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Category: News

How Google’s AI Mode Threatens Web Traffic: Insights from Yahoo CEO

As I delve into the evolving landscape of web traffic, I find Yahoo CEO Jim Lanzone’s insights on AI-powered search engines, particularly Google’s AI Mode, incredibly fascinating. He believes this technological evolution poses a significant threat to the web’s traditional traffic model.
Jim highlights a major concern: “I think that the LLMs are one big reason they’re under threat, with AI Mode in Google being the biggest challenge.” This makes me ponder the impact on publishers who rely heavily on these traffic flows.
I resonate with Jim’s view that publishers truly deserve this traffic. He articulates a fundamental truth: “Those publishers deserve [traffic], and we’re not going to have the content to consume to give great answers if publishers aren’t healthy.” This reflects the delicate balance required in the digital content ecosystem.
Why I care. Many websites, mine included, are noticing a dip in traffic coming from answer engines such as Google and OpenAI. It feels like a looming concern that could worsen. Yahoo’s dedication to maintaining the “search sends traffic” model is reassuring, as Jim passionately explains: “We have very purposefully highlighted and linked very explicitly and bent over backwards to try to send more traffic downstream to the people who created the content.”
Yahoo’s unique AI approach. Listening to Jim on the Decoder podcast, I learn that Yahoo is carving its own path with AI. Unlike the more conversational chatbot models, Yahoo isn’t pursuing to be an AI assistant: “Ours looks a lot more like traditional search and it is more paragraph-driven. It’s not a chatbot that’s trying to act like it’s a person and be your friend.” I see this as a move towards emphasizing informative search experiences.
Moreover, “We’re not a large language model. We’re not going to be the place you come to code. We’ve really launched Scout as an answer engine.” This strategy, I believe, could provide a clearer, more reliable information source online.
What’s next: Embracing personalization. In observing Yahoo’s strategy, I’m excited to see their efforts to evolve. They’re embedding AI across platforms: “You are very shortly going to see us get into very personalized results. You’re going to see us get into very agentic actions that you can take.” This indicates a future where user-specific solutions take precedence.
For instance, Jim notes, “There’s a button in Yahoo Finance that does analysis of a given stock on the fly… It is in Yahoo Mail to help summarize and process emails.” Such tools could transform how I interact with content on various platforms.
Yahoo vs. Google: A non-competition. Interestingly, Yahoo isn’t trying to directly outplay Google. Instead, as Jim points out, the focus is on existing users and enhancing their experience: “Nobody chooses, you will not be surprised, Yahoo over Google or somewhere else to search. The way that we get our search volume is because we have 250 million US users and 700 million global users in the Yahoo network at any given time. There’s a search box there. And infrequently, they use it.” It’s more about nurturing the loyalties of existing users.
A word of caution. The conversation also shines a light on the potential pitfalls of heavily relying on AI platforms. Jim references past experiences with Google: “You are tempting fate by opening up a way for consumers to access your product within a large language model.” This analogy resonates with me deeply, remembering the cautionary tales in tech history.
Yet, he warns: “The big bad wolf will come to your door and say everything’s cool.” It’s a timely reminder of the ever-competitive and unpredictable nature of tech alliances.
The interview. For those intrigued by Yahoo’s journey, check out Yahoo CEO Jim Lanzone’s full interview on reviving the web’s homepage.

Inspired by this post on Search Engine Land.

March 17, 2026
Unlock More Creative Control with Google Ads Editor Update

The latest update of Google Ads Editor has really opened up a world of possibilities for me as an advertiser. Now, I’m enjoying enhanced creative flexibility and budget control, which are crucial in today’s fast-paced AI-driven advertising landscape.

Google has significantly expanded its capabilities in the Ads Editor, providing us with better tools to manage creativity, automation, and budget precision. This is particularly handy as AI-driven campaign types continuously evolve.

What’s new. With the 2.12 release, I’m excited to explore the updates across Performance Max, Demand Gen, and video campaigns. The focus here is on scaling creative assets and enhancing workflow efficiency.

Creative expansion. I’m now able to include up to 15 videos per asset group in Performance Max campaigns. This is a game-changer, allowing me to offer more variations for Google’s AI to test. Additionally, the introduction of 9:16 vertical images caters to the growing demand for mobile-first formats.

Campaign upgrades. Demand Gen campaigns have seen several exciting enhancements. New customer acquisition goals, brand guideline controls, and hotel feed integrations are just a few updates. The new minimum daily budget and streamlined campaign build flow are set to improve campaign stability and setup.

Video & AI control. I’m appreciating the updates to non-skippable video formats and real-time bid guidance. They offer greater control over performance, and with new text and brand guidelines, I can ensure my AI-generated assets stay true to my brand.

Budgeting shift. The new total campaign budget feature is ideal for setting fixed spends over defined periods, like promotions or seasonal bursts. It’s great to see Google automatically pacing the delivery, ensuring every dollar counts.

Workflow improvements. With improvements like account-level tracking templates, better visibility into Final URL expansion performance, and clearer campaign status filters, my campaign management has become much more efficient.

Why I care. These updates provide me with enhanced creative flexibility and control over AI-driven campaigns, particularly in Performance Max and Demand Gen. Features like increased video limits and total campaign budgets empower me to test more, scale faster, and manage spend efficiently.

Moreover, the improvements in workflows and brand safeguards make it easier for me to guide automation while ensuring consistency and performance across Google Ads.

Between the lines. This update is part of a broader trend where, as automation rises, Google provides more ways to guide AI instead of manually controlling every aspect.

The bottom line. Google Ads Editor 2.12 isn’t about one standout feature. It’s about incremental improvements across creative assets, automation, and control, helping me refine my approach to increasingly AI-driven campaigns.

Inspired by this post on Search Engine Land.

March 17, 2026
Discover Google’s New ‘Sponsored Shops’ That Transform Shopping Results
I’ve recently stumbled upon a fascinating test by Google in their Shopping results. They’re experimenting with something called “Sponsored Shops,” which could totally change how we see competition in Shopping ads.

These “Sponsored Shops” spotlight entire stores rather than just individual products, meaning brands might need to rethink their strategy to gain visibility.

Imagine seeing a block in Shopping results that brings together several products from a single retailer, complete with store name, product ratings, and brand presence. It’s like a mini-storefront right there in the search results!

Why this matters to me. If this change spreads, it means the competition won’t just be about single products anymore. As a brand, I might need to ensure that my entire product feed is strong and diverse to capture these new ad placements.

Besides, this format has the potential to redirect traffic flow from individual product pages to broader store pages. For someone managing campaigns, it could mean prioritizing brand presence over just targeting specific product bids.

The bigger picture. It looks like Google’s trying to move Shopping ads slightly higher up the sales funnel. With one placement, I can emphasize a wide range of offerings and bolster my store’s identity.

Why this is notable for us. This approach can significantly boost exposure per impression by allowing multiple products to be showcased together. It’s an excellent way for us to strengthen brand presence in search results.

As a user, I find it makes discovery a lot simpler. I can easily browse a variety of items from one retailer without leaving the results page.

Reading between the lines. If this new format catches on, it’ll likely reward those, like me, who have invested in stronger product feeds and have great seller ratings. Merchants that depend solely on individual product listings might find themselves at a disadvantage.

What I’m curious about. I wonder how different parts of the ad unit will perform in terms of clicks. Stephanie Pratt, a Marketing Operating Lead, even pointed out the potential for consumer confusion between clicking on brand names versus individual products.
- “It’ll be interesting to see the split of clicks on each part of the ad unit, and how much is on the brand name vs product and if that will confuse some consumers
The bottom line for us. If “Sponsored Shops” goes beyond its testing phase, Google Shopping might lean more towards store-level competition. This could mean a shift in strategy for me—from product-centric optimization to enhancing brand presence across the platform.

Where I first encountered this. This intriguing development was spotted by PPC Specialist Arpan Banerjee, who shared it on LinkedIn.

Inspired by this post on Search Engine Land.
March 16, 2026
Unlocking the Future: OpenAI’s New Ads Manager for ChatGPT

As someone who’s been following OpenAI’s journey, I’m excited to share that they’re laying the groundwork for ChatGPT’s advertising business. These early steps reveal that OpenAI has more work to do to measure up against major players like Google when it comes to performance and ROI.
What’s happening. OpenAI has started testing an Ads Manager dashboard with a select group of partners, confirmed by sources at ADWEEK. This tool, aimed at marketers, allows for real-time campaign launching, monitoring, and optimization, drawing parallels with the established digital advertising management platforms.
Why it matters to me. OpenAI is building a self-serve advertising ecosystem around ChatGPT with the Ads Manager, in preparation for AI assistants becoming a significant channel. As conversational search becomes more prevalent, I believe it’s crucial for marketers like us to consider visibility in AI-driven responses, expanding beyond traditional platforms like Google Search.
Getting in on this early means we could gain unique insights into performance, formats, and optimization strategies within this fresh advertising landscape.
How it works now. For now, early testers are receiving weekly CSV performance reports, which include metrics like impressions and clicks. It’s evident that the ads product is in its initial stages, and more advanced analytics and tools are likely as the program matures.
The challenge: Initial tests indicate click-through rates for ChatGPT ads are lagging behind those of Google Search, marking a significant hurdle for OpenAI as they strive to showcase the value of advertising within conversational AI.
The cost of entry. Reports suggest that some early advertisers are being asked to commit a minimum of $200,000 in spend, significantly raising the stakes for OpenAI to deliver demonstrable performance and ROI.
Between the lines. Building an effective ad ecosystem entails more than just ad inventory. As marketers, we expect comprehensive reporting, optimization tools, and reliable performance — areas where established platforms like Google have a considerable head start.
The bottom line. OpenAI is laying the foundation for a revolutionary advertising platform within ChatGPT. The challenge is whether they can persuade brands to reallocate budgets by proving that conversational ads can compete with traditional search results.

Inspired by this post on Search Engine Land.

March 16, 2026
LinkedIn’s LLM-Powered Algorithm: Transforming Your Feed Experience
When I think about how often I scroll through LinkedIn, I’m excited to share that the platform is launching a cutting-edge AI-powered feed ranking system. It’s designed to analyze what we post, read, and engage with, thanks to large language models and advanced GPUs. This innovation aims to provide more personalized content updates for its vast user base of 1.3 billion.
Why this matters to me. Understanding LinkedIn’s content surfacing process can be a game-changer for anyone wanting their posts—or their brand’s—to gain visibility. The focus is on what’s relevant and engaging within our network. As LinkedIn Tweaked their system, posts that show expertise and contribute to trending professional topics have a better chance to go viral, regardless of our existing connections.
What’s under the hood. LinkedIn has revamped its feed recommendation mechanism using large language models and sophisticated transformer models, all powered by GPU infrastructure. The overhaul targets two key functions: the retrieval and ranking of relevant posts in our feeds.
Unified retrieval system. One of the most intriguing aspects for me is how LinkedIn has consolidated its discovery processes into a single model powered by LLMs (large language models). Previously, posts could come from various sources such as network activity and trending topics. Now, LinkedIn uses LLM-generated embeddings to interpret post content and align it with our professional interests.
For instance, by engaging with posts about small modular reactors, I might see content linked to renewable energy or other related fields, even if they use different terminology.
Ranked by your interests. Once posts are retrieved, LinkedIn ranks them utilizing a transformer-based sequential model. Instead of looking at posts individually, the model examines patterns in my past interactions, including likes, comments, and the time I spent viewing content.
This helps LinkedIn adapt to my evolving professional interests and recommend content that aligns with these shifts.
System performance and architecture. Powered by a GPU infrastructure that processes millions of posts, this system keeps our feeds fresh.
LinkedIn reports that this system can refresh content embeddings in mere minutes and retrieve suitable candidates in under 50 milliseconds.
Enhancing feed quality and authenticity. LinkedIn has also announced updates aimed at boosting content quality:
- Addressing automated engagement. They’ve started cracking down on tools that automate comments or use engagement pods to fake discussions. LinkedIn clarifies these violate platform policies and devalue genuine interactions.
- Cutting down on engagement bait and generic content. The platform will deprioritize content designed solely to provoke comments or clicks—such as posts begging for comments to inflate reach, irrelevant video-text pairings, and regurgitated thought-leadership content.
- Helping newcomers customize their feeds faster. New users can now utilize the “Interest Picker” during signup to select topics of interest, whether it be leadership, career growth, or job-seeking skills, ensuring relevance from day one.
Inspired by this post on Search Engine Land.
March 16, 2026
Mastering Google Ads: Avoid Costly Pitfalls & Optimize Performance

I recently had an enlightening chat with Chloe Varnfield, a seasoned digital marketer from Atelier Studios with nearly eight years of PPC experience. She shared invaluable insights on avoiding hidden Google Ads settings, steering clear of Friday mishaps, and the dangers of following Google rep advice blindly. These hard-learned lessons resonated with me deeply.

One of Chloe’s early eye-openers involved Google’s elusive account-level automated assets setting. It’s tucked away so deeply that I didn’t even realize it existed until I got an unexpected client message questioning a bizarre headline in their ad. It turns out Google had generated it automatically. This experience taught me the importance of auditing account-level settings and being proactive about Google updates.

Another lesson Chloe swears by is to never implement significant changes on a Friday. Once, she adjusted a campaign’s geographic targeting mid-conversation, only to accidentally exclude the UK. Recovery took three bewildering days. The rule I learned? Avoid major changes on a Friday and promptly audit your campaigns when things go awry.

Chloe’s most costly mistake unfolded when she followed a Google rep’s suggestion to switch bid strategies. What seemed like solid advice plummeted her campaign’s performance. It was a stark reminder of the high stakes involved in altering bid strategies, especially for businesses not hitting conversion volume thresholds. Patience and trusting my judgment emerged as crucial takeaways.

While auditing inherited accounts, Chloe often finds recurring issues like broken conversion tracking and brand-broad match campaigns—challenges that skew performance data and waste precious budget. These insights made me acutely aware of consistently vigilant account management.

Transparency in client relationships plays a pivotal role in Chloe’s success. Honest communication—explaining issues, solutions, and next steps—has shielded her from losing client trust. Her advice? Stay calm, be kind to yourself, and remember every problem offers a chance for growth.

Lastly, Chloe emphatically warns against over-relying on AI for generating ad copy without thorough review. AI should be a tool to enhance speed, not replace meaningful human oversight. It reinforced my commitment to always infuse my unique voice and critical review into AI outputs.

Inspired by this post on Search Engine Land.

March 13, 2026
SerpApi Challenges Reddit’s Allegations in Court Showdown

In a bold move, I’m witnessing firsthand how SerpApi is requesting a federal court to dismiss Reddit’s lawsuit. This legal battle centers around the alleged scraping of Reddit content from Google Search. From my perspective, SerpApi argues that Reddit is using copyright law to exert control over user posts and public search results.

Reddit’s initial complaint was amended in February, but I noticed that SerpApi remains firm. They argue that Reddit has not adequately demonstrated copyright ownership, technical circumvention, or tangible harm resulting from these actions.

SerpApi’s argument. From a blog post by SerpApi CEO Julien Khaleghy, I gather that the lawsuit is flawed for several reasons:

Reddit, interestingly enough, does not own the majority of the content in question, as user agreements clearly state that content ownership resides with the users themselves. It’s fascinating to see that Reddit only has a non-exclusive license to these posts.

The snippets Reddit presented, including dates and short fragments, don’t appear to be copyrightable at all from what I’ve read in the claims.

SerpApi’s stance is that they accessed Google Search pages, not directly interfacing with Reddit’s platform, which I believe weakens Reddit’s argument substantially.

DMCA concerns. In what I find a compelling argument, Khaleghy asserts that Reddit’s claim of a Digital Millennium Copyright Act (DMCA) violation lacks merit. SerpApi contends that their actions parallel what any user might see when conducting a Google search. Khaleghy strongly points out that:

There’s no evidence of encryption breaches or authentication bypass by SerpApi.

Accessing publicly available web pages doesn’t constitute “circumvention” under existing DMCA guidelines.

Reddit seems to be attempting to enforce copyright claims over content that doesn’t belong to them, which is an intriguing angle to this case.

Moreover, Reddit’s privacy policy acknowledges that public posts may surface in search results, supporting SerpApi’s use of the data.

Backstory. It’s clear to me that legal conflicts surrounding search scraping and AI data have gained high stakes lately:

Oct. 22: I came across information about Reddit filing lawsuits against SerpApi, Perplexity, Oxylabs, and AWMProxy, claiming they scraped large amounts of Reddit content through Google Search, referring to a decoy post created solely for Google’s crawler.

Oct. 29: SerpApi’s response, branding Reddit’s allegations as inflammatory, was a critical move, showcasing their resolve to defend access to public search data.

Dec. 19: Further intensifying the narrative, Google filed a lawsuit against SerpApi, accusing them of bypassing bot protections to scrape licensed search functionalities.

Feb. 23: SerpApi retaliated by requesting the court to dismiss the lawsuit filed by Google, arguing that Google is inappropriately leveraging the DMCA to limit access to public search results.

Importance. This case captivates me as it explores whether companies can legally extract information from Google’s search results without infringing on copyright laws or the DMCA, potentially impacting SEO tools and AI data training significantly.

Looking forward. I eagerly await the court’s decision on whether Reddit’s amended complaint holds up. A dismissal with prejudice would put an end to Reddit’s claims against SerpApi in this instance, which could send ripples through the industry.

SerpApi’s blog post. Check out Reddit’s Lawsuit is a Dangerous Attempt to Expand Platform Power for more on SerpApi’s perspective.

Inspired by this post on Search Engine Land.

March 13, 2026
Why Most ChatGPT Sources Aren’t Cited: Key Findings Revealed

When I think about how ChatGPT retrieves information, I find it fascinating that most sources it pulls in don’t make it to the final answers. According to a report by AirOps, a whopping 85% of the sources identified by ChatGPT never appear in its final response.

Why this matters to me. If I’m aiming to have my content mentioned in AI-generated answers, it’s clear that simply being discovered by the AI isn’t sufficient. Most pages that get retrieved ultimately don’t get the exposure I’m hoping for.

Key insight. It’s interesting to note that just because a page ranks and is retrieved doesn’t mean it gets cited. My content has to align closely with the prompt or the context it supports to be chosen.

Per the report: the focus shifts to how well I can optimize my content for selection in the AI synthesis process, beyond just showing up in the search results.

By the numbers:

82,108 citations appeared in final responses, but only 15% of the retrieved pages were mentioned. That means 85% of the pages that surfaced during research didn’t make it into the answers.

Citation rates also varied based on query type:

18.3% for product discovery queries, 16.9% for how-to queries, and 11.3% for validation searches.

Fan-out queries. I noticed that when ChatGPT generates an answer, it often triggers additional internal searches, resulting in a “second citation surface.” This stood out in the dataset findings:

89.6% of prompts prompted two or more follow-up searches. Fan-out searches expanded 15,000 prompts into 43,233 queries. Interestingly, 32.9% of the cited pages were results from these fan-outs and not the original prompt.

95% of fan-out queries had zero traditional search volume.

Google ranking correlation. I’ve learned that high rankings in Google significantly improve chances of citation:

55.8% of cited pages ranked within Google’s top 20. Pages in Position 1 were cited 3.5 times more often than those outside the top 20.

About the data. AirOps examined 548,534 pages from 15,000 prompts to understand how ChatGPT expands queries and selects which citations to include.

The study. For those interested in diving deeper, check out The Influence of Retrieval, Fan-out, and Google SERPs on ChatGPT Citations.

Inspired by this post on Search Engine Land.

March 13, 2026
Master Google Ads Audits: Navigate the Changes in 2026

I recently tuned into an episode of Google’s Ads Decoded podcast where Brandon Ervin, Director of Product Management for Google Search Ads, shared insights on campaign consolidation, AI Max, and the future of advertiser control as we approach 2026. It was enlightening to hear a product team so in tune with advertiser concerns.

However, I felt the podcast left some gaps. There’s a significant disconnect between Google’s narrative and what advertisers truly experience on the ground. While Ervin’s team is making strides, the fast-evolving platform presents new challenges, shifting performance measurement onto economic standards. This change fundamentally alters how we should approach search ad audits.

As I reflect on recent improvements, it’s clear that enhancements like brand exclusions in Performance Max and Demand Gen, exclusion of site visitors in PMax campaigns, and improved search term visibility are crucial. These are responses to issues caused by bundling and aggressive automation. It’s worth noting that these controls arrived after advertisers were already knee-deep in implementation.

In an era where Google’s product team pushes for advancement, it’s vital for us to audit whether these new tools genuinely expand control or simply restore baseline transparency lost with earlier automation efforts.

In building the foundation for a 2026 search audit, we need to start with the basics, ensuring full ad extensions, strategic automated bidding, and maintaining negative keyword lists, among others. These are undeniable essentials that set the stage for deeper audits.

Focusing on the intricacies of signal architecture, I realize that while traditional controls like exact match and manual bids gave us direct oversight, the new controls shift focus to data quality, density, and selectivity. These influence the algorithm, which ultimately makes the decisions.

An effective audit in this context addresses three core aspects: the quality of the data imported, the density of high-quality data available for modeling, and the selectivity of the data shared with Google. These elements are pivotal in shaping campaign success.

Being mindful of incrementality is another key consideration. Google optimizes towards reported conversions, often encompassing brand search and retargeting signals that may not truly reflect incremental gains.

It’s critical to analyze marginal returns as Google’s system operates on a blended cost-per-action model. Without understanding the incremental cost at each spend tier, advertisers risk overspending without realizing diminishing returns.

Furthermore, as Ervin acknowledged, AI-driven campaigns sometimes misalign with intended targets. Query mapping has deteriorated over time, and AI Max exacerbates irrelevant matches, underlining the need to rigorously classify queries by intent to maintain high-value engagements.

Lastly, the economics of network performance in bundled campaigns like Performance Max and Demand Gen need thorough examination as they obscure valuable insight into actual network-driven outcomes.

By focusing on value redistribution through audits, we can ensure that the surplus value generated by high-intent searches isn’t misallocated into Google’s weaker inventory, thereby optimizing ad spend efficiency and accountability.

Inspired by this post on Search Engine Land.

March 13, 2026
Google Hints at Ads in Gemini: A Shift in Strategy
I recently came across some interesting news about Google and its potential plans to incorporate ads into its Gemini AI app. A senior executive at the company shared with WIRED that ads in Gemini are not out of the question — a stark contrast to previous denials just a few months ago.

What’s changed: Back in January, Google DeepMind CEO Demis Hassabis assured reporters at Davos that there were no plans to introduce ads in Gemini. However, now Google’s SVP Nick Fox has hinted otherwise, mentioning that insights gained from ads in AI Mode could eventually be applied to Gemini.

The current strategy. Instead of rushing into ads within Gemini, Google is leveraging AI Mode — a search product powered by Gemini — as a testing ground for advertising formats in AI settings.

Here’s how they’re currently managing it:
- Ads are distinct from organic results and clearly labeled.
- Only relevant ads are displayed — if there’s nothing that fits, no ads are shown.
- Google’s extensive experience in search ads informs this approach.
Why we care. Advertising is at the core of Google’s business model. How they introduce ads into AI products like Gemini will have a significant impact on the industry and influence how AI companies monetize their free services. Brands that can position themselves effectively within these conversational AI platforms now will gain a crucial advantage.

The bigger picture. Google, with its strong financial backing, is in a comfortable position to proceed at a steady pace, having surpassed $400 billion in revenue in 2025. In contrast, OpenAI is under pressure to more than double its $30 billion revenue target this year and has already begun testing ads in ChatGPT’s free tier.

Between the lines: Fox’s remarks are strategically cautious but enlightening. By framing Gemini ads as a “prioritization question” rather than a debate of values, Google hints that it’s more about when the ads will appear, not if.

What to watch: There’s an intriguing aspect of Gemini called Personal Intelligence, which extracts data from a user’s Gmail, Photos, and Calendar. Fox considers personalization to be critical for search, and it may eventually integrate into the broader Search experience. If that occurs, advertisers could tap into a new realm of contextual targeting, though user data will strictly remain unsold and unshared.

What’s next. Advertisers should start preparing now. As Google fine-tunes AI ad formats in AI Mode, these insights will make their way to Gemini. Brands that master the art of being relevant in context-driven, conversational AI environments will be well ahead when the opportunity for advertising in Gemini fully materializes.

Dig deeper. For a more detailed exploration of Google’s advertising strategy in Gemini, check out the full article on WIRED.

Inspired by this post on Search Engine Land.
March 12, 2026

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