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Google Completes September 2026 Spam Update After Nearly 14 Days Introduction: Google Completes September 2026 Spam Update, But The Questions Aren’t Over Google completes September 2026 spam update as of October 8, 2026 — closing out a rollout that took nearly 14 days, by far the longest spam update Google has run all year. For SEOs and site owners who’ve spent the past two weeks nervously refreshing Search Console, the news that Google completes September 2026 spam update brings a kind of relief, but it also opens a new phase: figuring out what actually happened, and what to do next. At digitalmachao.com, where we track Google algorithm news and SEO developments as part of our ongoing coverage, this update stood out immediately because of how unusually long it ran compared to every other spam update this year. Now that Google completes September 2026 spam update, here’s the full timeline, how it compares to past updates, what early third-party data is showing, and the concrete steps worth taking before assuming this update is the reason behind any ranking changes you’ve noticed. The Full Timeline: How Long It Actually Took Key facts: Google’s Search Status Dashboard confirms the rollout ended at 1:00 a.m. PDT, with the update listed as complete as of October 8, 2026. The update originally began on September 24, 2026. At launch, Google said it would apply globally, across all languages, and could take “up to two weeks to complete.” Google’s own ranking update history logs the total rollout duration at 13 days and 16 hours — just shy of a full two weeks. As of publication, Google has not disclosed what specifically the September update targeted, or why it ran significantly longer than earlier updates released earlier in the year. Why This Matters The fact that Google completes September 2026 spam update almost exactly at the upper edge of its own stated two-week estimate is worth noting on its own. Google rarely gives a timeframe this specific in advance, and when it does, running right up against that limit suggests this rollout genuinely needed the full window — not that Google was simply being cautious with its initial estimate. For site owners, the practical takeaway is straightforward: any ranking or traffic movement observed between September 24 and October 8 falls inside the official rollout window, and should be read with that context in mind. How This Compares To Every Other Spam Update In 2026 Key facts: The March 2026 spam update ran for just 19 hours and 30 minutes. The June 2026 spam update took 2 days and 1 hour. The August 2026 spam update ran for 2 days and 16 hours. By contrast, the September update — the one where Google completes September 2026 spam update after nearly 14 days — dwarfs all three earlier 2026 rollouts combined. For additional context, the August 2025 spam update ran for roughly 27 days, from August 26 to September 22, 2025, showing that extended spam rollouts aren’t entirely unprecedented, even if they’re unusual. Why This Matters Seeing Google completes September 2026 spam update after nearly 14 days immediately stands out next to a year of updates that mostly wrapped up in a few days. This kind of gap matters because it changes how confidently anyone can interpret short-term signals. A ranking fluctuation that might have resolved itself within 48 hours during the March or June updates could plausibly still be in motion for well over a week during this particular rollout, simply because Google’s systems were still actively processing and adjusting. What Google’s Documentation Actually Says About Spam Updates Key facts: Google officially describes a spam update as a notable improvement to its automated spam-detection systems — language it uses consistently across these rollouts. Google has not stated publicly what the September 2026 update specifically addressed, nor explained why this particular rollout took considerably longer than its three predecessors earlier in the year. This lack of specificity is consistent with how Google typically handles spam update disclosures — broad confirmation that systems were improved, without naming exact targets or mechanisms. Why This Matters Even now that Google completes September 2026 spam update, the absence of a stated target leaves site owners without a clear checklist to compare their own sites against. That’s frustrating, but it’s also fairly standard practice from Google, which generally avoids giving detailed spam-detection specifics that bad actors could use to reverse-engineer workarounds. The practical response remains the same one Google consistently recommends: review your site against Google’s published spam policies broadly, rather than searching for one specific technical cause. What Early Third-Party Data Is Showing Key facts: Analytics platform Diib analyzed 32,569 sites with connected Google Analytics accounts during the rollout window. The median share of visits coming from unpaid Google search fell from 34% to 31% across the sample. Within that same dataset, 10% of sites saw their organic search share drop by half or more, compared to just 5% of sites in Diib’s earlier comparison period. Diib was careful to frame this as “an early read, not a verdict,” noting explicitly that the data doesn’t identify which specific sites Google actually flagged as spam versus sites affected by unrelated fluctuations. Separately, rank-tracking platform SEOmonitor reported that many third-party rank trackers showed sudden, dramatic ranking changes starting around September 26 that real users searching Google weren’t actually seeing. SEOmonitor attributed this to incomplete or inconsistent results being returned to the automated, bot-driven searches that rank trackers rely on. SEOmonitor also noted an important distinction: genuine spam-related ranking changes tend to persist for several consecutive days and show up clearly in Search Console impressions and clicks data — unlike the brief, inconsistent spikes some rank trackers displayed. Why This Matters Now that Google completes September 2026 spam update, this early data offers a useful gut-check against panic. A 3-percentage-point median drop in organic share across tens of thousands of sites is a real signal worth watching, but it’s an aggregate figure across a huge, varied

The Business Case For SEO Is Changing; Budget Justifications Should Too Introduction: The Business Case For SEO Is Changing Faster Than Most Budgets Can Keep Up For years, justifying an SEO budget was simple: show the organic traffic chart, point to the upward trend, and the line item renewed itself. That model is breaking down. The business case for SEO is changing because the thing SEO actually produces — consistent, structured, discoverable information about a business — now feeds far more than a ranking in Google’s blue links. It feeds AI Overviews, chatbot answers, shopping agents, and internal systems that have nothing to do with organic search traffic at all. The problem is that most budget justifications haven’t caught up. Finance teams, marketing leads, and even SEO practitioners themselves are still defending this work using a single metric — organic sessions — that increasingly misrepresents what the work is actually worth. When the business case for SEO is changing this fundamentally, the way teams justify and structure that budget has to change with it. At digitalmachao.com, where we cover SEO and AI search developments as part of our regular news coverage, this exact shift has come up again and again in recent industry discussions. This article breaks down why the business case for SEO is changing, what a smarter budget structure looks like, and how to build a proposal that actually survives scrutiny. Why SEO Has Outgrown Its Old Budget Category Key facts: SEO budgets have traditionally been justified almost entirely by organic traffic growth — the assumption being that more clicks from search directly equals more business value. This traffic-based model misses a significant category of benefit: consistent, accurate product and business information that supports discovery systems well beyond classic search, along with internal operational use cases that depend on that same clean data. When organic returns decline — as they increasingly do in an AI-mediated search landscape — these other, harder-to-measure capabilities become vulnerable to budget cuts, even though the underlying work is often more valuable than ever. Why This Matters This is the central reason the business case for SEO is changing. The technical and content work behind strong SEO — clean structured data, accurate product information, well-organized content — was always doing more than driving clicks. It just wasn’t being credited for the rest. Now that AI systems depend heavily on that same foundation to generate accurate answers, the gap between what SEO actually delivers and what the budget narrative claims it delivers has become impossible to ignore. The GEO Budget Paradox: Same Work, Different Label Key facts: A recurring pattern in how organizations respond to this shift is creating a brand-new “GEO” (Generative Engine Optimization) budget line, separate from the existing SEO budget, to fund essentially the same underlying work. Google’s own guidance states that its AI search features rely on standard SEO fundamentals rather than requiring a wholly separate technical discipline — though this may not hold true across every AI platform equally. Before creating a new budget category, organizations should first map how much proposed “GEO” work actually overlaps with SEO work that’s already funded, rather than assuming it’s an entirely new cost center. Why This Matters This is one of the clearest signs that the business case for SEO is changing in a way that creates real risk of waste. If a company funds structured data improvements, content clarity work, and technical fixes twice — once as “SEO” and again as “GEO” — because leadership believes they’re different disciplines, that’s not smart budgeting. It’s duplicated spend justified by a label change. The smarter move is recognizing that, in large part, the business case for SEO is changing to include AI visibility, not being replaced by a separate discipline that needs its own line item. A Practical Example: Reallocating A Budget By Purpose, Not Expense Type Key facts: One illustrative example reorganizes a hypothetical $1.2 million ecommerce SEO budget — keeping the total spend identical, but restructuring it around purpose rather than traditional expense categories. The reallocation breaks the same total budget into four functional buckets: shared discovery infrastructure (35%), commercial search (30%), AI experimentation (15%), and measurement and operations (20%). These percentages are explicitly illustrative, not a recommended universal ratio — every organization’s right mix depends entirely on its own business model. Why This Matters This reframing is a genuinely useful template for any team trying to explain why the business case for SEO is changing to a finance stakeholder who only sees one number on a spreadsheet. Instead of asking for “more SEO budget” as an undifferentiated lump sum, breaking the ask into functional purposes — infrastructure that other systems depend on, revenue-generating search work, forward-looking AI experimentation, and the operational cost of measuring it all — gives decision-makers something they can actually evaluate line by line, rather than a single vague request riding entirely on a traffic trendline. Different Types Of SEO Investment Need Different Tests Key facts: A major reason the business case for SEO is changing is that not all SEO spending serves the same purpose, and treating every dollar the same way distorts the justification. Four distinct categories each need their own evaluation standard: Work that generates revenue should be judged on expected incremental return — the traditional ROI conversation most people already associate with SEO. Work that protects revenue — such as technical work during a platform migration — should be judged on expected loss avoided relative to the cost of the mitigation effort, not on new traffic gained. Work that maintains existing capabilities should be judged on operational value relative to its ongoing cost, similar to how a business would evaluate any infrastructure maintenance spend. Work that reduces uncertainty — genuine experimentation — should be judged on the value of the information the experiment produces, with clear decision criteria set before any money is spent, not after. Why This Matters This four-category framework is arguably the most practical shift in how the business case

Using AI To Assist With SEO Work, Not Replace The Worker Introduction: Using AI To Assist With SEO Work Is Not The Same As Replacing SEOs Every few months, a new wave of panic rolls through the SEO industry: “AI is going to replace SEOs.” It makes for a dramatic headline, but it misses what’s actually happening on the ground. The real shift isn’t AI replacing the worker — it’s smart practitioners using AI to assist with SEO work in ways that make them faster, sharper, and more valuable, not obsolete. According to insights shared by Chris Green, Technical Director and Senior Consultant at Torque Partnership, the right way to think about using AI to assist with SEO work is through a clear line: some tasks demand rigid, deterministic logic that AI shouldn’t be anywhere near, while others genuinely benefit from AI’s ability to interpret, summarize, and explain. Confusing the two is where most AI-in-SEO strategies go wrong. This article breaks down that framework — what AI should never be trusted to decide on its own, where using AI to assist with SEO work genuinely pays off, and how to build a workflow that keeps the human practitioner firmly in the loop rather than replaced by it. Why Some SEO Tasks Should Never Be Handed To AI Alone The starting point for using AI to assist with SEO work responsibly is understanding which tasks are deterministic — meaning they have one correct, verifiable answer — versus which tasks are probabilistic and benefit from interpretation. Key facts: Deterministic SEO tasks include things like whether a page returns an HTTP 404 status code, whether a canonical tag exists on a given URL, how the DOM renders after JavaScript executes, and whether robots.txt actually permits or blocks crawler access. Green argues these tasks should run through a “firm framework rather than a probabilistic token-eating word-predictor,” because they produce the same correct answer every single time they’re checked. Using AI to assist with SEO work on tasks like these — where a large language model essentially guesses at an answer that software can simply verify — introduces unnecessary risk of error into checks that should be 100% reliable. Why This Matters This is the single most important guardrail in any strategy for using AI to assist with SEO work. A 404 status code is not a matter of interpretation — it either returns or it doesn’t. Handing that kind of binary, verifiable fact to a language model, which generates probabilistic text rather than running a deterministic check, introduces a failure point that simply doesn’t need to exist. The practical takeaway: keep technical, verifiable SEO checks running through actual software and crawlers, and reserve AI for the layer that sits above those facts — not for generating the facts themselves. Where Using AI To Assist With SEO Work Actually Pays Off Once the deterministic layer is handled by proper tools, there’s real, substantial value in using AI to assist with SEO work on the interpretive side of the job. Key facts: Language models are genuinely effective at converting dense technical data — JSON exports, spreadsheet dumps, crawl reports — into readable, digestible summaries a stakeholder or client can actually understand. AI is well suited to explaining what changed between two versions of a page, a site, or a dataset, turning a raw diff into a plain-language explanation. Generating ticket descriptions for developers is another strong use case, since it saves practitioners from manually re-explaining the same technical context over and over. AI also genuinely helps interpret ambiguous semantic shifts — situations where meaning or intent has changed in a way that isn’t a simple binary fact, but requires judgment-informed explanation. Why This Matters This is where using AI to assist with SEO work delivers real, measurable time savings without sacrificing accuracy. None of these use cases ask AI to be the source of truth — they ask AI to take facts that are already established and make them faster to understand, communicate, and act on. That distinction is what separates a genuinely useful AI workflow from a risky one. The AI model provides supporting evidence and explanation; it does not become the final decision-maker on whether something is actually a problem worth fixing. A Real Experiment: What Happened When AI Judged Instead Of Supported One of the most instructive parts of this discussion comes from a direct experiment testing the limits of using AI to assist with SEO work. Key facts: Testing an on-device model (Gemini Nano), Green found it performed well at describing DOM changes — accurately summarizing what had shifted in a page’s rendered structure. However, the same model struggled significantly when asked to combine multiple structured facts into a nuanced SEO judgment — the kind of multi-factor reasoning an experienced practitioner does almost instinctively. Rather than trying to fix this by piling on more detailed instructions or prompts, the practical solution was reassigning the model’s role entirely: instead of asking it to judge findings, it was repositioned to support the practitioner’s own judgment. Why This Matters This experiment is a useful real-world test case for anyone experimenting with using AI to assist with SEO work. The instinct when an AI model gets something wrong is often to write a longer, more detailed prompt — adding more rules, more context, more constraints. But the better fix, based on this experience, isn’t a bigger prompt. It’s a smaller job. Describing a change is a task AI handles well. Judging whether that change matters, and what should be done about it, is still a job for the practitioner. Recognizing which category a task falls into — description or judgment — is the real skill in using AI to assist with SEO work effectively. The Practical Workflow: Facts, Then AI, Then Judgment Put together, this points to a simple, repeatable three-step workflow for using AI to assist with SEO work without losing control of the outcome. Key facts: Establish the facts with software. Technical, deterministic

AI Cites Your Brand: How To Choose Your Next Move Introduction: AI Cites Your Brand, Now What? You searched a question, and AI cites your brand in the answer. Exciting, right? But here’s the catch most marketers miss: when AI cites your brand once, it doesn’t automatically mean you’re winning. And when AI cites your brand less often than a competitor, that doesn’t automatically mean you’re losing either. This is the core problem a lot of marketing teams run into. They see that AI cites your brand in one ChatGPT answer or one Perplexity response, get excited, and move on. Or they see that AI cites your brand’s competitor instead, panic, and start firing off emails demanding corrections. Neither reaction is the right one. According to insights shared by Constance Tan, Product Marketer at Ahrefs, in a recent industry discussion, the smarter approach is to treat every single instance where AI cites your brand — or fails to — as one data point inside a much bigger pattern. The question isn’t “did AI cite my brand today?” It’s “what does AI consistently say about my brand across the entire customer journey, and what should I actually do about it?” In this article, we’ll break down exactly how to build that bigger picture, and how to choose your next move whether AI cites your brand accurately, inaccurately, rarely, or not at all. Why One Citation Doesn’t Tell You The Whole Story The first mistake most brands make is reacting to isolated moments when AI cites your brand, rather than looking for patterns. Key facts: AI systems like ChatGPT, Google AI Overviews, and Perplexity pull from a huge range of sources — articles, reviews, forum discussions, videos, and comparison pages — to decide whether AI cites your brand in a given answer. A single prompt result is just one sample. The same question, asked slightly differently, can produce a completely different answer about whether AI cites your brand or a competitor instead. Tan recommends organizing your monitoring around three categories of questions that map to the real customer journey: problem-solving queries (how customers discover solutions before they even think of specific brands), positioning and comparison queries (which brands AI recommends for a given use case), and business-fact queries (whether AI accurately states your pricing, features, and capabilities). Why This Matters If you only track whether AI cites your brand for branded searches, you’re missing almost the entire funnel. Most buyers don’t start by typing your brand name — they start with a problem. Tracking only the moments when AI cites your brand by name means you never see how often AI recommends a competitor before your brand even enters the conversation. As Tan put it, “the idea is that you want a representative view from every angle of the customer journey” — not a lucky screenshot of AI citing your brand once. When A Competitor Gets Cited Instead Of You It stings when you ask a question and AI cites your brand’s competitor instead. But before reacting, dig into why. Key facts: When a competitor gets more AI citations than you do, the first step is examining the specific source pages actually driving those answers — not just the brand name that shows up. Reviews, forum discussions, and videos can influence whether AI cites your brand just as much as traditional articles and blog posts do. For outreach efforts aimed at getting these sources corrected or updated, prioritize pages that are both frequently cited by AI and realistically reachable — chasing every single source wastes time you don’t have. Why This Matters It’s tempting to assume AI cites your brand less because of some mysterious algorithmic bias. In reality, it usually comes down to which sources AI is actually pulling from, and whether those sources favor a competitor’s language, structure, or recency. Once you identify exactly which pages are feeding AI’s answer, you can decide whether the fix is outreach, new content, or simply accepting that particular source isn’t worth chasing. Fixing The Information Gaps That Stop AI From Citing Your Brand Accurately Sometimes the problem isn’t that AI ignores your brand — it’s that AI cites your brand with outdated or wrong information. Key facts: The first and most important fix is internal: update outdated pricing pages, stale product descriptions, and old content on your own website before chasing anyone else’s corrections. External outreach is harder than it looks. In one real example shared by Tan, Ahrefs contacted 26 authors about inaccuracies in their content. Only 10 responded, and just 4 actually made updates. When outreach isn’t realistic, Tan suggests a different approach entirely: “Sometimes it’s better to create the new sources of information, new pages that answer or cover the topic in a better way” — rather than spending months trying to get someone else’s page corrected. Why This Matters This data point matters enormously: out of 26 outreach attempts, only 4 resulted in an actual fix. If your strategy for improving how often AI cites your brand accurately depends entirely on convincing other website owners to update their content, you’re playing a low-odds game. The better long-term move is often publishing your own authoritative, up-to-date page on the topic — one strong enough that AI naturally prefers citing your brand’s own source over an outdated third-party one. Showing Up In Communities Without Looking Like You’re Selling AI increasingly pulls from forums, Reddit threads, and community discussions when deciding whether AI cites your brand in an answer. That makes authentic participation a real lever. Key facts: The right way to participate in these communities is by answering genuine technical questions and correcting factual errors — not by inserting product pitches into unrelated discussions. AI systems can detect patterns that look like coordinated self-promotion, so a heavy-handed approach can backfire and reduce the chances AI cites your brand as a trustworthy source. Authentic, helpful contributions build the kind of reputation signal that naturally increases the odds AI cites your brand

Google Shows How Long Crawling, Indexing & Recovery Can Take For years, one of the most frustrating parts of SEO has been the silence around timing. A new page goes live, and nobody can say with any confidence whether Google will discover it in hours or weeks. A core update hits, and the recovery conversation is pure guesswork. Clients ask “how long will this take?” and most SEOs have learned to answer with a shrug dressed up as professionalism: “it depends.” That changed, at least partially, on October 2, 2026, when Google’s Gary Illyes shared real timing ranges for the most common search processes at the Search Central Live Deep Dive event in Barcelona. The data comes from an attendee recap by John Campbell of ROAST, since Google hasn’t published the official slides yet — but the numbers themselves are specific enough to actually be useful. Here’s every range Illyes shared, broken down by stage, along with what each one means for how you plan, report, and set expectations. Crawling And Indexing: From Discovery To Full Visibility Key facts: New URL discovery: typically around 20 hours, but can take anywhere from weeks to never happening at all in the slowest cases. Refresh of already-known URLs: typically around 30 days, with the slowest cases also stretching to weeks or never. Sitemap processing: typically around 24 hours, but can take up to 14 days, or never complete, if Google flags quality issues. End-to-end indexing (crawl to index): typically around 1.5 hours, but can take months or never happen at all, depending on content quality signals. Why This Matters This is the first time many SEOs have seen Google put an actual number on “how long should indexing take.” The headline figure — 1.5 hours for end-to-end indexing in typical cases — is genuinely fast, and it confirms something many practitioners suspected: when indexing drags on for weeks, quality signals are usually the real bottleneck, not some arbitrary queue delay. If a client’s new page still isn’t indexed after a month, the conversation shouldn’t default to “Google is slow.” It should shift toward reviewing the page itself — thin content, duplicate signals, weak internal linking, or anything else that would make Google hesitate to treat it as worth indexing quickly. The sitemap processing number is also worth flagging to anyone who treats sitemap submission as a formality. A typical 24-hour turnaround is fast, but the 14-day-or-never range for sites with quality issues is a real warning: a sitemap full of low-value or problematic URLs can actively slow down how Google treats the rest of your submissions. Technical Changes: Canonicalization And Site Moves Key facts: Canonicalization changes: typically 1 to 3 weeks, but can stretch to months when Google receives conflicting signals about which version of a page is canonical. Site moves: typically 1 to 3 months, with the slowest cases taking anywhere from 6 months to over a year. Smaller sites, however, can sometimes complete a move in just a few weeks. Why This Matters Anyone who has managed a domain migration knows this timeline intuitively, but having Google confirm it changes the conversation with stakeholders. A 1-to-3-month typical window for a site move means that panicking over a traffic dip in week two is premature — but it also means that if you’re still seeing major instability after three or four months, something in the migration likely needs auditing rather than just more patience. The canonicalization number deserves particular attention from technical SEOs. “Months when signals conflict” is a direct warning about what happens when canonical tags, redirects, internal links, and sitemaps all point to slightly different versions of the truth. Cleaning up conflicting signals isn’t just good practice — based on this data, it’s the difference between a multi-week fix and a multi-month one. Search Results: Title And Snippet Updates Key facts: Title updates: typically 1 to 2 days to reflect in search results, but can take several weeks to months in slower cases. Snippet changes: also typically 1 to 2 days, with the same several-weeks-to-months range for slower cases. Why This Matters This is one of the more immediately useful ranges for day-to-day SEO work. If you’ve rewritten a title tag to improve click-through rate and it still hasn’t updated in search results after a week, that’s a reasonable point to start investigating rather than just waiting longer. On the other hand, this range also gives you language to set expectations with clients or stakeholders who want to see a title change reflected in search results the same day they ask for it — a 1-to-2-day typical window is fast by web standards, but it’s not instant, and “several weeks to months” in edge cases is a legitimate possibility worth mentioning upfront. Removals: Search Console Requests And Manual Actions Key facts: Search Console removal requests: typically around 2 hours, with the slowest cases taking up to roughly 24 hours. Manual action removal: typically 1 to 2 weeks after a successful reconsideration request, but can stretch to 4 to 6 or more weeks for sites that have been inactive. Why This Matters The removal request numbers are genuinely reassuring — a 2-hour typical turnaround for Search Console removal requests means this remains one of the fastest, most reliable levers SEOs have for emergency situations, like a page that was accidentally indexed with sensitive content. The manual action timeline is more nuanced. A 1-to-2-week typical recovery window after a successful reconsideration request is useful to know, but the 4-to-6-week-plus range for inactive sites is an important caveat. If a site has been dormant and then gets hit with a manual action, or is trying to recover from one, the process of re-establishing enough activity and trust signals appears to meaningfully slow down how quickly Google processes the recovery. Core Update Recovery: The Timeline Everyone Actually Wants Key facts: Core update recovery: typically 3 to 6 months, but in the slowest cases can take anywhere from 6 months until the next core

Shopify Is Auto-Enrolling Your Store In Every New AI Shopping Channel — Here’s What That Actually Means If you run a Shopify store, there’s a good chance your products are already being shared with AI shopping apps you never explicitly signed up for. One setting, switched on by default, is quietly enrolling eligible stores in every new AI shopping channel Shopify adds — and most merchants have no idea it’s happening until they go digging through their admin panel. This isn’t a future possibility. It already happened. When Meta launched its AI agent Muse on September 8, 2026, eligible Shopify stores’ products were shared with Meta that same day, without any merchant having to click “yes” to anything new. Here’s a full breakdown of what this default setting does, how direct checkout changes what shows up in your analytics, and the practical steps every Shopify merchant should take right now. Shopify’s “Allow Shopify To Manage For Me” Setting Signs You Up Automatically Key facts: The setting is called “Allow Shopify to manage for me,” and it sits at the top of Sales channels > Agentic inside the Shopify admin. It stays switched on unless a merchant manually turns it off. According to Shopify’s own documentation, agentic storefront channels get access to a store’s products through Shopify Catalog, direct checkout gets activated for relevant channels, and the store is auto-enrolled into any new agentic storefront channel Shopify adds. Shopify Catalog is how most of these AI shopping apps pull in a store’s product list. Google is the one exception — it reads product data from Google Merchant Center instead. Shopify first turned this auto-enrollment on for merchants back in January 2026. Why This Matters Most merchants who checked this setting earlier in the year and confirmed a channel was switched on have no reason to assume anything changed since. But the same setting that enrolled them in one channel is the same setting feeding their product data into every channel added afterward — including ones that didn’t exist yet when they last checked. Meta is the clearest example: stores didn’t opt into sharing products with Meta’s new AI agent. They were enrolled automatically, through a setting most store owners checked once and forgot about. Meta’s Muse Arrived Through This Setting — And Amazon Chose A Different Path Key facts: Meta launched Muse, an AI agent described as able to open a browser, fill out forms, and negotiate on a user’s behalf, on September 8, 2026. Shopify’s own changelog for that date confirms that eligible stores’ products are shared with Meta by default through Shopify Catalog. Twelve days later, on September 20, 2026, Amazon blocked Muse from accessing its store entirely. Shopify’s admin also includes an “Other channels” row, switched on by default, described as covering independent developers, startups, and new third-party partners building experimental shopping agents that have recently gained access to Shopify Catalog for testing. Why This Matters Two of the biggest names in e-commerce took opposite approaches to the same AI agent on the same week. Amazon actively blocked it. Shopify’s default setting shared merchant data with it automatically. Neither approach is inherently wrong, but it highlights how much control individual merchants actually have — and how easy it is to miss that control exists, since the default setting makes the decision for you unless you go looking for it. Direct Checkout Lets Customers Pay Without Ever Visiting Your Website Key facts: On Meta, Google AI Mode and Gemini, and Microsoft Copilot, eligible stores have direct checkout switched on by default. The customer pays inside a Shopify checkout embedded in the chat app and never opens the merchant’s actual website. Eligibility varies by platform: Meta covers stores selling to the US, Canada, or Mexico; Google requires a US store selling to US customers with a Google Merchant Center account; Microsoft Copilot requires a store selling in the US. ChatGPT works differently — it sends the customer to the store’s own checkout, either inside a ChatGPT in-app browser or in a new tab on ChatGPT web. When a customer completes a direct checkout, standard website tracking doesn’t fire. Shopify confirms that Google Analytics and custom pixels won’t trigger during Google and Copilot checkouts, and third-party analytics pixels don’t fire during Meta’s either. Only server-side tracking captures these sales on the analytics side. The order itself still shows up normally inside the Shopify admin, the same way any other sales channel’s orders do. Why This Matters This is the detail with the most immediate, practical impact for merchants. A sale made through direct checkout is real, and it will appear in your Shopify orders — but if you’re used to checking Google Analytics, an ad platform dashboard, or any pixel-based tracking tool to judge how a channel is performing, you may be looking at numbers that don’t include these sales at all. Anyone running paid campaigns or judging channel performance off third-party analytics needs to start cross-checking directly against Shopify’s own order data, filtered by channel, rather than trusting external tracking tools alone. You Can Turn Off Auto-Enrollment Or Disable Individual Channels Key facts: Switching off “Allow Shopify to manage for me” reveals three separate controls: Shopify Catalog access, auto-enroll for new storefronts, and direct checkout. Each individual channel also has its own separate switches. Meta and Copilot both offer a catalog access toggle and a direct checkout toggle. ChatGPT only offers a catalog access toggle. Google only offers a direct checkout toggle, though the store’s products still need to qualify for Shopify Catalog in the first place. Turning off catalog access for a channel doesn’t take effect immediately — Shopify says it can take up to seven days before product data stops being shared through Shopify Catalog. To hide a single product from all AI shopping apps, a merchant has to mark that product as unlisted. Doing so also removes the product from sitemaps, standard search engines like Google, and the store’s own on-site search. Why This Matters Merchants

Yes, You Can Change AI’s Opinion. Here’s How. You did the hard part. Your brand finally shows up when people ask ChatGPT, Gemini or Claude about your market. Then a customer types one more question: “Do you recommend them?” And the AI hesitates. It mentions a bad review, adds a word of caution, and suggests a competitor instead. A new article published on Search Engine Journal on September 29, 2026 says this doesn’t have to happen. Its author, Robert Maynard Jr., founder of AnswerShare, claims that brands can shift AI answers from warnings to recommendations within days. Let’s look at what he found, how it works, and where you should stay cautious. The Quick Answer Can you change AI’s opinion of your brand? According to this study, yes, you can influence it. The idea is to publish a full, sourced, public account of your business, including the story behind any complaints, and make it easy for AI crawlers to read. In the author’s client test, AI warnings fell from 27 of 42 answers to zero. One important note: the article is sponsored, and the results are self-reported. Key Takeaways AI tools can lean too hard on a few negative reviews when they don’t know how big a business is. The author calls this a “numerator without a denominator” problem. In one client test, recommendations rose from 10 of 42 answers to 40 of 40. The method uses a public llms-full.txt file and a CDN “worker” that serves the same content to AI crawlers. The data comes from the vendor’s own tests, so independent checks are still needed. Why AI Warns People Away From Good Businesses Here is the core problem, in simple terms. AI companies want their assistants to be careful, especially on topics where a wrong answer could hurt someone. That means health, money and safety, the area SEOs call YMYL (Your Money or Your Life). The article points to OpenAI’s public model guidance, Anthropic’s January 2026 constitution for Claude, and Google’s Gemini policy guidelines as examples of this cautious approach. Caution is good. But the author argues it can go too far. If even a few complaints exist online, the AI may bring all of them up, attach a warning, and send the customer somewhere else. His example makes the point well. One client had more than 75 positive reviews, five negative ones and two Better Business Bureau complaints. Seven complaints sounds serious if a company has 20 customers. But this company had served 35,000 customers over 13 years. By the author’s math, that works out to a 99.98% satisfaction rate. The AI could see the complaints. It just couldn’t see the size of the business. That is the missing denominator. What the Test Found The team asked leading AI systems the same neutral questions about the client’s reputation. They ran them through APIs, without chat history or leading wording, so the models couldn’t simply agree with the user. They then added full context and repeated the test after 14 days. What the AI did Before After Warned the user 27 of 42 (64.3%) 0 of 40 (0%) Recommended the company 10 of 42 (23.8%) 40 of 40 (100%) Explained complaints with scale or context 1 of 42 (2.4%) 40 of 40 (100%) Here is the detail I find most interesting: the complaints did not vanish. They still came up in every “after” answer. The difference was that the AI now placed them next to the company’s size, history and response. That is a fairer picture, not a cleaned-up one. According to the author, balanced answers started appearing in about three days and settled in roughly two weeks. He also shared two other examples, a regional ad agency and a luxury resort in Phoenix and Scottsdale, where recommendations reportedly reached 40 of 40. How the Method Works The approach has three parts. 1. A public llms-full.txt file. Think of it as a complete company profile written for machines: who you are, what you offer, and what the record shows. 2. A CDN “worker.” This is a small script running at the edge of a content delivery network. It sends AI crawlers to the machine-friendly version of your content, while human visitors and Googlebot see your normal site. The author says both versions should match, byte for byte, apart from the routing code. 3. The “billboard” effect. The author noticed AI crawlers visited the worker far more often than the text files. Over two months, the client’s llms files were crawled 154 times. The worker was crawled 744,566 times. So he started treating the worker like a billboard that tells the brand’s story on every AI visit. He backs the value of repetition with a peer-reviewed arXiv paper about how language models handle retrieved information. What Goes Into a Good Brand Briefing The article suggests building your record around five areas: The business: who you are, who owns you, how long you’ve operated and how many customers you serve, with links to sources like Wikipedia, Wikidata and your official business registration. The offering: what you sell, where, and what truly sets you apart. The evidence: credentials, case studies, customer reviews and third-party sources. The reputation record: each public complaint, when it happened, its scope, and your response. The boundaries: dates, attribution, open questions, and what the evidence does not prove. Two habits make this work. First, keep context right next to the claim it explains, so the AI doesn’t have to connect scattered facts. Second, attribute everything. Label company-reported numbers as such, and link independent facts to their source. There’s also a lesson in honesty here. The author says his own company disclosed a three-day technical failure in its case studies, and AI reviewers reportedly read that openness as a sign of credibility. Being upfront can actually help you. One more rule stands out: don’t try to trick the AI. The author warns that models quickly spot “marketing slop.” And if a brand truly has a bad reputation,

Google Generative AI Report Feels Broken — Here’s How To Actually Make It Useful Google finally gave SEOs first-party visibility into how often their content shows up inside AI Overviews and AI Mode. For a lot of teams, the excitement lasted about five minutes — right up until they realized the report shows impressions, but almost nothing else that’s actually actionable. No clicks. No query-level data in most views. Just a rising or falling line that tells you your content is being seen somewhere inside Google’s AI features, with very little context on what that actually means for your business. Here’s what’s changed, why the report looks this thin, and a practical way to turn it into something genuinely useful anyway. Google’s Generative AI Report Shows Visibility, Not Value Search Console’s Generative AI Performance report tracks how often your pages appear in AI Overviews and AI Mode. Key facts: The report gives sitewide and page-level impression data for content appearing in Google’s generative AI features. It does not include click data or reliable query-level detail for most sites. According to Google’s own John Mueller, impressions are counted based on links to a site being shown inside AI Overviews or AI Mode — a link shown directly can count without any user action, while a link that requires activation (like expanding a section) only counts once a user actually activates it. The page-level export is also limited to roughly the first thousand rows, so larger sites are typically seeing a sample rather than the complete picture. Why This Matters Impressions alone answer one question — “is my content showing up in AI features?” — and leave the far more important ones unanswered: is that visibility costing us clicks, and does it matter for the business either way? Without click or query data, it’s genuinely hard to separate a section of your site that’s thriving inside AI answers from one that’s quietly losing traffic to them. That gap is exactly why treating this report as a standalone dashboard, rather than one input among several, leads a lot of teams to write it off entirely — when the smarter move is to combine it with data you already have. Turning Visibility Into A Real Exposure Score The fix isn’t waiting for Google to add more columns. It’s layering the AI visibility data against information you already collect elsewhere. Key facts: A workable exposure model combines four inputs: AI Visibility (where impressions concentrate by subfolder or content type), Traffic Dependency (what share of a section’s traffic comes from organic search), Commercial Value (revenue, leads, subscriptions, or conversions tied to that content), and, optionally, AI Substitution (how often relevant queries trigger an AI Overview at all, using third-party SERP data). A simple version of the formula — without the optional substitution step — multiplies AI Visibility, Traffic Dependency, and Commercial Value together and takes the cube root, producing a single 0–100 score per content area. The logic is deliberately conservative: heavy AI visibility on a page with almost no commercial value shouldn’t produce a high exposure score, and a highly valuable page with little AI visibility or search dependence shouldn’t either. Why This Matters This reframes the conversation from “our AI impressions went up” to “here’s exactly which parts of the business are most exposed if AI search keeps growing.” That’s a very different conversation to bring into a leadership meeting, and it’s the kind of analysis that turns a thin report into a genuine planning tool — one built from data most SEO and analytics teams already have sitting in Search Console and their analytics platform. Not All Exposure Is Equally Risky — Resilience Fills The Gap Two content areas can carry the same exposure score and still face very different levels of real risk, depending on how defensible that content actually is. Key facts: A resilience score can be built from four inputs: branded search volume (demand specifically for your brand name), direct audience share (traffic that comes straight to your site rather than through a search intermediary), returning audience share (how much of your traffic is repeat visitors), and content defensibility — a qualitative judgment of how hard your content is to replicate, based on its uniqueness, effort, expertise, and access to proprietary information. The first three inputs can be measured as straightforward percentages from your analytics platform. Content defensibility needs a manual, consistent scoring framework, since it’s a judgment call rather than a metric. Averaging the four gives a single resilience score, where a higher number means a content area is better protected against further substitution by AI-driven search. Why This Matters A site or section with a strong direct and returning audience, high brand-search demand, and genuinely hard-to-replicate content is in a fundamentally different position than one relying almost entirely on generic organic traffic for informational queries — even if both show identical AI visibility numbers. Resilience scoring is what separates “this looks exposed but is actually fine” from “this looks fine but is quietly vulnerable.” Server Logs Add A Demand Signal Google Won’t Give You Key facts: Server logs can reveal AI crawler access patterns by bot type, letting teams separate training bots, search bots, and retrieval bots instead of lumping everything together as generic “AI traffic.” This creates a proxy for AI demand: what content is being accessed, how often, and by what kind of crawler. Crawler access alone doesn’t prove that content was used to train a model or that any AI company intends to pay for it — it only shows that the content is attracting AI crawler activity consistent with training, search, or retrieval behavior. Why This Matters Combined with commercial value and content defensibility, server-log demand data can point to a genuine opportunity rather than just a threat. High-value, hard-to-replicate content that’s being heavily accessed by retrieval crawlers is exactly the kind of asset worth a closer look before deciding whether to block AI crawlers, pursue a licensing conversation, or lean further into

Google Spam Update, Image Search Data In GSC – SEO Pulse For anyone working in SEO, the past week must have been quite busy indeed. Google has started rolling out yet another anti-spam update with an extended timeframe for its deployment. A new visualization of search queries triggered via images appeared on Search Console. De-indexing panic related to the casino website of some unknown person was raging through the SEO community. Finally, two studies revealed fresh statistics regarding the loss of traffic by means of the traditional blue links. Taken separately, those could be considered independent events. But when viewed altogether, they tell the same unpleasant story – Google is providing you with different perspectives on your traffic, while the explanation of the reason why this happened still relies primarily on your own logs. This article will help you understand what each of the five events means, how it affects you and your website, and which specific actions can be taken to distinguish between a threat and noise. Google Rolls Out The September 2026 Spam Update Google began rolling out its September 2026 spam update on September 24, and this time it warned that the process may take up to two weeks to finish. Key facts: The rollout started at 9:15 a.m. PDT, according to Google’s Search Status Dashboard. It applies globally and across all languages. It is the fourth spam update of 2026, following earlier ones in March, June, and August. Google described each of those earlier updates as needing “a few days,” and none of them ran longer than two days and sixteen hours. Google has not said what this update specifically targets. Why This Matters The two-week window is noticeably longer than anything Google gave a spam update earlier this year. That has a practical consequence: the first few days of data may not show the full effect. A dip on day three doesn’t necessarily tell you where your site will land on day twelve, and a quiet first week doesn’t guarantee you were untouched. The safest approach is to line up any ranking or traffic movement in Search Console against the rollout dates, rather than reacting to daily swings. Google’s own advice for sites that feel affected is to revisit its spam policies and honestly compare your pages against them. Since the company hasn’t named a target, that policy review is the only reliable starting point. Move Fast In The First 24 Hours Send a welcome email within 24 hours of signature. The excitement and momentum built during the sales process fades fast if the client hears nothing for several days. A quick, organized welcome message reinforces that the project is already moving. That first email should set up the shared communication channel, request the access you’ll need (analytics, Search Console, the CMS, and so on), and include an onboarding questionnaire built around the specific business — not generic boilerplate. Useful questions include who the client’s core customers are, what makes their business different from competitors, their biggest current challenges, and what success actually looks like to them. Speed here isn’t just politeness. It’s the first real proof point that this agency is organized and easy to work with. A quick checklist while the update rolls out: Note your baseline clicks and impressions from the two weeks before September 24. Avoid making large, reactive site changes mid-rollout, because they muddy the data. Wait until Google marks the update as complete before drawing conclusions. If you do see a drop, review scaled content, expired-domain abuse, site reputation abuse, and manipulative link patterns against Google’s published spam policies. Search Console Adds A Filter For Image-Based Searches The second big development is a genuinely useful one. Google is rolling out a multimodal filter in the Performance report of Search Console, built to show searches where someone used an image rather than typing words. Key facts: The data covers Google Lens, Circle to Search on Android, image uploads to Google Search, and the “Search this image” option in Chrome’s right-click menu. The filter sits under the Web search type, and the rollout is global. Query data is not available, because these searches mostly rely on images rather than text. Google says multimodal reporting is also coming to the generative AI performance report. Why This Matters For e-commerce stores, publishers, and any site with strong product or visual photography, this is the first clear, page-level view of what people land on after searching with an image. You can compare those pages against text-search traffic by country and device, which may reveal that a certain product category performs far better in visual search than in traditional search. There is an important limit, though. A product page showing multimodal clicks will not tell you which image the visitor started with, and there are no queries to study. So the data answers “which pages?” but not “why?” You’ll need to combine it with what you already know about your catalog, your image quality, and your on-page context. Practical ways to use the new filter: Identify which product or article pages attract image-based visits, and make sure those pages have descriptive alt text, clean structured data, and fast-loading, high-quality images. Check whether image-driven visitors land on the pages you’d expect, or on old, thin, or out-of-stock pages. Track the trend over several weeks before making decisions, since this is brand-new reporting. Mueller Responds To A De-Indexing Case Involving A Casino Site The third story came from a Reddit thread, where a site owner reported that pages were dropping out of Google’s index while an unrelated casino betting page appeared as their canonical. Key facts: The owner said Google was treating a casino page as the canonical version for pages about companies and suppliers. Another user described the same issue on their own site and noticed that the third-party canonical was indexed under a generic JavaScript error title. Their theory was that Googlebot had crawled an error page. Google’s John Mueller said that could be