limits. During a recent discussion, Google elaborated on its crawling limits. Gary Illyes provided these insights:
Googlebot fetches up to 2MB for any individual URL, except for PDFs.
This means it crawls only up to 2MB of a resource, encompassing the HTTP header.
For PDF files, the limit is notably higher at 64MB.
Image and video crawlers have varied threshold values, contingent on the product they serve.
By default, other crawlers have a 15MB limit, regardless of content type.
What exactly occurs when Google initiates crawling?
Partial Fetching: For HTML files exceeding 2MB, Googlebot will not dismiss the page. Instead, it halts the fetch exactly at the 2MB mark, including HTTP request headers.
Processing the Cutoff: The downloaded section is then forwarded to Google’s indexing systems and the Web Rendering Service (WRS) as if it were the entire file.
The Unseen Bytes: Any data beyond the 2MB cutoff won’t be fetched, rendered, or indexed.
Resource Handling: All referenced resources in the HTML, except media, fonts, and certain files, are fetched by WRS independently, with their own byte count not affecting the parent page’s size.
Rendering Bytes with Google. Once the crawler accesses these bytes, WRS takes over. It processes JavaScript and executes code like a modern browser to grasp the final visual and textual state of the page. This process doesn’t request images or videos but does respect the 2MB threshold for each resource.
Best Practices You might want to embrace these recommended practices:
Streamline Your HTML: Shift large CSS and JavaScript to external files. While the main HTML document is capped at 2MB, external scripts and stylesheets can be fetched separately, under their own constraints.
Prioritize Content: Position crucial elements like meta tags, <title>, <link>, canonicals, and vital structured data high in the HTML to ensure they’re not overlooked.
Monitor Server Logs: Keep track of server response times. If your server struggles to deliver data efficiently, our fetchers may slow down to avoid overloading, reducing crawl frequency.
Don’t Miss the Podcast! Google also released a podcast on this topic. Check it out:
I’ve noticed a significant shift in the SEO industry toward senior, strategy-focused roles. As AI increasingly handles execution tasks, the demand for seasoned strategists has grown, along with an increase in salaries and responsibilities that span multiple channels.
The change in hiring trends is evident when looking at a recent Semrush analysis of 3,900 job listings. It appears companies are now prioritizing leadership skills, innovative experimentation, and cross-channel visibility over purely technical execution.
Why it matters to me. The landscape for SEO careers and skillsets is evolving. Entry-level positions are mostly focused on execution, while leadership roles require a firm grasp of strategy across various domains such as search, AI assistants, and paid channels, ensuring they drive significant revenue.
What’s changing now. Senior roles account for 59% of job listings, clearly dominating the landscape. In contrast, mid-level positions like specialists and managers are less prevalent, with only 15% and 10%, respectively.
Companies are redirecting their budgets towards strategic roles as AI tools begin to absorb more of the technical workload.
The shift in skills. The skills in demand now extend beyond traditional SEO to include coordination, experimentation, and decision-making capabilities:
Project management is mentioned in over 30% of the listings, highlighting its importance.
Communication is highlighted in 39.4% of non-senior roles, indicating its fundamental role in the industry.
Experimentation is noted in 23.9% of senior roles, compared to just 14% of other roles.
Technical SEO appears in approximately 6% of postings, showing its niche but crucial role.
Tools and channels. The modern SEO toolkit now includes analytics, paid media, and comprehensive data tools.
Google Analytics is cited in up to 47.7% of job listings, underlining its importance.
Google Ads features in 29% of the listings, showcasing its growing relevance.
Demand for SQL skills is rising, especially at the senior level.
AI tools, such as ChatGPT, are increasingly mentioned, reflecting their future role in SEO.
AI expectations. AI literacy is shifting from being a nice-to-have to an essential skill:
31% of senior roles now reference AI capabilities.
Nearly 10% of listings highlight familiarity with LLMs.
Concepts such as AI search and AEO are increasingly common in job descriptions.
Pay and positioning. SEO is being increasingly recognized as a vital business function:
The median salary for senior roles has reached $130,000, markedly higher than the $71,630 for other roles, with some positions offering even more.
Preferred degrees are leaning towards business and marketing, reflecting the strategic emphasis.
Remote work prevalence. Remote options are available in over 40% of job listings, indicating a shift towards flexible work environments across all levels.
About the data. This analysis by Semrush covers 3,900 SEO job listings in the U.S., gathered from Indeed as of November 25. The roles were deduplicated and segmented by seniority before a semantic keyword extraction analysis was applied.
I’ve got some exciting news for those of us using Microsoft Advertising! Now, we can update our Merchant Center store names and domains without the hassle of submitting a support ticket. Everything is streamlined directly through the platform.
Why does this matter? As businesses grow or undergo rebranding, being able to quickly adjust names and URLs is crucial. Previously, I had to go through a cumbersome process, but now I have full control to make these changes seamlessly.
Here’s how it works:
When I want to change my store name, it goes through an editorial review. The best part? My ads continue to run under the old name, so there’s no downtime for my campaigns.
If I decide to switch my domain or URL, verification of the new domain is needed. Meanwhile, my ads still serve on the old domain, keeping my advertising efforts uninterrupted. Once approved, I’ll update product URLs to reflect the new domain.
Reusing store names or domains is perfectly fine as long as everything passes the editorial checks and domain verification. This provides me with flexibility while maintaining quality standards.
The bottom line? This update empowers me with more control over my store settings. It also ensures compliance by having robust checks like editorial reviews and domain verifications in place, safeguarding the quality of my ads.
I’ve got some exciting news to share—Reddit has just opened up its Pro publishing tools to all publishers! No more waiting lists. Now, anyone can dive into the public beta and ramp up their content distribution and engagement strategies, all for free.
Why this matters to us. Reddit Pro offers me a centralized hub to monitor where my content spreads, simplifying my posting process, and helping me pinpoint the right communities to engage with. It’s transforming Reddit from being a place of manual posting to a well-organized distribution channel.
Here’s the scoop. I can now easily sign up for Reddit Pro, verify my domain (usually within three business days), and jump into the Links tab. With Reddit Pro, I can:
Keep track of where my content is shared all over Reddit.
Quickly auto-import articles through RSS, speeding up my posting.
Receive AI-powered tips on the most relevant communities to connect with.
Reddit has also rolled out some features based on early adopter feedback:
Community snapshots that display rules, stats, and top discussions.
Community notes that let me track strategy and context over time.
By the numbers. Back in 2025, Reddit revealed there were over 55 billion views of publisher-related discussions. Since some publishers started testing in September, they saw:
A 46% uptick in median post views.
An almost doubled amount of profile views.
A 48% climb in median comments.
What else to look forward to. Reddit is also expanding profile flairs to every Pro user. This means I can organize posts on my profile, making it easier for users to browse my coverage and get involved with stories.
As someone who closely follows advancements in technology, I was thrilled to learn about Google’s latest breakthrough with the TurboQuant algorithm. It’s designed to enhance the speed of vector searches, fundamentally changing the way we interact with AI-powered data searches.
If you’re like me and value precision in data retrieval, this algorithm is exciting news. A tiny error-correction signal maintains compressed vectors’ accuracy, enabling AI systems to retrieve data more broadly and precisely than ever before.
Google’s TurboQuant is a compression algorithm that can shrink and organize large AI datasets with nearly zero indexing time. This technology might just obliterate one of the major speed bottlenecks in modern search engines.
What TurboQuant Is. For me, TurboQuant represents a monumental way of handling the data behind AI and search by keeping it compact without losing precision. It significantly reduces memory usage and cuts down the time to build searchable AI indexes almost to zero, according to Google’s research paper.
How It Works. Modern search systems, which convert content into vectors, can be resource-heavy. These numeric representations cluster based on similarity, allowing searches to match the closest ideas. But let’s face it, these vectors are massive and expensive to store. That’s where TurboQuant steps in, using efficiently compressed data that mirrors the original extremely well through:
Smart Compression. It rotates data mathematically, organizing it like neatly packed boxes, an image that resonates with how I like to visualize innovative data solutions.
Error Correction. By introducing a 1-bit signal, it corrects minor compression mistakes, ensuring the data remains accurate, which is quite a comforting thought for anyone concerned about data integrity.
What This Means. For those of us deeply engaged with AI, TurboQuant signifies a shift. Vector search systems, the backbone of semantic search and AI-driven answers, have traditionally been slow and costly. Google claims TurboQuant makes these operations quicker and more cost-effective, enabling faster similarity searching, lower memory consumption, and real-time processing of colossal datasets.
Why It Matters to Us. Imagine Google being able to analyze far greater volumes of documents per query, not just a limited subset. Should Google implement this into its Search, AI Overviews could access a wider, more accurate range of sources, making instant summaries from large data sets far more accessible.
I recently stumbled upon a tricky issue in Google Ads Editor that’s affecting many advertisers. A bug is causing structured snippet extensions copied between accounts to unintentionally stay linked. Whenever I change the language setting in one account, it seems to magically update the extension in another account too.
Why this matters to us. For those of us running multi-market campaigns, this bug could introduce hidden inconsistencies, especially if we’re managing accounts that require different languages.
What I’ve been experiencing. This issue came to light for digital marketer Marcin Wsół while handling Czech and Slovak e-commerce accounts. A change in snippet language in one account inadvertently altered the same setting in another.
The extensions appear separate at first glance but act like they’re mysteriously synced.
Zoom in on the details. If you use the Google Ads web interface, you can temporarily correct this, but any further edits in Editor might cause the language settings to toggle again.
A deeper issue. This bug isn’t confined to cross-account use. PPC News Feed founder Hana Kobzová discovered that even copying structured snippets within the same account can lead to incorrect language settings after making additional edits.
Reading between the lines. For those of us who depend on bulk edits in the Editor, there’s a risk of unintentionally overwriting localization settings, which could lead to mixed messaging across our markets.
The bottom line. Until Google fixes this, I recommend double-checking structured snippet languages after copying or editing in Google Ads Editor, especially when you’re working across different accounts or regions.
When this issue was first seen. This was initially identified by Marcin Wsół and later reported by PPC News Feed.
