AMD confirms the MSRP of its Ryzen 9 9950X3D2 Dual Edition processor AMD’s David McAfee has unveiled the official MSRP for its Ryzen 9 9950X3D2 Dual Edition CPU, setting it at $899.99 in the US. This makes the Ryzen 9 9950X3D2 Dual Edition AMD’s most expensive AM5 CPU to date. This price is $200 above […]
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Noir Prompt is a prompt manager for people who use AI generation tools like Midjourney, DALL-E, ChatGPT, Runway, and more. Save your prompts, tag them, organize by type, and find them instantly. No more digging through notes apps or Discord threads wondering what you typed weeks ago.
Every edit is saved automatically so you can roll back to any previous version. Build reusable templates with variable placeholders and swap out subject, style, or mood on the fly. Free to start, it works for image, video, and text prompts, all in one place.
Celavii helps brands and agencies find creators, manage outreach, and run campaigns from one place. It maps creator networks as a graph, showing audience overlap, bridge creators, and where your budget reaches new people.
Instead of clicking through filters, you ask questions in plain English and AI agents handle discovery, CRM, campaign tracking, and video generation. It works from your dashboard, WhatsApp, Slack, or Discord and starts at $49/month with no annual contracts. It was built because other tools required $2,000+/month and a yearly commitment just to search a database.
BayPoint AI is an all-in-one platform for eBay sellers who want to run their business smarter. The flagship Preflight app analyzes qualitative aspects of your listings — title strength, description quality, photo guidance, and keyword relevance — and gives you AI-generated improvements before you publish. Supporting apps cover shipment tracking, buyer feedback management, sales analytics, and marketplace intelligence, all working together from a single dashboard.
Our AI assistant, Riley, is available in every app. Riley analyzes your actual listing and sales data. Morgan answers eBay strategy questions in real time. No more guessing — just a clear picture of what to fix and why.
If you shelved your inbound strategy this past year, you can shelve your Inbound conference mugs and swag with it.
HubSpot renamed its annual Inbound conference in Boston this September to Unbound. A note on the event site explains the thinking:
Inbound is outbound. HubSpot pioneered inbound marketing, which uses content and search rankings to attract visitors, then convert them on-site.
Recent Google core updates appeared to hurt the HubSpot blog, possibly because its content drifted from core topics like CRM, sales, and marketing into broader business areas like interview tips.
Inbound strategy has declined as search shifts from platforms like Google to LLMs like ChatGPT, which drive fewer clicks to websites.
From inbound to loop marketing. In 2025, HubSpot introduced its Loop marketing strategy to replace inbound. Loop focuses educating consumers in an AI-driven world.
The conference rebrand acknowledges that no single framework works for you in today’s marketing landscape.
Two new improvements to Colab’s Gemini agent give you more control over how Google Colab works and how it helps you learn.Google Finance brings AI tools to 100+ countries. Use AI to research stocks and follow live earnings in your preferred language.AI bot activity surged 300% in 2025, with media and publishing among the most targeted sectors, according to a new Akamai report.
Why we care. AI bots are reshaping how content is discovered and consumed, shifting users from search clicks to instant answers in chat interfaces. Publishers are seeing fewer visits from organic search and often don’t get attribution in AI-generated answers. It’s also eroding ad and subscription models.
The threat is real. Publishers now face two threats:
The impact. Pageviews are declining, costs are rising (because scraping bots increase infrastructure costs by consuming server and CDN resources without generating revenue), and brand visibility is weakening.
What publishers are doing. Publishers are adopting nuanced controls (rather than blanket blocking AI bots), such as:
What they’re saying. According to Akamai’s report:
What’s next? A “pay-per-crawl” model is emerging. Tools like identity verification (Know Your Agent) and platforms like TollBit aim to authenticate bots and charge for access in real time.
About the data. The report analyzed Akamai bot management data from July to December 2025, covering application-layer traffic across websites, apps, and APIs.
The report. SOTI Security Insight Series: Navigating the AI Bot Era (registration required)
Google may be making local search ads more interactive, potentially changing how advertisers showcase multiple locations and capture nearby demand.
What’s happening. Google Ads appears to be testing a new format that displays multiple business locations in a swipeable carousel within search ads, allowing users to browse options directly in the ad unit.
How it works. Instead of listing locations separately, the new format groups them into a horizontal carousel with business details like ratings and proximity, enabling users to swipe through locations without leaving the search results page.
Zoom in. Early comparisons show a shift from static, stacked location assets to a more dynamic experience, where multiple listings are consolidated into a single, scrollable unit.
Why we care. Advertisers with multiple locations could gain more visibility within a single ad, while users get a quicker way to compare nearby options.
Between the lines. This format could increase engagement with location-based ads, but may also intensify competition within the carousel itself as businesses vie for attention.
What to watch. Whether the feature rolls out more broadly and how it impacts click-through rates and local ad performance.
First spotted. This update was spotted by Founder of Adsquire Anthony Higman who shared spotting this ad type on LinkedIn.
Google is consolidating its advertising and measurement resources into a single destination, aiming to make it easier for developers and technical marketers to build, automate and scale campaigns.
What’s happening. Google has introduced a new Advertising and Measurement Developers Hub, a centralized site designed to help users access tools, documentation and support across its ad ecosystem.
The hub brings together resources for products like the Google Ads API, Google Analytics and publisher tools such as AdMob and Google Ad Manager, all organized into categories including advertising, tagging and measurement.
How it works. The site offers a streamlined homepage with quick access to documentation, blog updates and community channels, along with dedicated sections to explore products, connect with support and engage with Google’s developer relations team.
Why we care. Google is making it easier to access and implement advanced tools that power automation, tracking and campaign optimization. This can help teams work more efficiently, especially those relying on APIs, tagging and data integrations. As advertising becomes more technical and AI-driven, having a centralized hub lowers the barrier to building more sophisticated, scalable setups.
The big picture. As advertising becomes more automated and API-driven, Google is investing in infrastructure that supports developers and technical users who manage complex integrations across platforms.
Zoom in. New features include a “meet the team” section, a centralized support page linking to Discord and GitHub resources, and a media hub featuring content like Ads DevCast.
What to watch. Whether this hub becomes the primary entry point for developers working across Google’s ad products — and how it evolves with new AI and measurement tools.
Bottom line. Google is simplifying access to its ad tech ecosystem, betting that better developer support will drive more innovation and adoption.
Dig deeper. Introducing the Google Advertising and Measurement Developers Hub!
Most agencies present prospective clients with an account audit as part of their sales process. The purpose is twofold:
But how often do brand marketers turn the tables and audit their agencies in their RFP?
I’m the head of performance marketing at a marketing agency, so I’m clearly writing from a biased perspective. However, over my decade-plus in the industry, I’ve seen too many brands settle for “good enough” because they didn’t know which questions would reveal the cracks in a potential partner’s strategy and approach.
If I were a brand looking for a true growth partner, here are the specific questions I’d ask to separate the top performers from the rest.
A lot of agencies claim to be “full service,” but rarely are they “full excellence.” I’d be looking for where an agency truly spends its time versus where they’re just trying to upsell me.
It’s less about the channels in question (although if, say, LinkedIn is a key growth driver for your brand, they’d better demonstrate proficiency there), and more about how their strengths align with your needs.
If an agency claims to be experts in SEO, creative strategy, and paid media, but 90% of their client base only uses them for paid search, that’s a red flag. You want a partner whose core competencies align with your primary needs.
If you need high-volume creative testing, you want an agency where 80%+ of clients use its creative production frameworks, not one that treats creative as an add-on service.
Dig deeper: Confessions of a PPC-only agency: Why we finally embraced SEO
I miss the days when knowledge of the manual controls at your disposal could set you apart as a high-performing marketer. But those days have been gone for a while.
In 2026, there’s a real danger of over-optimization with the controls we have left. This can reset algorithmic learnings and prevent them from fine-tuning in service of your goals. Agency teams that strike this balance most certainly have a healthier approach than those who either blindly trust algorithms or can’t help tinkering excessively.
One control you can and must be diligent about using is first-party data for enhanced conversions and offline conversion tracking. Part of the job of a great marketer is training the algorithms on which leads and which conversions to target, and first-party data is a huge lever to pull in that regard.
Don’t just ask for a sample report. Anyone can make a PDF look pretty. You need to understand their philosophy on data.
You’re looking for an agency that’s willing to move upstream. If the majority of their clients are measuring success on clicks, traffic, or even MQLs, run the other way.
A performance-driven agency should be obsessed with revenue, ROAS, and pipeline velocity. Ask them how they handle attribution. If they rely solely on in-platform metrics, which often over-claim credit, they aren’t looking at the full picture.
Dig deeper: What successful brand-agency partnerships look like in 2026
This is actually a pretty common question and has been for years. Too many marketers know the pain of integrating rotating sets of agency teams because the agency can’t hold onto top employees, and you should be evaluating the answer from this perspective.
There’s another factor to consider. Generally speaking, the more experienced a marketing team is, the more effectively it uses AI tools.
Whereas junior marketers might be more avid proponents of AI and quicker to adopt its functionality, they’re also far more likely to use it for things like creative ideation and strategy. Both are areas where high-quality human thought is a true differentiator.
For this answer specifically, remember that you have some great research tools like Glassdoor that you can and should access. Employee tenure is one thing, but a Glassdoor profile with a bunch of red flags is an indicator that the agency might struggle to keep the talent it really wants to retain.
Again, you’re looking for a balance here. Agency teams that don’t use AI at all are almost certainly burning resources on manual tasks, but agency teams that overuse it to replace perspective, critical thinking, and creativity are commoditizing their own client service.
Two follow-up questions to ask:
You’re looking for firm answers and redundant layers for each of these questions — at the very least, someone relatively senior should approve any output before it goes live.
Dig deeper: Why PPC teams are becoming data teams
This is the ultimate litmus test for technical proficiency. A great performance marketer knows where the ad platforms hide the waste buttons. If I were a brand marketer, I’d want to hear about:
If an agency can’t rattle off these specific checks, they’re likely missing the “low-hanging fruit” of budget efficiency. Fixing some of these takes seconds, but missing them costs thousands.
Remember: when you’re choosing an agency partner, it’s the job of each agency to sound as good as they possibly can, but what an agency considers to be a great answer might not be a great fit for your brand.
By focusing on utilization rates of services, strategic application of AI, and approaches to budget efficiency, you’ll find a partner capable of driving actual performance, not just spending your budget.
Dig deeper: How to find your next PPC agency: 12 top tips
Overclockers UK unveils pricing for AMD’s Ryzen 9 9950X3D2 Dual Edition CPU Overclockers UK has unveiled the pricing of AMD’s Ryzen 9 9950X3D2 Dual Edition CPU, which AMD unveiled last month. This CPU is AMD’s new AM5 flagship, offering 16 cores and 192 MB of total L3 cache. This is the first consumer-grade X3D CPU […]
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ogsm.io helps small businesses create a clear, one-page OGSM strategy in about 10 minutes. It guides you through a brief conversation and turns your answers into objectives, goals, strategies, and measures you can download as a PDF or share online, so you can act with focus and track what matters.
ChoreChomp is an AI-powered chore coach for families. Parents assign custom chores with reference photos, kids snap a photo when they're done, and the AI checks the work and gives age-appropriate feedback. Parents approve final scores, award points, and set reward goals to keep motivation high. The app also has a homework helper that guides with Socratic hints without giving answers. It protects privacy with no child accounts, strict guardrails, person detection, and short-lived photos, and one subscription covers unlimited kids.
Maskerade.ai lets you deploy unlimited, highly accurate AI personas to browse the web and navigate your site. Get deep, actionable insights into the thoughts and feelings of your hardest-to-reach audiences.
Analytics tools tell you what happened on your site, but not why. Customer research tells you what a group of people thinks and why, but it's slow, labor intensive, and costly. With Maskerade, you can combine the best of both worlds.
Google is laying the groundwork for “agentic commerce,” where users can complete purchases directly inside AI-driven search experiences.
What’s happening. Google has published a new onboarding guide for its Universal Commerce Protocol (UCP) in Merchant Center, outlining how merchants can integrate with the system and enable checkout directly from product listings in AI Mode and Gemini.
The big picture. As AI search evolves from discovery to transaction, Google is pushing to keep users within its ecosystem by embedding shopping and checkout into conversational experiences.
How it works. Merchants must first complete a technical integration, then submit an interest form and wait for approval before gaining access to onboarding tools in Google Merchant Center, including a sandbox environment to test integration, identity linking and checkout APIs.
Why we care. Google is moving search closer to transaction, meaning users may complete purchases directly inside AI experiences instead of visiting your website. This shifts where conversions happen and could change how performance is measured, attributed and optimized. Early adopters of the Universal Commerce Protocol may gain a competitive advantage as shopping becomes more integrated into tools like Gemini.
Zoom in. The protocol acts as an open standard for connecting product data, user identity and payment flows, enabling seamless purchases without redirecting users to external sites.
What to watch: The rollout is gradual and currently limited to the U.S., with a dedicated UCP integration tab expected to appear in Merchant Center accounts over the coming months.
Bottom line. If widely adopted, the Universal Commerce Protocol could redefine how online shopping works — turning search into a full-funnel, AI-powered checkout experience.
Dig deeper. How to onboard to the Universal Commerce Protocol in Merchant Center
Meta Platforms is making it easier for advertisers to implement tracking, reducing technical friction for teams running campaigns across platforms.
What’s happening. Meta released an official Pixel template inside Google Tag Manager, replacing the need for third-party or community-built workarounds.
How it works. The new template allows advertisers to reuse their existing GA4 dataLayer, meaning events already configured for Google Analytics 4 can be leveraged without rebuilding tracking from scratch. It also automatically maps enhanced e-commerce events such as purchases, add-to-cart actions, content views and checkout initiations, eliminating the need for duplicate tagging.
Why we care. This reduces implementation time, lowers the risk of tracking errors and ensures consistency across platforms, especially for advertisers managing both Google and Meta campaigns.
What to watch. Whether this leads to broader adoption of Meta Pixel tracking among advertisers who previously avoided complex setups, and if similar cross-platform integrations follow.
Bottom line. Meta is removing one of the biggest headaches in ad tracking — making it faster and easier to get reliable data across platforms.
First seen. This update was spotted by Paid Media expert Thomas Eccel who shared spotting the update on LinkedIn.
Ask most ecommerce brands who owns their product feed, and the answer is almost always the same: the paid media team.
Maybe a feed management tool sits under PPC. Maybe the shopping team built the feed years ago, and nobody’s touched the titles since. Either way, SEO rarely has a seat at the table, and it’s often forgotten as part of the broader feed management strategy.
Whether you’re worried about AI search or traditional clicks, you’re missing out on opportunities by excluding SEO from your feed management strategy.
Up to 83% of ChatGPT carousel products match Google Shopping’s organic results, according to a recent Peec AI study analyzing more than 43,000 listings. And 60% of those matches came from Shopping positions 1-10.
On Google’s side, the Shopping Graph now contains more than 50 billion product listings and feeds directly into AI Overviews, AI Mode, and Gemini. AI Overviews appear in roughly 14% of shopping queries, up from about 2% in late 2024. Like many other things we’ve discovered about AI search, the generative results are informed by traditional SERP.
SEO needs to be the strategic quarterback for brand authority. This is a highly valuable opportunity to work cross-channel toward a common goal of improving visibility across search surfaces. It really requires SEOs, commerce, and paid media teams to get in the same room.
The SEO toolkit you know, plus the AI visibility data you need.
Typically, brands run a single product feed optimized for Google paid shopping campaigns. Titles are written for bid relevance, descriptions are built for Quality Score, and the feed exists to win auctions, with less consideration for user search behaviors.
As user behavior shifts, search surfaces favor stronger semantic alignment between queries and product data. A title stuffed with paid-friendly modifiers or branded terms isn’t the same as a title that mirrors how someone conversationally searches for a product.
We tested this with a large ecommerce brand. Our agency’s AI SEO team partnered with the commerce team to launch a dedicated product feed for free organic listings, with titles and descriptions optimized specifically for organic visibility, rather than replicating what was already running in the paid feed.
After the organic feed was pushed live:
Rather than replacing our paid feed strategy, we recognized that organic and paid shopping solve different problems and have different needs that require optimizing accordingly.
Organic feed titles should reflect how your customers actually search, not how your bidding strategy is structured.
Dig deeper: How AI-driven shopping discovery changes product page optimization
Not every feed attribute carries equal weight. If you’re building a dedicated organic feed or just auditing your existing feed for gaps, here’s where you could start.
Google’s algorithm heavily favors feed titles when matching products to queries, and its own documentation emphasizes including important attributes to “better match search queries and drive performance lift.” Consider how a customer might describe what they’re looking for in a conversational way, and how that aligns with product attributes.
Google’s GTIN documentation makes clear that products with correct GTINs receive significantly more visibility. Industry data has consistently shown that properly matched products can drive up to 40% more clicks. They’re also the primary signal for aggregating product reviews across sources.
They’re still the most common source of Merchant Center disapprovals. Products with both standard and lifestyle images typically see significantly higher engagement.
If budget or bandwidth has kept better product images on the back burner, Google’s Product Studio can help handle some of the editing, so you can test and improve creative at scale without a full reshoot. It’s also a way for SEO and creative teams to collaborate on feed-specific assets and testing.
Loginproduct_highlight and product_detail product_highlight lets you add scannable benefit statements that appear in expanded Shopping views. For instance, “water-resistant for light rain commutes” is doing more work than “high-quality material” for both the shopper and the AI. product_detail provides structured specifications that power Google’s faceted filters in organic product grids.The same semantic work SEOs are doing to optimize product detail pages (PDPs) for conversational search — like defining ideal buyers, naming use cases, and articulating compatibility — should inform feed attributes.
Product and content teams already understand what drives someone to buy. That context should be in the feed, not just on a brand’s PDPs.
Dig deeper: How to make ecommerce product pages work in an AI-first world
Here’s what makes this investment compound: the feed optimization work done today for organic shopping visibility will also help build brand readiness for agentic commerce standards and applications.
Google’s Universal Commerce Protocol, announced in January, is a framework that enables AI agents to discover products, build carts, and complete transactions directly inside AI Mode and Gemini. The shopper may never land on the brand website to make a purchase. UCP isn’t a replacement for Google Merchant Center, because it’s built directly on top of GMC data.
Feeds are how products enter the Shopping Graph. The Shopping Graph is the dataset AI agents query when processing a shopping request. The new native_commerce attribute added to feeds is what signals that a product is eligible for the UCP-powered “Buy” button in traditional and AI-driven Google services.
Google has also announced the eventual rollout of several new Merchant Center attributes designed specifically for conversational commerce:
These are additions to an existing GMC feed that give AI agents the contextual understanding they need to match products to natural-language queries like “what’s a good waterproof jacket for bike commuting?” These new conversational attributes are rolling out to a small group of retailers first.
This is where feed data and on-page content need to stay tightly aligned. Search surfaces cross-reference a brand’s feed against:
When those layers contradict each other, trust erodes at the domain level.
Dig deeper: 7 organic content investments that drive ecommerce ROI
Product feed strategy and optimization is an opportunity for genuine cross-team collaboration to test, execute, and measure visibility. A holistic approach to managing product details across every surface will benefit brands in both traditional and AI-driven search.
These teams must work together to coordinate their insights and effectively establish an AI SEO operating system. The product feed sits at that intersection as it’s an owned asset managed by commerce infrastructure that directly feeds AI-powered visibility.
The first step is to pull a current feed and compare organic titles to paid titles. The second step is getting the right people in the room to build something better. SEO is most successful when more channels align toward the same goal: better brand visibility.
The March 2026 core update finished rolling out today after 12 days and 4 hours, completing Google’s first broad ranking update of the year.
What happened. Google confirmed the rollout ended at 06:12 PDT, per its Search Status Dashboard. The update began March 27 and impacted search rankings globally.
The timeline. Google originally estimated the March 2026 core update would take up to two weeks to complete.
The context. This was the first core update of 2026. It followed the March 2026 spam update and the February 2026 Discover update.
What to do if you were impacted. Google didn’t issue any new guidance for the March 2026 core update. Its standing advice remains:
Google continues to point site owners to its core update and helpful content guidance.
Why we care. Now that the rollout is complete, you can assess impact with more confidence. Analyze ranking and traffic changes, identify winners and losers, and adjust your content strategy based on what the update appears to reward.
Previous core updates. Here’s a timeline and our coverage of recent core updates:
Apple’s next-gen MacBook Neo should feature up to 12GB of memory and an A19 Pro silicon upgrade According to MacRumours, citing a report from Tim Culpan, Apple plans to release a new MacBook Neo in 2027. This new model will reportedly feature Apple’s new A19 Pro processor, the same chip as the Apple iPhone 17 […]
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Google's March core update finished rolling out. Here's what to know about the rollout and when to check your data.
The post Google Confirms March 2026 Core Update Is Complete appeared first on Search Engine Journal.
How retailers can adapt to the rapidly evolving advertising landscape, on the Ads Decoded Podcast.Hreflang has long been a core mechanism in international SEO, directing users to the right regional version of a page. That approach worked when search engines primarily returned static results.
AI-driven synthesis changes that. Instead of returning lists of links, AI systems construct answers. They don’t need, nor want, your perfectly implemented hreflang tags. They aren’t looking for instructions on which page to serve. They’re trying to determine which answer is best supported across sources.
Your content has to hold up when the model compares it against everything it’s seen, regardless of language or origin. If it doesn’t, it won’t be used.
We need to address a fundamental misunderstanding of the hreflang attribute. Hreflang has always been a switcher, not a booster.
If your brand lacked organic authority in Australia before implementing the tag, adding the en-au attribute wouldn’t magically improve your rankings in Sydney. Its only function was to ensure that if you did rank, the user saw the correct regional version.
In AI search, this “you vs. you” dynamic has become a liability. While traditional search still relies on these tags to organize traffic, AI models often bypass them during the synthesis phase. If a brand’s U.S.-based .com site possesses decades of authority, the AI’s internal logic may determine that the U.S. site is the true source of information.
Consequently, even when a user in Berlin searches in German, the AI may synthesize an answer based on the U.S. data and simply translate it on the fly, effectively ghosting the brand’s localized German site despite perfectly implemented hreflang tags.
AI models don’t just answer the query you see. They expand it into dozens of hidden checks, comparing sources, validating claims, and pulling in information across languages to see what aligns.
ChatGPT often translates and evaluates queries in English even when the user searches in another language, research from Peec AI shows. This reinforces how query fan-out operates across markets. If your local entity doesn’t hold up in that broader comparison, it doesn’t get used.
A second issue happens before retrieval even begins. During training, LLMs compress what they see so it can be stored and reused at scale.
When multiple regional pages look too similar, they don’t stay separate. They’re folded into a single representation, also known as canonical tokenization.
Local details — phone numbers, office locations, and market-specific references — don’t always survive that process. They’re treated as minor variations rather than meaningful signals.
By the time the model is asked a question, your local site is often no longer competing. In many cases, it’s already been absorbed into the global one.
Dig deeper: What the ‘Global Spanish’ problem means for AI search visibility
To compete globally, expand your strategy to include signals that resonate with AI’s data supply chain.
Meta tags tell systems what you intend. Infrastructure often tells them what to believe. Datasets like Common Crawl use geographic heuristics, IP location, and domain structure to make sense of content at scale. That happens early in the process, before anything resembling ranking.
This means your content may already be placed in a market before the model ever evaluates it. If your regional domains aren’t supported by local infrastructure or delivery, you’re sending mixed signals. Those are hard to recover from later.
To break the semantic gravity that leads to entity compression, you need what I would call a clear “knowledge delta.” Most global teams fail here because they think localization means translation. It doesn’t.
There’s no universally accepted magic number for unique content. From a semantic vector perspective, I speculate that a divergence threshold of at least 20% of the content on a local page must be unique to prevent the model from collapsing your local identity into your global one.
To address this, front-load market-specific data, such as regional shipping logistics, local tax identifiers, and native case studies, into the first 30% of your page. This lets you provide the mathematical proof the model needs to cite your local URL as a distinct authority.
AI models interpret market relevance by looking at the company you keep in the text. Incorporate geographic anchoring by referencing local neighborhoods, regional landmarks, or specific transit hubs (e.g., “located near the Alexanderplatz station” in Berlin).
These co-occurrence signals pull your brand’s vector embedding toward the specific local coordinate in the model’s training data, creating a geographic fence that helps the AI disambiguate your local office from your global headquarters.
Dig deeper: How to craft an international SEO approach that balances tech, translation and trust
The origin of your links is a primary signal of market authority. During the fan-out phase, AI models look for regional consensus.
This is one of the areas where traditional link building logic starts to break. It’s not just about getting links. Consider where those links originate, along with their authority and contextual relevance.
If your Australian page has backlinks primarily from U.S.-based websites, the model has little evidence that you actually belong in or are relevant to the Australian market. Local sources, including high local trust and location-specific news outlets, change that. Without them, you’re often treated more like a visitor than a participant.
LLMs pick up on regional language nuances far more than most teams expect. This is where simple translation starts to break down. Unique market- or colloquial-specific terms, formatting, and even small legal references signal whether something actually belongs in a market.
Use the terms people in that market actually use — things like “incl. GST,” local identifiers like ABN, and even spelling differences. Without these signals, the page may be technically and linguistically correct, but it won’t register as truly local.
As mentioned, LLMs often generate multiple incremental queries during their research phase. These invisible queries may focus on local friction points, such as “How does this product comply with [name of local regulation]?”
By incorporating local FAQ clusters that address these nuances, you ensure your local URL survives the fan-out check, making your global .com too generic to be cited in a localized answer.
Dig deeper: Why AI optimization is just long-tail SEO done right
Expand your SEO reporting beyond traditional rank tracking. Incorporate AI citation audits by using a local VPN to query the most popular generative engines in your target markets.
If the AI consistently pulls from your global .com domain for a local query, it’s a clear signal that your local domain lacks the necessary evidence chain. Identify where this market drift is occurring and reinforce those specific pages with more unique local data and infrastructure signals.
Hreflang and traditional technical signals still shape how search engines organize and deliver content, but they don’t determine what AI systems use.
AI models evaluate which sources to use based on evidence of local relevance. Without a distinct presence in each market, they default to the version of your brand they trust most, which often isn’t the one you intended.
Translation alone doesn’t establish that presence. Your content needs to demonstrate that it belongs in the market it’s meant to serve.
Dig deeper: Multilingual and international SEO: 5 mistakes to watch out for
You’re facing a major shift as familiar manual targeting levers disappear in favor of AI-driven discovery. Platforms’ automated tools are collapsing campaign types, obscuring data, and replacing manual targeting with intent-based algorithms.
This is a shift from selection to prediction. You won’t adapt by holding onto old controls — you’ll adapt by learning to engineer the inputs that replace them. Here’s how to make sure you have the tools to stay on top.
You previously relied on granular keyword lists, demographic filters, and custom exclusions to target ideal customers. You told platforms exactly who to target and paid to access that inventory.
Now, platforms have eliminated those controls:
Targeting didn’t disappear — it moved inside the platform’s black box. The algorithm now targets based on data within its own ecosystem.
Platforms are clear: manual segmentation is gone, and automation is here to stay.
If targeting is now internal to the algorithm, your role changes. It’s less about selecting your audience and more about engineering it.
The distinction is critical. Traditional targeting focused on selecting audiences. Audience engineering focuses on instructing the algorithm through high-quality conversion signals, precise creative, and first-party data. It teaches AI systems who to find and what to optimize for.
Here’s how this changes your workflow:
In the past, to target CFOs, you might use job title filters and negative keyword lists. With audience engineering, you instead upload high-quality data (e.g., “deal closed” signals) to define a high-value prospect. You also tailor creative to CFO-specific pain points, teaching the AI to reach people who engage with that message.
If you fight the algorithm and resist this shift, you’ll struggle. If you embrace it, you’ll succeed by optimizing conversion signals, refining creative, and strengthening your data infrastructure.
As manual levers disappear, the gap between strong and average performance comes down to signal quality. Audience engineering is what closes that gap.
You must optimize three critical inputs the AI uses to segment for you:
Tell the algorithm what matters. If you optimize for cheap, top-of-funnel leads, it will get efficient at finding people who fill out forms but never buy — that’s not what you want.
Focus on meaningful business outcomes, not top-of-funnel metrics. Integrate Offline Conversion Imports (OCI) and Conversions API (CAPI) to feed data on final sales, not just initial clicks. With value-based bidding, you teach the algorithm to prioritize users who drive revenue — effectively targeting high-value customers without using demographic checkboxes.
In a world without demographic filters, your creative becomes your primary targeting mechanism. The specificity of your message does the filtering.
If your creative speaks broadly, the AI shows it broadly. If it speaks to a niche pain point, the AI finds users who resonate with that pain point.
Build ad sets around motivations, not product categories.
Your customer lists, CRM data, and engagement signals are the foundation the algorithm learns from.
This data replaces third-party signals and becomes a critical competitive advantage. You’re giving the algorithm a cheat sheet to identify your best customers.
The shift to AI-driven targeting isn’t theoretical. As an agency managing over $215 million in annual paid media spend, we’ve tested this across platforms and validated it with performance data. Here’s what we’ve learned:
A long-time client had a well-established view of its target audience based on years of campaign performance and customer data. Campaigns used manual age caps and layered targeting to protect efficiency.
When we transitioned those campaigns to Advantage+ Audiences, manual exclusions were removed, allowing the algorithm to optimize based purely on conversion signals and creative performance.
During testing, Meta identified and scaled into an older demographic that had previously received minimal budget. This segment delivered a 37% higher CTR than the campaign average and drove stronger downstream conversion performance.
As spend shifted into this audience, conversions came at a lower cost per result while total revenue increased. Broader targeting improved return on ad spend (ROAS) compared to the prior manual strategy.
This reflects a broader trend with Advantage+ Audiences. Paired with strong conversion goals, accurate data signals, and high-quality creative, it consistently identifies high-value segments that manual targeting restricts or misses.
For another client, we implemented a Microsoft PMax test, using advanced audience targeting and first-party data to reach high-intent prospects across Bing, Outlook, MSN, and the Microsoft Audience Network.
With in-platform placement insights, we monitored performance closely and reacted quickly early on. The campaign drove a 10% increase in conversion rate, a 14% decrease in cost per lead, and a 4x increase in form fills in the first month — followed by another 2x the next month.
This reinforced a key principle: automation performs best with strategic human oversight. While we fed strong audience signals and conversion data, performance drifted as the system expanded into less efficient placements. With Microsoft support and ongoing monitoring, we excluded underperforming placements and refined targeting without over-constraining the campaign.
By letting PMax handle scale and optimization — while maintaining disciplined oversight and guardrails — we preserved efficiency and improved overall performance.
Automated targeting is powerful, but not benevolent. It optimizes for the math you give it. Here are pitfalls to avoid.
This is the most important risk. Poorly defined conversion events, incomplete data pipelines, or low-quality first-party data limit performance and train the algorithm on the wrong outcomes.
If you feed it noise, it will scale that noise — wasting budget on low-quality traffic.
If your goal is too broad or lacks strong quality signals, the algorithm will maximize volume, even when that volume doesn’t drive real business value.
If your seed data is biased, the AI will keep optimizing toward that bias — potentially missing valuable adjacent audiences. This “sampling bias” in training data is a real, underappreciated risk in automated systems.
Platforms have a financial incentive to push broader automation. Without your oversight and willingness to intervene, campaigns can drift from your business goals. “Set it and forget it” fails. You need to monitor campaigns and nudge them back on track when they drift.
As targeting automates, creative becomes your primary differentiator. Neglect it and you lose.
Build creative that directly answers your audience’s pain points. Stand out.
So how do you operationalize this? Here are three steps to start engineering your audiences today:
The era of manual targeting is over, but precision matters more than ever. Audience engineering is your competitive advantage. By teaching algorithms who to target and what matters, you unlock AI’s full potential and win in this evolving landscape.
Intel adds “Gaming Support” to its ARC PRO B70 and ARC PRO B65 GPUs with its newest ARC GPU drivers With the release of its ARC Graphic Driver 32.0.101.8629 WHQL, Intel has given its ARC Pro B70 and ARC Pro B65 GPUs official “gaming support”. This means that users of Intel’s “Big Battlemage” GPUs will […]
The post Intel delivers official “gaming support” to its Big Battlemage ARC Pro B70 and B65 GPUs appeared first on OC3D.
Rythm adds a bouncer to your email so you control who reaches you. It builds a guest list from your Gmail or Outlook contacts, lets known senders through, and files unknown senders into a separate folder you can check on your terms. Strangers can pay a small cover charge to reach your inbox, with paid messages marked PAID and funds sent to your wallet. Rythm scans messages only to detect payment proofs and discards contents, never storing or sharing any email content.
DaySet calculates your daily guilt-free spend after all your bills and subscriptions are accounted for. Enter your income and expenses once and get one number every morning telling you exactly what you can freely spend that day. Unspent amounts roll over to tomorrow. It also includes an AI coach, recipe photo scanner, tax deduction tracker with PDF export, goals, habits, and a bill calendar with reminders. It's built for anyone who wants to stop guessing and start owning every day.
AMD's Ryzen 5 5500X3D extends AM4's life once again, but is it worth it? We tested 14 games to see how this cut-down 3D V-Cache chip stacks up against Zen 3, older Ryzen parts, and newer CPUs.
Axiom enables enterprise teams to turn complex decisions into action quickly. It centralizes procurement and alignment workflows, lets AI agents research options, propose criteria, and score vendors against documentation and RFPs, and generates audit-ready Architectural Decision Records.
Use Axiom to compare human intuition with data-driven scores, collaborate asynchronously to resolve gaps, and approve outcomes with clear traceability. Replace weeks of meetings with days of structured, transparent evaluation.
Google's John Mueller answers question about how Google handles multiple URLs and duplicate content.
The post Google Says It Can Handle Multiple URLs To The Same Content appeared first on Search Engine Journal.
Naftiko turns existing data and APIs into governed, reusable capabilities for AI. Teams declare what they consume and expose in YAML specs, run them with an open-source engine, and publish them to a runtime where discovery, composition, and observability are built-in. Policy-driven controls, identity propagation, and audit trails keep agents inside trust boundaries while reuse metrics and consistent packaging reduce duplication and speed delivery.
Apricot AI provides 24/7 tech support through a lightweight Windows taskbar app. It reads your hardware, drivers, and software to deliver personalized, step-by-step fixes in seconds instead of generic search results.
For $19/month, you get unlimited questions across common issues like Wi‑Fi, printers, slow PCs, drivers, app errors, and more. Apricot AI keeps your data private and uses system info only to answer your questions, so you can solve problems fast without appointments or jargon.
Hexys is a privacy-first behavioral recovery platform built to break compulsive digital habits. Its name comes from the ancient Greek "hexis," Aristotle's concept of stable character built through repeated practice. Every check-in, honest journal entry, and day you show up is a deposit toward who you are becoming. Hexys encrypts your sensitive data on your device with AES-256-GCM zero-knowledge encryption before it reaches our servers, storing only ciphertext. Features include a journal, Arcos AI companion, streak tracking, XP progression, anonymous accountability pods, and a content blocker.
Local real-time voice transcription for Mac
Free business calculators — instant results, no signup
An AI-powered Job Search System built on Claude Code
One command to back up every Git repo you have; and more!
Share only part of your screen in video calls
Prove your private GitHub work and contributions
The SEO agent that lives inside Claude & via MCP
Bet your friends on Strava challenges and losers pay in USDC
Your projects. Your way.
Not just dictation and private AI voice toolkit
Verify passports, ID cards, and digital credentials via API
View large JSON files in your browser. Nothing uploaded.
Spell check for video - catch quality issues before you post
Shield your keyboard from kids and pets
Mechanical keyboard sounds for your Mac
Post your startup. Set your terms. Find investors.
Checks PRs against decisions your team approved in Slack
Hire an AI outbound sales rep as your next coworker
Launch on-brand pages for every campaign, ad, and prospect.
Business intelligence that doesn't just answer — it acts.
Mac Metrics In Your Menu Bar
The Mac app your body thanks you for
The roommate matching app built for college students
The all-in-one workspace for content agencies & editors
Open-source benchmarks for cloud browser infrastructure
Public changelog for builders to share product updates
Better lighting and larger scales for 3D world generation
Chrome now supports vertical tabs and immersive reading mode
Open-source AI voice input
Peer-to-Peer Podcasting for Agents
A flexible AI writing assistant for selected text on macOS
Share anything as video messages
How well do your gut decisions actually hold up? Convexly is a decision intelligence platform that measures this. Log predictions with probabilities, resolve outcomes, and see how your confidence matches reality. It calculates Brier scores, calibration curves, and runs Monte Carlo simulations to stress-test your choices. Start with a free 2-minute calibration quiz, then track real decisions to improve over time.
Research Rocket helps founders validate ideas before building. Launch waitlist landing pages and smoke tests in minutes, run pre-launch surveys and concept tests, and get AI-scored demand signals with clear insight summaries. Use built-in tools for card sorting, tree testing, interview guides, and qualitative analysis to map user needs and make evidence-based decisions.
BeatMusic is an online AI music generation platform for creators, musicians, and content producers. No music theory or expensive equipment needed—just describe what you want and get professional-quality songs in minutes. It offers 20+ professional tools including AI Cover to transform any song with 100+ vocal styles and genres, AI Music Video Generator to turn static images into music videos, and AI Singing Photo to make anyone in a picture sing your song.
Mygomseo is an AI marketing agent that helps you rank across Google and AI search. It scans your site in seconds, runs 40+ technical checks, connects to Search Console, and uncovers issues and keyword gaps. It learns your brand voice, plans a content calendar, writes SEO articles, and auto-publishes to 13+ platforms including WordPress, Shopify, and Webflow. Mygomseo tracks rankings, backlinks, and anomalies, delivers reports, and answers questions on demand so you grow search visibility with minimal effort.
Ka.I Health builds Kai Companion, a local safety app that triggers a help chain. It sends a personalized SMS with your location to selected contacts and automatically calls a primary contact so you get support fast on iOS.
APPROXINATION is an AI agent-focused search engine that delivers answers powered by an "Inverse Arena," a system where personal AI agents continuously test, compare, and rank the best tech tools to give you data-driven recommendations.
DutyDesk helps importers and trade professionals look up US and EU tariff rates, calculate total landed costs, and classify products. You can search by name or HTS code to see full rates including trade actions and fees, or use AI classification backed by GRI rules and official rulings. Set alerts for rate changes, organize codes by client or shipment, and access data via a REST API. Data sources include USITC, CBP, the Federal Register, TARIC, EUR-Lex, and EU BTI.
The app also updated reply settings, allowing paying X users to give second degree connections the ability to comment on posts.
The brief comment function is being expanded beyond mutual followers and could potentially become a new way for creators to broadcast information.
The app highlighted the popularity of its public discussions during March Madness, though Threads and X still have more active users during live events.
New demographic data points could be valuable for brand partners, while Google’s latest Nano Banana model will help with image generation.
There may be opportunities for wellness brands that want to engage with people beyond the confines of a doctor’s office, according to a new study from the company.
New insight from the platform highlights the importance of variable signals within Pin recommendations.
Google CEO Sundar Pichai said AI models could expose more software vulnerabilities and agreed it was plausible AI is affecting zero-day exploit markets.
The post Google CEO Says AI Could ‘Break Pretty Much All Software’ appeared first on Search Engine Journal.
Google is giving advertisers new visibility into whether its automated recommendations actually drive performance — a long-standing blind spot in the platform.
What’s happening. A new “Results” tab within Recommendations shows the incremental impact of bidding and budget changes after they’ve been applied, allowing marketers to evaluate outcomes instead of relying on assumptions.
How it works. The feature attributes performance changes to specific recommendations, helping advertisers understand what effect adjustments like budget increases or bid strategy shifts had on results.
Why we care. Marketers can now validate whether recommendations improved performance, making it easier to decide which automated suggestions are worth adopting in the future.
Between the lines. Google has a vested interest in encouraging adoption of its recommendations, so providing performance data could build trust — but it also raises questions about how that impact is measured.
The catch. Advertisers may question whether the reported results are fully objective or skewed toward showing positive outcomes, given Google’s incentives.
What to watch. How detailed and transparent the reporting becomes — and whether advertisers see mixed or negative results alongside wins.
Bottom line. Google is moving from “trust us” to “here’s the proof,” but advertisers will be watching closely to see how impartial that proof really is.
First seen. This update was first spotted by Arpan Banerjee who shared seeing the new tab on LinkedIn.
Google is giving advertisers more control over how AI generates ad copy, making it easier to scale campaigns without losing brand consistency.
What’s happening. Google Ads is rolling out a beta feature that allows marketers to copy text guidelines from existing campaigns and apply them to new ones, eliminating the need to rewrite brand rules from scratch.
How it works. Advertisers can replicate approved tone, style and messaging rules across campaigns in one click, ensuring AI-generated ads stay aligned with brand standards while reducing setup time.
Why we care. The feature helps teams launch campaigns faster by reusing what already works, while maintaining consistency across large accounts where multiple campaigns run simultaneously.
Between the lines. This shift reflects a growing demand from marketers to “train” AI systems rather than rely on them blindly, effectively turning brand guidelines into reusable inputs for automation.
Bottom line. AI is speeding up ad creation, but control is becoming the real differentiator — and Google is starting to hand more of it back to advertisers.
First spotted. This update was spotted by Paid Media expert Arpan Banerjee when he shared spotting the alert on LinkedIn.
ZeroTwo lets you access the combined capabilities of Claude, Perplexity, ChatGPT, Manus, and Higgsfield. These top AI platforms each have unique features that give them special abilities beyond their models. Now you can use all of them without paying for several subscriptions. Perplexity's agentic search, Claude's agentic connector, ChatGPT's apps, and Higgsfield's AI tools for creatives are all available on one platform.
The platform also offers deep research, canvas mode, and shared access to threads and projects. Plans include unlimited messages, expanded memory, priority performance, and team features for businesses.
OrbitMeet is a browser-based AI meeting co-pilot that listens to your meetings in real time, surfaces questions you might miss every 75 seconds, and builds your summary as you talk with no plugins or installation.
It detects action items by speaker name, generates follow-up documents such as emails, memos, and action trackers in seconds, and works across Zoom, Teams, Google Meet, or in-person meetings. It's designed for consultants, founders, and distributed teams working in multiple languages. A free plan is available, with Pro at $20.5 CAD/month.
Google says its AI-powered advertising tools are starting to deliver meaningful results, including major revenue gains for some retailers, as it experiments with how ads work in AI-driven search.
The big picture. Fears that AI chatbots like ChatGPT would disrupt Google’s core search business haven’t materialized, and instead the company’s ads business continues to grow, suggesting AI may be expanding how people search rather than replacing it.
By the numbers:
What’s happened. Google is embedding ads into its AI-powered search experiences, including AI Mode powered by Gemini, while introducing new ad formats designed for conversational queries and tools that allow brands to shape how they appear in AI-generated answers, with a new “business agent” feature enabling companies like Poshmark and Reebok to control how their products are represented.
Driving the results. AI-driven campaigns like Performance Max and AI Max match ads to more detailed and conversational search intent, and Google says queries in AI Mode are often two to three times longer than traditional searches, giving the system more context to connect users with relevant products, as seen with Aritzia, which reported an 80% increase in revenue after adopting AI Max.
How it works. The system scans a retailer’s website and creative assets, interprets user intent from conversational queries, and dynamically matches products and messaging in real time. This is increasingly important given that 15% of daily searches are entirely new (according to Google) and cannot be predicted through traditional keyword targeting.
Why we care. Google is shifting from keyword-based ads to intent-driven, AI-matched advertising, meaning campaigns can reach consumers with far more precision at the moment they’re ready to buy. As search becomes more conversational and unpredictable, advertisers who rely on traditional targeting risk falling behind those using AI-driven formats that automatically adapt to new user behavior.
Zoom in. Google is testing new formats such as “direct offers,” which deliver personalized promotions when users show purchase intent, using Gemini to analyze conversational context and behavior, with brands like E.l.f. Beauty, Chewy and L’Oréal participating in early trials.
Commerce push. Google is also advancing its commerce strategy through a Universal Commerce Protocol developed with Shopify, which allows purchases to happen directly within AI conversations.
Yes, but. Google is not alone in experimenting with ads in AI search, and early results across the industry have been mixed, as Amazon has reportedly seen limited traction from ads in its AI shopping assistant, OpenAI continues to explore monetization models, and Perplexity AI has begun phasing out ads after underwhelming performance.
What they’re saying, Google positions itself as a “matchmaker” rather than a retailer, emphasizing that AI helps deliver more relevant and personalized ads while allowing brands to maintain control over their messaging and build user trust by showing the right product at the right moment.
What’s next. Gooogle says it has no current plans to introduce ads directly into Gemini but will continue testing and expanding advertising within AI Mode, including more personalized offers and AI-driven shopping experiences.
Bottom line. AI is not replacing search but reshaping it, and for Google that shift is making advertising more conversational, more targeted and, in some cases, significantly more profitable.
Dig deeper. Google says its AI-powered ads help some brands lift online sales by 80%.
Google Search is evolving beyond links and answers into a system that completes tasks, potentially fundamentally changing how users interact with the web. That’s according to Alphabet CEO Sundar Pichai, speaking on the Cheeky Pint podcast.
Why we care. Google is signaling a move from information retrieval to task execution.
Search becoming agentic. Traditional search behavior is already changing and will continue to, Pichai said.
Pichai also described a future where Google Search acts less like a list of results and more like a system that coordinates actions:
AI Mode is already changing queries. Users are already adapting their behavior in Google’s AI-powered search experiences, Pichai said:
Search vs. Gemini overlap. Despite the rise of Gemini, Pichai said Google isn’t replacing Search with a chatbot. Instead, the two will coexist — and diverge (echoing what Liz Reid said last month):
The interview. The history and future of AI at Google, with Sundar Pichai
Google’s AI Overviews answered a standard factual benchmark correctly 91% of the time in February, up from 85% in October, according to a New York Times analysis with AI startup Oumi.
However, Google handles more than 5 trillion searches per year, so that means tens of millions of answers every hour may be wrong.
Why we care. We’ve watched Google shift from linking to sources to summarizing them for more than two years. This report suggests AI Overviews are improving, but still mix correct answers, weak sourcing, and clear errors in ways that can mislead searchers and reshape which publishers get visibility and clicks.
The details. Oumi tested 4,326 Google searches using SimpleQA, a widely used benchmark for measuring factual accuracy in AI systems, the Times reported. It found AI Overviews were accurate 85% of the time with Gemini 2 and 91% after an upgrade to Gemini 3.
What changed. Accuracy improved between October and February, but grounding worsened. In October, 37% of correct answers were ungrounded; in February, that rose to 56%.
Examples. The Times highlighted several misses:
Google’s response: Google disputed the Times analysis, saying the study used a flawed benchmark and didn’t reflect what people actually search. Google spokesperson Ned Adriance told the Times the study had “serious holes.”
The report. How Accurate Are Google’s A.I. Overviews? (subscription required)
PeaZip 11.0 refines one of the most capable free archivers with faster browsing, smoother drag-and-drop across tabs, and a cleaner, more responsive UI. The update also improves scaling, adds flexible icon rendering, and introduces batch archive testing, alongside the usual fixes and cleanup.
Shadow OS is the first decision-making app built on 64 hexagrams, the same system Carl Jung studied for over two decades and called his most significant method for surfacing what the unconscious already knows. Other decision apps use random spinner wheels, AI chatbots validate whatever you say, and astrology apps offer forecasts open to interpretation. Shadow OS gives you one committed answer: move forward, hold, or pull back.
BeMusic AI is a free AI music generator that turns text prompts into fully produced, royalty-free songs in under 30 seconds. Choose from 50+ genres, adjust mood, tempo, and energy, and download high-quality WAV or MP3 for videos, games, podcasts, and ads. It also offers tools to write lyrics, create instrumentals, convert audio to MIDI, edit MIDI, make AI covers, remove vocals, extend tracks, and analyze songs. Use it to avoid copyright issues and keep full ownership of every track.
These new features are designed to streamline your browser and help you maximize productivity in Chrome.Google has begun placing sponsored ad units directly inside the Images tab of mobile search results — a new placement that eligible campaigns can access without any changes to existing keyword targeting.
What’s happening. When a user navigates to the Images tab within Google Search on mobile, they may now see sponsored units appearing within the image grid. Each unit shows a full image creative as the primary visual alongside text, and is clearly labelled “Sponsored” — consistent with how Google labels ads elsewhere in search results.
How it works. Eligible campaigns can serve into the Images tab without any changes to keyword targeting or campaign structure. The placement draws from existing image assets, meaning advertisers running Search or Performance Max campaigns with strong visual creative are best positioned to benefit. No separate image-only campaign setup is required.
Why we care. This is a meaningful expansion of Google’s paid search real estate. For product-led and catalog-heavy advertisers, the Images tab is where purchase-intent discovery often starts — and now ads can appear right in that moment. If your campaigns already use strong image assets, you may be picking up incremental impressions without lifting a finger.
The big picture. Early indications suggest this placement behaves more like a visual discovery surface than classic paid search. Expect high impression volume but lower click-through rates — more in line with display or Shopping than traditional text ads. That said, the assist value in multi-touch conversion paths could be significant, particularly for retail and direct-to-consumer brands. Treat it as upper-funnel reach, not a last-click channel.
What to watch. Google has not made a formal announcement, and there is no dedicated reporting breakdown for Images tab placements yet. Monitor your impression share and segment data closely to understand whether this placement is contributing — and whether it’s eating into organic image visibility for competitors.
First seen. The placement was spotted by Google Ads Expert – Matteo Braghetta, who shared seeing this update on LinkedIn. No official documentation has been published by Google at the time of writing.
Over 30% of outbound clicks go to just 10 domains, with Google alone taking more than 20%, according to a new Semrush study published today.
ChatGPT also relies less on the live web, triggering search on 34.5% of queries, down from 46% in late 2024.
The big picture. ChatGPT’s growth has plateaued, and its role in how users navigate the web is evolving unevenly.
The details. Most ChatGPT referral traffic still goes to a small set of sites, even as more sites receive some traffic.
Why we care. Visibility in ChatGPT doesn’t translate evenly into traffic, and you’ll likely see marginal referral impact. The decline in search-triggered queries also limits your chances to earn citations and traffic.
When ChatGPT searches. It defaults to pre-trained knowledge and uses web search in specific cases, including:
Behavior shift. Most ChatGPT prompts still don’t resemble traditional search queries.
About the data. Semrush analyzed more than 1 billion lines of U.S. clickstream data from October 2024 to February 2026 across a 200 million-user panel, tracking prompts, referral destinations, and search usage.
The study. ChatGPT traffic analysis: Insights from 17 months of clickstream data
Google doesn’t train Gemini using personal emails. Here’s how Google keeps private data secure in Gmail amid new AI model upgrades.Android XR adds spatial conversion for 2D apps, the ability to pin apps to your walls and more ways to watch, create, and explore.We’re making it easier for people to share photos and videos, and keep track of their progress.Google is rolling out new Google Maps features that make it easier to contribute photos, reviews, and local insights, while adding Gemini-powered caption suggestions.
Local Guides redesign. Contributor profiles are getting more visibility. Total points now appear more prominently, Local Guide levels are easier to spot, and badge designs have been refreshed.
AI caption drafts. Google is also introducing AI-generated caption drafts. Gemini analyzes selected images and suggests text you can edit or discard.
Media sharing. Google Maps now shows recent photos and videos directly in the Contribute tab, making uploads faster.
Why we care. Google is making it easier to create and scale fresh local content, which can directly affect rankings and visibility. At the same time, stronger contributor signals may influence which reviews users trust and which businesses win clicks.
Google once attributed two of Barry Schwartz’s Search Engine Land articles to me — a misclassification at the annotation layer that briefly rewrote authorship in Google’s systems.
For a few days, when you searched for certain Search Engine Land articles Schwartz had written, Google listed me as the author. The articles appeared in my entity’s publication list and were connected to my Knowledge Panel.
What happened illustrates something the SEO industry has almost entirely overlooked: that annotation — not the content itself — is the key to what users see and thus your success.
Googlebot crawled those pages, found my name prominently displayed below the article (my author bio appeared as the first recognized entity name beneath the content), and the algorithm at the annotation gate added the “Post-It” that classified me as the author with high confidence.
This is the most important point to bear in mind: the bot can misclassify and annotate, and that defines everything the algorithms do downstream (in recruitment, grounding, display, and won). In this case, the issue was authorship, which isn’t going to kill my business or Schwartz’s.
But if that were a product, a price, an attribute, or anything else that matters to the intent of a user search query where your brand should be one of the obvious candidates, when any aspect of content is inaccurately annotated, you’ve lost the “ranking game” before you even started competing.
Annotation is the single most important gate in taking your brand from discover to won, whatever query, intent, or engine you’re optimizing for.
Indexing (Gate 4) breaks your content into semantic chunks, converts it, and stores it in a proprietary format. Annotation (Gate 5) then labels those chunks with a confidence-driven “Post-It” classification system.
It’s a pragmatic labeler and attaches classifications to each chunk, describing:
Importantly, it’s mostly unopinionated when labeling facts, context, and trustworthiness. Microsoft’s Fabrice Canel confirmed the principle that the bot tags without judging, and that filtering happens at query time.
What does that mean? The bot annotates neutrally at crawl time, classifying your content without knowing what query will eventually trigger retrieval.
Annotation carries no intent at all. It’s the insight that has completely changed my approach to “crawl and index.”
That clearly shows you that indexing isn’t the ultimate goal. Getting your page indexed is table stakes. Full, correct, and confident annotation is where the action happens: an indexed page that is poorly annotated is invisible to each of the algorithmic trinity.
The annotation system analyzes each chunk using one or more language models, cross-referenced against the web index, the knowledge graph, and the models’ own parametric knowledge. But it analyzes each chunk in the context of the page wrapper.
The page-level topic, entity associations, and intent provide the frame for classifying each chunk. If the page-level understanding is confused (unclear topic, ambiguous entity, mixed intent), every chunk annotation inherits that confusion. Even more importantly, it assigns confidence to every piece of information it adds to the “Post-Its.”
The choices happen downstream: each of the algorithmic trinity (LLMs, search engines, and knowledge graphs) uses the annotation to decide whether to absorb your content at recruitment (Gate 6). Each has different criteria, so you need to assess your own content for its “annotatability” in the context of all three.
And a small but telling detail: Back in 2020, Martin Splitt suggested that Google compares your meta description to its own LLM-generated summary of the page. When they match, the system’s confidence in its page-level understanding increases, and that confidence cascades into better annotation scores for every chunk — one of thousands of tiny signals that accumulate.
Annotation is the key midpoint of the 10-gate pipeline, where the scoreboard turns on. Everything before it is infrastructure: “Can the system access and store your content?” Everything after it is competition:
When you consider what happens at the annotation gate and its depth, links and keywords become the wrong lens entirely. They describe how you tried to influence a ranking system, whereas annotation is the mechanism behind how the algorithmic trinity chooses the content that builds its understanding of what you are.
The frame has to shift. You’re educating algorithms. They behave like children, learning from what you consistently, clearly, and coherently put in front of them. With consistent, corroborated information, they build an accurate understanding.
Given inconsistent or ambiguous signals, they learn incorrectly and then confidently repeat those errors over time. Building confidence in the machine’s understanding of you is the most important variable in this work, whether you call it SEO or AAO.
In 2026, every AI assistive engine and agent is that same child, operating at a greater scale and with higher stakes than Google ever had. Educating the algorithms isn’t a metaphor. It’s the operational model for everything that follows.
For a more academic perspective, see: “Annotation Cascading: Hierarchical Model Routing, Topical Authority, and Inter-Page Context Propagation in Large-Scale Web Content Classification.”
When mapping the annotation dimensions, I identified 24, organized across five functional categories. After presenting this to Canel, his response was: “Oh, there is definitely more.”
Of course there are. This taxonomy is built through observation first, then naming what consistently appears. The [know/guess] distinctions follow the same logic: test hypotheses, eliminate what doesn’t hold up, and keep what remains.
The five functional categories form the foundation of the model. They are simple by design — once you understand the categories, the dimensions follow naturally. There are likely additional dimensions beyond those mapped here.
What follows is the taxonomy: the categories are directionally sound (as confirmed by Canel), while the specific dimension assignments reflect observed behavior and remain incomplete.
Across all five levels, a confidence score is attached to every individual annotation. Not just what the system thinks your content means, but how certain it is.
Clarity drives confidence. Ambiguity kills it.
Canel also confirmed additional dimensions I had not initially mapped: audience suitability, ingestion fidelity, and freshness delta. These sit across the existing categories rather than forming a sixth level.
In 2022, Splitt named three annotation behaviors in a Duda webinar that map directly onto the five-level model. The centerpiece annotation is Level 2 in direct operation:
Annotation runs before recruitment, which means a chunk classified as non-centerpiece carries that verdict into every gate that follows. Boilerplate detection is Level 3: content that appears consistently across pages — headers, footers, navigation, and repeated blocks — enters a different competition pool based on its structural role alone.
Off-topic routing closes the picture. A page classified around a primary topic annotates every chunk relative to that centerpiece, and content peripheral to the primary topic starts its own competition pool at a disadvantage before Recruitment begins.
Splitt’s example: a page with 10,000 words on dog food and a thousand on bikes is “probably not good content for bikes.” The system isn’t ignoring the bike content. It’s annotating it as peripheral, and that annotation is the routing decision.
In Sydney in 2019, I was at a conference with Gary Illyes and Brent Payne. Illyes explained that Google’s quality assessment across annotation dimensions was multiplicative, not additive.
Illyes asked us not to film, so I grabbed a beer mat and noted a simple calculation: if you score 0.9 across each of 10 dimensions, 0.9 to the power of 10 is 0.35. You survive at 35% of your original signal. If you score 0.8 across 10 dimensions, you survive at 11%. If one dimension scores close to zero, the multiplication produces a result close to zero, regardless of how well you score on every other dimension.
Payne’s phrasing of the practical implication was better than mine: “Better to be a straight C student than three As and an F.”
The beer mat went into my bag. The principle became central to everything I’ve built since.
The multiplicative destruction effect has a direct consequence for annotation strategy: the C-student principle is your guide.
At the annotation stage, misclassification, low confidence, or near-zero on one dimension will kill your content and take it out of the race.
Nathan Chalmers, who works at Bing on quality, told me something that puts this in a different light entirely. Bing’s internal quality algorithm, the one making these multiplicative assessments across annotation dimensions, is literally called Darwin.
Natural selection is the explicit model: content with near-zero on any fitness dimension is selected against. The annotations are the fitness test. The multiplicative destruction effect is the selection mechanism.
The system doesn’t use one giant language model to classify all content. It routes content to specialized small language models (SLMs): domain-specific models that are cheaper, faster, and paradoxically more accurate than general LLMs for niche content.
A medical SLM classifies medical content better than GPT-4 would, because it has been trained specifically on medical literature and knows the entities, the relationships, the standard claims, and the red flags in that domain.
What follows is my model of how the routing works, reconstructed from observable behavior and confirmed principles. The existence of specialist models is confirmed. The specific cascade mechanism is my reconstruction.
The routing follows what I call the annotation cascade. The choice of SLM cascades like this:
Each level narrows the SLM selection, and each level either confirms or overrides the routing from above. This maps directly to the wrapper hierarchy from the fourth piece: the site wrapper, category wrapper, and page wrapper each provide context that influences which specialist model the system selects.
The system deploys three types of SLM simultaneously for each topic. This is my model, derived from the behavior I have observed: annotation errors cluster into patterns that suggest three distinct classification axes.
When all three return high confidence on the same entity for the same content, annotation cost is minimal, and the confidence score is very high. When they disagree (i.e., the subject SLM says “marketing,” but the entity SLM can’t resolve the entity, and the concept SLM flags the claims as novel), confidence drops, and the system falls back to a more general, less accurate model.
The key insight? LLM annotation is the failure mode. The system wants to use a specialist. It defaults to a generalist only when it can’t route to a specialist. Generalist annotation produces lower confidence across all dimensions.
Content that’s category-clear within its first 100 words, uses standard industry terminology, follows structural conventions for its content type, and references well-known entities in its domain triggers SLM routing.
Content that’s topically ambiguous or terminologically creative gets the generalist. Lower confidence propagates through every downstream gate.
Now, this may not be the exact way the SLMs are applied as a triad (and it might not even be a trio). However, two things strike me:
Here is something I’ve observed over years of tracking annotation behavior. It aligns with a principle Canel confirmed explicitly for URL status changes (404s and 301 redirects): the system’s initial classification tends to stick.
When the bot first crawls a page, it selects an SLM, runs the annotation, assigns confidence scores, and saves the classification. The next time it crawls the same page, it logically starts with the previously assigned model and annotations. I call this first-impression persistence.
The initial annotation is the baseline against which all subsequent signals are measured. The system doesn’t re-evaluate from scratch. It checks whether the new crawl is consistent with the existing classification, and if it is, the classification is reinforced.
Canel confirmed a related mechanism: when a URL returns a 404 or is redirected with a 301, the system allows a grace period (very roughly a week for a page, and between one and three months for content, in my observation) during which it assumes the change might revert. After the grace period, the new state becomes persistent. I believe the same principle applies to content classification: a window of fluidity after first publication, then crystallization.
I have direct evidence for the correction side from the evolution of my own terminologies. When I first described the algorithmic trinity, I used the phrase “knowledge graphs, large language models, and web index.” Google, ChatGPT, and Perplexity all picked up on the new term and defined it correctly.
A month later, I changed the last one to “search engine” because it occurred to me that the web index is what all three systems feed off, not just the search system itself. At the point of correction, I had published roughly 10 articles using the original terminology.
I went back and invested the time to change every single one, updating every reference, leaving zero traces. A month later, AI assistive engines were consistently using “search engine” in place of “web index.”
The lesson is that change is possible, but you need to be thorough: any residual contradictory signal (one old article, one unchanged social post, and one cached version) maintains inertia proportionally. Thoroughness is the unlock, rather than time.
A rebrand, career pivot, or repositioning is the practical example. You can change the AI model’s understanding and representation of your corporate or personal brand, but it requires thoroughly and consistently pivoting your digital footprint to the new reality.
In my experience, “on a sixpence” within a week. I’ve done this with my podcast several times. Facebook achieved the ultimate rebrand from an algorithmic perspective when it changed its name to Meta.
Get your annotation right before you publish. The first crawl sets the baseline. A page published prematurely (with an unclear topic or ambiguous entity signals) crystallizes into a low-confidence annotation, and changing it later requires significantly more effort than getting it right the first time.
The system doesn’t annotate in a vacuum. When the bot classifies your content at Gate 5, it cross-references against at least three sources simultaneously. This is my model of the mechanism. The observable effect — that annotation confidence correlates with entity presence across multiple systems — is confirmed from our tracking data.
The bot carries prioritized access to the web index during crawling, checking your content against what it already knows:
Against the knowledge graph, it checks annotated entities during classification — an entity already in the graph with high confidence means annotation inherits that confidence, while absence starts from a much lower baseline.
The SLM’s own parametric knowledge provides the third cross-reference: each SLM compares encountered claims against its training data, granting higher confidence to claims that align, flagging contradictions, and giving lower confidence to novel claims until corroboration accumulates.
This means annotation quality isn’t just about how well your content is written. It’s about how well your entity is already represented across all three of the algorithmic trinity. An entity with strong knowledge graph presence, authoritative web index links, and consistent SLM-domain representation gets higher annotation confidence on new content automatically.
The flywheel: better presence leads to better annotation, which leads to better recruitment, which strengthens presence, and which improves future annotation.
Once again, better to have an average presence in all three than to have a dominant presence in two and no presence in one.
And this is why knowledge graph optimization (what I’ve been advocating for over a decade) isn’t separate from content optimization. They are the same pipeline. Your knowledge graph presence directly improves how accurately, verbosely, and confidently the system annotates every new piece of content you publish.
If you’re thinking “Knowledge graph? That’s just Google,” think again.
In November 2025, Andrea Volpini intercepted ChatGPT’s internal data streams and found an operational entity layer running beneath every conversation: structured entity resolution connected to what amounts to a product graph mirroring Google Shopping feeds.
OpenAI is building its own knowledge graph inside the LLM. My bet is that they will externalize it for several reasons: a knowledge graph in an LLM doesn’t scale, an LLM will self-confirm, so the value is limited, a standalone knowledge graph can be easily updated in real time without retraining the model, and it’s only useful at scale when it stays current.
The algorithmic trinity isn’t a Google phenomenon. It’s the architectural pattern every AI assistive engine and agent converges on, because you can’t generate reliable recommendations without a concept graph, structured entity data, and up-to-date search results to ground them.
Google and Bing own their crawling infrastructure, indexes, and knowledge graphs. They can afford grace periods, schedule rechecks, and maintain temporal state for URLs and entities over months.
OpenAI, Perplexity, and every engine that rents index access from Google or Bing operate on a fundamentally different model. They have two speeds:
The Boolean gate inherits Google’s and Bing’s annotations. Whether your content appears at all depends on whether it was recruited from the index those engines draw from, and that recruitment depends on annotation and selection decisions made by the algorithmic trinity. But what these engines show when they cite you is fetched in real time.
For Google and Bing, you’re optimizing for annotation quality with the benefit of grace periods and gradual reclassification. For engines that don’t own their index, the Boolean presence is inherited from the rented index and is slow to change, but the surface-level display changes every time they re-fetch.
That means what you are seeing in the results is not a direct measure of your annotation quality. It’s a snapshot of your page at the moment of fetch, and those two things may have nothing to do with each other.
The SEO industry has spent two decades optimizing for search and assistive results — what happens after the system has already decided what your content means. We should be optimizing for annotation.
If the annotation is wrong, everything downstream suffers. When the annotation is accurate, verbose, and confident, your content has a significant advantage in recruitment, grounding, display, and, ultimately, won.
Make your topic category obvious within the first 100 words. Use standard industry terminology. Follow structural conventions. Reference well-known entities. The goal: specialist model, not generalist.
Clear signals for subject (what is this about?), entity (who is the authority?), and concept (what established ideas does this connect to?). Ambiguity on any axis reduces confidence.
First-impression persistence means the initial annotation is the hardest to change. Publish only when topic, entity signals, and claims are unambiguous.
Knowledge graph presence, web index centrality, LLM parameter strengthening, and correct SLM-domain representation all feed annotation confidence for new content. Invest in entity foundation, and every future piece benefits from inherited credibility.
Change every reference. Leave zero contradictory signals. Noise maintains inertia proportionally.
A page can be indexed and still misannotated. If the AI response is wrong about you, the problem is almost certainly at Gate 5, not Gate 8.
Annotation is the gate where most brands silently lose. The SEO industry doesn’t yet have a vocabulary for it. That needs to change, because the gap between brands that get annotation right and brands that don’t is the gap between consistent AI visibility and permanent algorithmic obscurity.
You’ve done everything within your power to create the best possible content that maps to intent of your ideal customer profile, you have methodically optimized your digital footprint, your data feeds every entry mode simultaneously: pull, push discovery, push data, MCP, and ambient, so they are all drawing from the same clean, consistent source
So, content about your brand has passed through the DSCRI infrastructure phase, survived the rendering and conversion fidelity boundaries, and arrived in the index (Gate 4) intact. Phew!
Now it gets classified. Annotation is the last moment in the pipeline where you have the field to yourself. Every decision in DSCRI was absolute: you vs. the machine, with no competitor in the frame.
Annotation is still absolute. The system classifies your content based on your signals alone, independently of what any competitor has done. Nobody else’s data changes how your entity is annotated.
But this is the last time you aren’t competing. From recruitment onward, everything is relative. The field opens, every brand that passed annotation enters the same competitive pool, and the advantage you carried through the absolute phase becomes your starting position in the competitive race you have to win.
That means:
Warning: First-impression persistence (remember, the first time you are annotated is the baseline) means you don’t get a clean retry. Changing the baseline requires thoroughness, time, and more effort than getting it right on the first crawl.
Annotation isn’t the gate that most brands focus on. It’s the gate where most brands silently lose.
This is the eighth piece in my AI authority series.
SOVOL is about to enter its multi-material era SOVOL has started teasing “something new”, a new 3D printer that promises to be both “multi-material” and “multi-colour”. Until now, SOVOL has specialised in single-colour/material 3D printing solutions, promising “open-source freedom” and a wealth of customisation options. Based on their teaser image, SOVOL’s new 3D printer appears […]
The post SOVOL teases its first multi-filament 3D printer appeared first on OC3D.
Java 26 is here with fresh language features, faster performance, stronger security, and a wave of library and tooling upgrades. Early developer reaction has been upbeat, with many praising Java's steady pace of meaningful improvements.
PanelShot generates realistic AI personas, shows them your website, and delivers structured feedback in minutes. Pick audience segments or create your own, select a research rubric, and let AI evaluate screenshots, copy, and accessibility to surface insights. Review an executive summary and per-page analysis, replay the same personas on new versions, track sentiment trends over time, and chat with any persona for deeper understanding, all for cents per persona.
REWRITE is a 30-day interactive story and voice-first coaching platform that measures personal transformation through your voice. You follow the narrative, talk with an AI coach by text or voice, and see objective signals like stress, confidence, engagement, cognitive load, and authenticity. After the story, daily prompts and monthly voice reports track your progress, giving data you can act on. Coaches get a dashboard with client trends, attention flags, and AI-generated prep notes.
Many of today’s PPC tools were designed to be easily accessible to ecommerce. That doesn’t mean lead gen can’t take advantage of them, but it does mean more intentional application is required.
Lead gen with AI still requires a creative approach, and many conventional ecommerce tools still apply — but not always in the same way.
Here are the priorities that matter most for succeeding with lead gen using AI.
Disclosure: I’m a Microsoft employee. While this guidance is platform-agnostic, I’ll reference examples that lean into Microsoft Advertising tooling. The principles apply broadly across platforms.
This is the single most important thing you can do as AI becomes more embedded in media buying.
Between evolving attribution models, privacy changes, different platform connections, and shifts in how consumers engage with brands, it’s reasonable to ask whether your data is still telling an accurate story.
Start by auditing your CRM or lead management system. Make sure the data you pass back to advertising platforms is clean, consistent, and intentional.
In most cases, data issues stem from human choices rather than technical failures. Still, there are a few technical checks that matter:
If AI systems are learning from your data, you want to be confident that the feedback loop reflects reality.
Dig deeper: How to make automation work for lead gen PPC
Lead gen campaigns often have multiple conversion paths, which can be helpful for users. But from an AI perspective, ambiguity is a risk.
Your landing pages should make it clear:
Redundant or unclear conversion paths can confuse both users and systems. If AI crawlers detect that anticipated outcomes are inconsistent, they may begin to question the accuracy of what your site claims to do. That can limit eligibility for certain placements.
Language clarity matters just as much. Avoid jargon, eccentric terminology, or internally focused phrasing when describing your services. Clear, plain language makes it easier for AI systems to understand who you are, what you offer, and how to match creative to the right audience.
A practical test: Put your website content into a Performance Max campaign builder and review how the system attempts to position your business. If you agree with the messaging, imagery, and framing, your site is likely easy to understand. If not, that feedback is valuable.
You can also paste your site content into AI assistants and ask them to describe your business and services. If the response aligns with reality, you’re in a good place. If it doesn’t, that’s a signal to refine your content.
Behavioral analytics tools, like Clarity, can help you understand exactly how humans are engaging with your site and how often AI tools are crawling your site.
Dig deeper: AI tools for PPC, AI search, and social campaigns: What’s worth using now
Lead gen has always struggled with long conversion cycles. That challenge doesn’t go away, and in some ways, it becomes more pronounced.
AI-driven systems increasingly weigh sentiment, visibility, and contextual signals, not just last-click performance. If all of your budget and reporting focuses on immediate traffic, you may miss meaningful impact higher in the funnel.
That means:
In many lead gen models, citations, qualified leads, and eventual revenue tell a more accurate story than clicks alone.
Dig deeper: Lead gen PPC: How to optimize for conversions and drive results
You may not think you have a “feed” in your lead gen setup, but that absence can put you at a disadvantage.
Feeds help AI systems understand your business structure, services, and site architecture. Even if you don’t have hundreds of pages, a simple, well-maintained feed in an Excel document can provide valuable context when uploaded to ad platforms.
Feed hygiene matters. Use clear, specific columns. Follow platform standards for text, images, and categorization. Make sure all relevant categories are represented.
On the local side, claim and maintain all map profiles. Ensure information is accurate and consistent. If you use call tracking in map placements, review your labeling carefully. AI systems may pull data from map listings or your website, and mismatches can create attribution confusion, particularly for phone leads.
Account for potential AI-driven inflation in reporting, whether you’re looking at map pack data, direct reporting, or site-level performance. Any changes you make should also be reflected correctly in your conversion goals.
Creative assets may be mixed, matched, or shortened using AI. In some cases, you may only get one headline to explain who you are and why someone should contact you.
If your value proposition requires three headlines, or a headline plus a description, to make sense, that’s a risk.
Review your existing creative and identify assets that stand on their own. You should have at least some options where a single headline clearly communicates:
If that clarity isn’t there, AI-driven placements can quickly become confusing.
Dig deeper: Why creative, not bidding, is limiting PPC performance
Lead gen today doesn’t need to be complicated.
Most of the actions that matter today are things strong advertisers already do: clean data, clear messaging, intentional budgeting, and disciplined execution. What changes is how attribution may shift, and how much weight systems place on different signals.
The fundamentals still win. The difference is that AI makes weaknesses more visible and strengths more scalable.
If you focus on clarity, accuracy, and alignment across your funnel, you give both people and systems the best possible chance to understand your business — and that’s where sustainable performance comes from.
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For most people, “Mad Men” means the TV show. But the phrase points to something more specific: Madison Avenue in the 1950s and ‘60s, when agencies grew brands through persuasion, positioning, and earned trust in a world of scarce media channels and powerful gatekeepers. If you wanted attention, you bought your way in, then made your product the obvious choice.
When the internet arrived and Google made the chaos navigable, an entire industry was built on getting brands found. Search and SEO became one of the most commercially valuable disciplines in marketing.
That model isn’t disappearing. But something new is taking shape on top of it — and most of the industry is still using the wrong language to describe what’s happening.
AI is exposing everything SEO has neglected. Brands that win recommendations from AI systems won’t do so by publishing more content. They’ll win through positioning, persuasion, and corroborated proof.
In other words, they’ll win the way Madison Avenue always did.
One of the strangest things about the current industry conversation is how many people talk as if the job of SEO is to create content. It isn’t. Not for most businesses.
If you’re a publisher, content is the product. Traffic is the commercial engine. But for most brands, content never did what people thought.
Early on, people wrote content for customers, and it worked. Then it changed. Content became a keyword vehicle. “Get people to our site” replaced good marketing comms.
Traffic became a proxy for exposure. It worked because search rewarded retrieval: type a query, get a page, get a click. All you needed to sell that model was the belief that any traffic was good traffic. That traffic somehow led to revenue that your agency could keep delivering.
That model is now under serious pressure.
Google and ChatGPT are increasingly taking the click. Every serious large language model is trying to satisfy informational intent before the user reaches the source. They aren’t trying to be better search engines. They’re trying to make search engines unnecessary — and that’s the entire point.
There’s too much information on the web. People don’t want to open 10 tabs and read five near-identical blog posts to find a basic answer. They want the answer. The AI systems exist precisely to give it to them.
So if informational retrieval gets absorbed into the interface, what remains? Marketing. That’s the part many SEOs are still not fully grappling with.
Dig deeper: The three AI research modes redefining search – and why brand wins
The cleanest way to understand this shift is through the “4 Ps” of marketing: product, price, place, and promotion.
Traditional SEO has been, almost entirely, a place discipline. It’s been about getting your products, services, or information onto the digital shelf when people go looking.
Keyword rankings are shelf position. Paid search is just a more expensive version of the same principle. In commercial search, you pay for premium placement in a digital aisle.
That still matters enormously.
Buyer-intent search remains valuable. Google hasn’t solved its commercial transition to a fully AI-led interface, and won’t overnight. Search is too important to Google’s revenue to disappear fast. But another layer is emerging above it, and this is the layer that most agencies aren’t yet equipped to compete on.
As AI systems become the first interaction point for more users, the game shifts from being present to being preferred.
Users don’t just search. They ask. They describe a problem. They want the best CRM for a mid-market SaaS company, the best estate agent in their area, the best sandwich shop near the office. And the system responds with recommendations.
If classic SEO was about rankings, the next phase is about recommendations. If classic SEO was about digital placement, the next phase is about shaping preference. And recommendation, in practice, is advertising.
Not a display banner. Not a 30-second TV spot. But advertising in the oldest and most commercially powerful sense: influencing the choice someone makes before they’ve even consciously made it.
An AI-generated recommendation is an invisible ad unit. It doesn’t bill by impression.
When an LLM recommends a brand, it can’t know with certainty what will work best. So it infers. It weighs signals: past success, prominence, reviews, case studies, corroborating sources, and repeated associations between a brand and a specific type of problem.
Humans do something almost identical.
Where performance is clearly bounded, we can identify a winner. We know who won the Oscar. We know which film topped the box office.
But when performance isn’t obvious in advance, we rely on proxies. We ask friends, read reviews, and scan for authority. We use familiarity, logic, and social proof to estimate what is likely to be right.
That’s exactly the territory AI recommendation is now entering — the consideration set problem. If I ask an LLM to find me a reliable accountant for a small business, I’m not asking it to retrieve a blog post. I’m asking it to build me a shortlist.
Unlike traditional search, the recommendation layer is invisible to brands unless they test for it actively. You don’t see the prompt or the source chain. You don’t even know why one brand made the cut and another didn’t.
But the commercial effect is real, possibly stronger than anything traditional search produced. If you’re in the recommendation set, you’re in the running. If you’re absent, you’ve lost the sale before the conversation started.
Dig deeper: Rand Fishkin proved AI recommendations are inconsistent – here’s why and how to fix it
The first practical consequence: your website can no longer function like a polite digital brochure. Despite being optimized for search, many commercial web pages simply:
Still, they’re weak where it matters most: actual selling.
In the Mad Men era of SEO, your landing pages and service pages need to function like sales pages, not in a cheesy direct-response way, but in the strategic sense that they must clearly answer four things:
This comes down to positioning, which is key to GEO. If seven brands do broadly the same thing, the model needs distinctions. It needs enough clarity to say: this brand is best for X kind of buyer with Y kind of problem because it does Z better than everyone else.
Your website copy must surface real performance attributes: the specific things you genuinely do better or more distinctively than competitors. Your pages must become machine-readable arguments for preference.
Actual commercial copywriting — not fluffy brand storytelling or word count for its own sake — identifies a target customer, sharpens the problem, articulates the value, and makes the offer easy to recommend.
Good copy isn’t optional.
Take a local sandwich shop. The old SEO conversation runs to “best sandwich near me,” local pack, and review acquisition. It’s useful, but limited.
The GEO version starts with the shop’s actual performance attributes.
Those claims must be clear on the website first. Then they need corroboration everywhere else:
They’re specific, repeated, retrievable evidence of why this shop is the right recommendation for a particular type of customer.
Scale that logic to a B2B software company, and the principle holds. Pages that clearly explain who the product is for, which problems it solves, and why it outperforms rivals. Then build mentions, customer reviews, and gain trade-press coverage — the body of evidence to support recommending you to buyers — and let the AI find it.
That’s pretty much GEO in a nutshell.
Keywords are a human workaround. Approximations of intent, built for a retrieval system that needed exact string matching. LLMs process fuller context, layered needs, and comparative requirements. They move from keyword matching toward problem understanding.
Keyword research still matters for classic search, paid search, and buyer-intent pages. But the center of gravity shifts.
Instead of asking only “what terms should we rank for?”, the better question is: what attributes make us the right recommendation for the buyer we actually want, and what evidence exists across the web to support that claim?
The future of SEO is starting to look like the old agency model, as the work is increasingly promotional. Once your website clearly expresses your positioning, the challenge becomes promoting that position across the wider web through credible, repeated, relevant signals.
These are the things you go after, create, and encourage. Sadly, many “AI visibility” conversations flatten this into nonsense.
The goal isn’t merely to have content cited by AI. It’s to gather enough market evidence that AI systems repeatedly encounter your brand in the right contexts, with the right associations.
The work stops being optimization and becomes maximization: building the largest possible volume of persuasive, corroborated, retrievable evidence that your brand is a sensible recommendation for a specific kind of buyer.
That’s a fundamentally different model from anything the SEO industry has been selling. It’s promotional and strategic brand marketing.
Dig deeper: How to design content that AI systems prefer and promote
SEOs need to grow up. There’s still significant value in buyer-intent search, technical site architecture, entity clarity, internal linking, and structured data. SEOs are well placed to monitor recommendation environments, test prompts, and identify where visibility is being won or lost.
But the identity crisis is real. Many agencies were built for a world of rankings, informational blogs, and monthly traffic graphs. They aren’t equipped to lead a world defined by positioning, copy, PR, brand evidence, and recommendation science.
Tracking brand citations inside AI outputs isn’t a complete strategy. It’s a temporary metric.
Winning agencies look like hybrid commercial strategy firms: part SEO, part copywriting, part PR, part brand strategy, part technical infrastructure. They know how to protect buyer-intent search revenue today while building the fame, clarity, and corroborated authority that earns recommendation tomorrow.
This is the Mad Men model of SEO. Persuasion, positioning, and clear claims backed by public proof matter again. And the job is to become recommended by AI.
Google explains why it doesn't matter if websites are getting heavier and the takeaway has everything to do with SEO.
The post Google Explains Why It Doesn’t Matter That Websites Are Getting Larger appeared first on Search Engine Journal.
I’m getting a mid-career executive MBA. Last week, in class, we discussed the interaction between automation and advertising. The lecture covered why A/B testing in Meta is less valuable now, since Facebook can auto-optimize faster and better than marketers can on their own.
A classmate took the logical leap and asked the professor, “If digital channels have more data and more processing power, why don’t advertisers just give them a URL and a credit card and let them go wild?”
The argument has real merit. Google, Meta, and LinkedIn have access to more data than any agency ever will. Their optimization engines are improving fast. Handing them a budget and a URL and walking away isn’t entirely crazy.
But that means we’d need to have faith in the channels to optimize media in a business’s best interests, and there’s a long, proud history of that not being the case.
About six years ago, we met with a Google rep who pitched a product that introduced broader, more aggressive targeting and bidding. We listened to the pitch and said no. We didn’t want to try it. The reps turned it on anyway.
What happened next was what we predicted. The campaigns spent significantly more money and didn’t generate any additional conversions.
We had to comp the client for the wasted spend, which was bad enough. But what made it worse was the principle of the thing: we hadn’t agreed to this. Google made unauthorized changes to our account.
When I tried to get the money back, Google’s position was that we’d set our campaign budgets at a certain level, and they were within their rights to spend up to that amount. That framing ignores that a budget cap is a ceiling, not an invitation.
Our agency methodology is to never hit a budget cap. We set those numbers based on the strategy we’d approved, not the one they decided to test. I hounded them for weeks, but never got any resolution. It still makes me angry.
The reps were clearly incentivized to get adoption of the new feature. When it didn’t work, there was no accountability and no recourse. We were left covering the cost of a decision we explicitly declined.
Budget caps were treated as implicit consent to spend. A product we declined was activated without authorization, and when it failed, the platform pointed to our own settings as justification.
The incentive structure rewarded the reps for turning it on. There was no corresponding mechanism to make the advertiser whole when it didn’t work.
Dig deeper: Google rep’s unauthorized ad changes spark advertiser concerns
This was years ago for a successful retainer. A pair of senior Google reps sat across from us and asked what our client’s gross margin was. Around 50%, we said. They went to the whiteboard and wrote out: if overall revenue/2 – overall media cost >= 0, then we should keep spending money on ads.
On the surface, the math sounds right. In practice, it has two problems.
The model treats all reported conversions as incremental and assumes cost per conversion is constant across spend levels. Both assumptions are wrong, and together they can justify significant overspend.
This one still happens all the time. The pitch is that if you raise your CPCs, you’ll get access to higher-quality traffic. The implied logic is that conversion rate is influenced by CPC, and that if your investment isn’t high enough, you’re missing the best clicks.
There’s a version of this that has some truth to it. Higher CPCs can mean higher ad positions, which can mean higher impression frequency against the same users. More frequency can drive higher aggregate conversion rates, because repeated exposure matters.
But the argument glosses over the other side of that equation.
In practice, raising CPCs to chase quality traffic is almost always correlated with substantially worse overall return on ad spend.
This is a variant of the marginal return problem seen across these cases. The pitch frames the upside without acknowledging the cost curve. More spend gets positioned as access to better outcomes, when it often delivers the same outcomes at a higher price.
CPC and conversion rate are presented as if higher bids unlock better traffic. In most cases, the incremental cost outpaces the incremental return. The pitch frames diminishing returns as an opportunity, rather than a constraint.
Dig deeper: Dealing with Google Ads frustrations: Poor support, suspensions, rising costs
“If your Meta campaigns are underperforming, it’s because the algorithm just needs more time to learn.”
“Don’t make changes, and don’t reduce budget, just give the platform more data.”
This is sometimes true. Machine learning systems need volume to optimize effectively, and premature intervention can reset progress.
But “it needs to learn” has become a catch-all explanation that’s almost impossible to disprove in the short run. It explains away poor CPAs, delays accountability, and keeps spend flowing when a reasonable advertiser might otherwise pull back and reassess.
There’s rarely a clear definition of when the learning phase ends, which makes it a moving target. The learning phase ends when performance improves. If performance doesn’t improve, more learning is prescribed.
A real technical concept is being used in ways that resist falsification. When there’s no defined endpoint and no stated criteria for success, “it needs to learn” serves as a blank check for budgetary continuity.
In many cases, YouTube or display campaigns aren’t driving measurable conversions. The rep’s suggestion: let’s look at brand measurement. We can measure recall rates, positive sentiment, and intent to purchase. These are real signals of brand health, and they matter in the long run.
But the shift from conversion to sentiment metrics tends to occur when conversion metrics are poor, not as a principled measurement strategy. Brand lift surveys measure awareness under controlled conditions, but they rely on self-reported intent and don’t connect to downstream revenue.
Recall is almost never translated into a cost per point of lift that can be compared across the media plan. You end up with a number that’s positive and presented as evidence of success, with no agreed-upon framework for what sufficient lift would look like.
A softer metric is substituted for a harder one after the harder one fails. Brand lift is a legitimate measurement tool when defined upfront as a success criterion. Introduced afterward, it functions as a consolation prize.
Dig deeper: PPC mistakes that humble even experienced marketers
Upper-funnel and lower-funnel campaigns serve different purposes and perform differently on a cost-per-acquisition basis. When a channel reports blended CPA across all campaign types, an average that looks acceptable can hide the fact that some portion of the media plan is wildly inefficient at the margin.
The argument for blending is that upper-funnel spend creates the conditions for lower-funnel performance. That is plausible, but plausibility isn’t the same as demonstrated causality.
Often, it’s assumed the upper funnel is directly contributing and that, in aggregate, the system is profitable and fully incremental. This is never the case.
Aggregate CPA can look fine while specific segments of spend have no measurable return. Blending is a reporting choice, and it can obscure where money is and isn’t working.
A view-through conversion is counted when a user sees an ad, doesn’t click it, and then converts within some attribution window, often 24 hours or more. Platforms report these alongside click-through conversions by default.
For retargeting campaigns, which by definition serve ads to people who have already visited your site, view-through attribution is particularly problematic. These users were likely going to return and convert regardless. The ad may have had nothing to do with it.
The issue isn’t that view-throughs aren’t meaningful. For a cold audience, some brand-influenced conversions happen without clicks.
The issue is that those conversions are almost never broken out proactively (you have to ask). And when you remove view-throughs from retargeting campaigns, the ROAS numbers can change dramatically.
We’ve seen cases where removing VTAs cuts reported conversions by more than half. I would note that by moving to incremental measurement options, Meta has become substantially more transparent.
View-through conversions inflate reported performance, particularly in retargeting, where incrementality is already low. Default reporting includes them without flagging the methodological problem.
Dig deeper: Outsmarting Google Ads: Insider strategies to navigate changes like a pro
This one is a pattern. A channel rep brings industry benchmark data to a meeting showing that your competitors are spending at a level above your current budget. The implication is clear: you’re being outspent, and you should close the gap.
Industry benchmarks are among the most valuable inputs a channel can provide. Knowing where you sit relative to the market is useful context for planning. The problem is how they get deployed. More often than not, benchmark data shows up as a tool to expand media spend, not as a neutral input into strategy.
And it works. CEOs and CMOs are particularly susceptible to this framing. Nobody wants to hear that a competitor is outspending them.
The emotional pull of “they’re investing more than you” is hard to counter with a measured conversation about marginal returns or strategic fit. The benchmark becomes the argument, and the argument is almost always “spend more.”
What gets lost is any discussion of whether:
Competitive spend data without context is just a number that makes your budget feel inadequate.
Benchmark data is real, but it’s selectively introduced to justify budget increases rather than treated as one input among many. The framing skips over whether the comparison is meaningful and relies on competitive anxiety to sell.
This one is hard to frame as a single incident because it’s everywhere. I’ve talked to so many people trying to break into the industry, or launch their first campaigns, and the story is almost always the same.
They follow the platform’s setup guide, accept the default settings, and end up opted into programs that have close to zero chance of being successful.
This is true across pretty much every major channel.
Each of these defaults, taken individually, could be defended as a reasonable starting point. Taken together, they create a setup that maximizes the platform’s revenue from day one, before the advertiser knows what’s happening.
A new advertiser following the guided setup is accepting a configuration that the platform designed, and the platform’s incentives aren’t aligned with efficient spend.
This one is genuinely difficult to solve. Platforms need to provide default settings, and they can’t expect every new advertiser to understand every option.
But there’s something predatory about the gap between what people think they’re signing up for and what they’re getting. The defaults are revenue-optimized for the channel, not performance-optimized for the advertiser.
Setup guides and default settings are presented as best practices when they’re actually configurations that favor the platform’s revenue. New advertisers trust the guided experience, and have no reason to suspect the defaults are working against them.
Dig deeper: Are you being manipulated by Google Ads?
Privacy regulations and platform changes have created real limitations in conversion tracking. GDPR and Apple’s App Tracking Transparency aren’t invented problems.
We have less visibility than we used to, and the platforms have responded by layering probabilistic modeling and modeled conversions on top of deterministic tracking.
But the tracking gap has also become a convenient shelter for underperformance. The argument goes like this:
Each of those can be true in isolation. Modeled conversions take time to appear. Attribution is harder than it was five years ago. Proxy metrics can be useful when direct measurement breaks down.
The problem is when all of these caveats get stacked together and used to justify sustained spend in the absence of any measurable result. At some point, “the data will come in” stops being a reasonable expectation and becomes an article of faith.
The tracking gap is real, but it cuts both ways. If you can’t measure the result, you also can’t prove the spend is working. The platform’s default position is to assume it is, and keep going. The advertiser’s job is to ask what happens if the modeled conversions never materialize, and what the fallback plan looks like if they don’t.
Legitimate tracking limitations are used to defer accountability indefinitely. When measurement is hard, the platform’s recommendation is always to maintain or increase spend, never to reduce it. The uncertainty gets resolved in the channel’s favor by default.
None of this is an argument that agencies are irreplaceable in their current form. We used to question tCPA, and now it’s a preferred bidding strategy. Automation handles execution-level work that used to require skilled practitioners. In-house teams are viable for more companies than they used to be.
But the argument for fully autonomous, channel-run advertising assumes the channel will optimize for your outcomes rather than revenue. Even if we imagine new profit-sharing contracts, this assumption carries real risk.
And I’m not blaming reps or the channels. They believe in their products, but they’re also measured on metrics that create a predictable drift in how they frame data. I should note that agencies struggle with misaligned incentives as well.
The advertiser’s job, with or without an agency, is to keep asking the inconvenient questions.
Maybe the answer to everything is eventually full automation. But the entity building the machine shouldn’t be the one telling you when it’s ready.
For years, Salesforce Marketing Cloud was the safe choice.
Powerful. Enterprise. Trusted.
But lately, we’re hearing something different:
Sound familiar? If so, this fireside chat is for you.
We’ve helped dozens of brands migrate off Salesforce and into modern, composable engagement architectures built for real CRM performance. Not because it’s trendy — but because marketers needed more speed, flexibility, and innovation.
In this April 14 session, we’ll cover:
To be clear: this isn’t a Salesforce-bashing session.
It’s a candid conversation about innovation velocity, marketing ownership, and what the next era of marketing actually requires.
Disclaimer: To ensure a candid and open conversation, the live session is open only to brand-side marketing leaders. Registrants who are not verified brand-side marketing leaders will not be permitted to attend the live session. However, the recorded session will be made available to all registrants upon completion of the event.
Intel’s first CPUs to integrate Nvidia graphics chiplets are reportedly called “Serpent Lake”, and they could launch in late 2028 Last year, Intel struck a deal with Nvidia that would allow them to “build and offer to the market x86 system-on-chips (SOCs) that integrate NVIDIA RTX GPU chiplets.” According to the leaker Jaykihn, Intel’s first […]
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We’re sharing an update on our mental health work, including some new changes to better connect people with the right information.STALKER 2 is getting some free content ahead of its Cost og Hope DLC this summer GSC Game World has confirmed that STALKER 2 is getting a free content update this month. This update is “Sealed Truth”, which will allow players inside the X-18 Lab. STALKER fans should already be aware of the Lab X-18, […]
The post STALKER 2 is getting a free content update this month called “Sealed Truth” appeared first on OC3D.
Giraffe Gold lets you build ownership of a physical gold, silver, or platinum bar through small monthly contributions and automatic round-ups. Connect your bank and spending card, set a contribution starting at $50, and watch your bar balance grow in real time with market prices.
When you hit the bar price, Giraffe Gold purchases from certified refiners and ships your bar fully insured to your door. The platform uses Plaid for secure, read-only connectivity and partners with Upstate Coin & Gold and ShipSecure to ensure authenticity and safe delivery.
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Google's John Mueller says that those who self-identify as SEO gurus are clueless imposters.
The post Google’s Mueller On SEO Gurus Who Are “Clueless Imposters” appeared first on Search Engine Journal.
FORMLOVA is a chat-first form service powered by MCP. Create forms from ChatGPT, Claude, or Cursor in under a minute, then manage response routing, follow-up emails, reminders, analytics, and CRM handoffs from the same conversation. It integrates with 118 tools across 24 categories, focusing on the 95% of form work that happens after the form exists.
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Battery dying? WiFi gone? Your Mac finally talks back.
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Simmerce lets e-commerce teams run synthetic customer simulations to validate features before launch. It generates diverse shopper personas—budget shoppers, brand loyalists, impulse buyers, and researchers—and simulates how they search, browse, and convert to reveal friction and opportunities. Upload a product pages, search results, or recommendations and get insights in minutes, including which segments convert, where users get stuck, and what content is missing. Use pay-as-you-go credits to test ideas fast and ship with confidence.
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TrackACert.io is a simple tool for IT managers who need to know what certifications their team holds, what's about to expire, and where the gaps are. We connect GIAC, CompTIA, ISC², AWS, Microsoft, and more in one place. Key features include IT certification tracking, certification expiry notices for your team, skill gap analysis to identify missing skills and certifications, and full reports to help justify financing for more certifications.
