Measurement & Monitoring
Methods, tools, dashboards, and metrics for tracking AI mentions, citations, citation share, sentiment, prompt coverage, source influence, referral traffic, and conversions.
42%
Best tweets about AI Visibility
50 Best Tweets About AI Visibility (2026)
Find the best tweets about AI visibility, covering brand mentions, citations, answer engines, LLM monitoring, measurement, content, and search strategy.
Brand and content visibility inside AI assistants and answer engines, including citations, measurement, monitoring, experiments, and strategy.
- Creators
- 37
- Updated
Original Xholic analysis
What 50 top AI Visibility posts reveal
The conversation frames AI visibility as a measurement-and-brand problem as much as a content problem: track realistic prompts, citation share, and perception; build credible signals across owned and third-party surfaces; and treat technical or automation claims as hypotheses to test. Posts present conflicting views on schema, referral economics, and citation speed.
- Dominant tone
- Positive
- Median score
- 5.91
- Leading format
- Announcement
- Recent posts
- 52%
44% of posts
All-time engagement
34% of posts
Published in 90 days
Conversation map
The themes creators return to
AI-Citation Content Strategy
Content formats and editorial practices aimed at earning AI citations, including direct answers, comparisons, listicles, Q&A structure, freshness, research, and product information.
36%
Third-Party Authority & Earned Media
The role of earned media, editorial coverage, reviews, communities, social platforms, and branded mentions as sources that AI systems cite or use to validate brands.
30%
AI Search vs. Traditional SEO Outcomes & Attribution
The relationship between traditional SEO, AI answers, and business outcomes, including differing rankings versus citations, AI referral volume and quality, buyer discovery behavior, and attribution.
22%
Brand Selection & Reputation
How brands are selected, recommended, described, and positioned in AI-generated answers; emphasizes category fit, trust, reputation, and accurate brand narratives.
20%
Prompt Intent & Query Strategy
Prompt research, intent segmentation, personas, query/topic clusters, citation fingerprints, and the need to use realistic buyer prompts rather than biased or generic tracking prompts.
20%
Technical AI Visibility Optimization
Experiments and debate around technical and on-page optimization for AI visibility, including schema, metadata, crawlability, site architecture, structured data, llms.txt, and formatting.
16%
Agents & Automation
Automation and agent workflows for researching AI answers, identifying citation gaps, generating or refreshing content, publishing, and iterating based on visibility data.
8%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 19
- Median reposts
- 2
- Median replies
- 3
- Median views
- 1.8K
Posts with media make up 62% of this collection. Their median all-time score is 5.49, compared with 6.33 for text-only posts.
Format mix
- Announcement 34% · score 6.33
- Opinion 34% · score 4.81
- Case Study 16% · score 7.09
- Story 8% · score 34.2
Consensus and debate
Where creators agree, and where they do not
Shared view
Measure context, not just mentions
Posts advocate moving beyond raw citation counts to track query intent, topics, citation share, historical patterns, and persona-specific results. One contributor also warns that branded, blended, or seeded prompts can inflate dashboard outputs.
Shared view
Visibility includes brand perception
Visibility is framed as presence plus how a brand is positioned and described. Contributors emphasize category fit, clear product narrative, trust signals, and perception—not merely appearing in an answer.
Shared view
Third-party signals matter
Posts repeatedly present third-party editorial, community, and social surfaces as relevant to AI visibility alongside owned-site work. Reddit, LinkedIn, YouTube, and earned coverage are specifically named.
Open debate
Schema’s citation impact is disputed
One automation-oriented post recommends schema as part of a broader content system, while posts citing an Ahrefs study report no major citation uplift after pages added JSON-LD. The evidence set therefore does not support schema as a standalone primary lever.
Open debate
AI referral value remains unsettled
Posts make conflicting claims about AI-referral economics: one says AI referrals tend to convert better, another reports low traffic and poorer conversions in its sample, and another cites a higher-conversion study with stated scope limits.
Open debate
Citation speed has no single timeline
Some case posts report AI citations within days, while another argues that citations emerge after months of consistent publishing. These posts do not establish a single reliable citation timeline.
What performs
Patterns behind standout posts
Outliers span strategy, experiments and evidence
The five supplied score outliers cover a strategic framework, a third-party-authority argument, an automation tutorial, a comparison-site experiment, and a schema study. Their supplied all-time scores range from 66.49 to 252.09.
Monitoring is the dominant theme
Measurement & Monitoring is the largest supplied theme at 42% (21 tweets) and has a 10.805 median all-time score, ahead of content strategy by share.
Content strategy is frequent, not highest scoring
AI-Citation Content Strategy accounts for 36% of the supplied theme mix (18 tweets), but its 4.83 median all-time score trails Measurement & Monitoring’s 10.805.
Statistical standouts
- View standout post 1 Score 252.1 · 42.65× median
- View standout post 2 Score 178.5 · 30.2× median
- View standout post 3 Score 87.5 · 14.81× median
- View standout post 4 Score 69.3 · 11.73× median
- View standout post 5 Score 66.5 · 11.25× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Elvis
@elvissun
2 posts
-
2. Klaas
@forgebitz
2 posts
-
3. Gaetano DiNardi
@gaetano_nyc
2 posts
-
4. Glenn Gabe
@glenngabe
2 posts
-
5. Jan-Willem Bobbink
@jbobbink
2 posts
-
6. Kaleigh Moore
@kaleighf
2 posts
DiNardi: strategy before dashboards
Gaetano DiNardi argues for a positioning-led diagnostic and warns that AI-visibility reporting can be inflated by branded, blended, or biased prompts.
Gabe: richer reporting, limited schema lift
Glenn Gabe highlights Bing’s expanded reporting for intents, topics, and citation share, while sharing an Ahrefs study reporting no major citation uplift after pages added schema.
Klaas: test engines and tools directly
Klaas reports an experiment in which comparison pages were crawled and cited despite little traditional-search visibility, while criticizing visibility claims based on outdated, non-web-enabled models.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best AI Visibility tweets
Top AI Visibility tweets from 37 creators
Ranked 01–50
- 01
@gaetano_nyc ·
I’ve spent the last 18 months doing AI SEO / GEO / AEO for B2B SaaS companies... and here's the 10 biggest things I've learned: 1. Most companies do not have an "AEO problem." Most do not have an unfixable technical SEO problem either. They have a positioning, category alignment, and market validation problem. 2. I've spent too many hours in meetings explaining how the old-world SEO model is dying (clicks and rankings) and the new model is about being selected for AI-powered answer recommendations. 3. Brand selection depends on whether the market, your website, your customers, and third-party sources all tell the same story about why your brand belongs in the answer. This is not about llms.txt or paragraph chunking. 4. Bulldozing your way to the top with listicles and backlinks may help you rank, but not get recommended. Especially if your brand doesn't belong in that category. 5. The best AEO strategy starts with positioning and category alignment. Here is the proper diagnostic framework for: “What's our AEO strategy?” Do we have clear product positioning? Do we have messaging that's aligned to the category? Does the website make our category alignment obvious? Do we have a BOFU content strategy aligned with the desired category? Do we have an external authority gap? 6. AI referral traffic is negligible for most B2B SaaS companies. For most of the companies I work with: ChatGPT / Perplexity / Gemini / Claude, etc. is a tiny fraction compared to Google. It's common to see a range of 5% (AI) vs 95% (Google). Why? Because AI SEO eliminates top of funnel, whereas traditional SEO sends mostly informational traffic. 7. Visitor to page conversion rates from AI referral traffic tend to be higher than traditional Google traffic. This is because of personalization, long-tail BOFU intent, and the elimination of TOFU intent. 8. There's a "Dark SEO" funnel emerging where buyers are shortlisting vendor options with AI search, verifying the brands they want to evaluate with Google search, and then converting directly on the website from branded search much later in the buying process. 9. Off-site authority matters, but the GEO vendor claims are getting out of control. I'm skeptical on things like: “90% of AI visibility comes from third-party sources.” etc. Claims like these are confusing and pulled out of context. This causes marketers to have the wrong reactions and think irrationally. 10. Buying AEO tools is not a strategy. I have personally worked with companies that purchased AEO tools with no strategy in-place. The mere fact that a tool was purchased only buys them time with the board... "we're working on it" ... but it's not a real strategy. Bonus: There's a "research" study out there which fits any narrative you want to push. Be wary. And be skeptical.
- 02
@jbobbink ·
94% of AI citations come from non-paid sources. Gartner is telling CMOs to double their PR budgets by 2027. Gartner just published their 2026 comms predictions. The headline: PR and earned media budgets will double by 2027 as LLMs replace traditional search. They're telling CMOs to reallocate spending from paid channels toward PR and earned media because that's what AI answer engines actually cite. Data backs this up. Muck Rack analyzed over a 1M links cited by ChatGPT, Claude, Gemini, and Perplexity. Around 94% of those citations come from non-paid sources. Earned media alone accounts for 82%. Journalism makes up 20 to 30% of all AI citations depending on the time period. Paid placements and advertising barely register. Semrush found that visitors arriving from AI search convert at 4.4 times the rate of traditional organic search visitors. That number comes from analysis of 500+ topics in digital marketing and SEO verticals specifically, so the exact multiplier will vary by industry. The direction is clear. There is a recency factor too. Muck Rack's February 2026 update found that more than half of all AI citations come from content published in the last 12 months. The highest citation rate occurs within seven days of publication. Press release citations alone grew 5x between July and December 2025. AI systems are actively prioritizing fresh content over older material. That recency signal matters for SEOs. This is not a one-time optimization play. It requires ongoing editorial presence. When CMOs start doubling their earned media budgets, that money has to come from somewhere. Gartner explicitly says to reallocate from paid. But in their 2025 CMO Spend Survey, paid media was already at 30.6% of total marketing spend. If earned media doubles, the budget pressure will hit every digital channel. Including SEO. We have spent two decades building an industry around optimizing for Google's algorithm. Now the discovery layer is fragmenting across a dozen AI systems that each weigh signals differently. And the signal they all seem to agree on is third-party editorial validation. Not backlinks. Not technical SEO. Editorial trust. Across analytics data from hundreds of properties, the pattern is consistent. Sites gaining visibility in AI-driven discovery are the ones with genuine brand authority and regular editorial coverage. Not the ones with the cleanest technical audits. Technical SEO is not dead. But it is no longer enough on its own. If your entire strategy is optimizing for crawlers and you are ignoring how AI systems evaluate brand trust, you are building on a foundation that is shifting. The PR industry just got handed the playbook that used to be ours. The smartest SEOs will learn from it. Sources: - Gartner, Top Predictions to Inform 2026 Comms Strategies (Feb 2026) - Muck Rack, What Is AI Reading? report (Feb 2026) - Gartner, 2025 CMO Spend Survey (May 2025) - Semrush, We Studied the Impact of AI Search on SEO Traffic (Jul 2025)
- 03
@codyschneider ·
How to Build an Agent That Researches and Publishes Content for AEO Automatically The shift is happening. Instead of scrolling through Google links, people ask ChatGPT, Claude, and Perplexity for answers. This is AEO (Answer Engine Optimization)—and it's where visibility lives now. Here's how to build an autonomous agent that does the work for you. What AI Engines Actually Want Structure beats length. AI parses content in 100-300 token chunks. Lead with declarative statements. Use clear headings and bullet points. Freshness matters. 70% of AI-cited pages were updated in the last 12 months. Your content needs regular refreshes or you lose citations. Answer directly. No more keyword stuffing. AI extracts the best answer to the user's question—hit the point immediately or don't get cited. The Agent Architecture 1. Query Intelligence Monitor what questions AI engines are being asked about your topic Use tools like Searchable to track ChatGPT, Claude, Perplexity citations Build a prioritized list of questions to target 2. Competitive Intelligence Query AI engines for your target questions Extract and analyze what's being cited Find gaps—what isn't being answered well? 3. Content Generation Generate content based on question + competitive analysis Apply brand voice and style guidelines Include schema markup (FAQ, Article, HowTo) 4. Automated Refresh Monitor citation decay Trigger refreshes when visibility drops or quarterly minimum Implementation Workflow Daily Trigger → Query AI Engines → Extract Citations → Analyze Gaps → Generate Content → Apply Schema → Human Review → Publish → Monitor Performance Content Standards Every piece needs: Question-based headline Direct answer in first 2 sentences H2s for each sub-question 800-1500 words (focused, not verbose) 2+ stats or citations FAQ/Article schema markup 2-3 internal links Measuring Success AI Citations: How often you're cited in AI responses Share of Voice: Your % vs competitors Traffic from AI: GA4 attribution to AI referrals Pitfalls to Avoid Publishing without human review – AI makes mistakes Ignoring structure – AI can't cite wall-of-text Wrong goals – Optimize for citations, not rankings Skipping technical SEO – Schema, crawlability, speed still matter Start Simple Pick one topic cluster to test Build the research loop first Add generation once research works Add publishing and refresh as you scale The companies winning at AEO treat content as a machine-driven system. They use automation for research, creation, and refresh—so humans focus on strategy. You set the strategy. The agent executes.
- 04
@forgebitz ·
now back to work using claude code, we created a 100% ai-generated comparison (listicle) website called surferstack we let claude research hundreds of saas companies to dynamically generate comparison pages based on what it could find on their website the goal was simple: understand how comparison websites are used by ai search engines the website is now offline, but the data is wild we published comparison pages across a variety of saas categories and monitored how they were crawled and cited by ai models pages types included: best social media scheduling tools 2026 brand a vs. brand b compared 2025 best tools for xyz 2026 what surprised us was that these pages were being scraped and revisited by ai search engines despite having little to no visibility in traditional search ranking in google doesn't necessarily mean you'll be cited by ai models, and the reverse can also be true. traditional SEO appears to have very little impact on ai citations in some cases we also ran multiple experiments using exact-match domains, long-tail domains, and surferstack interestingly outperformed all of them, showing that exact match domains have very little impact on ai search visibility
- 05
@glenngabe ·
Interested in Schema impact on AI citations? Here's the latest study from @ahrefs -> We Tracked 1,885 Pages Adding Schema. AI Citations Barely Moved "We tracked 1,885 web pages that added JSON-LD schema between August 2025 and March 2026, matched them against 4,000 control pages, and measured citation changes across Google AI Overviews, AI Mode, and ChatGPT. Adding schema produced no major uplift in citations on any platform." https://t.co/cinIs15p9M
- 06
@SEOKeval ·
It can take 6-12 months to rank in Google. But it only takes a few days to show up in AI models. They're so easy to manipulate. It feels like a crime. I blasted out "best X" listicles to help a brand's AI visibility a week ago. Within three days... They were appearing at the top of Google's AI Overviews, ChatGPT, and Claude. Three. Friggin'. Days. I wasn't optimizing for Google back in the early 2000's... But I imagine this is what it felt like. It's comical to think back to 2-3 years when AI was gaining steam and everyone thought the SEO industry was cooked. You know who's actually cooked? Humanity. Because everyone trusts AI with their lives. And I can change its results before the weekend.
- 07
@neilpatel ·
The next version of SEO isn’t about ranking number one. It’s about what AI says about you when someone asks. Because Google, ChatGPT, Gemini, Claude, they’re not just showing links anymore. They’re forming opinions. They’re comparing brands. They’re deciding who sounds credible, who sounds risky, who gets recommended, and who gets ignored. That means your biggest search problem in 2027 might not be that you don’t show up. It might be that you show up wrong. Maybe AI describes your competitor as the trusted expert… and describes you as generic. Maybe it mentions your brand… but the sentiment is neutral while your competitor is glowing. Maybe it pulls from old reviews, outdated articles, weak bios, messy positioning, or random third-party pages you didn’t even know were shaping your reputation. That’s the part most businesses are missing. AI search visibility is not just a traffic game. It’s a reputation game. And that’s why we added the Sentiment tab inside Ubersuggest’s AI Search Visibility tool. It doesn’t just track whether your brand appears across ChatGPT, Gemini, Claude, and Google AI. It shows how those engines describe you. Positive, neutral, or negative. And it charts that sentiment over time against your competitors. So now you can answer the question that actually matters: Not just, “Am I showing up in AI answers?” But, “What is AI actually saying about me?”
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- 08
@elvissun ·
your AI visibility sucks because your content is never built for it. I just open-sourced an /ai-visibility-writing skill to fix that: first I analyzed 448 prompts and 3445 citations to see what actually makes chatgpt and AI overview cite a page. it cost me $8.91 to collect the results, then it took 6 hours for 5.6 sol to run the analysis. from there I gave the analysis to fable together with 15 papers on AEO, eval'd it over 48 pairs of outputs to build this skill. fun times. run it to over any content to improve your AI visibility: https://t.co/fV2l7TwHZr
- 09
@glenngabe ·
Big news from Bing. And you can compare changes over time. This is what @kmadhavan77 shared in April and now it's rolling out -> New AI Visibility Insights in Bing Webmaster Tools: Intents, Topics, Citation Share, Compare "AI-generated answers are dynamic, contextual, and often synthesized from many sources at once. Understanding visibility in these systems requires more than a single metric or surface-level citation count. With these expanded preview capabilities, Bing Webmaster Tools is expanding first-party reporting to provide deeper insight into the query context, thematic patterns, relative citation presence, and changes over time that shape how content appears in AI-powered experiences." "With the new Intents feature, grounding queries in the AI Performance Report are now classified into broader categories such as Informational, Commercial, Navigational, Learn and Solve, Research, Creation, Local, and more. This helps publishers move beyond simply seeing which queries triggered citations and begin understanding the broader query context our systems associate with those citation appearances." "We are also introducing Topics, which group related grounding queries into broader thematic clusters. AI systems reason across concepts and themes rather than isolated keywords. Topics help publishers understand visibility in the same thematic structure that modern AI systems use to organize information." "While total citation counts show how often your content appears in AI-generated answers, Citation Share shows how much of the citation space your site receives for a specific grounding query. It is calculated as the percentage of citations attributed to your site out of all citations shown across all sites for that same grounding query." https://t.co/FEoz44fFxz
- 10
@semrush ·
The work you put into social media SEO can now shape your brand's visibility across multiple search experiences beyond social platforms, including AI Overviews, AI Mode, ChatGPT, and more. For years, "social media SEO" meant winning in-platform search and appearing in the feed. Brands optimized captions, hashtags, and bios to climb TikTok's For You page or surface in Instagram's Explore. That still matters. But using social media to influence external search visibility and shape how LLMs talk about your brand is now just as important. Our AI visibility study found that Reddit, LinkedIn, YouTube, Facebook, and Instagram are among the top domains LLMs cite. When a buyer asks ChatGPT "which managed WordPress host should I pick," answers are often pulled from Reddit threads, YouTube tutorials, and LinkedIn posts https://t.co/mI3b6Yg5V8.
- 11
@ShubhAgrawal26 ·
something really basic I did today that made me feel like - "damn! AGI is almost here and technology has gone so far" I was building some content and growth infra which had an elaborate directory of thousands of LinkedIn posts, tweets, newsletters, and internal docs inside an Obsidian vault on my Mac mini. I then had to visit a hospital to meet a friend for his surgery, while in the hospital, I had my macbook with me. I realized I needed this obsidian vault's data to connect with ahref's, google search console and promptwatch - check our AI visibility and self create structures of blogs for AEO from existing past content, by identifying whatever is missing or noot ranking in our sitemap. but this vault was on my mac mini at home , which is also where I had the claude code instance running. I was able to ask claude code via remote control using my phone -> make a copy of the vault and paste it from my local mac mini folder to my icloud -> opened this vault on my macbook and complete the entire task. insane!! we're truly living in the future API's and MCP's fetch real time metrics and data from SaaS tools markdown files that seld update with relevant company context Agents that can control and take actions on your computer while you run them from ur phone complete analysis to great results end to end. beautiful
- 12
@emilylai ·
Playing around with an AI visibility tool and looking into what people prompt into gpt, claude, perplexity For "make money" there's a few prompts around clipping, ugc, and content creation For "Ethereum" the first one is "Will Ethereum's usage increase if Base gains more traction" lol alongside prompts asking ai to help with announcements and articles For "Solana" the first one: "Provide a detailed trading strategy using MobyScreener for Solana." and other prompts around price predictions For "crypto" i'm seeing all sorts of prompts from troubleshooting to tax questions if certain exchanges are available in geos
- 13
@semrush ·
AI search platforms like ChatGPT, Google AI Mode, and Perplexity are changing how content gets discovered. But what makes one piece of content get cited while another gets ignored? To answer this, we analyzed thousands of citations and compared them to similar pages ranking in Google. Our goal was to identify which text-only qualities most strongly correlate with AI citation behavior, and whether these patterns differ from traditional SEO signals. Based on our research, we found five content qualities that showed a strong positive correlation with AI citations, plus one that showed a negative correlation: • Clarity and summarization: +32.83% • EEAT signals: +30.64% • Q&A format: +25.45% • Section structure: +22.91% • Structured data elements: +21.60% • Non-promotional tone: -26.19% In short: content that delivers clear answers, demonstrates expertise, and uses structured formatting is more likely to be cited. Full breakdown: https://t.co/iv0Gzu2BYl.
- 14
@jmoserr ·
"This GEO tactic gets you cited in LLMs!" "Interesting. How do you know that...?" "My research bro. I analyzed 10,000 prompts bro. All of the brands cited in ChatGPT have pages with expert quotes, statistics, inline citations, structured formatting, and topical depth…" "Ok…sure, but did the page get cited because it had those features? Or did it have those features because it was already high-authority content that performs well everywhere...and AI search just picked it up too?" Nobody peddling GEO fairy dust wants to answer this question. Right now, a huge chunk of GEO research is treating correlation like causation. Like these content traits are brand new levers you can pull to "get cited in AI search." Do X, get cited. Simple. Except: if everyone can just add charts and tables to a post and get cited, how does AI determine YOUR brand gets mentioned in a short output of 5 solutions in a space where thousands exist? It can't just be "do X tactics." That math doesn't work. What actually separates the brands getting cited? The same thing that's always separated them: real authority. Google's own "How Search Works" documentation states openly that links are a core signal for content quality. AI is very similar. HubSpot found that 92% of AI mentions come not from your own content, but third party sites mentioning you. So when someone says AI engines cite content with quotes, data, and good structure, they're simply observing a trait of authoritative content in general, not a "do x and get Y" causation factor. And even if these content tweaks do influence AI citation likelihood, SparkToro's recent research found there's less than a 1 in 100 chance that ChatGPT or Google AI will give the same list of brand recommendations in any two responses...even on the identical prompt run 100 times. The lists are different. The order is different. The number of results is different. Nearly every response is unique...so the agencies selling revolutionary GEO hacks are building on two broken assumptions: that these tactics directly cause citations (unproven), and that the outputs are consistent enough for those tactics to track reliably (they're not). AI search is real. It matters. Heck, we generate tons of leads from it. But the brands winning there are the same ones that have been winning everywhere... because they built actual authority over years, not because they added a data table to a blog post last Tuesday. Our own AI visibility increased the most by doing one thing: acquiring mentions + links on publications talking about our niche: being in more places that AI pulls from, increasing the likelihood AI mentions our brand.
- 15
@levikov ·
Writing for LLMs is the most valuable and most ignored skill on the entire internet right now and the operators who master it in the next 12 months will control where every AI-generated recommendation points for the next decade… When someone asks ChatGPT or Perplexity or Claude "what's the best tool for X" or "should I buy Y" the AI doesn't browse the internet in real time and make a fresh judgment call. It synthesizes information from sources it was trained on and sources it can access. If your brand, your product, your name isn't in those sources in a way the AI can understand and extract, you literally don't exist inside the world's fastest-growing discovery channel Google searches are declining for the first time in the platform's history. Not because people stopped looking for information. Because they started asking AI instead. And AI doesn't show 10 links. It gives one answer. Maybe two. The brand that AI names first gets 80-90% of the attention. Second place gets scraps. Third place doesn't exist This is a completely different optimization game than SEO. With Google you could rank for hundreds of keywords and get traffic from all of them. With AI answers you either ARE the recommendation or you aren't. There's no page 2. There's barely a page 1. There's the answer The people figuring this out right now are doing things that sound boring but print stupid money. They're publishing original research that AI systems cite as sources. They're building structured data layers on their product pages so AI can parse specs without guessing. They're getting their brand mentioned in Reddit threads and forums that LLMs scrape as training data. They're making sure that when an AI system needs to recommend something in their category, their brand is the most referenced, most cited, most structured option available I know someone in the project management software space who figured this out 8 months ago. Instead of spending $80k/month on Google Ads he hired 3 people full time to do nothing but create original comparison content, publish detailed methodology pages, and plant structured recommendation threads across Reddit, Quora, and niche forums. His brand went from appearing in 0% of AI-generated software recommendations to appearing in roughly 40% within 6 months. Organic signups from AI-referred traffic are now his largest growth channel. Cost: $18k/month for the team. Revenue from the channel: $120k+/month. The CAC is absurd The wildest part is that LLM training data creates a compounding moat. Once your brand is embedded in the training data that these models learn from, removing it requires retraining the entire model. Your competitor can't just outbid you the way they can on Google Ads. They'd need to generate MORE mentions, MORE citations, MORE structured data than you across the entire internet. First mover advantage in LLM visibility is the most durable competitive advantage in marketing right now because the switching cost for the AI itself is astronomical (btw this is also why the Reddit SEO play I've talked about before is getting 10x more valuable. Reddit threads don't just rank on Google anymore. They get scraped into LLM training data. A well-crafted Reddit post recommending your product doesn't just drive Google traffic for 2 years. It potentially gets baked into the AI's knowledge permanently. One post influencing millions of AI-generated recommendations for years. The ROI on that is incalculable) the entire marketing industry is still optimizing for an algorithm that shows 10 links while the discovery layer that shows 1 answer is eating everything. the window where you can cheaply establish yourself as the AI's default recommendation is 12-18 months. after that the training data moat makes it almost impossible to displace the incumbents whoever owns the AI's answer owns the customer. and right now almost nobody is even trying
- 16
@neilpatel ·
20 years of SEO obsession over backlinks. Turns out branded mentions on credible sites predict AI visibility better. That's not a small shift. That's the whole strategy flipped. #SEO #AIOverviews #ContentMarketing #DigitalMarketing
Watch video
- 17
@aleyda ·
🤖 How AI Systems Surface Your Brand: From Inputs to Outcomes 👇 Before a brand can perform well in AI answers, AI systems need three things from it: 1. Access: They need to be able to reach and process your content. 2. Usefulness: They need relevant content worth reusing. 3. Recognition: They need enough signals to connect that content to a credible brand. If one of these inputs is weak, your chances of being included, cited, or represented accurately are lower. Once those three inputs are in place, you can assess the four outcomes they produce in AI answers: 1. Presence: Do you show up? If your brand does not appear in relevant AI-generated answers, the rest matters less. 2. Position: How prominent are you? Not all visibility is equal. A brand that is central to a recommendation, comparison, or cited explanation has more impact than one that is mentioned in passing. 3. Perception: How are you described? A brand can be visible and still lose if AI systems describe it in weak, generic, or inaccurate ways. 4. Influence: Do you shape the answer? Influence is the highest level. It goes beyond being mentioned. It's when your brand's data, concepts, definitions, or frameworks help shape how the category is explained. I'll be going through how to optimize and measure these through an AI Search Brand Authority framework in the following days that I'll share over here and in more in seofomo(.)co (subscribe now to avoid missing out) 👀
- 18
@elvissun ·
"is this shit legit?" run this prompt against your website right now. here's why: last week, we shipped https://t.co/aq7DBz7en5 as open source, and the install flow asks people to point their agent at a remote script via curl | bash. modern agents are smart. they will not execute random code on your machine just because you asked nicely. so i tested this by running the install in a fresh claude session and watched it interrogate my own brand. round 1: agent flags the install command as high risk. fix: link to where the install command actually lives. round 2: agent says the website could be hosting a different script than the repo. fix: open source the entire website. round 3: agent says the authors are unknown. fix: link my x, linkedin at the bottom of the page. round 4: agent says there aren't enough trust signals. fix: ship the thing, get real users, stars catch up. every round was the agent doing what a careful journalist would do before running a pitch. check the source. check the source of the source. check the byline. this is the test everyone should run on their own brand. open your site in claude right now. ask the agent if the company is legit in the most skeptical way possible. watch it research. watch it doubt. read the gaps in its reasoning back as a list of missing evidence. that list is your AI visibility roadmap. not "more press." specifically: which signals does the agent need before it stops being suspicious of you. trust used to be a feeling. now it's a checklist a model walks before it lets your name through the door. welcome to the new earned media.
- 19
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@gaetano_nyc ·
I just took a VP of marketing through a “LLM visibility monitoring” dashboard to reveal all the ways they are getting tricked in the reporting. 1. Filter includes brand prompts which inflates their “share of voice” score. 2. Visibility dashboard shows a blended “share of voice” where they already have high recommendation rates for their flagship category. 3. Company has a strategic priority to break into a new category, which is not being tracked or monitored. This is marketing’s way of not reporting on a “bad” visibility score. 4. Prompts are biased and contain seeded phrasing where the brand will be dominant by default. Example: they are a compliance tool and the prompt leads with “For a VP of compliance at a mid-market company…” These tools are highly manipulate-able and executives don’t understand how it all works.
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@RobHoffman_ ·
don't buy into the hype on X that spamming SEO articles with Claude will get you traffic/leads. our SEO client made $180,000 last month and we didn't create any new pages. We just optimized their site architecture. here's how: the client is a managed IT and security compliance company. the problem: they were trying to rank for everything at once: managed IT, security compliance, and more, with no clear hierarchy between them. here's how we fixed that and got their money pages ranking: 1. Focused the homepage on their most valuable keyword: "managed IT service provider" 2. Created dedicated pages for their core compliance and security services 3. Rebuilt the heading hierarchy to signal topical relevance 4. Rewrote entire sections of content to read like a buying page, not a blog post 5. Added a localized "Why Choose Us" section to their content for trust 6. Built an FAQ section covering both branded and unbranded high-funnel queries 7. Used Mentions to see what phrases were getting the most AI visibility, then optimized their content around those to rank in LLMs (that last one matters more than most people realize). The lesson: before you publish more pages, make sure your site is optimized.
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@jbobbink ·
I analyzed who Google AI Mode actually cites. The antitrust case just wrote itself. SE Ranking studied 68,313 keywords across 20 niches. They looked at 1.3 million citations inside Google's AI Mode answers. The finding that should make every SEO uncomfortable: Google(.)com appears as the source in 17.42% of all citations. Nearly one in five sources in AI Mode points back to Google itself. Add YouTube and it climbs to roughly 20%. Google is building an answer engine that treats its own properties as the most authoritative source on the internet. That is not a search engine. That is a closed loop. Here is where it gets worse. Ahrefs just published new data on AI Overviews using 863,000 keywords and 4 million URLs. In July 2025, 76% of pages cited in AI Overviews ranked in the top 10 organic results. By early 2026 that number dropped to 38%. Cut in half in eight months So Google is simultaneously citing itself more and citing top-ranking pages less. If you built your entire strategy around ranking on page one to earn AI visibility, that bet just collapsed. The timing is not a coincidence. Google upgraded AI Overviews to Gemini 3 globally on January 27. SE Ranking found that Gemini 3 replaced about 42% of previously cited domains overnight. Your citations are not earned. They are rented. And Google just changed the landlord. Meanwhile, the European Publishers Council filed a formal antitrust complaint with the EU in February 2026. Their argument is simple. Google uses publisher content to generate AI answers, then cites itself instead of the publishers who created the original information. The numbers support the complaint perfectly. Separately, eMarketer found that fewer than 10% of sources cited in ChatGPT, Gemini, and Copilot rank in the top 10 organic results for the same query. BrightEdge research shows the overlap between top Google links and AI cited sources dropped from 70% to below 20%, currently at 17%. Traditional SEO rankings and AI visibility are becoming two completely different games. And in the AI game, Google gave itself home court advantage. This is not a ranking problem. This is a market structure problem. When the platform that controls 90% of search also controls the answer layer and cites itself as the primary source, the word for that is not optimization. The word is antitrust.
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@foley_seo ·
Businesses desperate for AI SEO / GEO need a reality check. This isn't funny this is REAL data, and it's not isolated either. LLMS delivered such a miniscule amount of traffic to so many sites that I had to hide GSC clicks from the chart because they make all LLM traffic non-legible to read. 100+ websites, 40 of which are "high traffic" - 100k+ clicks per month: ➜ Aggregated LLM traffic accounted for less than 5% of ALL clicks ➜ chatGPT showed the greatest volume of clicks to websites across all 100+ websites where there was sufficient data ➜ Grok, Copilot and Perplexity showed the lowest traffic throughputs With organic click decline or stability - NONE of the websites had any degree of meaningful compensation from LLM traffic. ➜ Whilst LLM traffic generally accounted for less than 5% of all clicks, conversion rates were significantly poorer averaging 0.2% to 0.5% compared to organic's average of 2.75% ➜ 15 of the 100 websites showed sustained LLM click growth but, this dropped off recently suggest chatGPT has reduced link prominence or that the ads on free tier are now impacting click throughput further I'm still dumbfounded how much HYPE and INVESTMENT has gone into LLM prompt tracking and OVERALL VISIBILITY scores when the GA4 data suggests otherwise.......... That's NOT to say that AI/LLM traffic sources aren't emerging, but, the heavy shifts we see in "SEO focus" towards "LLM focus" just isn't warranted that much at this point. I am PRO getting to a party early, so I'm not downplaying AI citations being important, but I think a lot of people are sacraficing budget and effort on SEO to chase "the shiny thing" because it's new. Sales, conversions and clicks would suggest otherwise............
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@BritneyMuller ·
The AI Search Gold Rush: What's Real vs What's Snake Oil? 🐍 @MiaRSato at The Verge wrote one of the most thorough pieces I've seen on what's actually happening in SEO + AI Search right now. Grateful to have been included. Most coverage hypes up AI or dismisses it. This stays neutral while going deep into research + educating the audience: → Those self-serving "best of" listicles gaming AI results? They're a search information retrieval problem, not an AI one. Google is working to clean them up. → Anyone claiming they can "influence AI" in guaranteed ways is selling you something. The AI and Search companies themselves are still figuring this out, we all are. → Third-party mentions, even without a link, are becoming more influential than ever. Gartner predicts PR + earned media budgets will double by 2027 specifically because of this shift. → Traditional SEO and AI visibility are still correlated. The brands showing up in AI aren't abandoning the basics; they're nailing & expanding upon them. → @randfish makes the powerful point that AI search is getting 10-100x more attention than the actual usage warrants. Traditional search still dominates desktop. @sparkto data also shows Amazon, Bing, and YouTube each drive more search activity than ChatGPT on desktop right now. The full piece is worth your time; Mia also digs into what's actually working for retailers tracking AI visibility, and the OpenAI ads backlash section is wild!! Link below 👇
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@yusukelp ·
I turned my AI visibility dogfooding for LandingBoost into a local dashboard. It now tracks: - Google index status - GSC movement - search positions - Reddit/source rankings - AI mentions/citations across Perplexity / OpenAI web search / online models - next actions to improve visibility Current state: - 13 dogfood runs logged - 4 target pages indexed by Google - LandingBoost is #7 for “landing page audit tool for saas” - Perplexity mentioned/cited LandingBoost for “best tool for a SaaS landing page audit” - OpenAI/Google-style answers are still not there yet This is the loop I wanted: 1. publish/edit pages 2. measure search + AI answers 3. see what sources are being used 4. decide the next page/FAQ/comparison edit 5. repeat Still early, but this finally feels like a system instead of random GEO guessing.
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@SEOKeval ·
A lot of people are running AI SEO strategies that are NOT future-proofed. "Oh, you just need to blast listicles?" *Proceeds to blast listicles on 100 bullsh*t PBN sites with no authority or keyword rankings* Yeah, that works right now. But, come on... Do you really think that will last? Do you really think Open AI, Anthropic and Google won't realize people are gaming the system that way? Doing that is akin to ranking sites in Google with a volume (not quality) approach to link building. Yeah, it worked for a while. But then all of sites that utilized that tactic tanked. It's absolutely in your best interest to run AI SEO strategies the right way. Post listicles on real sites. Make sure those sites actually rank well. Make sure those sites have good authority. Yeah, it'll be more expensive for you. But it'll significantly increase the odds of your AI visibility standing the test of time.
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@yusukelp ·
I started dogfooding AI visibility for LandingBoost. Perplexity already cited it once. Now I’m trying to make it repeatable across ChatGPT / Google AI / search. The loop: - find real landing page audit queries - improve pages / FAQ / schema - test AI answers - track what gets indexed, cited, or ignored - join relevant Reddit conversations Tiny signals so far: - the new AI landing page audit tools page started getting visitors - Google Search Console is showing more impressions - after ~1 useful Reddit comment/day, I got my first Reddit visitor lol Not a win yet. But the loop is finally visible in the data. If this works, the next post will be: “I got LandingBoost mentioned by ChatGPT/Google AI. Here’s exactly what I changed.”
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@peec_ai ·
AI content tools sell you the dream of LLM visibility. We checked what actually happens to their clients. We looked at the reference customers of several popular AI content generation tools. The ones they feature on their websites. The case study interviews. The success stories. 57% of them tanked in Google (Rank & Tank). The pattern is consistent: visibility spikes, then crashes. Some never even got the spike. They went from flat to down and skipped the peak entirely.
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@peeplaja ·
We surveyed 100 CFOs and VPs of Finance on how they buy software. The first question: where do you START the vendor search? → 38% ask peers / their network → 26% go to LLMs (ChatGPT, Claude, Gemini) → 20% Google it → 9% hit review/analyst platforms (G2, Gartner) → 7% go to known vendors directly Finance people trust their peers above everything else. LLMs have overtaken Google as the #2 starting point for software vendor search. Here are examples of what the CFOs told us on how they use LLMs: "When we are looking for a vendor, first I usually ask Gemini or ChatGPT to give me an overview of the available vendors, pros and cons." "I would describe to a LLM the nature of my business, desired outcome, and experience in attempting to solve the problem to date and inquire for suggested solutions." "Use LLM to produce a paper on options, feedback and cost. LLM will also help team to produce a tender pack." These aren't early adopters. These are CFOs running the buying process. What this means if you sell to the finance function: 1. Peers still dominate. Customer advocacy and CFO community presence aren't "nice to haves", they're your primary discovery engine. 2. Your SEO strategy alone won't cut it anymore. LLM visibility is now a parallel channel you need to win. Cold outreach? Not even on this chart. Zero respondents said "I start by looking at my inbox." Zero. The buying process is bifurcating: Trust channels (peers, network) → for shortlisting AI channels (LLMs) → for market mapping Google → still there but no longer alone
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@TheCoolestCool ·
Pro Tip: If you're using the default prompts that an LLM / AI Visibility tool is giving you... with no customization: You're tracking & working against AI-Slop prompts. I've seen 10 accounts this month filled with prompts that make absolutely no sense to track... This is why SEOs need a seat at the AI visibility table. SEOs understand intent. A lot of the new-age marketers have no clue what to track because they don't know how people search or discover products. It's a shame.
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@AgenticOperator ·
Got a message yesterday morning that made the last 3 months worth it. A SaaS founder I've been working with sent me his numbers. His product now shows up in roughly 90% of relevant prompts across Claude and ChatGPT. Renewals jumped 30% this quarter. Traffic and revenue both way up. A few months ago he was invisible. Same product. Same market. Zero AI presence. Here's what was actually broken. His product was solid. Customers loved it. But when a buyer asked AI "best tool for [his category]," AI had nothing to work with. His site was built to convert humans. Every page was a sales pitch. No page answered the question AI was being asked. His competitors weren't better. They just had one clean comparison page, a few Reddit threads from real users, and product data AI could actually parse. That's all it took to win the recommendation. What we did wasn't complicated. Rebuilt his key pages so each one answered a specific buyer question instead of pitching. Got his existing customers talking on Reddit and review platforms. Made sure his product data was structured for machines, not just humans. Added honest comparison content his team was scared to publish. No magic. No secret framework. Just making the brand visible in places AI actually looks and giving it something worth citing when it gets there. The part that surprised him most: Google rankings didn't change. Same positions. Same traffic. The entire lift came from AI visibility alone. A channel his team wasn't tracking 4 months ago is now driving 30% more renewals. He texted me "your AEO stuff is legit working." Honestly that's the whole pitch for this space right now. It works. Most brands just haven't started.
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@mehrab_build ·
Ahrefs tracked 1,885 pages that added schema The result? AI citations barely moved :)) Schema is on 53% of AI-cited pages, but those sites also have great content, authority, and backlinks! Next time a LinkedIn guru starts preaching about schema being the key to AI visibility, drop this study in the comments 🙂
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@profitfounder ·
I published one listicle on Medium and another on LinkedIn. Both ranked fast on Google. One was cited in Google AI Overviews in about 72 hours. Tanya's point: AI visibility can move faster than classic SEO, especially in a noncompetitive niche.
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@sharyph_ ·
Want to rank in search and get cited by AI? Great. Here is all I know about this 👇 It takes 6 months. Not 6 weeks. Not 1 viral post. 6 full months of consistent, optimized publishing. Here's what most creators do: → Optimize 3 posts → Check analytics after 2 weeks → See no traffic spike → Assume SEO doesn't work → Quit Here's what actually happens: Month 1-2: Google crawls, no rankings yet Month 3-4: A few posts start appearing on page 2-3 Month 5-6: Rankings improve, AI citations start, traffic compounds I optimized 12 posts over 4 months. Months 1-2: Nothing. Month 3: One post ranked page 2. Month 4: Three posts ranked page 1. AI started citing them. The people who win aren't the ones who publish daily. They're the ones who publish strategically and stay consistent. SEO isn't a tactic. It's infrastructure. You don't see results from infrastructure in week 1. You see results in month 6, then every month after. If you're not willing to commit 6 months, don't start. But if you are? Your back catalog becomes a lead generation machine. Your content works 24/7. Your authority compounds monthly. That's the bet. Most creators won't make it. They'll chase the next shiny tactic in week 3. Which means the ones who stay the course will dominate.
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@RandallKanna ·
A successful mentor in tech once told me it's more important to do one thing well than many things poorly. And that's what I'm working on right now in my startup. For us, it's accuracy. AI visibility tracking can be a mess. Every answer can change based on the model, prompt, context, wording, user, etc. We’re not just running a prompt once and pretending that is the “truth" to our users. We're doing a few things differently to be a source you can trust: 1. We run prompts multiple times to get a more reliable baseline. 2. We track results historically so brands can see whether they are gaining or losing visibility over time. 3. We added persona types (today) so you can understand how your brand shows up for different customers. And we’re continuing to build around the idea that AI visibility is not about one perfect answer once. It’s about patterns. - Are you being mentioned more often? - Are competitors showing up ahead of you? - Are you being described accurately? - Are AI answers pulling from the right sources? - Are you visible in the kinds of searches your actual customers are making? That’s what Kelsey is focused on. Helping brands understand how they appear in AI answers, why they appear that way, and what to improve next.
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@therealobsty ·
Your site has 0 AI citations in 2026 and you still think you're doing SEO? Bro you're cooked This 4 months site has: - 12,000 organic visitors/mo -ChatGPT citing them 3,500 times -Google AI Overviews, Perplexity, Gemini all recommending them And all they did was setting up one Claude Code prompt + Arvow automation via api that took less than 15 mins Meanwhile you're still writing "what is [keyword]" blog posts like it's 2019 and wondering why your traffic is flat Every week you wait is a week someone else is getting recommended instead of you And LLMs don't rotate results like Google does But yeah keep doing keyword research in spreadsheets I'm sure that'll work out great 🤡
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@msftClarity ·
If you’re still measuring performance the same way you did two years ago, you’re missing part of the funnel. AI assistants are reshaping discovery and influencing decisions before users ever land on your site. Traditional KPIs aren't designed to capture that impact. From AI referral traffic and AI citations to the metric that ultimately matters, AI referral conversions, we break down five forward-thinking KPIs to help you measure AI’s role in growth. 📊🤖 🔗 Explore 5️⃣ KPIs for an AI-mediated web: https://t.co/1LFID1Z5AG
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@kaleighf ·
Why I'm leaning into YouTube for AI visibility: YouTube shows up in roughly 16% of AI answers, ahead of Reddit's 10% in cross-company data. Google's own AI features love it most: over half of their social citations come from YouTube. Perplexity leans on video heavily too (38.7% of its social citations). Two data details: 1. 94% of YouTube citations go to long videos, not Shorts. 2. Popularity doesn't drive it: a 40,000-subscriber channel that answers a question thoroughly beats a 4-million-subscriber channel that entertains around it.
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@JulianGoldieSEO ·
Google just made business dashboards feel ancient. Local business owners who ignore this are going to waste hours doing work Gemini can now handle in one sentence. What Changed: → Gemini now connects directly to Google Business Profile → It can read your reviews, customer questions, profile data, and performance stats → You can ask about calls, direction requests, search impressions, and customer engagement without opening another dashboard The Real Time-Savers: ✓ Draft review replies based on the exact words customers used ✓ Update holiday hours, menus, photos, attributes, booking links, and ordering links from chat ✓ Spot unanswered customer questions before they quietly cost you leads The Bigger Upgrade: ✔ Business Notebooks remember your website, profile, sources, and chat history ✔ Gemini can surface missed questions and forgotten holiday hours before you ask ✔ You stop managing fields and start asking business questions Practical lesson: Your Google Business Profile is no longer just a listing. It’s becoming the control panel for local AI visibility.
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@donnellycss ·
Over the past six months, I’ve gone deep on learning how to get companies cited by AI. If you’re looking for some immediate fixes, these should be your priority: ✅ Test your AI visibility ✅ Add basic schema ✅ Fix meta tags But if you want to get it right, I've pulled together eight quick wins to make your website AI-Ready in 2026…
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@TheCoolestCool ·
Your AI visibility strategy is probably misaligned. Why? Because every SaaS vertical has a different “citation fingerprint.” Example: - Fintech → affiliate sites dominate - DevOps → dev communities - Healthcare → institutional sources One playbook won’t work. https://t.co/dQBGmKkheW
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@kristakdoyle ·
Easing into my day with a giant coffee and @Reddit's Cannes panel called "Get Fluent in Fangirl" 🔥 The fangirl mindset and its focus on... 👯♀️ community 🙋🏻♀️ authenticity 🫡 loyalty 🫶 belonging and identity over transaction ...is what's gonna help marketers, especially SEOs, survive this next era of brandom-led search. I started Fan Out on the bet that the future of AI search visibility will be powered by fandom, brandom, and advocacy on third party platforms like Reddit, YouTube, LinkedIn, Quora, and more. Super excited to see these concepts taking root on the broader stage (literally) from Sara Wilson and panelists 🙏 I wrote a bit about this last month. Here are some thoughts on why SEOs should become fandom experts in this next era of search 👇 https://t.co/HrPbPzBaPL
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@RandallKanna ·
Everyone is talking about “ranking in ChatGPT,” but most teams still have no idea why competitors get recommended instead of them. That’s exactly why I built Kelsey. Instead of vague AI visibility scores, Kelsey shows: 1. The results for prompts that customers ask AI 2. Which competitors get recommended instead of you 3. The URLs influencing those recommendations (Reddit, review sites, listicles, blogs, etc.) 4. Whether you’re already mentioned 5. The specific citation gaps costing you visibility And most importantly.. “What content, mentions, or sources do we need to win?” AI search visibility is becoming an acquisition channel. Most brands just can’t see the map yet.
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