Sony makes its PlayStation Portal streaming device even better with its new “High Quality Mode” On March 18th, Sony will be giving its PlayStation Portal a major upgrade. Soon, owners of Sony’s game streaming device will gain access to “1080p High Quality Mode” for both Remote Play and Cloud Streaming, boosting the device’s image quality. […]
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echo99 records, transcribes, and summarizes your calls across Zoom, Google Meet, MS Teams, and Webex. It delivers accurate, speaker-labeled transcripts, AI summaries with action items and decisions, and a searchable archive for every conversation.
Send the meeting bot to attend for you, then review talk time, sentiment, and engagement, and run post-call analysis to extract quotes and trends. Flexible pay-as-you-go pricing and team options make it easy to adopt at any scale.
Google confirmed it removed "What People Suggest" from health searches. Additionally, the company announced new AI health tools for YouTube.
The post Google Removes ‘What People Suggest,’ Expands Health AI Tools appeared first on Search Engine Journal.
Ray Tracing is coming to Death Stranding 2 on PC PC players will have access to optional ray tracing upgrades in Death Stranding 2 Death Stranding 2: On the Beach is coming to PC on March 19th, with new content arriving on PlayStation 5 on the same day. New content includes a new difficulty mode […]
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FraudSentry is a personal fraud detective that analyzes suspicious texts, emails, links, screenshots, and documents in seconds. It uses AI with a curated database of 100,000+ threat patterns to trace links, surface red flags, and reveal how schemes operate. You receive a clear, actionable report with the evidence, recommended next steps, and easy sharing to protect family and friends. Coming soon to TestFlight for iOS, with Android and web later this year.
Google is expanding Personal Intelligence to free U.S. users in AI Mode, connecting Gmail and Photos to search. Gemini app and Chrome rollout starting.
The post Google AI Mode’s Personal Intelligence Now Free In U.S. appeared first on Search Engine Journal.
We're expanding Personal Intelligence across AI Mode in Search, the Gemini app and Gemini in Chrome.
Google DeepMind proposes a cognitive framework to evaluate AGI and launches a Kaggle hackathon to build capability benchmarks
Google is making new investments, building new tools and developing code security to improve open source security.
YouTube is experimenting with a format that keeps ads visible even after users skip — potentially reshaping how advertisers think about skippable inventory.
What’s happening. YouTube is testing a sticky banner overlay that appears once a user skips an ad. Instead of the ad disappearing entirely, a branded card remains on-screen until the viewer actively dismisses it.
How it works. After hitting “skip,” users return to their video as normal, but a persistent banner tied to the original ad stays visible within the player, extending the advertiser’s presence beyond the initial skip.
Why we care. This test from YouTube creates a way to maintain visibility even when users skip ads, potentially increasing brand recall without requiring full ad views.
It also changes how skippable performance may be evaluated, as impressions and engagement could extend beyond the initial ad, giving brands more value from the same inventory within Google’s ecosystem.
Why it’s notable. Skippable ads have traditionally meant lost visibility once skipped. This format changes that dynamic by offering a second chance for exposure, even when users opt out of the full ad experience.
Impact for advertisers. The update creates an opportunity for extended brand visibility and recall, but could also influence engagement metrics and how users perceive ad interruptions.
The bottom line. If rolled out widely, the sticky banner test could redefine what a “skipped” ad means — turning it into continued, lower-friction exposure rather than a full exit for advertisers on YouTube.
First seen. This update was first spotted by Founder & CEO of Adsquire Anthony Higman who shared spotting it on LinkedIn.
Google is incrementally improving metric visibility in Performance Max, giving advertisers more insight into how creative choices — particularly video — impact performance.
What’s happening. Google Ads has introduced a new “Ads using video” segment within Performance Max channel performance reporting, allowing advertisers to break down results based on whether video assets were included.
Why we care. Marketers can now compare performance across placements that used video versus those that didn’t, offering a clearer view into the role video plays across Google’s automated inventory.
It helps answer a key question in an automated environment: whether investing in video assets is driving better results, allowing you to make more informed creative and budget decisions inside Google Ads.
Between the lines. As video becomes more central across surfaces like YouTube and beyond, this update gives advertisers a way to validate the impact of investing in video assets within automated campaigns.
The bottom line. The new segment adds a layer of clarity to Performance Max, helping advertisers better evaluate video’s contribution without changing how campaigns are run inside Google Ads.
First spotted. This update was first spotted by PPC News Feed founder Hana Kobzova.
Google is expanding Personal Intelligence across AI Mode, Gemini, and Chrome in the U.S., moving it beyond beta into broader consumer use.
Why we care. Personal Intelligence pushes Google further into fully personalized search, using first-party data like Gmail and Photos. That makes results harder to replicate, rank against, or track — especially in AI Mode, where outputs may vary based on user history, purchases, and behavior.
The details. Personal Intelligence now works across:
How it works. Users can connect apps like Gmail and Google Photos so Google can tailor responses using personal context. Examples Google shared include:
Availability. These features are available only for personal accounts, not Workspace users, Google said.
Dig deeper. Google says AI Mode stays ad-free for Personal Intelligence users
Catch-up quick. Google introduced Personal Intelligence as a U.S.-only beta for Gemini subscribers in January. At the time:
Privacy and control. Google emphasized:
Google’s blog post. Bringing the power of Personal Intelligence to more people
Although Google continues to test ads in AI Mode, users who connect apps to enable Personal Intelligence won’t see ads — and that isn’t changing right now, a Google spokesperson confirmed.
What’s happening. Google has been testing ads inside AI Mode in the U.S.
The details. Google today expanded Personal Intelligence in AI Mode as a beta to anyone in the U.S., allowing Gemini to generate more tailored responses by connecting data across its ecosystem, including Google Search, Gmail, Google Photos, and YouTube.
Why we care. Ads are coming to AI Mode, but Google is moving cautiously where personal data is deepest. Personal Intelligence experiences stay ad-free for now while Google works out the right balance.
What Google is saying. A Google spokesperson told Search Engine Land:
Bottom line. Personal Intelligence positions Google’s Gemini app as a more personalized assistant, setting the stage for future ad experiences built on richer, cross-platform user context.
The Hormuz crisis is threatening TSMC and the global semiconductor supply chain We have now entered the third week of the Iran conflict, with Iran effectively closing the globally vital Strait of Hormuz in response to attacks from the US and Israel. Typically, the Strait would see 20% of the world’s natural gas and 25% […]
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PDF Template API lets you design dynamic PDF templates and generate business documents via REST API, Zapier, Make, Airtable, and other no-code platforms. Build real-world documents with reusable headers and footers, data binding, auto-growing tables, and on-the-fly QR codes and barcodes. Use expressions, system variables, and 100+ functions to format content, calculate totals, and control layouts, then deliver polished invoices, packing slips, certificates, and more.
At Google’s annual health event, The Check Up, we shared how our products, research and partnerships are making the most of AI to help everyone live healthier lives.
Learn how Fitbit is helping people take control of their health by linking medical records, improving sleep tracking accuracy and advancing metabolic research.
At The Check Up, Google announced a $10M investment in clinician AI training and how AI is upgrading Search and Fitbit for better health data.
Yahoo CEO Jim Lanzone said AI-powered search — especially Google’s AI Mode — is putting the open web’s core traffic model at risk and argues AI search engines must send users back to publishers.
Why we care. Many websites are seeing less traffic from answer engines like Google and OpenAI — and I think it’ll only get worse. So it’s encouraging to see Yahoo trying to preserve the “search sends traffic” model. As he said: “We have very purposefully highlighted and linked very explicitly and bent over backwards to try to send more traffic downstream to the people who created the content.”
Yahoo’s AI stance. Yahoo is taking a different approach from chatbot-style interfaces, Lanzone said on the Decoder podcast. He added that Yahoo isn’t trying to compete as a full AI assistant:
What’s next: Personalization + agentic actions. Yahoo plans to expand Scout beyond basic answers and is embedding AI across its ecosystem:
Yahoo vs. Google isn’t a thing. Yahoo isn’t trying to win by converting Google users directly. Instead, Yahoo is prioritizing its existing audience and increasing usage frequency over immediate market share gains:
A warning. Companies — including publishers — should be cautious about relying too heavily on AI platforms as intermediaries. Lanzone compared today’s AI partnerships to Yahoo’s past reliance on Google:
The interview. Yahoo CEO Jim Lanzone on reviving the web’s homepage
For a long time, a nonprofit’s digital presence hasn’t been a “nice-to-have.” It’s the central hub for mission delivery, donor engagement, and advocacy.
Many organizations struggle with the technical and strategic foundations needed to turn a website and a few social accounts into a high-performing digital ecosystem.
The goal isn’t simply to “be online.” It’s to build reliable infrastructure, so your organization owns its narrative, protects its assets, and measures the impact of “free” digital efforts.
Here’s a practical look at the critical elements of managing a nonprofit’s digital presence — and the common pitfalls to avoid — based on my experience helping several organizations throughout my career.
If you help an organization with digital marketing and they aren’t following these practices, your first step should be getting their digital house in order.
Owning your name and your story are essential parts of a proactive online reputation management strategy and a critical aspect of managing an online entity.
In my experience, the most overlooked risk in nonprofit digital management is the lack of direct ownership of technical assets.
A well-meaning volunteer or third-party agency often registers a domain or creates a social account using personal credentials. If that individual leaves the organization, you risk losing access to your primary digital channel — the domain you should own and control.
I’ve worked with several organizations that had to start over completely because they lacked control.
Dig deeper: Google Ad Grants now lets nonprofits optimize for shop visits
A common mistake for nonprofits is posting only when there’s an immediate need, which is often only when making a fundraising appeal. This “broadcast-only” approach often leads to donor fatigue and low engagement.
To build a community, you need a content plan that balances stories of impact with actionable requests.
Data is only useful if it informs future decisions. Many organizations get bogged down in “vanity metrics” like total likes or page views without understanding whether those numbers lead to real-world outcomes.
Most global web traffic is now mobile, and for nonprofits, this is critical. Donors often engage with your content on social media on their phones and expect a seamless transition to your donation page.
Dig deeper: Why now is the most important time for nonprofit advertising
Even well-intentioned nonprofits can undermine their digital presence with a few common mistakes.
One of the biggest mistakes is trying to reach everyone. A digital presence that tries to appeal to every demographic usually ends up appealing to no one. Define your “ideal supporter,” and tailor your language, imagery, and platform choice to them.
Accessibility is about inclusion. Ensure your images have alt text, your videos have captions, and your website colors have enough contrast for users with visual impairments. If a portion of your audience can’t interact with your site, you aren’t fulfilling your mission.
I often tell businesses to treat websites like any other business asset, and the same applies to nonprofits. Digital ecosystems require maintenance.
Links break, plugins need updates, and search algorithms change. A quarterly “digital audit” to check your site speed, broken elements, and SEO health is essential for long-term visibility.
Dig deeper: How to use Google Ads to get more donations for your nonprofit
A successful digital presence is built on the same principles as a successful mission: consistency, transparency, and clear communication. By owning your assets, planning your content, and grounding your decisions in data, you ensure that your digital ecosystem serves as a force multiplier for the people you’re trying to help.
We’ve signed an agreement with AMP to enable 200,000 tons of CO2 removal by 2030 and explore how their approach mitigates methane, a superpollutant that is 80 times more…
If you’re a content strategist, you might feel this isn’t your territory. Keep reading, because it is. Everything you build feeds these five gates, and the decisions the algorithms make here determine whether the system recruits your content, trusts it enough to display it, and recommends it to the person who just asked for exactly what you sell.
The DSCRI infrastructure phase covers the first five gates: discovery through indexing. DSCRI is a sequence of absolute tests where the system either has your content or it doesn’t, and every failure degrades the content the competitive phase inherits.
The competitive phase, ARGDW (annotation through won), is a sequence of relative tests. Your content doesn’t just need to pass. It needs to beat the alternatives. A page that is perfectly indexed but poorly annotated can lose to a competitor whose content the system understands more confidently.
A brand that is annotated but never recruited into the system’s knowledge structures can lose to one that appears in all three graphs. The infrastructure phase is absolute: pass, stall, or degrade. The competitive phase is Darwinian “survival of the fittest.”
The DSCRI infrastructure phase determines whether your content even gets this far. The ARGDW competitive phase determines whether assistive engines use it.
Up until today, the industry has generally compressed these five distinct processes into two words: “rank and display.” That compression muddied visibility into several separate competitive mechanisms. Understanding and optimizing for all five will make all the difference in the world.
The transition from DSCRI to ARGDW is the most significant moment in the pipeline. I call it the competitive turn.
In the infrastructure phase, every gate is zero-sum: does the system have this content or not? Your competitors face the same test, and you both pass or fail. But the quality of what survives rendering and conversion fidelity creates differences that carry forward.
The differentiation through the DSCRI infrastructure gates is raw material quality, pure and simple, and you have an advantage in the ARGDW phase when better raw material enters that competition.
At the competitive turn, the questions change. The system stops asking “Do I have this?” and starts asking “Is this better than the alternatives?”
Every gate from annotation forward is a comparison. Your confidence score matters only relative to the confidence scores of every other piece of content the system has collected on the same topic, for the same query, serving the same intent.
You’ve done everything within your power to get your content fully intact. From here, the engine puts you toe to toe with your competitors.
The algorithmic trinity — search engines, knowledge graphs, and LLMs — operates across four of the five competitive gates: annotation, recruitment, grounding, and display. Won is the outcome produced by those four gates. Presence in all three graphs creates a compounding advantage across ARGD, and that vastly increases your chances of being the brand that wins.
The systems cross-reference across graphs constantly. An entity that exists in the entity graph with confirmed attributes, has supporting content in the document graph, and appears in the concept graph’s association patterns receives higher confidence at every downstream gate than an entity present in only one.
This is competitive math. If your competitor has document graph presence (they rank in search), but no entity graph presence (no knowledge panel, no structured entity data), and you have both, the system treats your content with higher confidence at grounding because it can verify your claims against structured facts. The competitor’s content can only be verified against other documents, which is a higher-fuzz verification path — more interpretation, more ambiguity, lower confidence.
For me, this is where the three-dimensional approach comes into its own, and single-graph thinking becomes a structural liability. “SEO” optimizes for the document graph. Entity optimization (structured data, knowledge panel, and entity home) optimizes for the entity graph.
Consistent, well-structured copywriting across authoritative platforms optimizes for concept graph. Most brands invest heavily in one (perhaps two) and ignore the others. The brands that win at the competitive gates are stronger than their competitors in all three at every gate in ARGD(W).
Annotation is something I haven’t heard anyone else (other than Microsoft’s Fabrice Canel) talking about. And yet it’s very clearly the hinge of the entire pipeline. It sits at the boundary between the two phases: the last gate that applies absolute classification, and the first gate that feeds competitive selection. Everything upstream (in DSCRI) prepared the raw material. Everything downstream in ARGDW depends on how accurately the system can classify it.
At the indexing gate, the system stores your content in its proprietary format. Annotation is where the system reads what it stored and decides what it means. The classification operates across at least five categories comprising at least 24 dimensions.
Canel confirmed the principle and confirmed there are (a lot) more dimensions than the ones I’ve mapped. What follows is my reconstruction of the categories I can identify from observed behavior and educated guesses.
Canel confirmed the Annotation gate back in 2020 on my podcast as part of the Bing Series, in the episode “Bingbot: Discovering, Crawling, Extracting and Indexing.”
So we know that annotation is a “thing,” and that all the other algorithms retrieve the chunks using those annotations.
Annotation classification runs across five types of specialist models operating simultaneously per niche:
This five-model architecture is my reconstruction based on observed annotation patterns and confirmed principles. The annotation system is a panel of specialists, and the combined output becomes the scorecard every downstream gate uses to compare your content against your competitors.
They determine whether the content enters specific competitive pools at all:
Fail a gatekeeper, and the content is excluded from entire query classes regardless of quality.
This classifies the content’s substance: entities present, attributes, relationships between entities, and sentiment.
For example, a page about “Jason Barnard” that the system classifies as being about a different Jason Barnard has perfect infrastructure and broken annotation. The content was there, and the system read it, but filed it in the wrong drawer.
They add query routing: intent category, expertise level, claim structure, and actionability.
For example, content classified as informational never surfaces for transactional queries, regardless of how well it performs on every other dimension.
Think:
Weak chunks get discarded before competition begins.
These determine how much the system trusts its own classification: verifiability, provenance, corroboration count, specificity, evidence type, controversy level, consensus alignment, and more.
Two pieces of content can be classified identically on every other dimension and still receive wildly different confidence scores based on how verifiable and corroborated their claims are.
An important aside on confidence
Confidence is a multiplier that determines whether systems have the “courage” to use a piece of content for anything.
Once upon a time, content was king. Then, a few years ago, context took over in many people’s minds.
Confidence is the single most important factor in SEO and AAO, and always has been — we just didn’t see it.
To retain their users, search and assistive engines must provide the most helpful results possible. Give them a piece of content that, from a content and context perspective, appears to be super relevant and helpful, but they have absolutely no confidence in it for one reason or another, and they likely will not use it for fear of providing a terrible user experience.
Annotation failures are the most dangerous failures in the pipeline because they are invisible. The content is indexed. But if the system misclassifies it, every competitive decision downstream inherits that misclassification.
I’ve watched this pattern repeatedly in our database: a page is indexed, it appears in search results, and yet the entity still gets misrepresented in AI responses.
Imagine this: A passage/chunk from your website is in the index, but confidence has degraded through the DSCRI part of the pipeline, and the annotation stage has received a degraded version.
The structural issues at the rendering and indexing gates didn’t prevent indexing, but they were degraded versions of the original content. That degradation makes the annotation less accurate, less complete, and less confident. That annotative weakness will propagate through every competitive gate that follows in ARGDW.
When your content is included in grounding or display, and it’s suboptimally annotated, your content is underperforming. You can always improve annotation.
Annotation quality is the most important gate in the AI engine pipeline, but unfortunately, you can’t measure annotation quality directly. Every metric available to you is an indirect downstream effect.
The KPIs I suggest below are signals that clearly show where your content cleared indexing and failed annotation: the engine found the page, rendered it, indexed it, and then drew the wrong conclusions from it.
That distinction matters: beware of “we need more content” when the real problem is “the engine misread the content we have.”
These signals reveal how accurately the AI has understood who you are, what you do, and who you serve. The brand SERP (and AI résumé) is a readout of the algorithm’s model of your brand and, because it is updated continuously, makes it a great KPI.
These signals reveal which entities the system considers comparable — a direct readout of how annotation classified them. Annotation places entities into competitive pools, and if your brand doesn’t appear in comparison sets where it belongs, the engine classified it outside that pool. Better content won’t fix that. Improving the algorithm’s ability to accurately, verbosely, and confidently annotate your content will.
For me, that last one is the most telling. Weaker brand, higher placement.
Once again, what you’re saying isn’t the problem, how you’re saying it and how you “package” it for the bots and algorithms is the problem.
These signals reveal whether the AI can place your brand at the point of discovery, before the user knows you exist. Clearing indexing means the engine has the content. Failing here means annotation didn’t connect that content to the broad topic signals that drive assistive recommendations.
The difference between a brand that appears in “how do I solve [problem]” answers and one that doesn’t is whether annotation connected the content to the intent.
Three revenue consequences follow from annotation failure, one at each layer of the funnel.
Each tax is a direct read of how well annotation worked — or didn’t.
For you as an SEO/AAO expert, you can diagnose your approach to reduce these three taxes for your client or company as:
Annotation should be your focus. My bet is that for the vast majority of brands, the gate in the pipeline with the biggest payback will be annotation. 99% of the time, my advice to you is going to be “get started on fixing that before you touch anything else.”
For the full classification model in academic depth, see:
Recruitment is where the system uses your content for the first time. Every piece of content the system has annotated now competes for inclusion in the system’s active knowledge structures, and this is where head-to-head competition begins.
Every entry mode in the pipeline — whether content arrived by crawl, by push, by structured feed, by MCP, or by ambient accumulation — must pass through recruitment. No content reaches a person without being recruited first. We could call recruitment “the universal checkpoint.”
The critical structural fact: it recruits into three distinct graphs, each with different selection criteria, different confidence thresholds, and different refresh cycles. The three-graph model is my reconstruction.
The underlying principle (multiple knowledge structures with different characteristics) is confirmed by observing behavior across the algorithmic trinity through the data we collect (25 billion datapoints covering Google’s Knowledge Graph, brand search results, and LLM outputs).
The entity graph stores structured facts with low fuzz — who is this entity, what are its attributes, how does it relate to other entities, binary edges — and knowledge graph presence is entity graph recruitment, with entity salience, structural clarity, source authority, and factual consistency as the selection criteria.
The document graph handles content with medium fuzz — passages and pages and chunks the system has annotated and assessed as worth retaining — where search engine ranking is the visible output, and relevance to anticipated queries, content quality signals, freshness, and diversity requirements drive selection.
The concept graph operates at a different level entirely, storing inferred relationships with high fuzz — topical associations, expertise patterns, semantic connections that emerge from cross-referencing multiple sources — with LLM training data selection as the mechanism and corroboration patterns as the primary selection criterion.
The same content may be recruited by one, two, or all three graphs. Each graph has its own speed of ingestion and its own speed of output. I call these the three speeds, a pattern I formulated explicitly this year but have been observing empirically across 10 years of brand SERP experiments:
Recruitment stored your content in the system’s three knowledge structures. Grounding is where the system checks whether it should trust your content, right now, for this specific query.
Search engines retrieve from their own index. Knowledge graphs serve stored structured facts. Neither needs grounding. Only LLMs have the (huge) gap between stale training data and fresh reality that makes grounding necessary.
The need for grounding will gradually disappear as the three technologies of the algorithmic trinity converge and work together natively in real time.
In an assistive Engine, the LLM is the lead actor. When the user asks a question or seeks a solution to a problem, the LLM assesses its confidence in its own answer.
If confidence is sufficient, it responds from embedded knowledge. If confidence is low, it sends cascading queries to the search index, retrieves results, dispatches bots to scrape selected pages, and synthesizes an answer from the fresh evidence (Perplexity is the easiest example to see this in action — an LLM that summarizes search results).
But that’s too simplistic. The three grounding sources model that follows is my reconstruction of how this lifecycle operates across the algorithmic trinity.
The search engine grounding the industry currently focuses on is this: the LLM queries the web index, retrieves documents, and extracts the answer. That’s high fuzz.
Now add this: Knowledge graph allows a simple, quick, and cheap lookup: low fuzz, binary edges, no interpretation required, and our data shows that Google does this already for entity-level queries.
My bet is that specialist SLM grounding is emerging as a third source. We know that once enough consistent data about a topic crosses a cost threshold, the system builds a small language model specialized for that niche, and that model becomes a domain-expert verifier. It would be foolish not to use that as a third grounding base.
The competitive implication is huge. A brand with entity graph presence gives the system a low-fuzz grounding path. A brand without it forces the system onto the high-fuzz path (document retrieval), which means more interpretation, more ambiguity, and lower confidence in the result. The competitor with structured entity data gets verified faster and more accurately.
In short, focus on entity optimization because knowledge graphs are the cheapest, fastest, and most reliable grounding for all the engines.
Your content has been annotated, recruited into its knowledge structures, and verified through grounding. Display is where the AI assistive engine decides what to show the person (and, looking to the future that is already happening, where the AI assistive Agent decides what to act upon).
Display is three simultaneous decisions: format (how to present), placement (where in the response), and prominence (how much emphasis). A brand can be annotated, recruited, and grounded with high confidence and still lose at display because the system chose a different format, placed the competitor more prominently, or decided the query deserved a different type of answer entirely.
This is essentially the same thing as Bing’s Whole Page Algorithm. Gary Illyes jokingly called Google’s whole page algorithm “the magic mixer.” Nathan Chalmers, PM for the whole page algorithm at Bing, explained how that works on my podcast in 2020. Don’t make the mistake of thinking this is out of date — it isn’t. The principles are even more relevant than ever.
You may have heard or read me talking obsessively about understandability, credibility, and deliverability. UCD is absolutely fundamental because it is the internal structure of display: the vertical dimension that makes this gate three-dimensional.
The same content, grounded with the same confidence, presents differently depending on who is asking and why.
A person arriving with high trust — they searched your brand name, they already know you — experiences display at the understandability layer, where the engine acts as a trusted partner confirming what they already believe, which is BOFU.
A person evaluating options — they asked “best [category] for [use case]” — experiences display at the credibility layer, where the engine presents evidence for and against as a recommender, which is MOFU.
A person encountering your brand for the first time — a broad topical question in which your name appears — experiences it at the deliverability layer, where the system introduces you, which is TOFU.
The user interaction reveals the funnel position. The funnel position determines which UCD layer fires.
This is why optimizing only for “ranking” misses reality: Display is a context-sensitive presentation, not a list, and the same piece of content can introduce, validate, or confirm depending on who asked.
The system presents what it understood, verified, and deemed relevant. The gap between that and your intended positioning is the framing gap, and it operates differently at each funnel stage.
After annotation, framing is the single most important part of the SEO/AAO puzzle, so I’ll talk a lot about both in the coming articles.
Everything I’ve explained so far in this series collapses into a zero-sum point at the “won” gate. Here, the outcome is binary. The person (or agent) acts, or they don’t. One brand converts, and every competitor loses.
The system may have mentioned others at display, but at the moment of commitment, there can only be one winner for the transaction.
Won always resolves through three distinct mechanisms, each with different competitive dynamics.
Resolution 1: Imperfect click
Resolution 2: Perfect click
Resolution 3: Agential click
The trajectory runs from oldest to newest: Resolution 1 was dominant up to late 2025, Resolution 2 is taking over, and Resolution 3 gained a lot of traction early 2026. Stripe and Cloudflare are laying the transaction and identity rails. Visa and Mastercard are building the financial authorization infrastructure.
Anthropic’s MCP is providing the coordination layer. Google’s UCP and A2A are defining how agents communicate across the full consumer commerce journey. Apple has the closed-loop infrastructure to make it seamless on a billion devices the moment they choose to.
Microsoft is locking in the enterprise and government layer through Copilot in a way that will be extremely difficult to displace. No single company turns Resolution 3 on — but all of them together make it inevitable.
The competitive intensity increases at every gate — a progressive narrowing, a Darwinian funnel where the field shrinks at each stage. The narrowing pattern is my model based on observed outcomes across our database. The underlying principle (competitive selection intensifies downstream) is structural to any sequential gating system.
Five gates. Five relative tests. Competitive failures in ARGDW are significantly harder to diagnose than infrastructure failures in DSCRI because the fix is competitive positioning rather than technical.
The aim of this series of articles is to give you the playbook for the DSCRI infrastructure phase and the strategy for the ARGDW competitive phase. This 10-gate AI engine pipeline breaks optimizing for assistive engines and agents into manageable chunks.
Each gate is manageable on its own. And the relative importance of each gate is now clear for you (I hope). In the remainder of this series of articles, I’ll provide solutions to the major issues at each gate that will help you manage each individually (and as part of the collective whole).
Aside: The feedback I have had from Microsoft on this series so far (thank you, Navah Hopkins) reminded me of something Chalmers said to me about Darwinism in Search back in 2020.
My explanations are often more absolute and mechanical than the reality. That’s a very fair point. But then reality is unmanageably nuanced, and nuance leads to a lack of clarity and often paralyzes people to the extent that they struggle to identify actionable next steps. I want to be useful.
I suggest we take this evolution from SEO to AAO step by step. Over the last 10+ years, I’ve always done my very best to avoid saying “it depends.”
People often say it takes 10,000 hours to become an expert. The framework presented here comes from tens of thousands of hours analyzing data, experimenting, working with the engineers who build these systems, and developing algorithms, infrastructure, and KPIs.
The aim is simple: reduce the number of frustrating “it depends” answers and provide a clear outline for identifying actionable next steps.
This is the fifth piece in my AI authority series.
Search strategy once meant ranking on Google. We optimized websites and invested heavily in organic visibility. Entire marketing strategies were built around capturing demand from Google search results.
But search behavior doesn’t live on a single platform. Today, people search on TikTok for recommendations, YouTube for tutorials, Reddit for honest opinions, and Amazon for product validation.
Search behavior now spans a much wider set of platforms, creating one of the most overlooked opportunities in digital marketing.
Recent research from SparkToro and Datos analyzed search behavior across 41 major platforms, including traditional search engines, ecommerce platforms, social networks, AI tools, and reference sites.
The findings reinforce something many marketers are beginning to notice. Search is no longer confined to traditional search engines.
While Google still dominates search activity, a growing share of discovery now happens across a wider collection of platforms — a search universe, if you will.
The research suggests search activity is roughly distributed as follows:
Consumers search directly on platforms where they expect to find the most useful answers, in the formats they prefer, rather than relying on Google to send them elsewhere.
Dig deeper: Discoverability in 2026: How digital PR and social search work together
Much of the search industry conversation today is focused on AI. Questions like:
They’re constantly being posed, debated, and answered by SEO professionals on platforms like Search Engine Land.
I want to be clear, these are important questions. But the data within this study tells a more grounded story, especially when thinking about strategy over the next 12 months.
AI search tools currently account for roughly 3.2% of search activity, per SparkToro research. That’s meaningful. It will almost certainly reshape how people search and discover information in the future.
But today, AI search is still smaller than many established discovery platforms with far broader adoption. For example:
Yet many brands are pouring disproportionate attention into AI visibility while overlooking platforms where millions of searches are already happening every day.
For many users, social platforms are now core search destinations. People look to:
Each platform plays a different role in the discovery journey.
| Platform | What people search for |
| TikTok/Instagram | Discovery and recommendations |
| YouTube | Learning, tutorials, and reviews |
| Real opinions and community discussions | |
| Inspiration and planning |
These platforms are more than entertainment destinations. Users head to them with real intent to find a solution to a problem, need, or desire.
As users adopt social platforms for search, Google has begun aggregating and organizing information right within its SERPs. So yes, social and creator content appears directly inside Google search results.
Over the past year, Google has significantly expanded how it surfaces social content within SERPs. Search results now frequently include TikTok videos, YouTube Shorts, Reddit threads, Instagram posts, and forum discussions.
Google even partnered with platforms like Reddit to ensure that community discussions appear more prominently in search results. This means social content can now influence discovery in multiple ways:
Dig deeper: Social and UGC: The trust engines powering search everywhere
Social platforms are also important sources for AI-generated answers. AI systems rely on content that reflects real-world experiences, discussions, and opinions.
That’s why platforms such as Reddit, YouTube, Quora, forums, and creator-led content (i.e., Instagram, TikTok, and YouTube Shorts) are frequently cited in AI-generated responses.
Google’s AI Overviews often reference Reddit threads and YouTube videos.
Other AI tools regularly draw insights from community discussions, reviews, and creator content when generating answers.
This means content created for social discovery can influence visibility across multiple layers of search, including social platforms, Google search results, and AI-generated responses.
A single piece of content can now travel much further across the universe, consistently providing signals to audiences, developing a preference toward one brand over another.
When brands invest in social search visibility, they unlock a powerful compounding effect. For example, a useful YouTube tutorial could:
Unlike traditional website content, social content can move across platforms, dramatically expanding its reach. This creates an entirely new layer of discoverability.
And at a time when marketing budgets are under increasing scrutiny, the ability for content to generate visibility across multiple platforms makes the ROI of content strategies far more compelling.
Dig deeper: The social-to-search halo effect: Why social content drives branded search
Despite these shifts, most search strategies still revolve around Google SEO, paid search, website content, and AI/LLM interfaces.
Few brands have structured strategies for TikTok search optimization, YouTube search visibility, Reddit community engagement, and creator-led discovery strategies.
While Google SEO is incredibly competitive, social search remains relatively under-optimized. Brands that move early can capture visibility (presence) in spaces where demand already exists, thereby developing preference for their brand.
When brands invest in social search visibility, they aren’t just unlocking the 5.5% of searches happening directly on social platforms. They’re also influencing traditional search results, AI-generated answers, and wider discovery across the web.
Search is more than a channel. It’s a behavior that happens across a developing and evolving search universe.
Your audience searches wherever they believe they’ll find the best answer in the most useful format — whether that’s Google, TikTok, YouTube, Reddit, Amazon, Pinterest, or increasingly, AI interfaces.
Winning search today means being discoverable wherever those searches happen. The brands that win won’t be the ones that rank in just one place, even as traditional SEO remains an important part of the mix. They’ll be the ones that are discoverable wherever their audience searches.
That is the future of search. That is “search everywhere.”
Dig deeper: ‘Search everywhere’ doesn’t mean ‘be everywhere’
Nintendo has transformed Switch 2 handheld gaming with “handheld mode boost” With its newest firmware update for the Switch 2 console, Nintendo has added a new “Handheld Mode Boost” function to its system. When using it, Switch 1 software can be operated in “TV mode” on the Nintendo Switch 2 console in handheld mode. This […]
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ValidDraft verifies human authorship by capturing your real drafting behavior and turning it into auditable, tamper-proof certificates. It analyzes revision patterns, timing, cursor movements, and optional video presence to produce a clear humanity score and verification status.
Use it to protect bylines, uphold academic integrity, and meet compliance needs. Detect pasted blobs and impossible patterns, keep your process private, and share verifiable proof of authorship with newsrooms, universities, and publishers.
Pixelle is an AI-powered visual toolkit for indie developers and creators. It generates consistent app icons, marketing graphics, and App Store screens, guided by project-wide brand colors and design rules for a cohesive look. Export assets in one click for iOS, Android, web favicons, macOS .icns, and Windows .ico, with localization to 20+ languages. Start with 5 free generations, then pay $0.09 per image—no subscriptions.
Google is partnering with DTE Energy to develop a new data center in Michigan, and enabling new, clean resources to support the grid.
YouTube is a Preferred Platform for the FIFA World Cup 2026. People can watch match highlights, historical games and behind-the-scenes content from creators and official…
Google is expanding capabilities in Google Ads Editor to give advertisers more creative flexibility, automation control, and budget precision — especially as AI-driven campaign types continue to evolve.
What’s new. The 2.12 release introduces a wide set of updates across Performance Max, Demand Gen, and video campaigns, with a clear focus on scaling creative assets and improving workflow efficiency.
Creative expansion. Performance Max campaigns now support up to 15 videos per asset group, allowing advertisers to feed more variations into Google’s AI for testing. The addition of 9:16 vertical images also reflects growing demand for mobile-first formats, particularly across surfaces like short-form video.
Campaign upgrades. Demand Gen campaigns get several enhancements, including new customer acquisition goals, brand guideline controls, and hotel feed integrations. A new minimum daily budget and a streamlined campaign build flow aim to improve stability and setup.
Video & AI control. Updates to non-skippable video formats and real-time bid guidance give advertisers more control over performance, while new text and brand guidelines help ensure AI-generated assets stay on-brand and compliant.
Budgeting shift. A new total campaign budget feature allows advertisers to set a fixed spend across a defined period — ideal for promotions or seasonal bursts — with Google automatically pacing delivery.
Workflow improvements. Account-level tracking templates, better visibility into Final URL expansion performance, clearer campaign status filters, and bulk link replacement tools are designed to reduce manual work and improve account management at scale.
Why we care. This update to Google Ads Editor gives them more creative flexibility and control over AI-driven campaigns, especially in Performance Max and Demand Gen. Features like increased video limits, vertical assets, and total campaign budgets help you test more, scale faster, and manage spend more efficiently.
It also improves workflows and brand safeguards, making it easier to guide automation while maintaining consistency and performance across Google Ads.
Between the lines. The update continues a broader trend: as automation increases, Google is giving advertisers more ways to guide AI rather than manually control every input.
The bottom line. Google Ads Editor 2.12 is less about one standout feature and more about incremental gains across creative, automation, and control — helping advertisers better manage increasingly AI-driven campaigns within Google Ads.
As Google rolls out AI Overviews, AI Mode in Search, and the Gemini ecosystem, we face a growing challenge: what happens when users get answers — and soon complete purchases — without leaving Google’s interfaces?
Enter Google’s Universal Commerce Protocol (UCP), now in beta.
UCP is designed to help brands to sell to consumers without leaving the Gemini or LLM experience. Consumers can check out within the LLM, add rewards points, and fully execute the transaction. Here’s an example flow:
At its core, UCP standardizes how consumer AI interfaces communicate with merchant checkout systems. When a user tells Gemini, “Find me a highly rated, waterproof hiking boot in size 10 under $200 and buy it,” UCP is the invisible bridge that allows the AI to securely fetch inventory, process the payment, and confirm the order.
While Google’s developer documentation leans into technical jargon like “Model Context Protocol (MCP)” and “Agent2Agent (A2A) interoperability,” the implications are remarkably straightforward:
Dig deeper: How Google’s Universal Commerce Protocol changes ecommerce SEO
Like many LLM optimization recommendations, these steps come down to the fundamentals of managing your shopping feed and Merchant Center account.
Google outlined a few best practices. If you follow these four steps, you’ll be well-positioned for success.
In an agentic commerce environment, your product feed is your primary sales tool. To ensure the AI accurately matches your products to highly specific user queries, you need to enrich your feed with granular details.
Dig deeper: Google publishes Universal Commerce Protocol help page
To set your brand apart when AI is helping consumers make immediate, confident purchasing decisions, you must pass trust and convenience signals directly through your feed. The data shows that these elements directly impact the bottom line:
The shift to UCP requires foundational updates to how your backend systems interact with Google. You must work hand in hand with their development and SEO teams to prepare for these AI search experiences.
Google is actively rolling out pilot programs designed specifically for the agentic era. Be proactive in adopting these new solutions rather than waiting for wide release:
Dig deeper: Are we ready for the agentic web?
The launch of Google’s Universal Commerce Protocol signals a significant shift. The SERP is becoming a transactional engine that increasingly operates within large language models.
UCP presents a meaningful opportunity. By removing friction between discovery and purchase, conversion rates could increase.
However, taking advantage of this requires stepping outside the Google Ads interface and working directly in your feed data and technical integrations, much like with Google Shopping. While this isn’t new, it’s becoming more important.
Ultimately, this comes down to the quality of your product data.
Noctua’s first “Noctua Edition” PC case has landed It’s official, Noctua has released its first-ever Noctua Edition PC case, shipping with six of Noctua’s NF G2 series fans, a Noctua fan hub, and a custom Noctua colour scheme. After launching a Noctua Edition PSU and several graphics cards, a case was the logical next step. […]
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TestSprint 360 delivers AI-driven continuous testing for web, mobile, and APIs so teams ship faster with fewer defects. Its TS360 OmniTest platform streamlines setup, authoring, and execution with natural language test creation, a smart visual flow builder, and secure cloud or local runs across browsers and devices. Integrate with CI/CD pipelines like Jenkins, customize features and localization, and scale regression and in-sprint testing with reliable coverage.
Text Affirmations sends randomly timed text messages to help you build habits, practice gratitude, and stay focused. It starts with a 2-minute quiz, then writes messages based on scientifically vetted frameworks like positive psychology and CBT. You can talk to it to refine the tone and timing, and let the system learn your needs. Or write your own messages to yourself. There’s no app to download, just supportive coaching that meets you where you are.
The official WordPress Plugin Checker provides automated code review for security and best practices, perfect for checking vibe coded plugins.
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Google DeepMind, Google.org, and Google Skills unite to empower the next generation of AI researchers and educators with a free, high-impact curriculum.
Partners.ai is an AI-powered platform that helps local service businesses, like financial advisors, real estate agents, and med spas, find and connect with complementary, non-competing businesses to build referral partnerships. It uses AI to discover ideal partner matches nearby, automates personalized email outreach through the user's own Gmail, and manages the ongoing health of those partnerships. The goal is to generate warm leads that close at higher rates than cold advertising, at a fraction of the cost.
AI reads the fine print before you click "I Agree"
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ArchieNote is an AI-powered note-taking app that turns your notes into quizzes automatically, lets you chat with an AI trained on your own content, and supports PDF uploads for instant analysis. Unlike other AI tools, ArchieNote uses pay-as-you-go credits instead of a monthly subscription—you only spend when you generate a quiz, ask a question, or upload a PDF. Light month? Your balance barely moves. Exam week? Go all in. No subscriptions, no surprises. Beta users start with 1,000 free credits with no card needed.
Send files to predefined Emails via drag and drop
Your X.com feed as a podcast
The GPT moment for real-time computer graphics
Product Hunt for AI agents — where agents discuss products
Turn your friends into shareable content
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Get revenue from every email campaign with 99.9% inbox rate
Local-first AI orchestrator for software development tasks.
Automate files, apps, and workflows with Manus Desktop
Discord CLI for AI agents and humans
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High-fidelity Mac performance telemetry from your menu bar
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Open-source platform for managing MCP servers and clients
Askiva automates the entire user research process. You set a topic, choose a language, and upload your participants. The platform handles sending invites, booking meetings across timezones, and conducting interviews on Zoom using an AI researcher that follows your custom script.
After the conversation, Askiva delivers accurate transcripts, grouped themes, key quotes, and sentiment analysis. It helps product teams and universities skip manual work and move from interviews to clear decisions in hours instead of weeks.
ADHD Academic Agent is an executive function automation system that pulls assignments from your student's Learning Management System (LMS), organizes everything, and pairs it with a personalized AI tutor built on their cognitive profile. ADHD students often struggle with steps before learning like checking the LMS, downloading files, organizing folders, and setting reminders. Parents manage this manually or pay coaches $200/hour. ADHD Academic Agent automates the entire process so the student can focus on learning.
Rewarded Interest is your automatic consent agent. It passes your consent settings to sites as you browse, eliminating cookie popups. It protects your privacy by blocking unwanted cookies or trackers. Once enough people join, you can share an anonymous ID so advertisers can target you, earning 5% of what brands spend to reach you. Rewarded Interest doesn’t sell your data or show extra ads; it charges advertisers when they target your ID. Available free for Chrome, Brave, and Arc.
StayScore analyzes your Airbnb listing with AI and assigns a score out of 100. It then gives specific recommendations on photos, title, description, amenities, and pricing to help you get more bookings. It evaluates photo quality, staging, and copy from a guest's perspective and highlights what's missing.
Paste your listing URL to get a photo-by-photo breakdown, prioritized fixes, and a downloadable report in about two minutes. A single analysis costs $9.99, and you can re-run it after changes to track improvements.
The report looks at how Redditors are engaging with sports, and the related opportunities for brands.
The change will reduce the presence of Meta's ad disclosure labels
In-stream pop-ups could have a significant impact on reducing the spread of AI-generated misinformation.
YouTube paid out over $8 billion to the music industry between July 2024 and June 2025.
The lawsuit could lead to another potential penalty over X's controversial AI chatbot.
The change will provide more insight into local audiences and relative reach.
Affinity's latest update to introduces Light UI for a brighter and cleaner workplace, Convert to Curves to eliminate manual tracing by transforming objects into a fully editable vector curves, and Live Tone Blend Groups which blends layers dynamically and non-destructively.
Drop Beacon tracks product releases across top everyday carry (EDC) brands so you never miss a drop. Set your interests, follow brands, and get timely notifications when knives, pens, flashlights, fidgets, and more are released. Browse current and upcoming drops, filter by materials and mechanisms, and jump straight to the seller.
Drop Beacon also lets you create Pocket Dump photos to share with the EDC community and view in the Pocket Dump gallery. It is the perfect one-stop shop for all your EDC needs. Stop chasing, start carrying.
BrightSite is a website platform for small businesses and agencies tired of patching WordPress plugins. Analytics, forms, SEO, SSL, CDN, and staging are built in with no plugins or extra bills. Pages load quickly over WebSocket navigation. Manage content using AI tools via MCP, a ChatGPT app, publish llms.txt for AI search visibility, and use Lumi, an AI chat assistant. Plans start at $39/month.
Transform Coding Sessions & Code into a System of Context
Sony confirms that AMD has a new version of FSR in the works When announcing the rollout of its “Improved PSSR” AI upscaler, Sony confirmed that AMD is working on a new version of its FSR upscaler. AMD and Sony PlayStation are collaborating on AI algorithms and neural networks as part of their “Project Amethyst” […]
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PilotFI is a privacy-first financial independence planning toolkit for European investors. It models state and private pensions, supports EU tax profiles, and runs 1,000-run Monte Carlo simulations and 97-year backtests, visualizing your path to financial independence with clear projections.
Track all assets, income, expenses, loans, and dollar-cost averaging across currencies. Plan as a household and export to PDF, analyze with AI, and simulate projection scenarios. Data is preloaded based on user location and stays EU-hosted with no bank connections. Pro users can enable Local Mode to keep all financial data on-device.
SportBot AI turns hours of pre-match research into just 60 seconds. It aggregates injuries, form, head-to-head history, and real-time odds from over 50 bookmakers to calculate win probabilities, detect edges, and flag potential risks across soccer, NBA, NFL, and NHL. You can see model versus market lines, predicted scores, risk levels, and best prices, then make your own decisions. Start free, or upgrade for unlimited analyses, AI chat, and edge alerts, with a performance dashboard tracking every prediction.
Nvidia promises photorealistic graphics with DLSS 5 At GTC 2026, Nvidia has shocked the gaming world with DLSS 5, a new AI model that promises to deliver photorealistic visuals with today’s gaming hardware. DLSS 5 is due to launch this fall, and Nvidia calls it their “most significant breakthrough in computer graphics since the debut […]
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Sony’s upgraded PSSR tech is now available in several PlayStation 5 Pro games Sony has officially started rolling out its improved PSSR technology across a range of games. Several partners have already implemented Sony’s improved upscaler into their PlayStation 5 Pro games, and more game upgrades are on the way. PSSR upgrades are now available […]
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Popular Photoshop alternative GIMP has been updated to feature non-destructive Link and Vector Layers, an upgraded MyPaint Brush tool, and expanded file format support including SVG export. The update also brings UI and stability improvements.
ConvertlyAI.online is a text-based SaaS designed for speed and precision. Use our curated prompt library to turn simple ideas into structured digital assets, professional copy, and organized content in seconds. Built for creators and developers who need high-quality output without the friction of complex AI interfaces. Key features include 10+ asset conversion types, a pro-grade prompt library, global-ready text generation, and a minimalist, high-speed UI.
Town is an AI work assistant that connects to your email, calendar, docs, and chat to triage inboxes, draft in your voice, manage scheduling, and run multi-step workflows with your oversight. It learns your preferences and maintains a memory profile so briefs, drafts, and actions match how you work. Use it across web, email, Slack, iOS, WhatsApp, and desktop. Choose read-only, approval-required, or autonomous modes, set per-tool boundaries, and review a clear action log while Town executes tasks across Gmail, Google Calendar, Drive, Slack, Notion, and more.
Data from 4M AI citations shows syndicated press releases barely register in AI answers. Editorial content and owned newsrooms fare better.
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Google introduces new features in AI Studio to enable developers to set monthly spend caps and scale effectively.
OpenAI is beginning to build the infrastructure for a formal advertising business around ChatGPT — but early performance signals suggest the company still has work to do to match established search platforms.
What’s happening. OpenAI started testing an Ads Manager dashboard with a small group of partners, according to confirmation shared with ADWEEK. The tool allows marketers to launch, monitor, and optimise campaigns in real time, similar to the campaign management platforms used across digital advertising.
Why we care. OpenAI is beginning to build a self-serve ads ecosystem around ChatGPT with a dedicated Ads Manager, as they prepare for AI assistants becoming a scalable channel. As conversational search grows, paid media marketers may need to think about visibility inside AI responses, not just traditional platforms like Google Search.
Early testing also means advertisers who participate now could gain first-mover insights into performance, formats, and optimisation strategies in a new advertising environment.
How it works today. Early testers currently receive weekly CSV performance reports that include metrics such as impressions and clicks. The reporting indicates the ads product is still evolving, with more advanced analytics and tooling likely to follow as the program develops.
The challenge: Early tests suggest click-through rates on ChatGPT ads trail those seen on Google Search, highlighting a key hurdle for OpenAI as it tries to prove the value of advertising inside conversational AI.
The cost of entry. Some early advertisers have reportedly been asked to commit at least $200,000 in spend, raising the stakes for OpenAI to demonstrate measurable performance and ROI.
Between the lines. Building an ad ecosystem requires more than ad inventory. Marketers expect robust reporting, optimisation tools, and predictable performance — areas where mature platforms like Google have years of advantage.
The bottom line. OpenAI is laying the foundation for a new ad platform inside ChatGPT, but convincing brands to shift budgets will depend on whether conversational ads can deliver results that compete with traditional search.
Google appears to be testing a new “Sponsored Shops” format in Google Shopping results that highlights entire stores instead of individual products — a potential shift in how brands compete in Shopping ads.
What’s happening. Instead of displaying only single product listings, the new block groups multiple products from the same retailer into one sponsored unit. The format features the store name, several products from that shop, and signals such as ratings and brand presence, effectively creating a mini storefront directly inside the Shopping results.
Why we care. The new “Sponsored Shops” format in Google Shopping could shift competition from individual products to entire stores. Instead of winning visibility with a single SKU, brands may need stronger product feeds, better ratings, and broader assortments to appear in these store-level placements.
It also introduces multiple click paths within one ad unit, which could change how traffic flows between product pages and store pages. If the format scales, it may reshape how advertisers optimise campaigns across Google Shopping — prioritising brand presence and feed quality, not just product-level bids.
The big picture. The test suggests a move slightly up the funnel for Shopping ads. Rather than focusing solely on a single SKU, brands can showcase a broader product assortment and reinforce their store identity within one placement.
Why it’s notable. Store-level visibility means advertisers can highlight multiple products at once, increasing exposure per impression. It also strengthens brand presence by combining store name, ratings, and product range in one block.
For users, it makes discovery easier by allowing them to browse several items from the same retailer without navigating away from results.
Between the lines. If the format rolls out widely, it could reward brands with strong product feeds, high seller ratings, and clear brand trust signals. Merchants with well-structured feeds and competitive assortments may gain more visibility compared with those relying on a few individual product listings.
What to watch. One open question is how users will interact with the different clickable elements inside the ad unit. Marketing Operating Lead, Stephanie Pratt commented on this and what measurement split we may expect:
The bottom line. If “Sponsored Shops” expands beyond testing, it could push Google Shopping toward more store-level competition — shifting strategy from purely product-level optimisation to building stronger brand presence within the Shopping ecosystem.
Fist seen. This update was spotted by PPC Specialist Arpan Banerjee who shared a screenshot of the update on LinkedIn.
RPCS3 team adds “Create Steam Shortcut” option to its PlayStation 3 emulator RPCS3 is the world’s top PlayStation 3 emulator, and a new update for the tool has dropped that allows PC gamers to add their PlayStation 3 games to their Steam Library. Using the emulator’s new “Create Steam Shortcut” tool, gamers can add their […]
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A look at Google’s support for the Industry Accord Against Online Scams and Fraud and its ongoing work to fight scammers.
The words “incremental” and “incrementality” get thrown around in affiliate marketing, but they might not mean what they sound like. There may be no increase in actual sales, new customers, or revenue. Affiliate marketers who refer to incrementality often look at it only within the affiliate channel, not across your company as a whole.
To determine whether affiliates are truly incremental, ask a simple question: Would the sale have happened without the affiliate program?
The answer determines whether the partner is bringing you new customers and revenue or simply intercepting customers already in your checkout flow.
The word “incrementality” in affiliate programs is similar to an affiliate, an agency, or a network using “high intent” to describe the traffic. High intent means the person has a strong intent to purchase, which is a good thing. What is left out is whether that touchpoint would happen if there were no affiliate program at all.
High intent could be used by a coupon site where the touchpoint is a consumer already at checkout, going to Google and typing in “your brand + coupons.” If you close your affiliate program today, these same touchpoints will likely still happen. Your company saves money because you no longer pay commissions, network fees, manager salaries, or agency fees.
Yes, the traffic is high intent. It’s your customers already checking out of your shopping cart. It doesn’t get more “high intent” than that. The touchpoint may be low- or no-value because it happens whether you have a program or not, and you may be losing money on the sale because of it.
Note: Not all coupon sites or deal touchpoints are bad. Some shopping cart interceptions may add value (including brand + coupon), so don’t take action without testing. Use your data and test to see if the same or a similar amount of sales happens without an affiliate program before making decisions.
The more customers checking out of your own shopping cart, the more sales the affiliate in the top positions of Google make. The less you have, the less they make. They rely on you having your own traffic to intercept so they can make money, which is why they are sometimes called parasitic affiliates. And that’s where incrementality comes in.
If this touchpoint isn’t bringing in new customers, and it happens even when you don’t have a program, are the sales incremental? This starts with defining what incremental sales and value are.
You, as the brand, can feature a coupon code, a deal, or a bundle without an affiliate program. If you have no program, you can submit those same deals to the sites showing up for your brand + coupons and get the same or a similar amount of sales with the increased AOV or items in cart. But you don’t have to spend money on network fees, commissions and affiliate manager salaries.
If a deal or bundle exists only on the partner’s platform (website, videos, password-protected communities, newsletter blasts, etc.) and it doesn’t appear for your brand on Google, YouTube, etc., their active community is what drives sales. That’s something you can’t do without them. The affiliate is adding incremental value.
Dig deeper: Where affiliates can get traffic beyond Google search
Here are a few types of affiliate content and programs that can add real incremental value.
There are two types of comparisons: brands and products. Comparing two products from any brand (e.g., bandages sold at most retailers like CVS, Walgreens, Amazon, and Walmart), the affiliate controls where traffic goes and which brand gets the sale. This may not be customer acquisition for big brands, since they already have millions of customers, but it’s high-value because without that affiliate deciding to send the customer to you, you don’t get the sale.
The person could be comparing two types of electronics or adaptors for a specific purpose. Then they decide which retailer to send the consumer to and explain why they recommend that one. They could mention the service guarantees, extra guides, prices, or social causes the brands support. Each of these helps convince the consumer to shop with their recommendation, increasing the incrementality and value.
If no brand is mentioned at all in the content, they can change out the affiliate links and destination at any time, so your brand can be cut out, and you lose. This is where the affiliate holds the power, as they control their traffic and add incremental value.
Brand comparisons get tricky. Comparing you and a competitor adds credibility because it’s a “trusted third party” who is putting their name on the line. They likely do help the customer make a decision, but it isn’t new customer acquisition, as the customer is already in your funnel. But it’s a value-adding touchpoint in the customer acquisition funnel.
Tip: If you have a non-affiliate doing the brand comparison, you’re more profitable because you don’t pay commissions on it in perpetuity.
For example, you pay a one-time fee of $500 for an unbiased and honest comparison vs. paying $2,000 in commissions over the course of the year. Your company is more profitable by $1,500 the first year and $2,000 each additional year until the comparison is no longer accurate or shows up for your brand vs. the competitor.
Then there’s the big incremental value add for small brands. By being added to a comparison with the two big brands, you gain access to their comparison traffic and their customer funnel. The credibility from their brands and the reviewer may build trust for your brand, and this comparison is likely to be customer acquisition and incremental in revenue, not to mention getting your competitors’ customers.
These types of partners include:
Creators is a blanket term for anyone who creates content, including:
They create top-funnel and high-value traffic and mid- and low-value traffic.
I’ll break this section into two parts starting with the mid- and low-value.
When creators do a review only, the initial review gets distributed to anyone who subscribes, and this is top-funnel and builds trust. Then it gets tricky on incrementality.
Once the initial review is live and the subscribers have already viewed it, the top-funnel incrementality is over. Now, algorithms start to pick it up and show it for your own customers already in your funnel. Unlike the coupon example, where the sale is likely to happen just before the person clicks the pay now button, the customer review touchpoint isn’t as “high intent” yet.
This consumer is looking for validity and credibility before making a purchase. The reviewer provides credibility as a trusted third party and helps the consumer make a decision. When the algorithms show this review, it isn’t bringing you new customers, so there’s no full customer acquisition. But if you currently have only bad reviews showing up, and the affiliates have good reviews showing the benefits and presenting you in a good light, this can increase customer confidence, making the conversions happen. Not to mention it helps repair your brand reputation.
Affiliates will be faster to create review content than customers because they are incentivized with commissions. The same goes for non-affiliate ambassadors and influencers. Incrementality here is similar to comparisons.
If you pay an influencer or ambassador a one-time fee of $200 for the review, that’s the only cost. When you have affiliates doing the review and earning commissions, each affiliate earns on each one, which could be $500 in commissions each year, while network fees, affiliate manager salaries, bonuses, etc., cost your company more than the influencer or ambassador.
With that said, it’s easier to get affiliates to update their reviews and create new ones as your company updates, since they’re making money by keeping them up to date. You’d need to pay the influencer or ambassador again each time, unless they are in a good mood and decide to do it for free.
The ones that genuinely value their readers or visitors will do it free and quickly because they want to make sure their audience and visitors get accurate information. With that said, it’s almost impossible to do it for every brand they feature, especially if they’ve been around for 10 years. This is why a fee is normally required. It’s too much for any one or even four- and five-person team.
Stephanie Robbins from Right Side Up also shared a situation where a review can be highly incremental. New brands without a ton of branded search and without demand yet could benefit from review affiliates. By getting reviews going early in the company’s life, they have an established foundation for growth. These established reviews help block competitors from taking their branded search. Once the brand starts to pick up, it will need to replace affiliate reviews with non-affiliate reviews via SEO to save money.
Dig deeper: The best affiliate networks by need and use case
Non-review creators are huge for incrementality, and there’s no shortage of them.
Listicle affiliates
These affiliates create “top ten” and “best” lists, including media companies, PPC affiliates, and bloggers with roundups and shopping guides. The ones that don’t optimize for your brand + reviews or bid on your branded terms in search engines are bringing you customers with a higher intent to purchase.
The consumer here knows they need something and is shopping, but they don’t know which brand to choose. Being on these lists builds trust and may reach a consumer who hasn’t heard of you (especially if you’re not one of the big names in the space).
Tutorial creators
You can see them on YouTube, Skool, and other platforms, teaching workshops and creating written guides on how to fix a roof, bake a cake, set up a server, or take care of a goldfish, which likely provides a lot of incrementality for your brand.
The ones that don’t add “with [Brand]” to the title (How to take a photo with a Canon camera vs. how to take a photo) and throughout the content have a captive audience that you can’t reach without them.
Because their traffic does not need your brand, they control who gets the referrals. Being in these guides brings you high-value and incremental customers. The conversion rates may be higher because the tutorial presold the product, and the creator put their name on the line by recommending you.
Community
This same form of trust comes from community moderators and the highly respected members. When people are there because they love sharing parenting advice, common passions for bird watching or cooking gluten-free meals, video game or tabletop game enthusiasts, or anything else, they trust the community.
When the owner of the community says this is the brand to trust, that trust passes through, and the community shops. While they may not be new customers each time, they are incremental, and you get brand credibility, which is one of the hardest things to earn.
Apps
There’s no shortage of apps, and now that AI is powering features, affiliate sales are being made. Some apps may let you find celebrity styles you like and then use affiliate data feeds to find similar clothes and recommend them to the user.
Others might have you snap a photo of your room, then use affiliate datafeeds to show what furniture could look like in it and let you mix and match to create your perfect space. These are high-value touchpoints with incrementality because the app controls where the person shops and pre-sells the items by giving them an experience with the products.
Media buyers
Media buyers purchase ads across the web, in communities, and other spaces. As long as they’re not buying ad space via the pages in your checkout, targeting your own website if you run ads on it, or using your brand as a target, they’re adding incrementality by reaching the audience your ads can’t reach.
Some have a lot of experience on third-party platforms, and others may have a budget when you’re already tapping yours out, so they work as an extension of your own efforts.
Dig deeper: How amplifying creator content strengthens trust and lowers media costs
Incrementality in affiliate marketing means the affiliate brings you a new customer and drives a sale that likely wouldn’t have happened without them or without the program at all. When an affiliate relies on your existing traffic, incrementality drops substantially. You’ll often hear terms like “high-intent traffic” used to make this sound more valuable than it is.
Use your data and your knowledge to determine what is right for your business and what incrementality means for you. Don’t rely on one channel alone.
Key takeaways:
LinkedIn is launching a new AI-powered feed ranking system that uses large language models and GPUs to analyze post content and surface more relevant updates to its 1.3 billion members.
Why we care. Understanding how LinkedIn surfaces content is critical if you want your posts — or your brand’s — to be discovered. The new system prioritizes topical relevance and engagement patterns, LinkedIn said. Posts that demonstrate expertise and align with emerging professional conversations may travel farther across the network — even without existing connections.
The details. LinkedIn rebuilt much of its feed recommendation system using large language models, transformer models, and GPU infrastructure. The overhaul centers on two systems: retrieving relevant posts and ranking them in the feed.
Unified retrieval system. LinkedIn replaced several separate discovery systems with a single LLM-powered retrieval model.
Ranking that follows your interests. After retrieval, LinkedIn ranks posts using a transformer-based sequential model. Instead of evaluating posts independently, the model analyzes patterns across your past interactions — including likes, comments, dwell time, and other signals.
System performance and infrastructure. The system runs on GPU infrastructure designed to process millions of posts while keeping feeds fresh.
Improving feed quality and authenticity. LinkedIn also announced updates to improve content quality:
The webpage is no longer the unit of digital visibility.
For years, we’ve built our digital presence on a foundation of URLs and keywords, but that infrastructure was designed for a highway that AI has now bypassed.
In the search everywhere revolution, the most powerful atomic unit is the entity — a well-defined, machine-readable representation of a concept, product, organization, or person.
The brands establishing AI-era dominance are engineering entity authority. To survive the shift from traditional search to generative discovery, we must move beyond the page and focus on entity linkage to build a foundation of AI visibility.
To navigate this landscape, we must recognize that we have moved past simple information retrieval. We’re witnessing a three-stage evolution in how the web is indexed and understood.
In this third phase, the search engine has become a reasoning engine. It looks at your content and the logical role your brand plays within a broader ecosystem.
Dig deeper: The enterprise blueprint for winning visibility in AI search
This evolution is driven by a cold economic reality: the comprehension budget. AI systems read and compute content.
Every time an engine attempts to resolve an ambiguous brand or an implied relationship, it burns expensive GPU cycles. Understanding your content is a resource-heavy calculation.
If your data is unstructured or inconsistent, you force the AI to overspend this comprehension budget. When the computational cost of grounding your facts exceeds the limit, the model defaults. It hallucinates based on probability, substitutes a cheaper competitor, or ignores your entity entirely.
To win, you must provide a comprehension subsidy. Deep, nested Schema.org markup pre-processes your data, shifting the burden from expensive deep inference to fast, economical knowledge graph lookups. In a world of finite compute, the most efficient entity is the one most likely to be cited.
Dig deeper: From search to answer engines: How to optimize for the next era of discovery
Traditional SEO has shifted and created a new discipline — generative engine optimization (GEO) — moving from keyword targeting to relevance engineering, where interconnected semantic structures enable machines to interpret, verify, and reuse trusted information.
GEO focuses on maximizing your inclusion in AI-generated answers across platforms like ChatGPT, Perplexity, and Google’s AI Overviews. This requires:
Dig deeper: Chunk, cite, clarify, build: A content framework for AI search
Most enterprise sites have some structured data deployed, but basic, fragmented schema — the kind used only for rich snippets — is functionally inadequate for AI.
When markup is applied page by page without nested relationships, the AI encounters isolated data islands. It sees a product here and an organization there, but no declared connection. This forces the AI back into an expensive inference loop.
The architectural solution is a content knowledge graph: an interconnected network of entities built in Schema.org vocabularies and expressed in JSON-LD.
A correctly implemented content knowledge graph maps your entities hierarchically: Organization → Brand → Product → Offer → Review.
The ROI of schema:
Dig deeper: Why entity search is your competitive advantage
To achieve global authority, two properties are non-negotiable:
To implement a content knowledge graph that survives the scrutiny of AI models, you must move from tactical tagging to entity governance. This playbook establishes a single source of truth that AI systems can verify at scale.
Here’s the strategic deep dive into the five-step implementation.
Before deploying a single line of code, you must conduct a semantic audit to define your core entities (e.g., organization, products, people, locations) that will build your entity knowledge graph.
Success requires leveraging the full breadth of the Schema.org vocabulary — which now supports over 800 specific types.
Fragmented schema creates data islands. You must implement deep nesting to fully trace your business’s lineage.
To achieve global authority, you must signal to AI engine platforms that your entity is recognized by the world’s most trusted knowledge bases.
At enterprise scale, manual updates are a liability. You must treat schema as an ongoing operational discipline.
Dig deeper: From search to AI agents: The future of digital experiences
The current AI search experience — summarized text answers — is merely a transitional phase. We’re rapidly moving toward an agentic ecosystem, where AI agents inform users and act on their behalf. The AI agent queries your structured entity graph to find executable functions.
To survive this shift, your entities must be more than just “readable.” They must be callable. Implementing schema actions — such as BuyAction, ReserveAction, ScheduleAction, or OrderAction — is how you declare your brand’s operational capabilities to the machine.
If these actions aren’t explicitly defined in your code, your brand becomes a dead end. An AI agent might mention your product, but if it can’t verify price, availability, or a booking path through structured data, it will bypass you in favor of a competitor that is agent-ready.
At enterprise scale, the greatest threat to visibility is schema drift. This occurs when your human-visible content (e.g., prices, stock, hours) evolves, but your machine-readable schema remains static. When AI systems detect this inconsistency, they lower your confidence score. Reduced confidence leads to zero citations.
To maintain agentic readiness, you must establish four governance pillars:
Bottom line: In the agentic web, inconsistency is invisible. If your structured data is outdated, you’re functionally removed from the transaction layer.
As the customer journey becomes an algorithm-driven narrative, we must shift from measuring traffic to a page to measuring share of model. To dominate the agentic web, your dashboard must evolve to track how AI perceives, trusts, and socializes your brand entities.
The transition from page-based to entity-based strategy is a present operational priority. Brands building content knowledge graphs today are building structural trust advantages that compound as AI systems learn to rely on established authorities.
The page was never the point. The entity — and the trust AI places in it — is what determines who gets found next.
Ready to see the “next generation of AI” Today, Nvidia will be hosting its GTC 2026 keynote, with CEO Jensen Huang leading the event. Last month, Nvidia’s CEO unveiled that it would unveil “a chip that will surprise the world” at the event. Furthermore, team GeForce claims that we will also see the “future of […]
The post Watch Nvidia’s GTC 2026 Keynote here appeared first on OC3D.
Intel aims to transform gaming with its IBOT optimisation tool Alongside its new Core Ultra 200S PLUS CPUs, Intel is launching their “Intel Binary Optimisation Tool” (IBOT), which aims to boost CPU performance and push gaming forward. Intel’s IBOT tool is part of Intel’s one-two punch strategy for enthusiast gaming performance. On the one hand, […]
The post Intel promises HUGE gaming gains with IBOT appeared first on OC3D.
Gryffi is a cloud-based platform for interactive employee onboarding. It uses a visual drag-and-drop builder to create branching training journeys. Key features include AI-powered knowledge guides in 14 languages, 360-degree virtual tours, and automated quizzes. The system integrates with Microsoft 365 and Google Workspace and provides password-free access via secure magic links. Fully hosted in the EU (Germany/France), Gryffi prioritizes technical security and GDPR compliance.
RouteBot is an end-to-end transport operations platform that automatically handles routing and planning, enables real-time vehicle tracking, and lets drivers and passengers follow routes live through built-in mobile apps. No app is required — users can also receive route updates via SMS. It is already used in real-world transport operations at scale.
The rules of organic content are shifting from a “publish more” to a “prove more” mindset. Search results increasingly answer questions directly through AI summaries, shopping features, and other SERP integrations. Visibility alone doesn’t resolve buyer uncertainty.
For ecommerce brands, organic visibility now requires recognition and trust amid the noise on the SERPs. The 2026 game is both simpler and more demanding. Invest in organic assets that:
Today’s search is defined by three forces changing how content performs.
Generative AI has become a standard part of the organic search results through features like Google’s AI Overviews and AI Mode. These generative AIs answer broader questions directly, often pulling in citations from web content.
AI Overviews were designed to help people get the gist of a topic quickly, providing a jumping-off point to explore links. However, time has shown they also contribute to fewer direct clicks on traditional search results, as users might get their answer entirely from the AI summary.
So, if you want your ecommerce brand to earn organic visibility, you need content that AI will cite and that users will trust.
Nowadays, Google’s search results are saturated with shopping features (e.g., product carousels, price comparison snippets, “Popular Products” lists, and more). Sometimes, they look more like the search results on an ecommerce site than a traditional organic SERP.
These discovery surfaces are powered by structured product data and merchant feeds. Product pages must communicate clean data to Google.
Product results depend on the quality of the attributes you provide. Google recommends that ecommerce sites include structured data on product pages and share complete product feeds for richer search appearances.
The bottom line is that you need to invest in your product data infrastructure. When Google can reliably understand what you sell, it will showcase your products more prominently, helping you attract more qualified shoppers.
The traditional funnel, where a customer Googles something and clicks your link, is evolving especially for Gen Z. Search now takes place on social media in huge numbers.
Approximately 86% of Gen Z internet users report searching on TikTok weekly, almost as many as use Google. This means your potential customers might discover products through a TikTok video or an Instagram Reel long before they ever see your website.
Here’s the pattern I see with ecommerce:
This is demand creation. Keep in mind that these types of results are showing up on Google, too.
Meanwhile, AI platforms are already part of the discovery process. Social search behavior is here, so think of platforms like YouTube, TikTok, and Instagram as extensions of Google.
Dig deeper: The social-to-search halo effect: Why social content drives branded search
The SEO toolkit you know, plus the AI visibility data you need.
So, where exactly should ecommerce teams focus their content resources?
Start with the pages that directly drive revenue (e.g., your product detail pages (PDPs), collection pages, and other high-intent landing pages).
Make these pages conversion-ready. Go beyond the basic title, image, and price by adding content blocks that answer buyer anxieties.
For example, your PDPs should include clear information on sizing/fit, compatibility, materials, care instructions, warranty, shipping and return policies, and genuine FAQs from real customers.
To do this, find conversational queries through Google Search Console and look at one-star and two-star reviews, either on competitor products or your own, to see the exact questions, complaints, and doubts buyers have.
Alternatively, you can get full clarity on the three types of obstacles that every single client has and focus on the emotional one.
For each pain point, ask:
Here’s an example scenario: Imagine a mother who works remotely and has a baby who refuses to sleep:
That last one — the emotional obstacle — is the strongest. People buy relief. They buy confidence. They buy the feeling that things will be okay.
On category pages, add filters that guide users (e.g., “Shop by size, color, or use case”), highlight top sellers or award-winning products, and include comparison links (e.g., “Best for X vs. Y”).
Try to enrich these pages so that a customer who lands on them has all the info they need to feel confident making a purchase.
The goal is a page that precisely matches the user’s intent and resolves uncertainties.
We live in a visual search world. Consumers are searching with images and even combinations of images and text.
As Google itself noted, “… consumers are using their voices to find answers on the go, and their cameras to explore the world around them.” Search has expanded beyond the traditional text box. This shows ecommerce’s huge opportunity to invest in visual content optimization.
Throughout 2025, there were over 100 billion visual searches via Google Lens and related visual tools, with one in five of those searches driven by someone looking to buy a product they saw. Up to 39% of consumers have used Pinterest as their search engine, per an Adobe study, and Instagram is clearly moving in the same direction.
Shoppers are using images to find ideas, compare products, and determine what to buy. This means you need to optimize your ecommerce images and videos for organic search just as rigorously as your text content.
Treat every image and video as a piece of searchable content.
Dig deeper: 10 advanced ecommerce SEO tips that boost rankings and revenue
Structured data and product feeds aren’t optional. If you want Google to feature your products in shopping results (and pull correct info into AI answers), you need clean product data.
Start with the product pages. Add Product schema on every PDP and include all the basics: name, description, image, brand, SKU, price, currency, availability, and offers. If you show reviews on the page, mark up reviews and ratings, too.
If shipping cost, delivery time, or variants matter for the purchase, include that information as well. Only use FAQ/HowTo/Review schema when the content is actually on the page.
Next, treat the Google Merchant Center feed like an SEO asset because Google does. Keep it accurate: use titles that match the product, correct categories, accurate price and stock information, and no mismatches with your PDPs.
After you fix errors in Merchant Center, improve the feed by adding attributes like size, color, and material. Turn on automatic updates so Google can handle small changes. When Google can clearly read what you sell, it shows your products more often, and the clicks received are higher intent.
Create content that credibly demonstrates the quality and performance of your products. This includes:
For reviews, consider improving your review prompts to get more detailed feedback. For example, you can ask customers specific questions about fit, durability, or how they’re using the product.
Find ways to highlight these insights on the PDP (e.g., a summary of common pros and cons). This kind of content signals to Google and users alike that the site offers genuine insights. A shopper is more likely to convert when they see real evidence, and this directly leads to higher conversion rates.
If you publish in-depth product review articles or videos on your site, you can capture search queries for “[Product] review” or “is [Product] worth it,” because Google will “see” the first-hand expertise.
Additionally, ecommerce brands can create their own original testing and use-case content. This might be blog articles or video snippets where the brand tests the product’s claims or compares it to alternatives.
Essentially, brands should think like an in-house influencer evaluating their product.
Dig deeper: How to make ecommerce product pages work in an AI-first world
Νot all customers search for a specific product. Many start with broader questions. Capture these early-stage shoppers by creating both comparison and buyer’s guide content that funnels to your product pages.
If shoppers aren’t sure what to choose, use formats that reduce confusion and give them a clear path forward, like quizzes or selectors (e.g., “Find your ideal [product] in 60 seconds”) and criteria-led guides (e.g., “How to choose a [category]: 7 factors that matter”).
If they’re comparing options, help them narrow the shortlist with head-to-head comparisons (e.g., “[Product A] vs [Product B]”) and “best for” hubs (e.g., “Best [category] for small spaces” or “Best [category] under $X”).
And if they’re scared of making the wrong choice, publish risk-reducing content like “mistakes to avoid” articles and “who it’s not for” pages (e.g., “Don’t buy [type] if you have [constraint]”).
Each of these content pieces should be seen as an extension of your sales funnel: Design them to link directly to your relevant categories or products
This type of content is the bridge between informational queries and purchase-ready sessions.
One of the smartest content investments an ecommerce brand can make is in content created by real people, whether that’s your customers, your employees, or trusted influencers.
The reason UGC works so well is that it doesn’t feel like marketing. This isn’t surprising when you consider user behavior: People trust people.
Brands should encourage and showcase UGC at every turn. This can mean reposting customer photos showing them using your product on social media, integrating reviews and customer images into your product pages, or running challenges to generate buzz.
The key is to treat your customers as a content engine.
Another trend is employee-generated content, or in simpler words: leveraging your team to humanize the brand.
Forward-thinking ecommerce brands have employees take the stage in content, whether it’s a product development engineer doing a “behind the scenes” video, retail staff modeling new apparel on TikTok, or your founder writing thought-leadership articles. This insider perspective is paying off because it blends expertise with authenticity.
Beyond individual pieces of content, ecommerce brands should invest in building communities around their products and niche. A great example is Instant Pot’s official Facebook group, which has over 3 million members. This community of passionate users shares recipes, tips, and excitement about using the product, which means they generate endless organic content for the brand.
The best part? The group keeps existing customers engaged and serves as social proof to potential buyers. More brands are realizing that a community = continuous organic marketing.
Here’s one more reason to invest in social proof and community: It can influence your search rankings.
Google’s recent updates indicate that brand mentions across the web, engagement on social media, and UGC signals can all contribute to SEO.
Dig deeper: Why ecommerce SEO audits fail – and what actually works in 30 days
While we’ve talked about discovery on external platforms, another area for organic content investment is your own channels.
First, content-rich blogs or resources on your site are still a powerful organic asset. Yes, the content mix has shifted toward video and social, but consumers and search engines still value in-depth written content for certain needs.
According to a recent HubSpot marketing report, blog posts are the third-most-popular content format among marketers. That shows blogs are still very much in play, even if they’re not the hottest format. The key is to evolve the blog strategy:
Next, email newsletters. The value of email lies in its ability to directly reach a highly engaged audience. Unlike social media, where your reach can be limited by algorithms, emails land straight in your subscribers’ inboxes, giving you full control over messaging and design.
Keep in mind that your subscribers have opted in voluntarily, showing a clear interest in your content or offers. Investing in email marketing tools, hiring good copywriters, and designing emails with careful attention is worth it.
Finally, content diversification within your owned media can pay dividends. This includes:
The key here is aligning the content with what your customers care about. A smart organic content plan could look like this:
These channels work better when they work together.
A blog post can become social posts and newsletter content. Customer reviews and photos can be used in emails and on product pages. Videos can be added to blog posts and category pages.
When you connect everything, your content becomes one system that keeps bringing people in and turning them into customers.
Track, optimize, and win in Google and AI search from one platform.
Just as important as where to invest is knowing what content tactics to avoid.
If your strategy is to publish lots of generic blog posts just to target keywords, stop. Especially if that content is automated, templated, or written with minimal effort. You’ll spend time and money, and you will get zero results.
Google has strengthened its spam policies against scaled content abuse, which includes content farms and auto-generated pages made only to win rankings.
Google is cracking down on tactics where sites leverage shady methods to rank. For example:
If it looks like a shortcut, it’s probably risky. In short, deprioritize quantity-over-quality approaches and any borderline spammy shortcuts. The direction is clear: Google wants originality, real value, and content made for people.
Ecommerce brands should invest in a multi-channel content strategy that prioritizes quality and is truly user-centric.
You need to show up wherever customers search and measure success through visibility, engagement, trust, and sales. The best investment with the greatest ROI is content that’s both genuinely helpful and strong enough to reuse across different channels.
Over the past decade, I’ve reviewed hundreds of resumes, conducted countless interviews, and led numerous technical tests for SEO candidates.
Along the way, I’ve met many exceptional professionals — but I’ve also noticed a recurring pattern of common interview mistakes that can hold even the most talented candidates back.
Below are 11 common mistakes I’ve observed in SEO interviews — and how you can easily avoid them.
Confidence is great! While imposter syndrome is common in SEO, it’s important to maintain realistic confidence in your skills and experience. However, there is a fine line between projecting confidence and appearing arrogant.
For example, talk about your successes, such as:
Be clear about what you achieved and how. Show off your theoretical knowledge. Discuss ideas and theories with your interviewer.
Don’t assume they will agree with you, though. This can be arrogance.
SEO isn’t a “one-size-fits-all” practice. You may have different experiences from your interviewer, leading to different conclusions. This is fine. It happens in SEO all the time.
Some people make the mistake of thinking it’s OK to argue and dismiss others’ opinions. This rarely works well in any workplace and can be especially harmful during an interview.
When I interview, I look for team players — confident in their knowledge yet humble and open to learning. They embrace new evidence and contribute to discussions that elevate the entire team’s understanding, including their own.
If you stray too far into arrogance during an interview, you may come across as difficult to teach or lead and not open to feedback.
Interviews are your time to shine. They let you showcase some of your best work. Another mistake I’ve seen in interviews is assuming interviewers can fill in the gaps.
Candidates talk about a project or website they have worked on, but fail to convey its significance. They mention website migrations, expecting non-SEO interviewers to understand the complexities involved. They discuss turning around a traffic slump without giving any data. Avoid this.
Make sure to give the specifics. There’s a good acronym for constructing interview answers called STAR. It stands for:
Using this method, you may find it easier to hit all the salient points that give the interviewers clarity and perspective. Try to choose examples that have an outcome that you’re proud of or can at least explain what made it fall short.
Dig deeper: How to become exceptional at SEO
Candidates sometimes don’t have time to think of an answer to the question or feel they don’t have one. They try to talk around the question and bring it back to something they feel more comfortable discussing.
If an interviewer asks, “Talk about a time when you faced a complex website migration and what you did?” or “How would you handle a stakeholder not signing off on your recommendations?” that’s exactly what they want to know.
Avoid going off on a tangent and ensure you address the question directly. Often, interviewers have a list of questions they ask each candidate.
They may even use these to compare candidates. If you’re not directly answering them, you put yourself at a disadvantage.
Instead, take some time to think about the answer. Explain that you want to answer well and need a minute to organize your thoughts. If you don’t have an experience relevant to a question or have not encountered something before, explain that to the interviewer.
Tell them you haven’t “migrated a website before,” but mention what you would do in that situation. If you make something up, passing it off as a situation you faced, you risk being exposed.
You may be asked for details you can’t provide, or you may realize that a savvy interviewer has been researching the company or website as you talk about it.
Building rapport with interviewers is key to a successful interview. Answer their questions clearly so they can recognize your knowledge and experience.
To do that well, you need to understand your audience. You should address their questions using the language and tone they are using and gauge their level of SEO knowledge.
It may be tempting to impress non-SEO stakeholders with industry jargon, but if they don’t know what it means, they won’t understand the impact of what you’ve done.
Similarly, if you’re being interviewed by the head of SEO, relying on jargon or complex-sounding projects without substance can risk being seen as insincere or unqualified.
If you are talking to another SEO at the company or agency, don’t assume they are negligent in not addressing that JavaScript issue you’ve noticed on their site.
Don’t think their SEO approach is basic; there is still an obvious area for expansion. Be respectful. It’s OK to acknowledge that you noticed these issues with their sites, but assume you aren’t telling them anything they don’t already know.
Chances are, some procedural or technical blocks are stopping them from fixing it. Enquire about that instead. It will give you some insight into what challenges you may face if you do go on to work there.
Dig deeper: What 15 years in enterprise SEO taught me about people, power, and progress
Interviews are nerve-wracking. It’s understandable if your mind goes blank when asked to share specific examples of your work or knowledge.
One of the most frustrating mistakes I see in interviews (and have made myself!) is forgetting the details of the perfect example of a project that would have answered an interviewer’s question.
A good way to avoid this is to come prepared with projects or challenges that exemplify some core areas of SEO that you are likely to face in the role. Look at the job listing again and see what experience they hope candidates will have.
Given the scope, seniority, and complexity of the sites, consider the situations and tasks you may face in that role. For example, if you are interviewing for a senior technical SEO role, you may want to prepare examples of projects you’ve worked on that included:
If you’re interviewing for an SEO account manager at an agency, you may want to prepare for times when:
Come prepared with example projects you can adapt.
This may mean writing notes about these projects and key points, such as tasks and results, to jog your memory. Essentially, you want to have a few well-detailed and thought-out examples that you can adapt using the STAR method on the fly at the interview.
Waffle. Meandering. Stalling for time out loud. Whatever you want to call it, this is possibly one of the most common mistakes I’ve seen in interviews. Starting to answer the question before knowing what you are going to say.
Again, it’s understandable. We feel like we need to answer the question as soon as it is asked. In reality, though, it’s OK to take some time to think it through first.
Listen to the question and address that directly. Consider it a school assignment where you get a mark for every point you hit. Structure your answers clearly to help interviewers find the information they’re looking for.
Sometimes the waffling comes from a poorly asked question. Perhaps it isn’t entirely clear what the interviewer is asking. Don’t fall into the trap of trying to answer a question you don’t fully understand.
It’s OK to ask clarifying questions. If you still don’t have an answer, you can explain that it isn’t something you have encountered or even heard of. However, this gives you something to go away and look into.
You could even ask the interviewers what they think about the topic or what they would do in the situation you mentioned. Most interviewers seek team members who are willing to learn and expand their knowledge.
In the best case, they will see your willingness to learn and grow from others around you. Worst case, you have another side of SEO or interviewing techniques to study for the next role you apply for.
This should go without saying, but I’ve encountered it in interviews before.
Another mistake I’ve seen is a candidate getting too enthusiastic about standing out from the crowd. In doing so, they contact anyone in the company they can to make themselves known.
It’s great to show that you are interested in the company and the role. If the interviewers have said it’s OK for you to contact them after the interview, it is absolutely fine.
However, be considerate when contacting interviewers outside the interview process. It may come across as keen, but do it too much, and it can become difficult for people to respond, especially if they aren’t directly involved in the interviewing process.
Follow up sparingly and with the right people, but be mindful of how busy interviewers are when running hiring processes. Your keen attitude may be too much if it’s not appropriate.
Be truthful about your level of involvement in a project. Don’t claim you worked on a project just because it happened at your agency at the same time you were working there.
As soon as interviewers start asking in-depth questions about the project, your lack of knowledge will be apparent. Instead of it sounding impressive, you’ll come across as lacking knowledge and depth in your answer.
Focus your answers on the impact that you had on a project. Talk to what others did and how it fit into the whole approach, but don’t take credit for their work. This is important because interviewers want to know where your competencies lie.
It’s OK to talk about what you learned from others during the project and how you might use that insight in future work. It isn’t OK to claim that it was your idea when it wasn’t.
Dig deeper: 8 tips for SEO newbies
This is an SEO-specific interview mistake. Unfortunately, it’s quite common. I see it often during technical portions of interviews. When candidates are asked to think through how they would approach a situation, or explain why an approach may not work.
They don’t necessarily know why Google ignored a canonical tag. Or why a page that is blocked in the robots.txt is still indexed. So they panic and start blaming Google for lying about its practices and bot behavior.
I’ve heard a lot of sweeping statements during interviews about how you can’t believe Google spokespeople. How they outright lie to us to disguise how the bot and algorithm mechanisms work. Whether you agree with those statements or not, they are a poor way to get around not knowing the answer to a technical question.
If you don’t know why a page has been indexed even though it is blocked in the robots.txt, the answer isn’t to claim “Google ignores the robots.txt and just says they don’t.”
Yes, the SEO world is full of conspiracy theories and genuine questions about the integrity of the industry’s larger players. It’s good to question the status quo through experiments and thought exercises.
However, the better way to approach an interview question like that would be to think around the issue. Let’s assume Google isn’t lying — what could be the reasons the page has been indexed despite being blocked in the robots.txt?
If you start your interview answers from a place of assuming there is a logical answer to them, you are more likely to get to the right conclusions. This is a much better way of approaching SEO in general, rather than assuming you’re being lied to!
By avoiding these common mistakes, you can present yourself as a confident, prepared, and team-oriented candidate. With the right approach, you’ll be better positioned to impress interviewers and land your next SEO role.
Micron has just bought 300,000 square feet of 300mm fab space, and it will be dedicated to DRAM technologies Following their January announcement (see here), Micon has successfully acquired a large fab from Powerchip Semiconductor Manufacturing Corp. (PSMC). For $1.8 billion, Micron has purchased PSMC’s P5 site in Tongluo, Miaoli County, Taiwan. This gives Micron access […]
The post Micron successfully acquires PSMC fab to accelerate its DRAM build-up appeared first on OC3D.
Noctua’s getting ready to launch its first PC case Noctua has started teasing its first-ever PC case, showcasing a Noctua-themed IO panel and another component with neat wood trim. Noctua says that this is the “final element” that PC builders need to build a Noctua “quiet build”. Right now, the company has CPU coolers and […]
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Are you watching your team’s creative operations buckle under mounting pressure? You’re not alone. As project complexity skyrockets and client demands intensify, creative leaders face an unprecedented challenge: scaling operations without sacrificing quality or burning out teams.
The solution isn’t working harder, rather, it’s working smarter with technology that transforms your entire content lifecycle. Here’s how forward-thinking creative operations leaders are building resilient, scalable workflows that thrive in 2025’s demanding landscape.
Creative teams are caught in a maelstrom of expectations and pressures. Research shows that 77% of marketing teams report increased project volume year-over-year, while 45% struggle to keep up with increasing content demands for various channels. Meanwhile, client expectations for faster turnarounds and higher-quality output continue unabated.
Consider this scenario: Your team juggles 15 active campaigns across multiple channels, each requiring dozens of asset variations. Reviews pile up in email threads, designers waste hours hunting for approved brand elements and project managers lose visibility into actual campaign progress.
This chaos isn’t just frustrating, it’s expensive. Teams spending excessive time on administrative tasks rather than creative work see productivity drop by up to 40%.
Many creative leaders attempt to solve these challenges by adding headcount, or by implementing rigid processes that chafe at the creative drives of artists and designers. But throwing additional resources at systemic problems isn’t a guaranteed fix.
For many teams, the real issue lies in disconnected workflows and siloed tools. When your creative software doesn’t communicate with your project management system, and your digital asset management exists in isolation from approval processes, you’re fighting an uphill battle against inefficiency.
What you need is an integrated marketing and creative ecosystem that connects every stage of your content lifecycle.
Digital asset management: Your content foundation
Modern digital asset management (DAM) systems serve as the central nervous system, the single source of truth for creative operations. But not all DAM platforms are created equal. Look for platforms that offer:
Seamless creative tool integration
Your designers live in Adobe Creative Cloud, Figma and Canva, but the briefing and project data for your campaigns live elsewhere. This disconnect creates unnecessary friction and increases time to market. Advanced integrations between platforms should bridge this gap by:
Embedding project context: Bringing project briefs, deadlines, task assignments and feedback directly into creative applications.
Automating file management: Syncing creative files with project management systems without manual intervention.
Intelligent approval workflows
Traditional approval processes rely on email chains and manual tracking. Modern workflow automation transforms this chaotic process by:
Project management that actually manages
Generic project management tools often fail creative teams because they don’t resonate with creative workflows. Purpose-built solutions offer:
Start with process mapping
Before implementing technology, map your current content lifecycle. Identify every touchpoint from initial brief to final delivery. Where do assets get stuck? Which handoffs create delays? This analysis reveals your biggest pain points and prioritizes technology investments.
Implement incrementally
Don’t attempt a complete overhaul overnight. Start with your biggest bottleneck — often asset management or approval workflows. Success with one component builds momentum and buy-in for broader transformation.
Design for scale from day one
As you implement new systems, design workflows that can handle 3x your current volume. This forward-thinking approach prevents future growing pains and ensures your technology investment pays long-term dividends.
Measure everything
Establish baseline metrics for key performance indicators:
Track these metrics throughout your technology implementation to demonstrate ROI and identify areas for continued optimization.
Technology alone doesn’t transform operations — people do. Successful implementations require careful change management:
The most successful creative operations leaders aren’t just solving today’s problems — they’re preparing for tomorrow’s opportunities. Emerging technologies like AI-powered content generation and predictive project planning will further transform creative workflows.
Organizations that build flexible, integrated technology stacks now position themselves to rapidly adopt these innovations. Those stuck with legacy systems and manual processes will find themselves increasingly left behind.
The question isn’t whether to modernize your creative operations technology — it’s how quickly you can begin. Start by auditing your current tools and identifying the biggest gaps in your workflow integration.
Consider piloting a comprehensive digital asset management solution that integrates with your existing creative tools. Look for platforms offering robust approval workflows and project management capabilities that can scale with your growth.
Remember: every day you wait, your competition gains ground. The creative operations leaders who act decisively today will define the industry standards of tomorrow. Are you ready to transform your creative operations from a bottleneck into a competitive advantage? The technology exists — now it’s time to implement it strategically and watch your team’s potential unfold.
Team GeForce hints at potential gaming announcements at GTC 2026 Nvidia’s GTC 2026 Keynote starts later today, and the Nvidia GeForce team has announced that CEO Jensen Huang will discuss the “future of real-time rendering” at the event. Since it is Nvidia’s GeForce team teasing this, it is likely that Jensen will make some gaming […]
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FSR 4.1 DLL appears on AMD’s website Users on the Radeon subreddit have discovered a new AMD FSR 4.1 DLL that’s newer than the version that leaked last month. While AMD was quick to remove these DLL files from its website, Radeon fans were quick to download them and distribute them online. The appearance of […]
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