AI citations and recommendations
How AI Overviews, AI Mode, ChatGPT, Perplexity, and other answer engines select, cite, and recommend sources and brands.
52%
Best tweets about AI Search
50 Best Tweets About AI Search (2026)
Explore the best tweets about AI search, including AI Overviews, answer engines, citations, search behavior, traffic changes, and visibility strategy.
Evidence-based discussion of AI search products, citations, user behavior, publisher traffic, measurement, and search strategy.
- Creators
- 36
- Updated
Original Xholic analysis
What 50 top AI Search posts reveal
Discussion emphasizes citations, retrieval-oriented content, brand and third-party validation, and the measurement challenges created by AI search interfaces. Posts offer competing views on the relationship between conventional SEO and AI citations, and on whether AI-referral conversion comparisons are methodologically fair. The supplied evidence also repeatedly cautions that correlations between content tactics and citations do not establish causation; reporting and attribution capabilities are still developing. [2034728233154105838, 2032463564775047248, 2038997828190589375, 2077027856345874894, 2038756520314449952]
- Dominant tone
- Positive
- Median score
- 12.0
- Leading format
- Announcement
- Recent posts
- 46%
30% of posts
All-time engagement
100% of posts
Published in 90 days
Conversation map
The themes creators return to
Content design for AI retrieval
Content formats and retrieval-oriented publishing practices: direct answers, extractable sections, lists, comparisons, freshness, and information gain.
32%
Traffic, zero-click, and publisher impact
AI search traffic, zero-click behavior, CTR losses, referral conversion quality, attribution, and the changing publisher economics of search.
28%
Off-site authority and earned media
Third-party validation through PR, journalism, creators, Reddit, YouTube, reviews, links, and other off-site sources.
24%
SEO–AI search relationship
The relationship between conventional rankings and AI visibility, including evidence that overlap varies by platform, query, and experiment.
22%
AI search measurement and reporting
Measurement of AI visibility, including citation tracking, prompt studies, platform reporting, Search Console, Merchant Center, and attribution gaps.
20%
AI search strategy and operations
Practical AI-search strategy: query and competitor audits, citation-gap analysis, content and authority prioritization, and testing workflows.
18%
Brand authority and entity signals
Brand positioning, entity consistency, product quality, trust, authority, reviews, and differentiation as inputs to AI recommendations.
18%
Tone and stance
Sentiment Positive leads
Author posture Observational leads
Performance benchmark
- Median likes
- 35
- Median reposts
- 5
- Median replies
- 7
- Median views
- 3.8K
Posts with media make up 72% of this collection. Their median all-time score is 13.5, compared with 5.80 for text-only posts.
Format mix
- Announcement 100% · score 12.0
Consensus and debate
Where creators agree, and where they do not
Shared view
Retrieval favors usable content
Several posts converge on clear, self-contained answers, structured sections, and extractable content as useful characteristics for AI retrieval, while pairing them with credibility and accessibility rather than presenting formatting as sufficient alone.
Shared view
Authority is central but vulnerable
The conversation repeatedly frames brand trust, corroboration, positioning, and product quality as important to AI recommendations—and warns that weak or fabricated signals can distort outputs.
Shared view
Visibility measurement remains incomplete
Measurement is advancing through emerging Google reporting, but the posts flag limits: Merchant Center’s rollout is limited and Search Console reporting lacks some granular metrics, while URL tagging is proposed as a practical attribution improvement.
Open debate
Is AI visibility just SEO?
Posts disagree on how closely conventional rankings map to AI citations: some experiments report citations beyond strong Google visibility, while another argues strong SEO naturally supports AI citation growth. The shared evidence points to platform- and case-dependent overlap.
Open debate
Traffic volume versus conversion value
The thread distinguishes low AI referral volume and declining organic clicks from lead quality. Some posts describe AI referrals as small but higher converting; others argue such comparisons confound bottom-funnel AI intent with all-stage organic traffic.
Open debate
Tactics face a causality test
Evidence-based caution is a recurring counterweight to tactical claims: correlations between content traits or schema and citations do not establish causation, and one before/after schema test reported no measurable citation lift.
What performs
Patterns behind standout posts
Top engagement spans risks and strategy
The highest-scoring outliers span manipulation risk, strategic positioning, earned-media claims, retrieval-oriented content, and the SEO–AI relationship. The largest outlier is the post describing how a fabricated page was repeated by AI systems.
Media posts outperformed text
Deterministic analytics show media posts had a higher median all-time score than text posts (13.47 versus 5.8), and 72% of the 50-post set included media. The cited posts are among the evidence set’s engagement outliers.
Statistical standouts
- View standout post 1 Score 3307.3 · 275.61× median
- View standout post 2 Score 252.1 · 21.01× median
- View standout post 3 Score 178.5 · 14.87× median
- View standout post 4 Score 167.6 · 13.97× median
- View standout post 5 Score 87.5 · 7.3× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
-
1. Aleyda Solis 🕊️
@aleyda
2 posts
-
2. Charles Floate 📈
@Charles_SEO
2 posts
-
3. Cody Schneider
@codyschneider
2 posts
-
4. Klaas
@forgebitz
2 posts
-
5. Gagan Ghotra
@gaganghotra_
2 posts
-
6. Glenn Gabe
@glenngabe
2 posts
Solis prioritizes brand fundamentals
Aleyda Solis emphasizes that recommendation visibility depends on a brand being accessible, recognizable, corroborated, credible, differentiated, and current; technical and content work cannot fully offset weak product or trust signals.
Floate focuses on retrieval gaps
Charles Floate focuses on retrieval mechanics: identifying answer gaps, creating standalone answer chunks, and interpreting information gain as relevant to the evolving AI-search environment.
Schneider proposes an operating loop
Cody Schneider advocates an operational loop of query research, citation-gap analysis, structured content, monitoring, and refreshes, while also emphasizing bottom-funnel comparison and alternative queries.
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 Search tweets
Top AI Search tweets from 36 creators
Ranked 01–50
- 01
@jakezward ·
> Be this BBC tech reporter > Spend 20 minutes writing a fake blog post > Claim you're the world's #1 hot dog-eating journalist > Invent a fake championship to back it up > Watch ChatGPT and Google repeat it as fact within 24 hours > Realise you just manipulated 2 of the world's most powerful AIs with a single page > Find that users trust AI more than websites because it feels like the answer is coming from the tech company itself > Prove that tricking AI in 2026 is as easy as tricking Google was in the early 2000s Now imagine doing this legitimately for your business. That's how you win AI search.
- 02
@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.
- 03
@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)
- 04
@Charles_SEO ·
We're now being cited in Google AI Overviews for core SEO terms.. right next to Moz and SEMRush 👀 No links, no authority play, just clever old school NLP SEO re-tuned for the new AI search interface... Here is the STEP-BY-STEP play: 1 - Reverse engineer SERP gaps; Your site needs to be able to rank in the traditional algo first, so pages where you are already in the top 100 are a must and the higher the start, the better the result. 2 - Identify the gaps between what THEY cover and what the AI Overview is trying to answer. 3 - Write a section on YOUR page that fills that exact gap, formatted clearly with a definition, example, or list that Google can easily extract as a standalone chunk. I added several more anchor text types using this exact method, "generic" wasn't the only one I targeted here. 4 - Make sure that chunk can stand alone as a complete answer to a sub-question within the broader topic. That's it! You're not competing for the whole page, you're competing for ONE chunk of the AI Overview. It's very similar to how we used to optimize for featured snippets, but with a lot more dynamic inputs required - It's usually not the one H2 anymore, you need to optimize (at least) several chunks to have the best chance of getting visibility. In this case, we were cited for the definition because our page had the clearest, most concise explanation with a practical example - Something our competitors either buried in paragraphs or didn't isolate properly. Unfortunately, we usually don't get to directly "win" a #1 spot anymore in these new SERPs, BUT the upside is you can now get visibility for queries your page would NEVER have ranked #1 for traditionally.. and you can get cited MULTIPLE times across the same AI Overview if you optimize several chunks well. Less clicks, more visibility and "brand awareness" - A lot of corporate marketers would actually prefer this anyway.... And this all rewards going broader and deeper on your pages. The more well structured, extractable chunks you have, the more chances you give Google to pull from you. The traditional algo is linear, this new search is much more dynamic, less ranking, more retrieval.
- 05
- 06
@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.
- 07
@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
- 08
@timsoulo ·
Google lost ~5% of traffic share in the past 10 months (35.11% → 30.53%). Everyone thinks AI search ate it. Well… ▪️ AI search: 0.22% → 0.26% (+0.04pp) ▪️ Social: 7.67% → 8.24% (+0.6pp) ▪️ Paid: 13.99% → 17.15% (+3.2pp) ^ that’s across ~75k websites in @Ahrefs’ panel. (HINT: visit chatgpt-vs-google(DOT)com to see more data) ... AI search gained almost no traffic share. And it makes sense. AI search is zero-click by nature. It answers questions, it doesn't send traffic. The real winner? Paid. Businesses are losing organic clicks from Google and compensating with ad spend. They have no choice. They still need customers on their websites. So Google pushes AI Overviews, organic traffic drops... and businesses respond by giving Google more money for ads. ..or at least that's my read on the situation. What's yours?
- 09
@codyschneider ·
stop overcomplicating this AI search is just SEO these are the only levers you can pull to actually move the needle 1. publish articles for bottom of funnel keywords and go to page 1 - 3 to get referral traffic find best x for y, x alternative, tools like x, better than x, apps like x scrape what is ranking page one currently define the product differentiation of your product versus what you just scraped write a blog post that includes your product and how it is different who it is for publish that article make sure it gets added to your site map for indexing pro tips - if you have a ton of posts on the site, on the sitemap have 100 pages per sitemap URL this will make indexing faster - make the page load extremely fast due to being extremely small and you'll get more crawl budget. ) 2. Get mentioned on citations that are already ranking for those same keywords, find the citations that are being used by AI search platforms se something like prompt watch to identify this export all the citations, then find the email addresses of the site owner or the individual article writer use instantly AI to cold email them asking to be included in the citation, you're probably going to have to pay to get this placement pro tip: - not all citations are created equal. If you map all the citations within your category, you're going to find that certain links get cited more often than others. So you can rank stack the citations by priority and this means that it is ok to potentially pay more for those citations because it will create a higher impact faster
- 10
@neilpatel ·
I tracked which brands show up in ChatGPT answers and which ones don't, and what I found should change how you think about search entirely. Google's top ten results used to account for 76% of ChatGPT citations. That number has dropped to 38%, and 90% of the pages AI cites now rank 21 or lower on Google. In this video, I break down the 5 patterns we found across 4,308 prompts and 1,161 citations, covering everything from site structure and content freshness to entity association and platform differences across ChatGPT, Gemini, Claude, and Meta AI. If you're investing in SEO and assuming it carries over into AI search automatically, it does not. Here's what actually works.
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- 11
@eric_seufert ·
Google's AI Overviews decrease outbound organic clicks by 40%. A new paper by researchers at Carnegie Mellon and the Indian School of Business finds that AI Overviews were triggered in roughly 41% of observed Google searches and, when triggered, reduced outbound organic clicks by about 40%. The presence of AI Overviews also increased the likelihood of a zero-click search by roughly 35%. The authors implement a field experiment using a custom Google Chrome extension. Users with the extension installed were randomly assigned into three groups: 1) those who experienced Google Search with no changes, including AI Overviews when available (control), 2) those who experienced Google Search with AI Overviews dynamically removed and the remaining SERP elements shifted upward (treatment), and 3) those who were redirected to Google’s AI Mode when they attempted to use Google Search. The authors find that, for searches in which an AI Overview was triggered, users in the Control group clicked outbound organic links roughly 40% less frequently than users in the "Hide AIO" treatment group. They also find that Control users were roughly 35% more likely to conduct zero-click searches than users whose AI Overviews were removed. Further, the authors observe no statistically meaningful difference in sponsored clicks between the groups, suggesting that AI Overviews primarily substitute for organic publisher visits rather than ad engagement. This is consistent with my thesis in my Google's Gambit series: that Google would execute a wholesale, Ship-of-Theseus-like transformation of Search by introducing AI Overviews, transitioning it from a distribution mechanism to an engagement sink. Paper pre-print linked in comments.
- 12
@brodieseo ·
Ecommerce SEO News: Google Merchant Center is (surprisingly) the first Google product to give query data for AI Overviews and AI Mode. I now have access to the new AI performance insights for one of my sub-accounts for a client. Here's what you need to know about this rollout: The new report can be found under Analytics > Products > AI Performance. It is currently available to a limited number of accounts in the US. The reporting is purely for 'organic' visibility within AI Overviews and AI Mode, with the assumption being that the data is reflected for "free listing" results in Search. The reporting includes various sections related to Discovery, Evaluation, and Purchase, with some insights around query type, query frequency, and share of voice. It is quite interesting seeing "impressions" broken down into these 3 categories and the way that Google has described how queries are grouped under each e.g. in the Evaluation grouping, users search for product specifications, user reviews, and compare products. The most interesting aspect of the reporting is that there is a "frequently used AI shopping terms" section that shows your groups of queries for where products are surfacing within AI features. In its current form, similar to the recent rollout of AI reporting in Search Console, there isn't a great deal of actionability behind the data, though it is good to see at least some form of query data being included - something that has been lacking in GSC. The equivalent of this same dataset in GSC would be using the 'merchant listings' filter and having a way to filter for AI reporting, which doesn't exist within the current implementation. Looking forward to getting access to this new reporting for some of my large eCommerce accounts, where I'm sure there will be some interesting insights to uncover! h/t @rustybrick @hanakobzova
- 13
@neilpatel ·
We didn't guess this. We tested it: 500 keywords, 4,300 prompts, three platforms. The result: 75% of AI citations go to pages outside Google's top ten. Rank four on Google and you have a 2.6% chance of showing up in an AI answer. This isn't an edge case. It's the new normal. If you're not auditing which of your pages are getting pulled into AI answers, you're flying blind right now. #SEO #AISearch #ContentMarketing #SearchStrategy #GEO
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- 14
@gaganghotra_ ·
JUST IN 🚨 Google to show AI Search performance data in Search Console #SEO "To help you understand how pages from your site are shown, our new reports show the following information: Impressions: How often URLs from your site appeared in generative AI features in Search and Discover. Pages: Check which URLs appeared within AI features. Countries: Understand your visibility on a country basis. Devices: Identify the devices people are using when seeing your website (available for Search results). Dates: Monitor your performance over time with hourly, daily, weekly, and monthly granularity."
- 15
@Charles_SEO ·
Welcome to Day #11 of the Core Update 🚨 So far, we have seen: - The initial surge of listicle / parasite domains from the spam update falling back off again - Subdomain / canonical abuse getting more prevalent than ever, especially in international SERPs - The biggest winner so far? YouTube... - Reddit saw some initial declines, and is now GOING TO THE MOON, from our tracking this is because it hadn't rolled out as strongly internationally as it did in US/UK/CA but has now gone fully global 🌏 - Thin, low quality pages (and by extension, techniques like pSEO) are still being targeted - There seems to be large overlaps between these last two updates (and the testing we saw in the weeks leading up to it) and the August 2025 Spam Update - HubSpot, that got hit hard by Aug 2025 and several previous updates, has continued losing positions and traffic With all that being said, this update is still likely largely, or at least trying to, feed the ingestion of the AIOs; This is mostly through information gain scoring, aka, how much the content matches consensus vs. how much it's contributing new information against existing ranked sources. It's also getting increasing difficult to isolate variables with such a messy algo anyway... AI Overviews are increasing, again, and Google recently released research on "TurboQuant", a new compression framework that gives near instant retrieval for models while using significantly less processing resources - If they haven't already rolled out that tech for AIOs, they will do, which substantially lowers the cost to deliver and brings us one step closer to a default Google AI search experience.
- 16
@tibo_maker ·
Google gave Search Console its own AI visibility reports you can finally isolate impressions from AI Overviews, AI Mode, and Discover 2 things to know: - it's a phased rollout. only a subset of sites have it so far, starting with the UK - it shows impressions only for now. no clicks, no CTR, no queries yet. Google says more metrics are coming why this matters for founders: - AI Overviews now show on ~48% of queries. up from 31% a year ago - inside AI Mode, the zero-click rate hits 93%. users get their answer and never get to your sites - top-ranking pages see 58% lower CTR when an AIO appears - but brands cited inside the AIO get 120% more clicks per impression than uncited ones on the same query so citation is the new ranking - that's the whole game now but the play hasn't changed. nail the basic SEO, write genuinely useful content for your users, and get real contextual backlinks those are the only things getting you cited now it's exactly what we're building with Outrank
- 17
@aleyda ·
🚀 Remember how in SEO we say there's only so much we can do if the product is broken? AI search shifts this dynamic further: there's only so much optimization can do if the brand and product experience are weak. The core interaction model in traditional search is still largely a ranked list of sources, where users compare options and make decisions for themselves. AI platforms also compare, synthesize, and sometimes recommend. In traditional SEO, brand and product quality are strong multipliers. In AI search, they are increasingly closer to prerequisites, especially for prompts where the system is helping users evaluate options. You can improve technical accessibility, make content easier to extract, and build topical authority. But if the broader signals around your brand point to weak trust, poor satisfaction, or unclear differentiation, that can limit how often you're surfaced and how confidently you're described. There are still technical and content levers that matter. But when AI systems act more like recommenders than indexes, strong brand and product signals become much harder to compensate for. Welcome to the branding era of search.
- 18
@kimmonismus ·
I sat down with Robby Stein (@rmstein), Google’s VP of Product for Search, at @Google I/O. Robby is one of the most interesting product leaders in tech: he helped build Instagram Stories, Reels and Close Friends, and now leads core Google Search products including AI Overviews, AI Mode, Lens and ranking. We talked about one of the biggest shifts in the history of the web: Google Search becoming AI-native. Topics we covered: • AI Mode and whether it is an evolution of Search or a reinvention of it • how Google breaks complex questions into multiple searches behind the scenes • why AI search is much more expensive to run than traditional search • whether Google’s TPUs and infrastructure give it an advantage no one else can match • why Search volume is growing instead of being cannibalized by AI • the tension between great AI answers and traffic for publishers • how Google decides which sources and links to show • what a better internet could look like if AI Search works as intended The big question behind the whole conversation: If Google gives you the answer directly, what happens to the link-based web? A small caveat: sadly the microphones didnt work properly. Therefore the audio quality in this episode isn't perfect due to a recording issue - we appreciate your understanding.
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- 19
@aleyda ·
🤖 The 10 Key Characteristics of (Long-Term) AI Search Winning Brands 👇 What makes a brand consistently show up and get recommended across AI search platforms? After analyzing the patterns, these are the 10 characteristics that AI search winning brands share: 1. Accessible AI systems can only cite what they can reach. If your content can't be crawled, retrieved, and parsed by AI platforms, nothing else matters. 2. Useful AI systems tend to surface content that demonstrates clear utility beyond keyword relevance. If your content doesn't add genuine value, it is less likely to be surfaced. 3. Recognizable The stronger and more consistently reinforced your entity is across the web, the more likely AI systems are to identify and represent your brand accurately. 4. Extractable Your content needs to be organized in ways that machines can reuse, since many AI systems retrieve and process information in chunks. If your key insights are buried, they're unlikely to be surfaced. 5. Consistent AI systems build confidence through repeated and aligned signals across sources. The same positioning, terminology, and brand facts need to appear across all your digital touchpoints: your site, third-party profiles, directories, social platforms, and earned media. 6. Corroborated Independent sources need to validate your expertise and claims. Repeated references across credible sources strengthen the likelihood of inclusion. 7. Credible Visibility in AI search is supported by real expertise, evidence, and trust signals, not just claims. Brands with consistently negative sentiment or weak trust signals may be less likely to be recommended. 8. Differentiated If your positioning is indistinguishable from competitors, AI systems have fewer signals to select and represent your brand as a distinct recommendation. 9. Fresh Important content needs to remain current and useful. Freshness can play a role in AI citation selection, particularly for time-sensitive or evolving topics, as many systems incorporate retrieval mechanisms that consider recency. 10. Transactable For ecommerce brands specifically, product data needs to support AI-driven discovery, comparison, and, where supported, purchase flows. Read more: https://t.co/gTHC70m75c
- 20
@Marie_Haynes ·
The most important things for Search from Google I/O: -Instead of Search having AI features, it is now "AI Search through and through." -Gemini 3.5 Flash now powers the AI features of Search. -There is a new Search box which you can use any form of media to search with. -Gemini Spark Agents: 24/7, personalized and work in the background. -Universal Cart. Woh. You can put items from multiple merchants in the cart and Gemini works in the background to find lower prices and other things. -Agentic booking is expanding. -You can create custom apps within Search as Antigravity is now in AI Mode. More in my latest video:
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@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/z4pmPraCA6.
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@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.
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@ViperChill ·
Do Google's AI search competitors have a (growing) spam problem? By the metrics I have available, they're increasingly citing low-quality domains. Using Ahrefs Brand Radar, I analyzed the top 1,000 sites mentioned on each platform over the past three months. ChatGPT, Copilot, and Perplexity are referencing a growing number of sites that get little or no traffic from Google. Ranking well in Google doesn't automatically mean a website is valuable, but I've manually looked at hundreds that thrive in AI but struggle in search, and almost all are… questionable. Fake or no authors. Thousands of AI-created articles with no obvious theme. eCommerce stores with 100K+ random items, etc. I'm not here to judge, but you've probably seen the kind I mean. While this analysis definitely isn't perfect — don't quote it just yet — it almost looks like Google's major competitors are going backwards on the quality front. I expect we'll see improvements from all of them this year, so I'll keep tracking the numbers. I've added a few more notes at /zero.txt (link at the bottom of the graphic), but I'm always open to feedback and suggestions to improve this analysis going forward. 🤝 I just find it super interesting to watch them tackle a challenge Google has been working on for so long. P.S. It’s admittedly possible the zero traffic domains rank for *something* in Google, but currently nothing across the entire Ahrefs database.
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@aigleeson ·
SEO is dead. Google, OpenAI, and Perplexity don't rank pages anymore. They write answers. The University of Toronto just published the first real blueprint for this shift. It's called GEO (Generative Engine Optimization) and it changes everything about how visibility works. Here's what their experiments found: → AI search ignores your blog and social posts almost completely → ChatGPT and Claude barely show brand pages, Google still does → Language and phrasing shift what gets cited across regions → Big brands dominate unless you build verifiable third-party authority The fix: → Engineer your site for machine scannability (schema, structured data) → Get cited by authoritative reviewers and publications → Build local-language authority, every region's AI runs on different media → Treat your website like an API, not a brochure Stop optimizing for clicks. Start optimizing for citations. The brands AI trusts will own the next decade of traffic. Everyone else is building for a search engine that's already dying.
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@semrush ·
In Jan–Feb 2026, we analyzed 325K unique prompts across ChatGPT Search, Google AI Mode, and Perplexity. Every search platform has signals that determine who gets surfaced. For AI search, one of the strongest signals is trust. The domains earning the most citations today offer an early look at how authority is being redistributed in the AI era 👇 https://t.co/3ZcHQ1KW23.
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@foley_seo ·
I love a good case study of which Assertive has many. We've done SO much SEO work in some of the most competitive niches on earth so think of Pharma, Health, Casino, iGaming, Finance. Traditional search is NOT DYING, if you see click bait / engagement bait on LinkedIn around AI sources outperforming Google organic - don't be fooled by it. I have access to HUNDREDS of sites across GSC / GA4 across ALL kinds of niches from mainstream publications to news, health publications, you name it. CLICKS from AI SOURCES (chatGPT, Claude, Copilot, Grok, Gemini) account for less than 5%. And yet there is SO SO SO much hype around "being found and cited in LLMS" - whilst so many probably neglect the fact that traditional search is STILL, BY FAR the FIRST place most people who "aren't in the industry" go to find what they want. We didn't lose clicks to LLMS, we lost clicks to Google AI overviews. And whilst there IS a growing search market for LLMS, it's still TINY in comparison. If you are MORE PROMINENT ORGANICALLY with GOOD SEO you are far more likely naturally to appear in LLMS. Good SEO = Good GEO. Take this client - 1. Strong sustained organic growth - even though it's very likely a portion of medicine searches at a research phase are going through AI 2. These guys get around 1% of their clicks from AI searches (aggregated) 3. Good solid technical SEO, good content with proper fact checking and high quality links have propelled growth and, without trying, their CITATION METRICS in AHREFS show strong growth. NO GEO playbook, no hacks, just good SEO. IF you have a good SEO agency, you'll achieve good AI citation growth naturally. #seo
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@thinking_slow ·
does adding schema markup help your pages get cited in AI search? probably not 👇 we (@ahrefs) analyzed 6M URLs and found schema is more common on heavily cited pages BUT that's probably correlation, not causation: schema markup is more common on pages with good SEO generally so we tested 1,885 pages that actually added JSON-LD schema and compared their citations before and after verdict: across Google AI Overviews, AI Mode, and ChatGPT, we saw *no measurable difference* in citations (or even a tiny negative effect) schema matters for plenty of reasons in SEO and AI search, but adding schema to your pages is probably not some magic fix for improving your AI citations ✌
Watch video
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@glenngabe ·
Do News Publishers That Block AI Crawlers Get Cited Less Often by AI? "Using data from Citation Labs’ AI citation-tracking tool, XOFU, we examined 4 million citations from 3,600 prompts in ChatGPT, Gemini, AI Overviews, and AI Mode, across 10 industries." https://t.co/z6tNNL6Cb7 via @VincetheNero
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@jmoserr ·
I talk to 35 new companies every month about SEO and GEO. Almost all of them are making the same data mistake...and they're building 2026 budgets around it. They all say the same thing: "AI search leads are so much better." When I ask how they arrived at that conclusion, it's always the same story: They're comparing all of their Google organic traffic, for example 200k visits a month across every funnel stage, to AI search leads, which are almost exclusively bottom of funnel. Of course the AI leads convert better. You're comparing apples to burgers. Here's what actually happened over the last 12 months and why your data is sh**. CTR and traffic dropped massively for tons of keywords. Yet revenue from organic search stayed flat. Why? Because most of what you were ranking for was vanity top-of-funnel junk that never moved the needle. Broad, informational keywords now eaten up by AI overviews and LLMs. Zero-click searches (hat tip / coined by @amandanat!). You didn't lose revenue, just vanity traffic that never made a difference in the first place. Now look at AI search: Brands nearly only get mentioned when someone asks for a specific solution. "I run a small business and need payroll services, what's a good fit for a 10 employee company?" The LLM spits out 5-6 tools. That's it. That's the only time you show up. It's as bottom of funnel as it gets. So when you compare 200k monthly organic visits where maybe 2,000 are meaningfully contributing to revenue against a channel that is ONLY purchase-intent recommendations, what exactly are you measuring? Nothing useful. Without proper data filtering, you can't make this comparison. And if you're making resource allocation decisions without it, you're making a huge mistake. GEO and SEO are not the same when it comes to data, intent, and usage. Stop treating their numbers like they're interchangeable.
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@ttunguz ·
The internet is about to look a whole lot more like the online advertising world. No, I don’t mean there’ll be more ads. In fact, I think there’ll be far fewer. But the technology stack for content distribution will mirror the architecture that has been implemented in the online ad world. As we’ve reached the AI search tipping point, publishers face an existential challenge : ensuring AI systems use their content in answers to maintain relevancy. When you visit a website, your browser triggers an auction. The site’s supply-side platform sends your data to an exchange—like a stock market for ads. Dozens of advertisers bid for the chance to show you their message. Highest bidder wins. Total time: under 200 milliseconds. Now imagine the same system, but for content. Instead of bidding to show you ads, publishers vie to inform AI responses. The AI uses quality metrics, not money, to determine winners. Publishers compete on relevance, accuracy, freshness, and authority. The signals that make content useful. This is PageRank for real-time AI responses—algorithmic evaluation operating in milliseconds rather than batch processing. Here’s how it would work in practice : you ask Gemini: ‘What are the reviews of the new Google Pixel phone?’ That query broadcasts to participating publishers: tech reviewers, consumer sites, electronics retailers. They submit their best content to the auction. Gemini evaluates quality, recency & relevance, then synthesizes the winning submissions into your answer. Notice what disappears : the demand-side platform. No more advertisers optimizing for clicks. Just publishers competing to be the most useful source. The internet gets fewer ads, but every piece of content fights for attention in an auction measured in milliseconds. Publishers get traffic & attribution when selected, creating indirect revenue through brand building & subscriptions—but only if their content consistently wins on merit. https://t.co/BJj0fqP40X
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@peec_ai ·
Which content types get cited most in AI search? We looked at over 1 million citations. On ChatGPT, listicles dominate. 52% hit a citation rate above 2.0. Product pages have high presence but lower citation rates. On Google AI Mode, category and product pages from official brand sites perform best. It prefers brand-owned content. On Perplexity, listicles and articles win, but the numbers are much lower. Only 6% of URLs make it into the high citation bucket.
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@natmiletic ·
How to future-proof your site against AI search eating your clicks: 1. Sell something. Products, services, access. Make visiting mandatory. 2. Build tools and interactive resources that AI can't replicate. 3. Build a community or email list. Own the audience, not just the ranking. 4. Go deeper than any summary can. Original data, personal experience, proprietary insight. AI summaries are just a filter for thin content.
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@alex_prompter ·
If you're still optimizing for Google and ignoring AI search, you've already lost, and you don't know it yet. A buyer asks ChatGPT or Claude for the best [your service], and a name comes up. 7 checks that tell you whether it's yours, and what to fix if it isn't: 1. Mirror the buyer query. Ask an AI the exact question your prospect would, with web search on, and read where you land. The answer it gives is the one your buyers already see. 2. Map the query funnel. List the 15 questions a client types on the way to hiring someone like you, from early research to ready-to-buy. These are the rooms you need to show up in. 3. Reverse the named competitor. Find who the AI recommends first, then trace the signals that earned it: reviews, lists, comparison pages, their own content. Now the gap has a name. 4. Audit your proof trail. AI vouches for what the open web confirms. Count your reviews, mentions, and third-party features, then list the five a buyer expects to find but can't. 5. Test your machine-readability. Have a model read your site cold and describe what you do and who you serve. If it struggles, so will the model a buyer is asking. 6. Close the authority gaps. Models cite the source that answered the question best. Find the buyer questions you've never put in content, because that silence is why you get skipped. 7. Compress to one page. Pull the findings into three moves: the gaps costing you the most recommendations, the first action for each, and the order to run them.
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@thinking_slow ·
everything you need to know about Google's AI Overviews (and how they impact your rankings and traffic)* - AI Overviews appear on roughly 21% of all Google search keywords worldwide, including about 16% of U.S. search queries. - Google’s AI Overviews are significantly cutting into clicks – top-ranking pages see a 34.5% drop in clicks when an AI Overview is present. (I suspect this is even higher now. New research forthcoming.) - Countries with the highest AI Overview coverage are Indonesia (37.2%), the Philippines (29.1%), and Mexico (29.1%) (the U.S. is around 20.5%). - 99.9% of keywords that trigger AI Overviews are informational in intent (only 5.5% are commercial and 1.2% transactional). - 76% of AI Overview citations come from content already ranking in Google’s top 10 organic results. - Longer queries are far more likely to trigger AI Overviews – they appear for only 9.5% of 1-word searches but 46.4% of searches with 7+ words. - AI Overviews show up on 57.9% of question-type searches (versus just 15.5% of non-question queries). - 44.1% of medical “Your Money or Your Life” (YMYL) queries return an AI Overview . - About 71.7% of searches that trigger AI Overviews show no ads (no CPC data), meaning these queries are largely non-monetized . - Top categories by AI Overview prevalence are Science (43.6%), Health (43.0%), Pets & Animals (36.8%), and People & Society (35.3%). - Categories with the lowest AI Overview coverage are Shopping (3.2%), Real Estate (5.8%), Sports (14.8%), and News (15.1%). - AI Overviews are rare in sensitive and localized queries: they appear on only 6% of news searches, 4% of NSFW queries, and 7.9% of local searches. - 8.6% of AI Overviews do not appear at the very top of the page (some appear as low as position #6 in the results). - The top 50 domains mentioned in AI Overviews account for 28.9% of all AI Overview citation links. *sources in thread!
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@glenngabe ·
Interesting case study and backs what @lilyraynyc published about the evolving situation with self-serving listicles -> Self-Promotional Content Works — Until It Backfires (AI SEO Experiment) Some takeaways: *When AI cited a conference-promoting page, it still skipped that conference 43% of the time, recommending a competing event featured on the same page instead. *Most AI citations (and the AI mentions they earned) were temporary and came and went over time. *Self-promotional content seems most useful when AI has a gap in its understanding: your brand belongs in a specific category, but AI doesn’t consistently mention it there yet. *Regarding "found not cited", among answers where a conference-promoting page was found but not cited, 74% skipped mentioning Ahrefs Evolve (compared to 43% when a conference-promoting page was cited). https://t.co/cFjMuC2wfu
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@hustle_fred ·
crazy how much i learned at "AI Search" event🔥 my favorite insights: 1> AI pulls references from the first 200 chars of your article, keep the intro sharp and short 2> Reddit/YouTube dominate the references, but niche blogs often beat them on position 1. So don't default to Reddit, analyze first 3> listicles are the most powerful content type in AI search, if your app is #1 in a list, AI shows it first > some AI SEO tools use LLM APIs instead of scraping the web interface directly. Issue is API returns less results than users actually see on the web
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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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@jbobbink ·
I read Google's internal docs from May 2025 that refused publishers granular AI controls. Now regulators forced their hand and they call it "exploring." In May 2025, Google deliberately decided not to give publishers the ability to opt out of AI Overviews separately from regular search. They had the option. They chose not to build it. Fast forward to January 28, 2026. The UK's Competition and Markets Authority designated Google with "strategic market status" and proposed binding rules. Suddenly Google is "exploring updates to controls." Their words, not mine. Ron Eden, Google's principal for product management, wrote that any new controls need to avoid "breaking search in a way that leads to a fragmented or confusing experience for people." Two weeks later, Google's managing director for news partnerships called it a "huge engineering project" at a conference in London. A basic opt-out toggle. Huge engineering project. From the company that built AI Overviews in the first place. Meanwhile, publishers are not waiting. BuzzStream analyzed 100 top news sites and found that 79% already block at least one AI training bot. 71% block retrieval bots too. Publishers are voting with their robots.txt files whether Google builds the button or not. The numbers explain why. Seer Interactive research showed a 61% drop in organic click-through rates for queries where AI Overviews appear. Google's own cited research shows that using nosnippet to block AI Overviews costs publishers roughly 45% of their search traffic. That is not an opt-out. That is a hostage negotiation. The current tools were designed to be all or nothing on purpose. Google-Extended blocks AI training but has zero effect on AI Overviews. Nosnippet blocks AI Overviews but also kills your regular search snippets. There was never a clean middle ground because Google never wanted one. It took the CMA, the EU Commission launching a formal antitrust investigation, and independent publishers filing complaints across multiple jurisdictions to get Google to even say the word "exploring." One-third of publishers polled say they will opt out if the controls arrive. But Google has given no timeline, no technical spec, and no commitment. If you manage SEO for any site that creates original content, do not wait for Google to hand you the tools. Build your traffic diversification strategy now, because the opt-out button Google is "exploring" may arrive too late to matter. Sources: - The Media Copilot, "Google signals it may let publishers opt out of AI search features" (Jan 29, 2026) - Ron Eden, Google blog post on CMA consultation response (Jan 28, 2026) - Sulina Connal, FT Strategies conference, London (Feb 11, 2026), reported by PPC Land - BuzzStream, "Which News Sites Block AI Crawlers?" (Dec 18 2025) - Seer Interactive, AI Overviews CTR research (Nov 4, 2025) - Matt G. Southern, "Google May Let Sites Opt Out Of AI Search Features" SEJ (Jan 28, 2026)
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@peec_ai ·
We looked at 1,056,727 AI citations. ChatGPT, Google AI Mode, Perplexity. The interesting bit: your industry matters less than what the user is trying to do right now. Someone learning? Articles. Someone comparing? Listicles. Someone buying? Product pages. Sounds obvious but the numbers are wild. Articles go from 45% for informational queries down to 5% for transactional. Done with Wix, link in comments.
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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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@hdxswx ·
The 5 biggest myths about AI search optimization (and what actually works) I've analyzed millions of AI citations across ChatGPT, Perplexity, and Google AI Overviews. Most of what you hear about AI search is either hype or just wrong. Here are the biggest myths I keep seeing: Myth 1: "AEO is completely different from SEO" The truth is more nuanced. AEO builds on SEO fundamentals, but it's not just rebranded SEO. Success is measured by mentions instead of clicks. LLMs break down queries into multiple parallel searches. Responses are highly personalized and probabilistic. (more on this in a separate post) Myth 2: "Flood the internet with AI content" This will destroy you. Mass AI content triggers algorithm penalties, tanks your credibility, and makes you invisible everywhere. AI platforms cite authoritative sources. Spam doesn't scale sustainably. Myth 3: "Optimize for exact AI prompts" AI prompts are personalized and constantly changing. Chasing individual prompts is whack-a-mole. What works: Build topical authority around core themes. Myth 4: "Create separate content for AI vs. humans" Don't write for bots. Don't create scaled-down AI versions. The best content serves both by being clear, authoritative, and well-structured. Myth 5: "llms.txt is the secret" Lumen's data: Millions of citations tracked. Zero llms.txt references. It sounds technical, so it sells well. But there's no evidence it works If your AEO strategy sounds easy, scalable, and fully automated, it’s probably wrong. What is the craziest AEO take you have come across in the wild?
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@kaleighf ·
The thing I keep coming back to about AI search: The further a signal sits from a brand's own control, the more LLMs seem to trust/weigh it when deciding what to cite. Authority, in other words, now flows toward people— not brands. The uncomfortable part is that it requires brands let go of control + allowing humans with real expertise to be their mouthpiece (instead of hovering behind the faceless logo or branded account.)
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@sharyph_ ·
60% of Google searches now end without a click. Read that again. People search. AI gives them the answer. They never visit your site. Most content creators are panicking about this. I'm doubling down. Here's why: If AI is answering questions with your content, you're winning. Getting cited by ChatGPT or Perplexity is the new featured snippet. It's digital authority on steroids. Only 11% of websites get cited by both ChatGPT and Perplexity. That's not a problem. That's an opportunity for early movers. The shift isn't "make content AI can't steal." It's "make content AI wants to cite." How? → Clear question →answer structure → Original data and proprietary insights → Specific details over generic advice → Short paragraphs (60-100 words max) → Semantic variation over keyword stuffing I tested this on 12 posts. 3 are now cited regularly by AI models. Those same 3 rank on page 1 of Google. Here's the insight no one's saying: Content optimized for AI also wins in traditional search. Because Google's algorithm is becoming more AI-like. It's looking for: → Clear answers → Natural language → Semantic relevance → Structured information The creators who figure out AEO (Answer Engine Optimization) in 2025 will dominate discovery for the next 5 years. Everyone else will still be chasing SEO tactics from 2019. By 2026, 25% of search volume shifts to AI chatbots. You can complain about it. Or you can get cited by it. Your move.
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@ChrisClickUp ·
AI Overviews killed organic click-through rates by 61%. Zero-click searches are heading toward 70% of all queries. Non-cited brands in AI Overviews see a 65% CTR decline. Paid ads now capture 19-36% of clicks depending on the vertical, effectively doubling in one year. And Google's global search share fell below 90% for the first time in a decade. But go to any SEO conference and everyone is still talking about keyword clusters and backlink strategies like it's 2019. Here's what's actually happening in B2B SaaS: Your buyers aren't Googling "best project management tool" anymore. They're asking ChatGPT. They're asking Perplexity. They're asking Claude. And those AI tools don't care about your domain authority. The SEO industry has too many jobs, too many agencies, and too much revenue tied to the old model to admit what's happening. The era of "rank and bank" is ending. The era of "be the answer, not the link" is starting.
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@kaleighf ·
Creator AEO, the emerging term for creator-driven answer engine optimization, is the practice of earning AI citations through human expertise published on third-party surfaces. “Creator” can take many shapes here: LinkedIn creators, B2B influencers, customer advocates, and even employees all fit under this umbrella. (More on this in a bit.) This work sits inside the broader practice of answer engine optimization, but it targets the off-site piece of this puzzle. Most AEO and GEO work is largely on-site and focused around topical ownership via blog content published on the company’s website. That work is useful and still necessary, but it caps out at the small share of citations your own website or company blog can earn as a brand-owned piece of digital real estate. Creator AEO picks up where that ceiling hits. It treats the entire context of the internet (the videos, posts, forum threads, and reviews) as the real arena for AI visibility, and it treats individual people as the unit of credibility rather than the brand. This new term emerged because LLM citation data has been pointing in this direction for a while now. Once teams started to notice that the bulk of AI answers were assembled from third-party, human-authored sources, they took note, and started thinking about AI citation rate as a largely offsite initiative.
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@_vmlops ·
GOOGLE'S AI SEARCH IS PUSHING USERS AWAY AND DUCKDUCKGO IS WINNING turns out, not everyone wants AI answering their questions. after google's AI search overhaul: → duckduckgo's AI-free page (https://t.co/CDbji2cyC0) saw 3x more visits → overall app installs jumped ~33% → iOS installs peaked at nearly 70% growth they even launched browser extensions to replace google in chrome + firefox's address bar. the demand for "just show me the results" is real and it's growing
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