Advertise on podcast: Found in AI: AI Search Visibility, SEO, & GEO

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe Google is expanding AI Mode’s monitoring capabilities, letting users have Google continuously watch the web for new information instead of repeatedly running the same search. In this episode of Found in AI, Cassie Clark looks at what persistent search could mean for content freshness and AI search visibility. Cassie also breaks down new research from Graphite analyzing nearly 500,000 AI shopping responses across ChatGPT and Google. While large and small retailers appeared at similar rates among cited sources, major retailers were significantly more likely to become the actual recommendation — another example of why citation presence and recommendation presence should not be treated as the same AI visibility metric. Plus, Google Search Console adds reporting for multimodal searches originating from tools including Google Lens and Circle to Search, and new research from Perplexity explores why retrieval systems may need more than the single passage containing an answer to provide enough context for a useful response. In this episode: Why Google AI Mode monitoring changes the traditional search journeyWhat persistent search means for content freshnessWhat Graphite found after analyzing nearly 500,000 AI shopping responsesWhy being cited by an AI system does not guarantee your brand will be recommendedHow prompt language can affect which retailers AI recommendsGoogle Search Console’s new multimodal search reportingWhy search intent increasingly exists without a traditional typed queryWhat Perplexity’s contextual embedding research tells us about AI retrievalWhy structuring content for AI shouldn’t mean removing useful context-- Found in AI covers AI search visibility, generative engine optimization (GEO), answer engine optimization (AEO), AI search measurement, and the changes marketers need to understand as platforms like ChatGPT, Google, and Perplexity reshape search and discovery. Cassie Clark is an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email her at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe In this episode of Found in AI, Cassie Clark welcomes back Tommy Landry, founder of Return On Now and author of The Signal and the Source, for a conversation about the connection between AI governance and AI search visibility. Internally, companies are connecting AI to CRMs, reporting systems, content workflows, sales data, and other sources of business information. Externally, AI systems are interpreting websites, third-party platforms, brand messaging, reviews, documents, and other signals to understand what a company is and what it does. And when those signals are incomplete, outdated, inconsistent, or just plain wrong, AI can still produce a very confident answer. Cassie and Tommy discuss why companies need human checkpoints around AI-powered workflows, who should be responsible for those checkpoints, and why AI governance can't simply be handed to one department. They also explore what this means for GEO and AI search visibility, including why brands need to think beyond citations and start paying closer attention to how they're actually represented inside AI-generated answers. In this episode: How internal AI governance connects to external AI visibilityWhat happens when AI is working with inaccurate or outdated company dataWhy inconsistent messaging across sales, marketing, and other departments can become an AI visibility problemWhere humans need to remain in the loopWho should be responsible for reviewing AI-generated outputsWhy AI governance requires subject matter expertiseHow automated content workflows can create brand and messaging problemsWhy AI search visibility is about more than tracking citationsWhat it means to measure your brand's AI representationWhy accurate positioning matters before a customer ever reaches your websiteHow third-party signals can shape what AI systems understand about your companyWhy AI governance is becoming a cross-functional business problemTommy also shares the thinking behind his new book, The Signal and the Source, and his framework for managing AI across both internal workflows and external visibility. Find it on Amazon. -- Cassie Clark is an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe AI is moving beyond answering questions and toward taking action on our behalf. But as agents handle more of the customer journey, what happens to the websites and publishers that make AI-powered discovery possible? In this episode of Found in AI, Cassie Clark breaks down two developments that reveal different sides of the future of search. First, Meta introduces Muse, a personal AI agent designed to complete tasks on a user’s behalf. Cassie explores what the shift from AI answers to AI actions could mean for brand discovery, the customer journey, and why being recommended by an AI engine may no longer be enough. Then, we turn to a story that deserves more attention: Yahoo Scout. Yahoo CEO Jim Lanzone is making the case for an AI search experience that keeps publisher links visible and preserves opportunities for referral traffic. Cassie examines why that approach matters, what it could mean for the economics of the open web, and why marketers need to look beyond citations when measuring AI visibility. In this episode:  What Meta’s Muse reveals about the shift from AI search to AI agents  Why brands need to think about agent readiness, not just AI visibility  How Yahoo Scout approaches AI-generated answers and publisher links  Why citations and referral traffic are not interchangeable  What the future of AI discovery could mean for marketers, publishers, and the open web Stories covered: Meta: Introducing Muse, a Personal AI Agent The Next Web: Yahoo Scout, Jim Lanzone, and the Future of Publisher Links -- Cassie Clark is an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe Does ChatGPT recommend the same brands to everyone? In this episode of Found in AI, Cassie Clark talks with Joao from Friction AI about new research examining how personalization changes the brands recommended by ChatGPT, Gemini, Claude, and Perplexity. The study compared responses collected through APIs, anonymous AI accounts, and accounts that had been primed with specific user personas over two weeks. The results show that personalization does influence brand recommendations — but the effect varies considerably depending on the AI platform. Cassie and Joao discuss: How ChatGPT, Gemini, Claude, and Perplexity respond differently to personalizationWhat changed between API, anonymous, and personalized responsesWhy geography can affect the brands and sources AI systems surfaceWhat personalization means for local and global brandsWhy creating more content isn't automatically the answer when your brand isn't appearingWhat marketers should understand about the methodology behind AI visibility toolsWhy personalization doesn't make AI visibility measurement useless — it makes repeated measurement more importantRead the research: The original research report: The Personalization Gap: How a Model's Knowledge of the User Reshapes Brand Recommendations in Generative AI Cassie's insights Friction AI's insights -- Cassie Clark is an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe OpenAI is testing a new kind of ChatGPT ad that doesn't just send users to a landing page. It starts a conversation. In this Found in AI news update, Cassie Clark breaks down OpenAI's new Sponsored Agents, Google's latest agentic commerce tools, and a new publisher licensing program that raises some interesting questions about how much Google can actually measure inside AI search. You'll learn: How OpenAI's Sponsored Agents could change the traditional ad-to-website journeyWhy ChatGPT ads point toward a much bigger shift in agent-mediated commerceHow Google is bringing Business Agents and more AI shopping capabilities into the customer journeyWhy Google's new AI share-of-voice reporting for retailers is worth watchingHow Google's pay-per-value publisher program complicates the conversation around AI search measurementWhy marketers need to think beyond AI visibility and start preparing for agent readiness-- Cassie Clark is an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe In this episode of Found in AI, Cassie Clark talks with Leah Nurik, CEO and co-founder of Brandi AI, about why brands need to think much more broadly about what influences their visibility in AI-generated answers. Cassie and Leah discuss why GEO is becoming a brand marketing and communications problem, not simply an extension of traditional SEO. They also dig into the role PR and earned media can play in establishing authority, why the sources that matter for AI visibility vary by industry, and why authentic storytelling may be more durable than trying to find shortcuts for influencing AI systems. They also talk about what makes a story compelling enough to earn media coverage, how brands can identify stories that actually add something new to their industry, and what may happen as AI-generated answers become a bigger part of the buyer journey. In this episode: Why Leah believes AI search represents a new buyer journeyWhy GEO extends beyond traditional SEO and your websiteHow PR and earned media can influence AI visibilityWhy authority signals differ from one industry to anotherThe role of peer reviews, user-generated content, and third-party coverageWhat makes a brand story interesting enough for journalists to coverWhy uniqueness matters for both PR and AI searchHow brands can build visibility that may be more resilient to model changesWhere AI search could be heading over the next five years-- Cassie Clark is an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe In this Found in AI news update, Cassie Clark breaks down Peec AI’s research into OpenAI’s Labrador retrieval index, including the separate indexes identified for web content, news, shopping, YouTube, PDFs, and more. Then, we look at Common Crawl’s analysis of more than 584,000 llms.txt files and what it tells us about the confusion surrounding llms.txt, robots.txt, and AI crawler access. Finally, Microsoft is preparing businesses for a future where AI agents don’t just help consumers discover products — they help choose and buy them. Microsoft’s latest agentic commerce guidance shows why structured, accurate, and current product data could become critical as AI moves from answering questions to taking action. In this episode: What Peec AI discovered about ChatGPT’s Labrador search indexWhy ranking in Bing isn’t a proxy for ChatGPT visibilityHow freshness appears within ChatGPT’s retrieval infrastructureOpenAI’s experiments with its own shopping indexWhat Common Crawl learned from 584,107 llms.txt filesThe difference between llms.txt and robots.txtWhy llms.txt does not control AI crawler accessMicrosoft’s recommendations for preparing for agentic commerceWhy structured product data matters when AI agents are shoppingThe difference between AI visibility and agent readinessWhy businesses should prepare for AI discovery even while the technology continues to changeSources Peec AI: “ChatGPT Built Its Own Search Index”Search Engine Journal: “Common Crawl Finds Widespread Confusion Around llms.txt & robots.txt”Microsoft Advertising: “How Businesses Win When AI Does the Shopping”-- Cassie Clark is an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe Google is giving us a pretty clear look at what comes after AI search visibility: AI agents that don't just recommend brands, but help consumers take action. In this week's Found in AI news update, I'm looking at several recent Google developments that make much more sense when considered together. Google DeepMind says Gemini is evolving from a model into an agent. Meanwhile, Google is adding more of the customer journey directly into Search and AI Mode, including flight price tracking, hotel research and booking, product discovery, visualization, price comparison, and more. So, what happens when getting recommended by an AI engine is only step one? In this episode: Why Google DeepMind is thinking beyond the chatbotHow AI Mode is expanding from travel research into hotel bookingWhat Google's home decor features tell us about the changing buying journeyWhy AI search visibility is still critical, but may actually be the beginning of the journeyThe difference between being recommended by an AI engine and being ready for an AI agent to take actionWhy agentic commerce will require more than content and SEO teams alonePlus, I share why marketers should start asking a new question: Once an AI engine finds, understands, trusts, and recommends your brand, can an AI agent actually do something with it? Resources mentioned: Google DeepMind on Gemini's evolution from model to agent Google's new AI Mode travel and hotel booking capabilities Google's AI-powered Search features for home decor Programming note: Found in AI is taking a short Labor Day break, so there won't be a Tuesday episode next week. We'll be back with a new episode the following Thursday. I'm an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe Daniel Horowitz has spent more than a decade in SEO and now works as an enterprise SEO strategist at Salesforce, where he continues to lead AI search work for Informatica. One of the projects his team is testing challenges a pretty fundamental piece of the traditional B2B content funnel: gated content. Instead of locking valuable research, customer stories, and insights entirely inside PDFs, they're experimenting with a hybrid approach that makes the main ideas accessible as HTML while preserving the full gated asset for lead capture. Because if your best information is inaccessible to AI engines, they'll still answer your buyer's question. They may just answer it using someone else's information. In this episode: Why Daniel still considers SEO foundational to AI search — and where he thinks the two start to divergeWhy creating more content isn't necessarily the answer when valuable information is already buried inside your existing assetsHow Informatica is testing a hybrid gating strategy that makes the main ideas from PDFs accessible without completely abandoning lead captureWhat Daniel's team has seen so far from turning gated information into accessible, answer-first contentWhy strong point-of-view content can give AI engines something distinct to associate with your brandWhat "chunking" actually means — and why it doesn't mean chopping every article into tiny piecesWhy Daniel is experimenting with moving key takeaways from the bottom of articles to the topHow outdated website content can continue shaping what AI engines believe about your company, even when those pages aren't driving meaningful search trafficThe old Informatica product page that was reinforcing an outdated brand perception — and what happened after Daniel's team rewrote itWhy monitoring how AI engines characterize your brand can uncover a much bigger positioning or business problemWhat Daniel has learned about getting enterprise stakeholders and senior leadership on board with AI search initiativesI'm an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe AI search may have had a quieter news week, but several updates from Google and Yahoo point to a bigger shift in how people discover information online. Google is expanding personalization across Search and Discover, including new ways for users to choose the publishers they want to see more often. It's also bringing link carousels for developing topics into AI Mode, creating additional opportunities for publishers and brands to earn clicks from AI-generated answers. Meanwhile, Google's John Mueller says there's still “nothing really special” websites need to do for generative AI responses in Search. I explain why I largely agree with that advice when we're talking about Google, and why marketers shouldn't apply it to the entire AI search ecosystem. Plus, Yahoo is leaning into Yahoo Scout and its history as the original guide to the internet. Could AI give Yahoo another chance to become a meaningful discovery destination? In this episode: How Google's Preferred Sources feature changes the conversation around brand authorityWhy Google's new AI Mode link carousels matter for marketers and publishersWhat John Mueller's latest comments actually mean for GEO and AI search optimizationWhy Yahoo Scout is worth watching as AI search continues to fragmentWhat marketers should learn from Yahoo's broader push for users and relevanceA quick update on Google's August 2026 spam updateWhy brands need to become sources that both people and AI systems want to findMentioned in this episode: Google: New ways to personalize Search, Discover and Google News Search Engine Roundtable: Google Says Nothing Special Needed for Generative AI Search Responses Search Engine Roundtable: Google AI Mode Gets Link Carousels for Developing Topics Yahoo Scout I'm an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe Ryan Doser has been working in SEO for roughly a decade. But as search behavior has expanded beyond Google into ChatGPT, YouTube, TikTok, Reddit, and other platforms, he's started thinking about SEO differently: not just search engine optimization, but "search everywhere optimization." In this episode, Ryan and I talk about why long-form video can do much more than build a YouTube audience. It can give search engines and AI systems another surface for understanding who you are, what you know, and what your brand should be associated with. We also get into the growing importance of third-party trust signals, where traditional SEO still fits into AI search, and some of the GEO advice Ryan thinks marketers should be questioning. In this episode: Why Ryan thinks marketers should shift from "search engine optimization" to "search everywhere optimization"Why trust signals and third-party surfaces matter more as brands try to build visibility beyond traditional Google searchWhy Ryan has "never been more bullish" on YouTube as an AI search marketerHow YouTube can become the foundation for blog posts, newsletters, short-form video, and other contentWhere to start if your brand wants to build a YouTube presence, from defining your ICP to finding the topics they're actually searching forWhy Ryan recommends at least one long-form YouTube video a week — and why he favors evergreen content over constantly chasing trendsThe relationship between traditional SEO and AI search optimization, and why Ryan pushes back hard on claims that SEO is deadWhere llms.txt fits into Ryan's strategy — and why he considers it a small piece of a much bigger visibility puzzleWhy podcast appearances, media coverage, speaking, video, and other off-site signals can help establish what a person or brand is known forWhy starting your own podcast or another form of long-form content can create a foundation for visibility across multiple platformsI'm an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe Reddit's share of ChatGPT Search citations fell by 86% in just four days — and nobody, including the firm that tracked it, claims to know exactly why. In this episode, we break down what actually happened, why the drop looks completely different across ChatGPT versus Google's AI Overviews and AI Mode, and what the ongoing Reddit-Google tension might have to do with it. Then: a Google spam update rolled out with zero new policies, but a clarification from Google's John Mueller complicates the story trackers were already telling about it. We break down why blaming the wrong update for ranking volatility means fixing the wrong thing. Plus, a quick look at Google Trends' new Explore Maps feature and what it's actually useful for. In this episode: Why Reddit's ChatGPT citation share fell off a cliff, while its Google AI Overviews and AI Mode citations only slid graduallyWhat the Reddit-Google licensing tension might (or might not) have to do with itWhy volatility before an update announcement usually isn't that updateWhat Google Trends' new regional Explore Maps add for content researchI'm an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/ Let’s connect: LinkedIn → Cassie Clark | AI Search Visibility Consultant Website → https://cassieclarkmarketing.com

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe A few stories with real strategic implications, and one that's generating more heat than light this week. First: Microsoft Clarity adds branded vs. non-branded query segmentation to its AI Citations dashboard. Cassie breaks down what the new filtering actually shows you, why separating brand-led demand from category discovery changes how you interpret your citation data, and what Share of Authority looks like when you can finally split it by query type. Then: LinkedIn publishes its first comprehensive B2B AI search guide, backed by internal data and eighteen months of their own testing. Cassie pulls out what's actually useful, including why LinkedIn is the number one most-cited domain for professional queries in AI search, how articles and posts do different jobs in the citation ecosystem, and the org alignment point that most brands are still missing. Plus: Sundar Pichai announced Gemini hit one billion monthly users, Shopify's Q2 data shows AI-referred sessions growing 197% year-over-year with double the conversion rate in research-heavy categories, and the Claude watermarking story — what we actually know, what's still speculation, and why you shouldn't change your workflow yet. In this episode: What branded vs. non-branded grounding query segmentation actually tells youHow to use Share of Authority data now that you can filter by query typeWhy LinkedIn articles and posts do different jobs in AI searchThe org alignment problem LinkedIn's own guide surfacesWhat Profound's citation timing data means for how you evaluate new contentWhat Gemini's milestone signals about where AI search is headedWhat Shopify's Q2 data says about buyer behavior in research-heavy categoriesWhat we actually know about Claude's watermarks — and what's still noiseI'm an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/ Let’s connect: LinkedIn → Cassie Clark | AI Search Visibility Consultant Website → https://cassieclarkmarketing.com

Send us Fan Mail It's the official one-year anniversary of Found in AI!  In this solo episode, I go back through the entire Found in AI archive and pull out the five-step strategy for AI search visibility — built from a year of guest conversations, prompt tests, and a few things I got wrong along the way. Whether you've been listening since episode one or you just found the show, this is your starting point. What's covered: Why AI search visibility is a cross-functional problem that shows up looking like a content problemThe five-step strategy: Describe → Structure → Refresh → Corroborate → MeasureHow the FSA Framework (Freshness, Structure, Authority) fits into a broader visibility strategyWhy fixing your brand description across surfaces is step one — before you touch your contentThe weekly assignment you can do in an afternoon to find your actual roadmapEpisodes mentioned: Step 1 — Description Is Your AI Visibility Problem Actually a Messaging Problem? — with David KirkdofferHow Does Local SEO Translate to AI Search Visibility? — with Tommy LandryStep 2 — Structure (FSA) How Do AI Engines Decide What to Cite? The FSA Framework ExplainedStop Optimizing Keywords for ChatGPT — with Shane TepperWhat AI Engines Actually Want (And Why Your Blog Posts Aren't It) — with Bryan McAnultyStep 3 — Freshness (FSA) How Approval Layers Slow Down AI Search Visibility — with Rose Ann MulletWhy Aren't AI Engines Citing Your Content? (Hint: You're Missing Knowledge Graph Enrichment) — with Paul RoweStep 4 — Authority (FSA) What Does It Take To Actually Get Cited in AI Search? — with Jonathan BentzThe 24-Hour Reddit Citation — with Carl PetersonSEO and PR Are Finally Married (And AI Search Is Why) — with Basha ColemanStep 5 — Measurement What Do Bing's AI Performance and ChatGPT Ads Mean for Search?What is an AI Visibility Audit? (And Do You Need One?)AI Competitive Intelligence: How to Track What's Citing Your Competitors — with Vlad PivnevIf you're new here: New to AI search: What's the Difference Between SEO, AEO, and GEO?How Do AI Engines Decide What to Cite? The FSA Framework ExplainedShould You Skip SEO and Go Straight to AI Search? [2026 UPDATE]Trying to measure it: What is an AI Visibility Audit?How Should Brands Measure Visibility in AI Search?What Do Bing's AI Performance and ChatGPT Ads Mean for Search?Selling this internally: Is Your AI Visibility Problem Actually a Messaging Problem? — with David KirkdofferHow Approval Layers Slow Down AI Search Visibility — with Rose Ann MulletYou Can't SEO Your Way Into AI Search VisibilityWant the arguments: SEO Agencies Have 2 Years Left — with Gilad Pichar"Good SEO is Good GEO." But Is That True?AJ Ghergich / Botify three-part series (Parts 1–3)I'm an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/ Let’s connect: LinkedIn → Cassie Clark | AI Search Visibility Consultant Website → https://cassieclarkmarketing.com

Send us Fan Mail 📬 You like this podcast? You’ll love the newsletter. Join the weekly The Visibility Report: subscribe Two stories in a slow week. First, Microsoft launched its first Advertising product newsletter and used it to announce that Microsoft Clarity's AI visibility suite now reports citations, citation share, grounding queries, and share of authority — a platform-defined, free metric for how often AI systems pick your domain over everyone else's.  Cassie breaks down what's actually in the dashboard, why it matters that Microsoft framed this as bringing "the same rigor and transparency of reporting" to the AI era, and the org problem hiding in plain sight: this entity data landed in a paid media newsletter, with paid media recommendations attached. Then: Google told The Verge that Reddit gets no special preference in its ranking systems or AI search features — while Reddit absorbs a core update, a spam update, and a wave of people gaming it for AI placement. Cassie makes the case for why concentration risk on any single third-party surface is the real story there. Plus a note on why newsletter content is worth more than it used to be, and what Microsoft publishing this on LinkedIn might be doing. In this episode: What Microsoft Clarity's four AI visibility metrics actually measureWhy "share of authority" being platform-defined mattersThe question to ask your team this week about ClarityHow to treat Microsoft's conversion stats before you put them in a deckWhat Clarity shows you — and the layer it doesn'tWhy a newsletter with a public archive is a retrieval surface, not just a listThe gap in Google's Reddit denialThird-party mention concentration and why it's a riskResources: Microsoft's Product Newsletter August 4, 2026 I'm an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at [email protected].  Or request your 7-Day AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/ Let’s connect: LinkedIn → Cassie Clark | AI Search Visibility Consultant Website → https://cassieclarkmarketing.com