The source cites a test where ranking gains from backlinks persisted months after the links were removed.

Everybody knows how backlinks increase SEO authority. Nobody knows that the positive effects from backlinks remain even if the backlinks are removed. This was a test done. There were two websites. Both received 22 backlinks of equal quality. They both shot up in positions. Website A moved from position 31 to position No. 1. Website B moved from position 11 to position No. 5. This is the part nobody expected...

Treat link-ghost persistence as an experimental hypothesis; do not rely on temporary or artificial links without independent validation.


The creator warns that if link effects persist after removal, harm from illegitimate links may persist too.

Avoid bad, bought, or manipulative links because potential ranking harm may outlast link removal.


The creator names podcasts, niche directories, and journalist quote platforms as backlink opportunities.

Review these as safer earned-link channels when they are relevant, real, and non-spammy.


A robots.txt file that uses `Disallow: /` tells search engines not to index the site, making other SEO work ineffective.

Before you spend any time or money on SEO, you need to check this file on your website. I can't tell you the amount of times I've seen this file completely screw up your chances of ranking. Go to your website browser, type in yourdomain/robots.txt. If you see disallow: / this is telling search engines not to index your site. Anything else you've done is irrelevant...

Check robots.txt early in technical SEO audits and fix accidental sitewide disallow rules before content or link work.


Google usage may keep rising while Google replaces traditional results with AI responses, so focusing only on blue-link rankings misses the future search surface.

I think it's pretty clear at this point that AI is about to replace traditional search engines, not because people are going to stop using Google, Google use will continue to rise, but because Google themselves are trying to replace their own search results with an AI response as quickly as possible...

Track AI answer visibility separately from traditional ranking visibility and treat AI responses as a primary search surface.


Cloaking different site versions for AI systems, prompt injections, and overtly spammy AI-optimization tricks are predicted to backfire and may attract manual penalties.

risk/avoid: Exclude cloaking, prompt injection, hidden AI-only content, and other deceptive GEO tactics from approved workflows.


The source argues models rely on trusted web pages, so recommendations on pages a model trusts can shape AI recommendations.

d honestly, I've got to see any of that tricky stuff be very effective. What I'm talking about is what actually works right now. It's paying attention to the types of pages that these large language models site before giving their response and then placing recommendations for your brand on those types of pages, primarily by creating content...

Map cited and trusted sources in each niche, then assess whether accurate brand recommendations on those sources correlate with AI recommendations.


For smaller niche brands, the source says the brand website may be the main available corpus AI can use to understand the business.

es. However, you can just pay these review sites to be put at the top of all their lists and immediately the larger influencers will start recommending you more. Now at first you might think eventually these AI will be smart enough to just ignore the sponsored results. But if they ignore the sponsored results, then you'd actually get penalized for paying for placement...

Build complete, structured, verifiable first-party content so AI systems can understand the brand's offer, proof, and fit.


Paid placement at the top of review/list sites may influence AI recommendations, but it is ethically and policy-sensitive if treated as objective recommendation evidence.

risk/avoid: Flag paid review/listicle placement as high-risk; require disclosure, fit checks, and legal/policy review before use.


ChatGPT ads may be inefficient if sold across low-intent conversations, but potentially comparable to Google Search Ads when shown during product-recommendation conversations.

reportedly running ads in ChatGPT will cost about 3 times as much as running ads on Facebook or Instagram except that that's not the right comparison I'd like to talk about what people are getting wrong about this story and whether or not your business should be running ads on ChatGPT it's been reported that ChatGPT will charge on average $60 for every 1000 ad impressions this is often called your C P m your...

Treat ChatGPT ads as an intent-segmented experiment; separate product-recommendation placements from broad conversational inventory.


The creator argues CPM comparisons to Meta are misleading because search-like intent can justify higher impression costs if conversion rates are much higher.

Compare AI ad channels by effective CPA/CAC and intent context, not CPM alone.


The creator says GSC regex filters for best, top, vs, review, comparison, and alternative can reveal shopping-around queries.

This is hidden within Google. Go to Google Search Console, select performance, then search results. Click add filter, query custom regex. Paste this in. This is going to show you Google searches that contain words like best, top versus review, comparison, alternative. Now you see searches from people who are shopping around for what you offer trying to figure out what to buy or use...

Create a GSC report for comparison modifiers and prioritize pages already visible for commercial-intent queries.


For valuable search terms, the creator recommends using the term high on the page, adding an H2 section, or creating a dedicated page.

Add query-matched sections only where they improve usefulness and match real page intent; monitor rankings and conversions after edits.


If a commercial-intent query is valuable enough, creating a dedicated page and linking to it from the currently ranking page with exact-match anchor can improve fit and visibility.

Review near-ranking commercial queries for dedicated-page opportunities and add contextual internal links from source pages.


The creator argues that GEO, AEO, and AI SEO are not separate from SEO, and that strong SEO strategy also supports AI search.

You can't do GEO without doing SEO. GEO, AEO, AI SEOвЂ¦ actually really wish we would just agree on one thing and stick with it, because it's driving me nuts. None of it is separate to SEO. Same strategy that we were doing for SEO is what's working for AI search engines, too...

Compare AI-visibility tactics against core SEO work before buying a separate AI SEO service framed as fundamentally different.


Providers selling AI SEO as entirely different from normal SEO or narrowly focused on ChatGPT are framed as a red flag.

risk/avoid: Vet AI SEO vendors for evidence, overlap with durable SEO fundamentals, and avoidance of hype-only ChatGPT tactics.


GSC queries with existing impressions/clicks and average position greater than 3 can be prioritized as low-hanging-fruit optimization targets.

This is insane. Go to Google Search Console, click performance, then search results. Toggle on average position. Scroll down to queries, filter by position. Is greater than 3, change rows per page is 500. You don't even have to export. Copy everything, paste it into Claude, use the prompt. My business is this. Give your business...

Build a GSC workflow to filter near-ranking queries, map them to current pages, and prioritize refreshes before creating net-new pages.


Claude can help prioritize GSC keywords by business context, clicks, impressions, and closeness to ranking.

Use AI as a prioritization assistant for GSC exports, then manually verify intent, SERP fit, business value, and page suitability before edits.


After updating a page for a target query, requesting indexing in GSC is presented as a way to prompt Google to reprocess the change.

After meaningful content updates, submit priority URLs for indexing and record update/request dates for measurement.


The source recommends a ChatGPT, Gemini, or Claude brand ambassador project that understands the brand and supports content, questions, and strategy.

I talk to business owners everyday about how they're using AI, and one thing that they all have in common is they feel like they're getting left behind. And in a sense, we're all getting left behind. I spend all my time learning about AI and how to show up in AI search, and I'm getting left behind. And that's because the technology is just moving too fast for anyone to reasonably keep up with...

Create a reviewed internal brand knowledge project before scaling AI-assisted content, support, or strategy workflows.


The creator recommends using an AI project to identify brand-knowledge gaps, generate a questionnaire, and organize the answers.

Use AI-assisted gap analysis to collect missing brand facts, then verify every fact with internal owners before using it in outputs.


Splitting brand knowledge into about a dozen documents and defining when to use each can help the model pull relevant context.

mmend having Claude organize all that information and identify any gaps in the understanding. You can then ask it to create a questionnaire to fill in all of those gaps and record you or someone else from your organization answering those questions. And feed that transcript back in the cloud and have it organize all the information...

Structure brand projects into topic-specific documents with clear project instructions that tell the model when each document applies.


Brand ambassador projects initially misrepresent the brand; effectiveness depends on logging errors and iteratively updating or adding source documents.

Add a feedback loop for AI brand-project errors, with updates to source docs rather than only prompt tweaks.


The clip warns that reconsideration requests may be rejected more often and manual actions may become semi-permanent after spammy SEO behavior.

Google penalties are now becoming permanent. Breaking reconsideration requests are now getting rejected left and right. A reconsideration request is if you break the rules, you ask Google to review your fixes. By hitting Request Review you are asking Google to reconsider you. In the past, companies that broke the rules with SEO could ask for forgiveness. They could get it. Now getting stone cold rejected...

Treat spam-policy shortcuts as high-severity business risk; require conservative review before any tactic that may violate Google guidelines.


The source describes AI search as query fan-out: several searches, about 100 retrieved pages, a handful of citations, then a synthesized answer.

AI search is still a brand new industry and those of us who are focused on how to get recommended by large language models like ChatGPT and Google's AI mode are learning as we go I've been doing my own experiments and learning from others for the past few years now I'd like to share a few things I've learned and talk about one thing that I think a lot of people are still getting wrong I know a lot of people are...

Track prompts, observed retrieval queries, retrieved pages, citations, and final recommendations as separate AI visibility stages.


To get a brand recommended by an LLM, the cited pages need to recommend the brand, not merely be found by the model.

Audit cited pages for whether they explicitly recommend the brand and what support they give for that recommendation.

AI recommendation / cited-page recommendations


Instead of only pursuing recommendations on already-cited pages, the creator recommends creating better-fit pages that can replace cited pages.

on those pages problem is that's really hard to do typically we can easily get a recommendation on about 20% of these pages these are like commonly cited Reddit threads directories without too much competition and pages from your own website after that you're lucky if you can get a recommendation on 5% of these highly cited sources and this is the mistake that I think most people in the Geo space are making...

Test new pages for observed AI retrieval queries and measure whether they earn citations or displace existing cited pages.


The source lists owned sites, LinkedIn, Medium, third-party sites, and industry sites as possible places to publish AI-targeted content.

Review each publishing option for credibility, disclosure, editorial quality, and citation likelihood before using it for AI visibility content.


After testing, the creator says his team shifted most effort toward creating content and less toward getting onto already-cited pages.

Use the 80/20 content-versus-placement split as a benchmark to compare against project-specific citation and recommendation data.


Website redesigns without SEO oversight can damage rankings when headings, URLs, categories, products, or redirects are mishandled.

Some of you are putting way too much trust in your web designer when you absolutely shouldn't, because they're gonna tank your entire site's rankings just quickly of context, SEO Specialist, but I'm also a web designer. I do know how to do these things, and I'm telling you that the majority of web designers do not...

Require SEO review before redesign launch, including headings, URL mapping, redirects, category or product structure, and indexability.


Removing headings during a redesign is cited as a change that can cause immediate ranking drops.

Include H1/H2 preservation and semantic heading review in migration QA.


Restructuring thousands of product/category URLs without redirects or SEO research can cause serious ranking loss.

Before URL restructuring, prepare redirect maps, keyword/category impact analysis, and post-launch monitoring.


New ad platforms are often underpriced early because inventory exists before advertiser adoption catches up; ChatGPT ads may offer early-rate opportunities.

Ads are coming to ChatGPT. Let's talk about if you as a business should be running ads on ChatGPT and why. I think Google will be the real winner in all of this. Apparently these ads will only be in the free version of ChatGPT, and they'll show up as clearly labelled sponsored ads below the LLM response...

Treat early ChatGPT ads as a controlled paid experiment if access exists; cap spend and measure CPA by intent type.


Google AI Mode ads may matter more than ChatGPT ads because Google has much larger distribution and a mature ad platform.

ticles showing that Google is already experimenting with ads in their AI. Mode results. And that's because it's not really news. Right? We all expected Google to run ads in AI Mode, so unlike ChatGPT, they're not gonna have the same P. R problem. But Google also has two other huge advantages over OpenAI. One is obvious they have 10 to 20 times as much distribution as ChatGPT...

Monitor Google AI Mode ad formats and plan tests separately from ChatGPT ads.


AI Mode ads may create a new targeting category: reaching people who need a product/service but do not yet know what to search for.

Explore AI ad targeting for problem-aware but solution-unaware users, with strict measurement against search ads.


The source claims YouTube has overtaken Reddit as a leading social citation source for AI search.

You're tired of seeing Reddit in ChatGPT? Exclusive YouTube overtakes Reddit as a go to citation source on AI search. YouTube has eclipsed Reddit as the leading social source for large language models. Go to ChatGPT, put in your prompt, copy what comes after forward, slash C, right click inspect. Go to network, paste in what you just copied. Click refresh. Click the orange brackets with what you just copied...

Verify with citation tracking before making strategy changes; include YouTube and Reddit in AI citation audits for target prompts.


Inspecting ChatGPT network responses is presented as a way to see generated web queries for a prompt.

Treat this as experimental and brittle; document model, account state, date, prompt, and observed queries before using it for planning.


The creator suggests using observed ChatGPT retrieval language in YouTube titles, descriptions, and videos to improve AI-search citation chances.

Test YouTube videos around validated retrieval queries with clear direct solutions, then monitor AI citations, Google visibility, and YouTube performance.


As Google search results become more personalized, keyword research and single-position rank tracking may become less representative of visibility.

oo frequent hallucinations. So what does this mean for s E O and businesses that are trying to be found on Google? We'll have to wait and see how quickly personalisation is adopted but I think it's clear that results are getting more and more personalised...

Add AI-mode and personalized-search checks, and avoid treating one neutral rank position as the full visibility KPI.


The source says AI visibility work should study how models structure searches, which sites they trust, and which content they cite.

Build prompt and citation tracking that records query patterns, trusted domains, cited content types, and competitor recommendations by scenario.


The creator claims businesses can influence or replace the sources LLMs use to decide whether to recommend them or competitors.

Treat as a GEO experiment: identify recurring sources, then test ethical inclusion, better first-party content, or replacement content.

GEO strategy / influence or replace sources


Search-focused YouTube videos can rank in YouTube and Google, and may influence LLM responses because many YouTubers ignore search-term targeting.

If you wanna be found online in 2026, user generated content has never been more important. So I wanna talk about three platforms that I think you should be active on as a business. The third one I'm not hearing anyone talk about right now. The first is YouTube...

Repurpose high-value blog/search terms into YouTube videos with exact topic language in titles and clear answers.


Blog posts can be turned into presentation-style videos with NotebookLM as a lower-effort way to create YouTube assets.

Review NotebookLM-generated videos as draft assets; add human review, brand voice, accuracy checks, and visual QA before publishing.


Publishing a topic across blog, YouTube, TikTok, and Instagram can occupy multiple positions on page one for a target search term.

The other platforms aren't cited nearly as often by large language models, but they can still rank in Google search results. We had one client who took up five positions on page 1 for one of his main search terms, his blog post, a YouTube video, two TikTok videos, and an Instagram post. The second platform you should be paying attention to is Reddit...

For priority queries, create a multi-surface content plan and track SERP real estate across web, video, and social results.


LLMs tend to cite the same older Reddit posts repeatedly rather than every new Reddit mention, so comments on already-cited threads may matter more than broad posting.

Identify recurring cited Reddit threads for target prompts and contribute genuinely helpful, policy-compliant comments where brand mention is relevant.


Creating fake Reddit accounts or spamming brand mentions is discouraged; active accounts with history and helpful comments are less likely to trigger spam filters.

risk/avoid: Do not use fake-account Reddit spam; require community rules, account history, usefulness, and explicit relevance before brand mentions.


For some business prompts, Facebook groups may be cited almost as often as Reddit and sometimes more often.

Facebook groups. I don't know why no one's talking about this. I think that they're probably not actually tracking in large language model responses. In my experience, at least for business related prompts, Facebook groups cited almost as often as Reddit and some industries cited more often. Now we don't post to Facebook groups on behalf of our clients. So I don't have as much experience there...

Include Facebook groups in citation-source tracking and review whether group content appears in target AI responses.


Brand mentions in Reddit or Facebook groups should avoid angering the community and should explicitly state what the brand does only when relevant.

risk/avoid: Treat community posting as reputation-sensitive; use helpful participation, disclosure, and moderation-rule checks.


Being cited is not enough; AI-targeted content should name the brand and explain why it is a fit for the reader's likely problem.

If you want your brand to be recommended by ChatGPT or Google's AI, it's not enough that the models are finding you. You need to give them a reason to recommend your brand. You need to brag about yourself. Most of the current advice about AI search visibility is around being cited. You want the models to find your content and site it in their response. And yes, this is important, but it misses an important step...

Add accurate brand-solution statements near the top of eligible pages, posts, and profiles, with wording tied to the page context.

AI recommendation / explicit brand positioning


After positioning the brand as the solution, the creator recommends adding two supporting data points.

Choose two verifiable proof points for AI-targeted content and require evidence for any number, result, award, or recognition claim.


On third-party sites, brand self-promotion may need to be more subtle than on owned properties, but the recommendation still must be explicit enough for AI to use.

ion. I recommend picking two such data points and mentioning them right after you present your brand as the solution. If you're doing this on a third party site like Reddit, you might need to be a little bit more subtle. But if this is your own website or your own profile, you should be bragging every chance you get.

For third-party placements, balance disclosure, usefulness, and explicit recommendation; avoid deceptive astroturfing.

Third-party content / subtle brand mentions


The Google Business Profile category is framed as one of the strongest signals for what the business should rank for locally.

The 3 local SEO tips that I recommend to absolutely everyone, because without them, you just aren't gonna rank higher than your competitors. The first thing you need is the right category. This is way more important than what you put in your bio or your services because it establishes what you do as a whole and it's the biggest keyword you're giving Google about what to rank you for...

Audit GBP primary category against the broadest accurate revenue-driving category; keep services as secondary details.


Business location must match the area a company wants to rank in because proximity to the searcher strongly affects local results.

Validate target service areas against actual address and proximity constraints before promising local ranking outcomes.


A competitor with fewer but higher-rated reviews may outrank a business with more reviews but lower average rating.

Track both review volume and rating quality; prioritize consistent high-quality review generation over volume alone.


Reviews mentioning services performed and target locations act as strong confidence signals that the business offers those services in those areas.

Encourage customers to naturally mention the service and location in reviews without scripting or incentivizing prohibited review text.


Traditional search rankings can help AI visibility because models often search the web first, but rankings do not explain the whole AI visibility problem.

If you want to get recommended by ChatGPT or Google's AI mode, it certainly helps if you rank well in traditional search results. And this is because these large language models use a search engine. When someone goes to ChatGPT or Google's AI to ask for a recommendation, the first thing it'll typically do is a series of searches in a traditional search engine...

Keep SEO fundamentals in scope while separately tracking AI retrieval queries, cited pages, and recommendation outcomes.


LLM query fan-out can create more specific long-tail searches with less direct competition, changing how PageRank pressure shows up.

not they go back to the search results and click on another page. Google uses this behavioural data to determine if your page satisfied the searcher's intent. Depending on how well Google feels your content is satisfied the searchers intent, they will move you up or down in ranking...

Track AI retrieval queries and build useful pages for specific buyer intents where direct competition is low.


Hyper-relevant pages for AI retrieval may get only a few visits each, so the strategy requires many specific pages and an efficient creation process.

Treat hyper-specific page creation as a quality-controlled program, not bulk thin content; measure aggregate conversions and citations.


The creator claims visitors from large language models convert at about eight times the rate of traditional search visitors.

month. They might get a couple visits a month. So for this to work, you need to create a lot of hyper specific pages, but the visits they get and the recommendations you get from large language models as a result and to convert at a much higher rate. Visitors from large language models convert at about eight times the rate as traditional search engines...

Treat the 8x claim as a benchmark to validate with analytics segmentation for LLM referrals, recommendations, and assisted conversions.


PageRank and organic rankings still broaden visibility, but AI search creates opportunities for lower-authority sites through specific low-competition retrieval queries.

Combine authority building with long-tail AI retrieval pages instead of relying on either alone.


ChatGPT agent mode can be prompted as a Google Quality Rater to compare a ranking page with top competitors and split fixes by page and site level.

This ChatGPT marketing trick should be illegal for how invasive it is. Go to Google Search Console. This is Google's own tool for accessing their search index. Connect your site to it using domain property if you haven't already. Go to performance, then search results toggle on average position. Scroll down to queries. Filter by position is greater than three...

Use AI-agent competitor analysis as research only; manually verify SERP intent, quality issues, feasibility, and policy risk before acting.

AI-assisted SEO audit / competitor analysis


Combining GSC near-ranking queries with AI-agent competitor analysis can produce prioritized fixes for pages already close to ranking.

Build a repeatable workflow: GSC query selection → competitor/page analysis → prioritized fixes → human review → measured update.


LLMs are not scanning every brand mention; mentions matter most on the small set of pages retrieved and cited for relevant recommendation prompts.

If you want your brand to be recommended in AI search like ChatGPT or Google's AI mode, then you need to be mentioned on third party websites, websites other than your own. Getting these mentions is commonly referred to as mention building, but people often get a few things wrong. So I'd like to take some time to talk about which mentions actually help you get recommended by large language models...

Prioritize mention building by prompt-level citation data instead of broad mention volume.


A mere brand mention is insufficient; the third-party mention needs to function as a recommendation, often explicit or implied by list position.

Audit off-site mentions for recommendation strength, position, context, and support evidence.


Many industries may have only one to three directories that LLMs frequently cite, so review-building should focus on those rather than every directory.

elp you rank at the top of these listicles. However, as I was saying before, you don't need to be getting reviews on every directory. There's probably one to three directories that large language models really like for your industry and you should focus your efforts there...

Identify top cited directories by industry and focus review/profile optimization on the few that recur in AI responses.


For Reddit and Facebook-style sources, helpful comments on the specific threads repeatedly cited by LLMs can have more impact than random broad posting.

Use surgical, helpful, compliant comments on recurring cited threads only where the brand is a genuine solution.


Highly cited articles are difficult but valuable sources; the creator suggests asking authors what it would take to recommend the brand.

Review as digital PR/outreach: approach authors only where the brand is a legitimate fit and avoid undisclosed paid recommendations.


Paying authors or offering reciprocal mentions for recommendations can become manipulative if undisclosed or not based on real fit.

risk/avoid: Require disclosure, editorial independence, and genuine relevance for any paid/reciprocal inclusion.

Risk/avoid / paid or reciprocal mentions


Speed optimization, schema, indexing, redirects, sitemaps, and image alt tags are framed as low-effort one-and-done tasks for average sites, not the main SEO growth lever.

The reason why your website isn't ranking on ChatGPT, or Google even though you've done SEO is because you're spending time on the wrong task. Speed optimisation, schema markup, indexing and other technical SEO Are meant to be low effort, one and done tasks, meaning that you're actioning one thing that improves the SEO On lots of or all of the pages on your site...

Complete baseline technical SEO efficiently, then shift most effort to content, authority, and demand-aligned strategy.


Spending excessive time or budget on basic technical SEO tasks can distract from the work that drives rankings and AI visibility.

risk/avoid: Do not over-scope technical SEO retainers without showing the expected marginal impact versus content/authority work.

Risk/avoid / overinvesting in technical SEO


The creator says brand-name plus alternative searches are popular and can be addressed with an alternatives page on the brand's own site.

People search for Canva alternative. Canva is paying for an ad that says Canva, there's no alternative. So smart online design made easy. When people are searching for Basecamp alternative, Basecamp is paying for an ad. We don't want to run this ad. We're the No. 1 result. But this site lets company is advertise against us using our brand...

Create an honest alternatives page where users and AI systems can compare options without relying only on competitor pages.


Alternatives pages can target competitor-plus-alternative searches and explain how the brand compares.

Build comparison or alternatives pages only with accurate, fair comparisons and clear evidence.

Competitor comparison pages / purchase intent


The creator claims ChatGPT breaks prompts into separate web search queries that can be inspected in network responses.

People think ChatGPT uses your prompt to search the web. That's not how it works at all. Go to the URL of your chat. Copy the part that comes after for slash C, right? Click inspect. Go to the network tab, paste in the part of the URL that you copied. Hit refresh fresh, click on the orange brackets with your code. Go to the response tab, search the response tab for queries...

Treat as an experimental research method only; document date/model/UI and validate query findings with other tools before using them.

AI visibility / ChatGPT retrieval queries


The workflow uses headings from current top Google results to ask ChatGPT for an improved article outline for the target query.

Use competitor headings as research input only; add original expertise, examples, evidence, and differentiation before publishing.


The creator recommends putting a concise TLDR at the beginning of an article, claiming summaries can improve conversions and help users quickly get the answer.

Test short above-the-fold summaries/TLDRs on informational and commercial pages; measure conversion and engagement impact.


The creator recommends linking a new article from two or three existing ranking articles found in Google Search Console.

re the meta description reads confidently. You get all your SEO metadata right there. Take this article, add your own experience, your own pictures, proof read it, remove em dashes, remove ChatGPT buzzwords. I have this article on my site, how to humanize ChatGPT content. Remove these buzzwords. You can also use the custom instructions. Write like a human. Keep it professional but conversational...

Add launch-checklist steps to identify relevant visible pages in GSC and place internal links from them to new strategic content.


The workflow includes generating full articles with ChatGPT, which can become generic or duplicate if not edited with real experience, images, and proofreading.

risk/avoid: Do not publish AI-drafted articles without human expertise, originality checks, factual review, and brand-specific evidence.


The source says Google Brand Agent lets users chat with a brand representative in search results and is currently available to retailers.

Google just announced brand agents. If you have any kind of business, it's really important that you understand what this is. This is Google's latest attempt to keep people from visiting your website. That's right. Google does not want to send people to your website. It wants to keep them on Google search. The more time people spend on Google, the more ads they click and the more money Google makes...

For eligible ecommerce brands, check whether Brand Agent appears in search results and test its answers for accuracy.


The source says Brand Agent setup should be checked in Google Merchant Center under the Marketing tab.

n Google before it recognizes your brand. Currently, these brand agents are fairly simple. They're just using data from your website and product fee. But pretty soon Google is going to start allowing you to provide all kinds of documentation to help train your brand agent, things like brand guidelines and sizing guides...

Add Brand Agent and brand-panel claim checks to ecommerce AI visibility audits, then document any inaccurate agent answers.


The source recommends checking Google search for a brand panel, Brand Agent chat option, and claim control before trusting the agent's information.

Audit the brand panel and Brand Agent state in Google search, then claim the brand and review agent answers for accuracy where controls are available.


The creator predicts Brand Agents will use brand and product documentation, including guidelines and sizing guides, beyond website and feed data.

Prepare clean brand and product documentation for future review, but keep implementation decisions tied to confirmed Google Merchant Center controls.


Archiving or drafting product pages without handling the URL can waste rankings, topical authority, internal links, and traffic.

I cannot stress enough how important it is that you So many people do this, and it is one of the biggest SEO mistakes that you can make on your entire site. When you set your product to draft or archived without doing anything else with it, the page is gone, the URL doesn't work anymore, and what you're telling search engines is that your site gives the poor user experience and that it's no longer relevant to...

Add a discontinued-product workflow that redirects unavailable product URLs to the closest relevant product or category where appropriate.


Removing product pages without a redirect or alternative destination is framed as a poor user experience signal and SEO mistake.

Do not archive or draft indexed product URLs until redirects, alternatives, and internal links have been reviewed.


The creator predicts early ChatGPT ads may be underpriced because corporate advertisers tend to move slowly into new ad channels.

Please listen. This is the opportunity of a lifetime. ChatGPT is experimenting with advertising in their free subscription. I'm old enough to remember when Google started running ads. For a few years you could get warm leads for pennies. I also remember when Facebook launched ads. They had so many eyeballs, so few advertisers that it cost nothing to get clients for the first few years...

Monitor ChatGPT ad access and prepare small test budgets, attribution, and guardrails before treating it as a scalable paid channel.


The creator makes strong claims about cheap ChatGPT ad opportunities before public advertiser mechanics are available.

risk/avoid: Do not build recommendations on speculative ad economics; wait for official OpenAI ad products, targeting, policy, and performance data.


The source says LLMs may visit FAQ pages to learn more about a brand or topic after finding the site through search or prior knowledge.

nothing wrong with that. But if you're trying to rank for that specific question, consider whether this question is less competitive than the title of the page you're adding the FAQ to. If not, you should consider making a separate page with that question as the title. One thing that has never made sense for traditional search is a standalone FAQ page...

Build reviewed brand, product, and service FAQ pages around real buyer questions, with answers rendered on the page.


For traditional SEO, a standalone page targeting a specific question can outrank an FAQ entry when both sites have similar authority.

If you follow SEO or GEO advice at all, you've probably heard that FAQs or frequently asked questions can be very effective. And it's true they can be, but not for the reasons most people think. I often joke that half of the SEO agencies out there offering GEO now are just slapping extra FAQs on all your pages. Let's talk about whether this is a good idea and how you can be doing it effectively...

Use FAQ sections for support, but create standalone pages for high-value questions with meaningful search demand or competitive intent.


The creator warns that crawlers cannot reliably open accordion answers, so important FAQ text should be rendered directly on the page.

Avoid hiding critical FAQ answers in inaccessible accordions; verify rendered HTML and crawler accessibility during QA.

Technical content rendering / accordions


The clip promotes capturing Digg communities and usernames to exploit a high-authority relaunch for SEO links and rankings.

Digg just relaunched. It's the biggest opportunity for SEO slap you've ever seen. Usernames like Harry Potter are available. Many popular communities are up for grabs. No one has created a Pokemon cards community. Digg has an SEO domain authority of 92 out of 100. That's almost as high as you can get. You have to understand, Digg can rank on Google for anything...

Do not recommend squatting communities, usernames, or link manipulation; evaluate only legitimate, user-serving community building.


The creator argues freshness matters for LLM citations only in some searches; older pages and Reddit threads can still be cited frequently.

se. I do recommend updating important pieces of content once per year, including the year in the title. For your most important pages, you might consider updating them every three months. However, don't fall into the trap of thinking that your pages need to be recently updated to be cited by large language models...

Avoid blanket freshness rules; refresh content based on query freshness need, topic volatility, and citation evidence.


The source says ChatGPT may pass a relevancy time parameter to web search and can infer freshness from dates visible on the page.

If you've been following GEO advice at all, you've probably heard that large language models love fresh content. You need to update your content constantly, so ChatGPT will cite it. And there is some truth to this, but it's much more nuanced than people typically present it. You may have heard stats like 70% of the content that ChatGPT sites was updated in the last 12 months...

Expose accurate publish and update dates on time-sensitive pages, and avoid fake freshness that misrepresents unchanged content.


The creator recommends annual updates for important content and possible quarterly updates for the most important pages, while noting many stable topics do not need constant refreshes.

Set refresh cadences by page importance and topic volatility rather than updating every page on a fixed schedule.


The creator says older Reddit threads already cited by LLMs may matter more for visibility than many comments on new threads.

For Reddit/GEO experiments, identify threads already cited by LLMs and contribute only when helpful, transparent, and policy-compliant.


For AI search, the source recommends a clear heading hierarchy: one H1, H2 sections, and H3/H4 subsections with relevant search terms.

Let's talk about how to structure your content so it shows up in AI search. This is important if you want to be recommended by ChatGPT or show up in Google's AI mode. There's a certain way that these large language models like to see content structured on a page. I just wanna go over a few simple things that you can do to maximise the chance that the content on your page is not just found, but also cited...

Add heading hierarchy, section intent, and query-term checks to AI visibility content QA.


The source recommends framing headings as questions when natural, with FAQ sections as a fallback when question headings do not fit.

Use question-style headings where natural and grounded in real prompts or searches; add FAQs only when they answer genuine buyer questions.

AI content structure / question headings


The first sentence under each heading should directly answer that heading so the LLM can retrieve the needed answer more easily.

ly seem to like headings that are framed as questions. If you can't naturally frame your headings as questions, you can always include an FAQ on the page and you wanna make sure the first sentence in each section directly addresses the heading. So if your heading is what is the cost of pool maintenance in Dallas, Texas?...

Write each section as heading question, concise direct answer, then supporting details and proof.


The creator says more numbers, data points, facts, quotes, and testimonials can make LLMs more likely to trust and cite a claim.

Strengthen priority pages with verifiable proof points, quotes, testimonials, and data, and remove unsupported claims.


Lists and tables are claimed to be more likely to be cited by large language models.

edle to thread, but pick two or three data points that you can share naturally in that opening. Also, large language models love lists and tables. So anytime you're presenting a list of something, put it into a bulleted format. Anytime you can justify doing some kind of comparison, put it into a table. Elements like these are much more likely to be cited. This is the No...

Use bullets, tables, and comparison formats when they genuinely clarify the answer or decision.


The source cites ChatGPT ad principles saying responses are not influenced by ads and ads are separate and clearly labeled.

Ads are coming to ChatGPT, posted less than an hour ago. In the coming weeks, we plan to start testing ads in ChatGPT free and go tiers. We're sharing our principles early on how we'll approach ads. Guided by user trust and transparency first as we work to make AI accessible to everyone. Responses in ChatGPT will not be influenced by ads. Ads are always separate and clearly labeled...

Track official ChatGPT ad policy separately from organic AI visibility work, and do not assume paid placement changes answer ranking.


The source says ChatGPT conversations are private from advertisers and ad personalization can be turned off.

Verify current privacy and personalization settings in official product docs before using these claims in client-facing recommendations.


Informational blog posts can lose traffic when ChatGPT and Google AI answers satisfy users directly in search results.

One thing that more people need to know about SEO In 2026 is that blogging is often a complete waste of your time. blog post that ranks no.1 for something that's searched 1,000 times a day, and you still wouldn't get any traffic because ChatGPT and Google's ai Answers are giving them all of that information directly in search results...

Deprioritize traffic-only blog KPIs and evaluate informational content by authority support, assisted conversions, and AI visibility value.


SEO strategy needs product, category, and service pages for commercial or transactional searches that AI answers cannot fully satisfy.

Prioritize commercial and transactional pages before broad informational blogging, especially for ecommerce and service businesses.


The source tells ecommerce businesses to make product feeds available to ChatGPT and Gemini through ChatGPT's merchant program and Google Merchant Center.

if you're an e commerce business you need to make sure your product feed is available to ChatGPT and Gemini both Google and OpenAI are pushing agnostic commerce hard in 2026 I think by the end of the year it's gonna be common for people to be buying products directly from these large language models so I'm gonna go over the three things you need to do now to make sure you don't get left behind one if you haven't...

Verify merchant enrollment and product feed availability for ecommerce clients that want exposure on AI commerce surfaces.


Product data should appear on product pages, in Product schema, and in uploaded feeds, with descriptive names and details such as price, dimensions, and weight.

Audit product pages for schema/feed parity, descriptive product names, detailed descriptions, and complete product attributes.


The source treats Agentic Commerce Protocol and Universal Commerce Protocol as emerging purchase-through-LLM requirements for ecommerce stacks.

Track official ACP/UCP support in Shopify, WooCommerce, Google, and OpenAI channels, and evaluate implementation only when requirements fit the store stack.


Google Search Console comparison can identify pages losing clicks while retaining impressions, suggesting a page may need updating to match changed search demand.

Nobody knows this about Google. Go to Google Search Console, performance search results. Click more. Click compare. Compare last 28 days to previous period. Click pages now. Sort for negative clicks difference. Look for pages that lost clicks but still have strong impressions. This could mean a competitor improved their page or Google wants something slightly different...

Add a GSC decay report: compare periods, find pages/queries with click and position losses, then refresh relevant content.


Ecommerce SEO should first target transactional product pages and commercial category or subcategory pages based on search intent.

This is the ecommerce SEO strategy you need if you wanna boost your visibility on ChatGPT and Google and generate more sales. And it doesn't involve a single blog post. All the most successful ecommerce stores are using this strategy. It's super simple and any ecommerce site can do this. Your SEO strategy should target customers at different stages of their journey...

Build ecommerce SEO architecture from the product list, category groups, and niche subcategories before blog planning.


Niche subcategory pages can target commercial searches where customers are deciding what to buy.

Audit product taxonomy for missing commercial-intent category and subcategory pages.


The source says LLM web tools may decide whether to retrieve a page using only the title, URL, and meta description returned in search results.

If you want the content on your website to get noticed by large language models, you need to understand meta data relevance. We're talking about your title tag, your meta description and your URL structure. If you know anything about SEO, you're probably thinking, TJ, I already know about this. But it's actually a little different with large language models. In traditional search engines, your title tag and U...

Optimize title tags, URL slugs, and meta descriptions for AI retrieval confidence, while keeping the metadata accurate for humans.


The source suggests using prompt-simulation tools, or controlled guesses when tool data is unavailable, to learn what searches LLMs perform.

Collect likely retrieval queries through AI-search monitoring tools or repeatable experiments before optimizing metadata for AI discovery.


The creator recommends answer-first meta descriptions so an LLM can see that the page likely answers the user's prompt.

Test concise answer-first meta descriptions on AI-targeted pages, while avoiding inaccurate or clickbait summaries.


The creator claims X no longer suppresses reach for posts with links, enabling article links to drive visits and secondary shares.

Your X recently made a change where you don't get less reach if you share a link. I only have 14 followers on X. Yesterday I shared this article from my website. The post got 4.2 thousand views. This post converted into 145 visits from X to the article. This means from this 4.2 thousand views, 145 people clicked and visited the article...

Treat X link posts as a distribution experiment; measure referral traffic, shares, and downstream mentions.


A shared article URL with an accidental extra bracket led the creator to add a redirect so referral traffic reached the correct page.

Monitor meaningful malformed inbound URLs from social and community shares, then add safe redirects when they preserve real referral traffic.


Server response time, especially TTFB, is claimed to matter more for AI search because LLMs give up if pages load too slowly during retrieval.

Let's talk about another important factor for being recommended by large language models like ChatGPT. In Google's AI. Mode. This one's a little more technical, but I'm gonna try to make it simple for you. That is improving your server response time. And the metric we really care about here is your TTFB, or time to first byte...

Add TTFB and server-response checks to AI visibility technical audits.


The source recommends server-side caching as a major way to improve server response time for AI-search retrieval.

Prioritize server-side cache configuration for slow priority pages before spending time on minor performance tweaks.

Performance optimization / server-side caching


GSC queries of at least seven words may indicate AI-assisted or LLM-influenced searches around pages already ranking.

This is getting out of control. Go to Google Search Console, go to performance search results, click add filter, then query queries containing regex. Paste this in. This shows searches of at least 7 words. These are usually searches done with the help of LLMs. Take one of them. You're already ranking on Google for this, so we're gonna make you rank higher. Copy it, paste it into Google. Take the top 3 articles...

Create an exploratory report for 7+ word GSC queries and use them as inputs for content updates, while avoiding claims that every long query came from AI.


The workflow suggests using top-three headings and ChatGPT to draft articles for long queries; this risks generic copy if used mechanically.

risk/avoid: Do not mass-produce long-query pages from AI outlines without expert review, original media, examples, and quality thresholds.


Filtering GSC queries containing “I am” may expose long, self-descriptive searches where users ask Google/AI for recommendations.

It still blows my mind that Google does this. Go to Google Search Console. If you haven't connected your site already to it, connect it using this domain property. Then go to performance Search results, click Query queries containing. Here it is. I am. Hit apply. You're gonna be amazed. This is showing Google searches that contain I am...

Use “I am” and similar self-description query filters as exploratory inputs for persona/use-case content research.


Large language models may have a pre-search bias toward brands or sites already seen as trusted authorities, affecting which pages are retrieved first for AI answers.

Yesterday I read the best overview I've ever seen on AI search. It was from Kevin Indigo's newsletter Growth Memo, and he went over everything it takes to be recommended by large language models like ChatGPT or Google's AI mode. He broke it down to 11 factors to determine which brands AI Search recommends. And it was so good that I thought I'd do a video on each factor...

Treat as a review hypothesis: evaluate brand/entity authority and pre-existing web footprint when diagnosing AI retrieval visibility.


Smaller brands may have better AI-search opportunities by targeting hyper-specific terms because competitive broad terms amplify primary bias toward established authorities.

Prioritize specific use cases, service areas, industries, demographics, and long-tail recommendation prompts before broad competitive prompts.


Consistent brand naming and consistent descriptive language across the internet may improve how LLMs understand and retrieve a brand.

Audit off-site and on-site entity consistency: brand name, service descriptors, category terms, and repeated language used around the brand.


For competitive AI-search terms, smaller brands may need recommendations on websites already treated as authorities in their space.

cusing on hyper specific terms. They tend to be much less competitive. If you want to be recommended for competitive terms as a smaller brand, you're going to have to get recommendations placed on those websites that are already seen as an authority in your space. So what can you do to improve your primary bias and therefore improve your selection rate?...

Map authority/cited sites for target recommendation prompts and evaluate credible inclusion or PR opportunities.


The creator defines SEO strategy as coordinated work across content, technical, and reputation components, not isolated tasks.

Publishing four blog posts a month It is not an SEO strategy. Buying 500 backlinks is not an SEO strategy. Optimizing your website speed? Not a strategy. These are all just random tasks. Your website SEO strategy is the plan you follow to improve your visibility on search engines, guarantee you that your top competitors on ChatGPT, Google, etc are all using one...

Structure SEO and GEO plans into content, technical, and reputation workstreams with priorities and measurable outcomes.


SEO content should support buyer journey stages with product or service pages, category or roundup pages, and informational pages.

Map target pages to journey stage and search intent before creating new content.


Technical SEO should cover indexability, schema markup, and major site errors, with periodic checks instead of endless task churn.

Add recurring technical hygiene checks focused on indexability, schema, broken assets, and speed issues.


Brand reputation work includes backlinks, social activity, Google Business Profile, Merchant Center, directories, and roundup sites.

tools to do almost all of this for you, check in every so often to make sure it's all still good. The third and most important component is your brand's reputation. Unless you're new to SEO, you've probably already heard of backlink building. And yes, this does play a significant role in your brand's reputation, but it is not the only thing to do here...

Include reputation and distribution surfaces in SEO and GEO plans instead of treating link building as the only authority lever.


The creator warns AI-generated knowledge bases such as Grocopedia may be gameable if false information can be made to appear in search results used for verification.

Elon Musk's Rockopedia is one of the craziest stories in marketing history. November 16th it was at only 19 clicks a month from Google search. Now it is averaging 3.2 million clicks a month from Google search. All SEO clicks. They fixed their SEO. Google, however, told their quality raters to give AI content the lowest quality rating possible...

risk/avoid: Do not rely on AI-generated encyclopedic sources without source verification; flag them as potential misinformation surfaces.

Risk/avoid / AI-generated knowledge bases


The creator frames a contradiction between Google quality guidance against low-quality AI content and the rapid organic growth of an AI-generated knowledge base.

Treat as a contradiction/review item: investigate whether AI-generated authority sites are being ranked differently from ordinary scaled AI content.

AI content / quality guidelines contradiction


Paid or reciprocal networks of sites recommending each other are proposed as a GEO tactic, but they resemble manipulative link exchanges/PBN behavior even if links are not the main goal.

I wanna talk about what I think brands are gonna start doing in 2026 to get recommended more often by ChatGPT and Google's a I mode. I wanna share one simple way that you can take advantage of this. Now, I believe by the end of 2026, pretty much all searches will involve a large language model...

risk/avoid: Do not recommend undisclosed recommendation networks; require transparency, relevance, editorial value, and search-policy/legal review before any similar experiment.


The source describes a partner-recommendation tactic where related non-competing companies publish 'best X in Y' or selection-guide articles for each other.

to get than links. There's virtually no chance they'll be ignored or penalized. And when done right, they're far more effective. But you don't need to join or follow a network to get the benefit of this tactic. These markets haven't even taken shape yet. And by the time they do, I think the market will adjust and their prices will go up dramatically. But you can already do this yourself for free...

Treat this as a cautious PR experiment: use only honest, relevant recommendations, clear editorial standards, and no scaled or deceptive exchanges.


Early recommendations in LLM-visible content may create a compounding effect because future AI-generated content can repeat brands already surfaced by models.

Treat as a hypothesis; track whether early AI citations/recommendations correlate with later third-party content mentions.


Searching for indexed competitor “thank you for your interest” pages can reveal old signup pages, gated offers, or campaign angles.

This is kind of mental. Go to Google, write in site colon a competitor URL, space in quotes. Thank you for your interest. You'll often find old sign up pages, gated offers, or campaigns your competitors stopped promoting you. Show past angles, pricing tests, and offers that didn't scale or worked once then got shelved. Do this to see what your competitors already tested...

Use only public indexed pages for research; log offer patterns and messaging as inspiration, not as copied assets.


The source claims listicles are among the page types most commonly cited by large language models for recommendation-style prompts.

We're right in the middle of what might be the fastest transition we've ever seen in digital marketing. We're transitioning from a world where humans use search engines to find information to where humans use large language models, who then, in turn, use search engines to find information. And whenever there's a transition like this, the market needs to adjust...

Track which listicles, directories, and blogs are actually cited for target prompts before paying for placement or outreach.


Paid placement on listicles or directories may be underpriced if the page is frequently cited by LLMs, but only if the page is actually cited for relevant prompts.

Before paying for placement, verify citations across target prompts, model surfaces, dates, and competitor contexts.


Because AI answers often satisfy informational searches without clicks, website strategy should shift from random blogs toward product, service, and category pages.

If your website rankings have dropped off of a cliff, and you can't figure out why, chances are website's content strategy. Since AI Was introduced, there has been a huge shift in what generates traffic from search. And the websites who have seen an increase in visibility since this shift have all followed the same strategy...

Audit the content mix and prioritize detailed product, service, and category pages with images, reviews, features, and clear conversion actions.


Category pages can capture buyers who want to purchase but have not chosen a specific product or service yet.

Add or improve category and comparison pages for grouped services or products where buyer intent exists.


The creator describes using ChatGPT-generated brand regex in GSC to compare branded-search clicks across periods.

You want to see if your social media marketing is working? Do this. Tell ChatGPT my brand name is my brand name. My URL is my website.com. Give me rejects for Google Search Console to filter for only branded searches. Include any possible variations. ChatGPT will give you something like this. Copy it...

Build a branded-query GSC comparison report to estimate whether off-site or social activity is increasing demand.


Mere brand mentions are framed as weak for AI visibility; useful placements should be actual implicit or explicit recommendations for the brand.

ions could be worth literally tens of thousands of dollars. So I'd like to help you evaluate how valuable a specific brand mention is. First of all, as I alluded to earlier, if the mention isn't an actual recommendation, it's not very useful. Just having your brand name on the page doesn't do anything. It needs to be at least an implicit recommendation for your brand...

Score off-site placements by recommendation strength, not just whether the brand name appears.

AI visibility / recommendations vs mentions


Flat-rate “per mention” agencies are presented as a red flag because most mentions may be useless while a few cited recommendations can be extremely valuable.

I wanna talk about a brand new type of marketing agency that has emerged since the advent of large language models and whether or not it makes sense to hire one of these agencies as a business owner. So if you're at all familiar with S E. O, you've probably heard of link building agencies...

risk/avoid: Do not buy bulk flat-rate mentions without proof of citation relevance, recommendation context, and quality controls.


GEO work should verify that target pages are cited by popular AI surfaces for realistic buyer prompts, not obscure prompts no customer would use.

Build a fixed prompt set of realistic buyer/recommendation prompts and monitor citations/recommendations over time.


Searching Google for competitor PDFs can surface indexed strategy documents, case studies, white papers, positioning language, or campaign examples.

Fire your marketing agency. Do this instead. Go to Google, type in site colon your competitor website file type colon PDF. Competitors often upload strategy documents, case studies, or white papers as PDFs and forget to block them from Google...

Use only public indexed PDFs for competitor-language research; avoid private, leaked, access-controlled, or confidential material.


AI-search visibility should be tracked by prompts, mentions, and topic coverage rather than traditional keyword rankings alone.

The reason website isn't ranking on ChatGPT Is because you don't know what you're doing. I'm Kate, SEO Specialist, and whenever anybody tells ranking on AI Search, it's always one of these two issues. The First, you're not using the right tools. With traditional search engines, you're tracking your rankings to see what position you are target keywords. AI Search engines, you're tracking prompts & mentions...

Add AI visibility tracking that records prompt variants, brand mentions, cited pages, and topics where the brand appears.

AI visibility measurement / prompts and mentions


The creator frames AI visibility work as heavily dependent on reputation signals, including reviews, social presence, and industry mentions.

Include reviews, social presence, and third-party industry mentions in AI visibility audits alongside on-site content gaps.


The creator contrasts AI visibility reputation-building with buying hundreds of random links.

Avoid bulk link buying as a substitute for reputation, review, social, citation, and brand-mention work.


Searching industry terms with `intitle:"write for us"` can find blogs accepting guest articles for backlink and visibility opportunities.

You want more press and backlinks? Try this trick. Go to Google, search your industry, then in title colon, then write for us and quotes. Now you can see blogs in your industry that want guest articles. Write for these, get backlinks. Share your guest posts on LinkedIn if you need content ideas for the guest articles. Search site colon reddit com or site colon quora com your industry in quotes how do I?...

Use guest-post prospecting only with relevance, editorial quality, and disclosure checks; avoid low-quality guest-post farms.


Reddit and Quora searches around industry “how do I” or “why does” questions can reveal audience problems for guest posts or content ideas.

Mine public forum questions for recurring language and problems, then create genuinely useful content that answers them.


The creator says GSC regex filters can reveal commercial investigation queries where a site already ranks.

This is getting ridiculous. Go to Google Search Console, go to performance, then search results, click add filter, then query queries containing regex. Paste this in. This is what it looks like. Now you can see commercial investigation keywords that your website is ranking on Google for. These are search terms where people are actively reviewing and comparing options. These are also common searches used by AI...

Find comparison/review queries in GSC and add H2 sections or dedicated pages for valuable terms.


The clip describes buying dead Facebook groups, reviving them, editing keyword posts, and selling sponsored placements as an SEO exploit.

This is scary. This is a growth of Facebook Groups on Google search being predicted. Facebook Groups will be one of the easiest SEO exploits left somebody else buy dead Facebook groups with good names and member accounts from admins who stopped caring. Usually $50 to $200. Revive with consistent content for a few weeks, then sell sponsored posts to businesses for $100 to $500 depending on size...

Reject this as a growth tactic; treat bought-group SEO manipulation as platform-policy, ethics, and search-spam risk.


The creator argues GEO is not a subset of SEO but a broader process covering prompts, LLM behavior, search, and traditional search-engine retrieval.

Google keeps telling us that G E O. Is just a subset of S E O. I think it was Danny Sullivan who recently said that S E O. Is the big umbrella that G E O. Sits inside of, and I just have to say, that's the complete opposite of the truth. So if you don't know, G E O. Stands for generative engine optimization. It's everything you do to get recommended by large language models...

Use this as a terminology review point; define internal GEO/SEO scope explicitly instead of treating the source's framing as settled doctrine.


GEO cannot be separated from SEO because LLMs often perform traditional web searches before forming recommendations.

You can't do GEO without doing SEO. If you don't know, GEO stands for generative engine optimization. It's all the things you do to get recommended by large language models. I've been doing SEO for 16 years and my agency is now completely focused on GEO. But recently I've realized there are some agencies that only offer GEO and not SEO. And that doesn't make any sense...

Treat technical SEO, indexability, rankings, and off-site search visibility as prerequisites for many GEO outcomes.


GEO differs from traditional SEO because LLMs perform more searches, use more specific queries, and inspect more pages than humans typically do.

way large language models use search engines. Large language models perform many more searches. The searches are much more specific and less competitive. And they look at many more pages than humans do. So to do GEO well, you still need to optimize your website for search engines. You still need to rank for your main search terms...

Expand keyword/content research to include specific AI-retrieval queries, use cases, and third-party pages, not only head terms.

GEO differentiation / LLM search behavior


For GEO, a third-party page that ranks for an LLM's searched term and recommends the brand can be as valuable as the brand's own page ranking.

Map third-party ranking pages for target prompts and pursue credible recommendation inclusion where appropriate.


GSC crawl stats can be pasted into ChatGPT for a high-level technical SEO diagnosis.

Google's free Google Search Console is one of the craziest marketing tools that everybody misses out on. Do this if you've never connected your site to it. Connect it here with domain. Scroll all the way down, click settings, find crawl stats. At the bottom, click open report command A, select all, then copy everything you just selected. Paste this big mess of text into ChatGPT at the bottom...

Use AI diagnosis only as a first-pass checklist; verify recommendations manually in GSC, logs, and crawl tools before changing the site.


The source says 'how to choose the best X in Y' pages have performed similarly to listicles when they clearly recommend the brand near the top.

ove listicles, but that's not quite true. The truth is that large language models love to search for things like best X and Y. They search for recommendations. It just happens to be that the vast majority of pages that are optimized for these terms are listicles. And this is because the vast majority of these listicles are automatically generated by directory websites...

Test useful selection-guide pages as an alternative to self-serving listicles, with transparent evidence and an explicit, substantiated recommendation.


The creator warns that publishing thousands of recommendation posts can create Google scaled-content-abuse penalty risk.

going to have any impact on the large language model's recommendation. And you want that explicit recommendation as close to the top of the article as possible, preferably in the first sentence. Now, that might still feel a little icky to you, but it sure beats putting a listicle on your website with you at the top...

Do not scale recommendation pages without unique value, human review, truthful claims, and policy checks.


The source recommends using a products/services by demographics, use-cases, or service-areas matrix to plan recommendation-style articles.

Build a simple matrix of offerings against locations, demographics, or use cases, then prioritize only combinations with real customer demand.

Content planning / recommendation matrix


GSC queries where the site ranks positions 2–3 with modest impressions may be low-competition opportunities that can reach position 1 with small page improvements or dedicated pages.

This is the easiest SEO possible. Go to Google Search Console, click performance, search results, toggle on average position, scroll down to queries, filter to position is less than 3. Filter again. Impressions are greater than 80. Sort by impressions low to highest. Look for keywords where you're in position 2 to 3 which have less than 300 impressions...

Build a GSC near-top opportunity report and test targeted content/page updates for valuable low-impression terms.


The creator predicts Google's main results page will look more like AI Mode in 2026, likely as a hybrid of AI answers and traditional results.

These are my predictions for how digital marketing is going to change in the next year. I'm gonna try to be as precise as possible so that the end of 2026, we can look back on this video and see if I was right. My first prediction is that Google's main search results page will look more like a I mode does now than their traditional search results...

Treat this as a scenario to monitor: track SERP layout, AI answer presence, citations, and reporting metrics beyond classic rankings.


The creator predicts recommendation building will remain effective for LLM visibility: earn mentions on pages models cite or create better pages that cite the brand.

Track pages cited for target recommendation prompts and pursue ethical inclusion or stronger replacement content with clear evidence.


Filtering Google Search Console queries for prompt-like phrases such as “I am” may reveal AI Overview/AI Mode-style user prompts that led to the site.

It's insane that Google allows people to see this. Go to Google Search Console. This is Google's own tool for accessing their search index. Click performance search results, click add filter query. Type in I am. It shows people's actual prompts that they used within Google to get an AI overview or AI mode. This is insane...

Treat as an experiment: collect prompt-like GSC queries and use the language to improve relevant pages, while validating that the queries are meaningful.


For ecommerce sites, product, category, and subcategory pages should come before generic blog ideas because they capture broader buying searches.

Your business is gonna make more money in 2026. And I know this because I'm gonna help an SEO Strategy. My name is Kate. SEO Specialist, and this is literally what I do every day. No matter what your industry is, what your goals are, whether you wanna rank on Google, ChatGPT, search engines, The first thing that I'm gonna is disregard anything that from ChatGPT because unless you actually know what you're doing...

Prioritize product, category, and subcategory architecture for commercial intent before producing broad blog content.


ChatGPT can help map ecommerce content only when it receives real search topics and product inventory, not when asked for generic blog ideas.

Use AI as a mapping assistant with structured product and topic inputs, then have an SEO review the output for intent and architecture fit.


The creator warns that ChatGPT-generated SEO strategy is unreliable when the user lacks SEO expertise and business context.

Do not accept generic AI SEO plans without human SEO review, real inventory, and search-intent validation.


Google Search Console can be filtered for 7+ word queries, which the source treats as a signal of AI-overview or AI-mode-style searches.

Google will actually show you which searches came from AI. Almost nobody knows where to look. Go to Google Search Console. This is Google's own tool for accessing their search index. If you haven't submitted your site to it yet, use this one. Submit a domain property, go to performance, click add filter, then query, click rexe. Paste this in. This will show you searches that are at least 7 words long...

Use 7+ word GSC queries as exploratory AI-search research input, validating intent before making new pages or sections.


Long natural-language GSC queries can be turned into pages or H2 sections that answer the questions users are asking.

Create a workflow that maps validated long-tail natural-language queries to useful page sections or standalone pages.


The clip warns that publishers face falling Google search traffic, fragmented discovery, and risky dependence on Google Discover.

The internet could be collapsing. Google search traffic to news publishers is down 50% over only the last two years. Now news publishers are depending on the Google Discover feed, which is extremely risky. AI, short form video, and podcasts are primarily responsible for this major drop in Google search traffic. The news landscape has become extremely fragmented...

For publisher-like sites, model traffic and revenue concentration risk, then diversify beyond ads, affiliates, and Discover dependency.


Publishers with SEO authority may diversify by building niche freemium web apps and ranking purchase-intent landing pages for them.

Treat this as a product experiment: pair useful niche tools with bottom-funnel pages and measure signups, revenue, and support load.


The source says redesign traffic losses often come from removing pages or changing URLs, because Google treats a changed URL as a removed page plus a new page.

I keep talking to companies that have rebuilt their website and lost a bunch of their Google search traffic. It's so common. In my experience, I would say 90% of the time an organisation rebuilds their website, they lose a significant amount of their rankings in Google and it's always for the same reason and it's entirely avoidable, unfortunately...

Before redesign, inventory every URL with search traffic and preserve it when the replacement page keeps similar content, title, and H1 intent.


If a changed URL redirects to an irrelevant page, the old ranking usually is not preserved; blanket homepage redirects can behave like soft 404s.

l actually gain ranking. This is because typically if you make significant site wide changes to your website this will force Google to re evaluate the content of your site. So if you've been working on your content or you have more authority than the last time Google evaluated your site you're likely to see a boost in ranking...

Map changed URLs to the closest relevant replacement page, and avoid redirecting old ranking URLs to the homepage by default.


Sitewide changes can trigger Google to re-evaluate content, so improved content or authority may lift rankings when the migration preserves important pages.

Treat redesigns as controlled SEO migrations: preserve ranking URLs, improve page quality, and monitor rankings after Google re-evaluates the site.


The creator claims LLMs often use Google for recency-related answers, so strong Google visibility can support LLM visibility.

Is that. Is that Google Search Console? It's coming down from the clouds. Look at the grass, they're glowing. It reacts when I touch it. This is unreal. Google gives you a way to see how your site appears in their massive search index. This tool, Google Search Console, is one of the most interesting free tools in digital marketing. Connect your site to it. Use the domain property here. Connecting is very easy...

Treat Google performance as one input for AI visibility, especially for recency queries; verify with prompt/citation testing.


The creator suggests using GSC queries outside top positions to build internal links toward the strongest relevant page.

Test natural internal links from related lower-ranking pages to the top relevant page using query-aligned anchors.


Including the current year in blog titles can improve CTR and may help when LLM retrieval searches include the current year.

This is your reminder to update all your blog titles that say 2025 to 2026. Let's talk about why this is so important and the right way to do it. First of all, if you're not already including the year in most of your blog titles, I highly recommend you start doing that. This has always been an effective strategy for increasing click through rate in Google search results, which in turn can improve your rankings...

Refresh eligible evergreen pages with current-year titles only when the content itself is updated.


Changing only the year or publish date is not enough; refreshed posts should include real updated content.

Add a concise 'what changed this year' section near the top of refreshed posts and document the update.


Years should generally not be placed in URLs because changing URLs later can destroy or complicate existing rankings.

Avoid year-based URLs for evergreen content; if legacy year URLs exist, decide based on traffic and redirect only when safe.


Modern social and video algorithms can surface content to the right audience even when an account has little following.

If you have a business or any kind of brand, now is the time to be creating content. Most people just don't realise how much modern algorithms and AI have changed the strategy for content creation. So I wanna explain why. I think we've just entered the golden age of content creation and how to do it effectively. It's honestly so easy right now, and that's not gonna last forever...

Track ideal-customer reach and lead quality, not only total views or follower count, for short-form and video visibility.


LLMs can search with user context, creating hyper-specific long-tail queries around demographics, use case, and location.

cal video gets about 25 views, but most of those 25 people are my ideal customer. As a result, we've been able to scale the agency as fast as we want to and be very picky about the clients that we take on. If you're interested in how I create videos, I have another video that's, like six minutes long called I can easily create videos as a business or something like that...

Create content for specific demographics, use cases, locations, and case-study scenarios likely to match AI retrieval queries.


A specific case study may receive very little search volume but be exactly what a high-intent AI-assisted searcher needs.

Build lightweight, highly specific case studies where each addresses a concrete buyer situation and problem.


Brands recommended by AI and algorithms earlier may later be referenced in AI-generated content created by others, creating a first-mover advantage.

to be exactly what they were looking for. And if you can create this content quickly without putting in a lot of effort, it's well worth it. The thing is, your competitors are afraid to make videos, and your competitors are afraid to run experiments like this. Which is why it's so easy right now. Eventually, people will catch on, and there will be more competition...

Treat as a hypothesis: start AI visibility experiments early and monitor whether brand mentions compound across generated content.


Posting during holidays may face lower creator competition while more people are home consuming content.

Right now is the best time to post. A lot of people take off and don't post on social media during the holidays. This is the best time to do it. There are more people home looking for content to consume with tons of creators taking off so there's less competition. You can cross post videos between in Instagram, YouTube, TikTok, LinkedIn, Facebook text and image posts, LinkedIn X Facebook threads...

Treat holiday posting as a social visibility experiment; cross-post assets and measure reach against normal posting periods.


Google is claimed to reward topical authority and punish or devalue content far outside a site's core expertise, with HubSpot used as an example.

This graph changed the world of digital marketing this year. This is HubSpot's SEO traffic. For years, HubSpot was posting about things that were not in their niche. They were getting away with it. At the peak, they were getting 24 million clicks a month from Google. Again, for things that were unrelated to HubSpot's core business. But recently HubSpot has been punished for this...

Keep content expansion adjacent to the site's proven topical authority; avoid unrelated traffic grabs.


LLMs can validate weak ideas with convincing reasoning, causing users to overestimate AI advice outside their expertise.

I wanna talk about LLM psychosis. If you or someone close to you has not already been affected by this, I think you probably will soon. The word psychosis might be a little too strong given how common this is, but apparently that's the term we've agreed on. If you don't know, this is just when someone becomes overly confident in an idea after having a conversation with a large language model...

risk/avoid: Require expert review for AI-generated business, SEO, medical, legal, or other high-stakes recommendations.


LLMs can help novices become amateurs and help experts generate ideas, but they do not let novices leapfrog domain experts.

Use AI for ideation and drafting, but keep expert validation as a required step before strategic SEO/GEO decisions.


Queries in positions 7–20 with impressions are described as opportunities for quicker gains through title rewrites or added H2 sections.

Nobody knows this about Google. Go to Google Search Console, connect your site to it. Go to performance, toggle on average position. Scroll down to queries. Go to filter position greater than 7. Now you can see searches that your site shows at the bottom of page 1 of Google for or page 2. Stick to searches between positions 7 and 20. Sort by impressions...

Prioritize GSC queries in positions 7–20 for page-title and section-level optimization tests.


GSC can be filtered for comparison, review, versus, and alternative keywords to find bottom-funnel queries already showing for the site.

It's crazy that Google will show you all of this. Go to Google Search Console. This is Google's own tool for accessing their search index. Go to performance, add filter, query, custom. Paste this in. Now you can see all the versus, review, comparison, alternative keywords that you show on Google. For click apply, do rows per page 500...

Add a GSC report for comparison/review/versus/alternative queries and prioritize pages with commercial intent.


High-impression queries with CTR below 1% can reveal pages Google shows often but users do not click.

This is hidden within Google. If you go to Google Search Console, connect your site to it, go to performance, toggle on average, click through rate, scroll down to queries, sort by most impressions, filter by click through rate is less than 1%. Now you can see high impression searches that your site ranks on Google for but is doing a poor job of capturing...

Build a GSC low-CTR/high-impression report and test more relevant or more compelling titles/H2 additions.


Google's SEO guidance is partly aspirational: useful for direction, but not a complete explanation of how rankings work.

ave you reviews. Google's algorithm will see all of this and reward you for that high quality helpful content. And of course, there is some truth here, but it doesn't really tell you anything about how Google's algorithm works. The truth is all of this advice is aspirational, but Google isn't transparent about that. Google talks as if their algorithm is already perfectly aligned with their intentions...

Listen to Google guidance, but validate with experiments before treating official advice as a complete ranking playbook.


The creator argues Google's algorithm is strongest at understanding searcher intent, so intent matching remains central to SEO.

Add explicit search-intent checks to content briefs and on-page QA for every target keyword.


The creator claims Google repeatedly denied CTR as a ranking factor, but evidence now shows CTR is a ranking factor.

Treat as a contradiction/review item; do not assume CTR impact without corroborating evidence and safe testing.


The creator predicts Google will test hybrid search experiences that mix AI responses with traditional results.

tain question correct. However, there's no penalty for an incorrect answer. So while Gemini 3 Flash does get more correct answers than almost any other model, it's also one of the models least likely to say I don't know, which means it still has a high hallucination rate. In fact, over 90% of the time that it doesn't know the answer, it will still give a confident answer...

Track Google AI Mode and hybrid result formats separately from classic SERPs.


Fast smaller models like Gemini Flash may answer confidently even when wrong, making hallucinations a major constraint for AI search.

Gemini 3 Flash came out this week and I wanna talk about why this is actually a big deal and something worth paying attention to. So you might know Gemini 3 came out a while back. Gemini 3 Flash is the smaller, faster version and all the large language models have one of these nano or flash versions. It's the model you want to use if you need a quick answer and don't want to spend a lot of tokens...

risk/avoid: Do not optimize only for AI answer inclusion; also track accuracy, citations, and whether the answer is safe/trustworthy.


Recommendation spam may work now, but the creator warns against automating it or doing it at scale because brand spam creates risk.

There are all these new tactics for being recommended by large language models. Now some of them feel a little silly or even spammy, and I hear a lot of people asking, sure, these work right now, but are they really gonna continue working? And so I just wanna talk about why I think a lot of these tactics are going to continue working for the foreseeable future, and which ones you might want to avoid...

Risk/avoid: do not automate scaled recommendation spam; require brand-safety, disclosure, and manual quality controls for off-site mentions.


Some recommendation-based tactics may keep working because fully vetting every recommendation source would be expensive for search/AI systems; likely enforcement may target only major offenders.

Treat as a hypothesis, not a recommendation; track enforcement and avoid tactics that rely on deception or scale.


The creator says service-area pages can still be effective for neighborhood and city rankings despite being seen as spammy.

this take is it's from other SEOs. SEOs that would prefer to just keep doing what they've always been doing and who really hate this idea that they have to change their methods. It's a really good excuse to not have to learn something new. And honestly, I've fallen into this trap myself in the past...

Review service-area pages only where they add real local relevance; avoid thin doorway-page patterns.


The creator frames GEO as SEO plus recommendation building: brand recommendations on pages LLMs cite, not only links and rankings.

Several people from Google have been going on podcast in the last week telling people that Geo is just SEO. If you don't know Geo stands for Generative Engine Optimization. It's the things you do to get recommended by large language models like ChatGPT or Google's AI mode...

Add cited-page recommendation analysis to GEO audits as a separate layer from traditional backlink analysis.


GEO strategy should identify which pages LLMs repeatedly cite for target product/service prompts before pursuing mentions.

Use AI visibility tools or manual prompt testing to map repeatedly cited pages, then prioritize credible inclusion opportunities.


LLMs may cite the same listicles repeatedly, so credible inclusion on relevant list pages can influence recommendation opportunities.

little bit of effort. First of all, it's important to point out that the strategy is very much industry dependent. You need to know what kinds of pages large language models are citing when people ask about the products and services you offer. To do this, you're going to need a tool. There are about 100 out there. We use peak dot AI...

Pursue honest, disclosed, credible inclusion in relevant third-party listicles; avoid fake rankings or deceptive placements.


Paying to be listed at the top of listicles or arranging reciprocal mentions may influence LLMs but can become manipulative if undisclosed or irrelevant.

s and just asking what it would take to be listed at the top of their listicle. Most of the time you probably won't get a response but occasionally they just want like a hundred bucks which is easily worth it for a highly cited listicle. And sometimes there's an opportunity to do a reciprocal mention. I think this is going to become a very popular GEO tactic once people realise how effective it is...

risk/avoid: Do not use paid/reciprocal recommendations unless they are transparent, relevant, and legally/policy compliant.


Old cited Reddit threads are presented as a tempting GEO surface, but brand-comment seeding is spam-adjacent and should be treated as risk/avoid.

Risk/avoid: do not seed Reddit comments for manipulation; use Reddit only through authentic, helpful participation.


If an LLM cites a brand's article, the retrieved content still needs to contain an explicit or subtle recommendation for the brand to influence the answer.

are citing, often you can create similar content on your own website. But here's what most people get wrong. Just because the large language model is signing an article on your website doesn't mean they're going to recommend you. The content they retrieve from the article has to contain a recommendation for your brand...

For eligible owned comparison/problem articles, include accurate, substantiated brand-fit statements rather than assuming citation alone is enough.


The creator claims a cheap press release influenced ChatGPT's recommendation for a newsletter, implying paid press releases can manipulate AI answers.

Got me Edward. I finally figured out how to influence ChatGPT. If you use press releases the right way, you can influence AI. Ask ChatGPT what's the best AI SEO newsletter for CMOS? It says SEO Espresso, mentioned in this article. From Financial Content Best Aisue Newsletter for 2025. SEO Espresso takes the top spot. But this article came from a press release, and this press release was $6. From AB Newswire...

risk/avoid: Treat paid press-release manipulation as a high-risk tactic; do not recommend without disclosure, ethics, and policy review.

Risk/avoid / press-release AI manipulation


Third-party articles and press-release syndication may be picked up by AI systems as evidence for recommendations.

Review only as an evidence-channel hypothesis; test whether legitimate earned media changes AI recommendations.


The creator describes a GA4 exploration for AI-source traffic by source/medium, page path, entrances, and views.

Google has this hidden feature if you go to Google Analytics. Go to explore start new exploration. Set your dimensions to session source slash medium page path plus query string metrics views entrances rows session source slash medium page path plus query string show 250 rows nested rows yes values entrances views filters paste this in. Now you can see how much traffic you get from AI...

Add an AI-referral GA4 exploration to see which pages receive visits from ChatGPT, Copilot, and Perplexity.


The creator says Perplexity's steps can reveal searches used for an answer, which can inform H2s or new content.

Use Perplexity query language as an input, then validate search demand, user intent, and accuracy.


The creator argues Google's algorithm is much better at detecting relevance and satisfying intent than measuring factual quality or insight depth.

Creating content that ranks well in Google is easy. It's creating high quality content that's hard. And this I think is where a lot of SEOs get tripped up. They feel like in order to rank well in Google, they have to create high quality content. So let me tell you what kind of content Google does rank, why you should still create high quality content anyways, and how we do it...

Do not rely on ranking as proof of quality; include separate factual accuracy, expert review, and usefulness checks.


A brand-ambassador AI project can draft in the brand voice, but early content still needs organizational review and iterative guideline updates.

the H1 or the title of the article and any smaller secondary terms are in H2s or subheadings. To satisfy user intent, you need to increase engagement on this page. Best way to do this is with structural elements like a table of contents, tables, charts, graphs. You can make infographics with Gemini or gamma app...

Use AI-assisted drafting only with brand context, review from someone in the organization, and updates to source guidelines after edits.


The creator recommends structural elements like tables of contents, tables, charts, graphs, and infographics to help satisfy intent.

Add structural elements only where they genuinely help users understand the topic.

engagement / structural content elements


AI-generated relevant content may rank even if inaccurate, which creates brand and user-risk despite short-term SEO effectiveness.

risk/avoid: Do not publish AI content without accuracy checks, expert input, and brand-quality review.


Maintaining a brand-ambassador knowledge base and updating it after each editorial correction can reduce repeated content mistakes over time.

the Claude is able to speak on behalf of the brand. That brand ambassador alone will get us a long way, but we'll still need someone in the organisation to review the content, at least at first. For that first piece of content, if you have very high quality standards, it's not uncommon to have 20 or more edits. But here's the key. We don't just make the edits to that piece of content...

Build a living brand/content-guidelines file from client interviews, public information, and review feedback.


The creator claims LinkedIn reach is unusually strong and that frequent helpful posts can generate high impressions even on previously inactive accounts.

I've been posting one to three times a day on LinkedIn. I'm almost at 900,000 impressions per week. Something is seriously going on with LinkedIn. Friend of mine shared this a few days ago. I started posting five times per day. Just for context, the account had not seen any activity in years. The result 12,000 impressions with an account that wasn't posting before...

Treat LinkedIn frequency and repurposing as a distribution experiment; measure qualified visibility and downstream leads.


The creator recommends repurposing helpful LinkedIn content across Facebook, Threads, X, and short-form video formats.

Build a cross-posting workflow for genuinely useful content, adapting format and measuring performance per platform instead of reposting blindly.


In critical conversion flows, simple and clear design can outperform visually rich design such as video backgrounds.

Designers are going to hate me for redpilling you like this. Dark mode is dying. Apps known for being notorious AB testers are turning off dark mode by default. This includes Facebook and LinkedIn. If dark mode was increasing conversions, it would be on. Amazon, Airbnb, Venmo team all don't have dark mode. It costs more to maintain and implement than is worth it...

For lead/signup pages, test plain high-clarity layouts before adding heavy aesthetic elements.


Unlinked brand mentions may matter for ChatGPT/LLM visibility, while traditional Google SEO still depends more on actual links.

If you're seeing this without having watched part 1, it's fine, You can watch this and then go back. I'm gonna talk about off-page SEO. Mainly link building. Back in the day you could just go and buy links from high authority sites. There was a huge emphasis on domain authority and how this would help you rank for more competitive keywords. This isn't really working nowadays...

Track and test unlinked brand mentions as a GEO signal separately from backlink acquisition; keep as experimental until validated.


Fewer high-quality links from trusted publications are claimed to outperform bulk low-quality purchased links.

Prioritize expert quote / journalist response campaigns over cheap bulk link buying.


Buying hundreds of cheap backlinks is framed as obsolete and ineffective.

Mark cheap bulk backlink packages as risk/avoid; do not recommend as a client tactic.