AEO measurement and experimentation
Tracking AI mentions, citations, referral traffic, conversion quality, sentiment, and visibility; using controlled tests and skepticism toward unsupported causal claims and vendor hype.
46%
Best tweets about Answer Engine Optimization
50 Best Tweets About Answer Engine Optimization (2026)
Discover the best tweets about answer engine optimization, including AI answers, citations, content structure, visibility, measurement, and AEO strategy.
Evidence-based AEO strategy for earning visibility and citations in answer engines, with experiments, measurement, and practical methods.
- Creators
- 34
- Updated
Original Xholic analysis
What 50 top Answer Engine Optimization posts reveal
The conversation presents AEO as an evidence-led extension of SEO: contributors emphasize authority and third-party representation, clear answer-oriented content, engine-specific testing, and measurement beyond visibility alone. Measurement and experimentation is the largest analyzed theme, representing 46% of tweets (23 of 50).
- Dominant tone
- Positive
- Median score
- 8.96
- Leading format
- Opinion
- Recent posts
- 40%
48% of posts
All-time engagement
36% of posts
Published in 90 days
Conversation map
The themes creators return to
Third-party citations and distribution
Building visibility through earned media, PR, reviews, listicles, directories, newsletters, communities, Reddit, YouTube, Quora, LinkedIn, and other external sources AI systems cite.
40%
Citation-ready content design
Creating clear, extractable answers through question-led sections, concise answer blocks, definitions, FAQs, tables, comparisons, schema, and scannable structure.
38%
Brand authority, positioning, and trust
Earning recommendations through clear category fit, differentiated positioning, market validation, credible claims, brand sentiment, and durable authority rather than tactical content tricks.
36%
SEO as the foundation for AEO
AEO/GEO as an extension or second layer of traditional SEO: crawlability, rankings, technical quality, UX, backlinks, and topical authority remain prerequisites for AI visibility.
36%
Prompt tracking and retrieval behavior
Studying prompt outputs, cited sources, query fan-outs, grounding, retrieval-versus-citation differences, source patterns, and variations across Google, ChatGPT, Claude, Perplexity, and other engines.
24%
Commercial and bottom-funnel content
Targeting high-intent comparison, alternatives, reviews, buying guides, and category pages that support shortlisting, clarify differentiation, and convert AI-influenced buyers.
18%
Original data and non-commodity content
Using proprietary datasets, firsthand expertise, original research, practical perspectives, and fresh evidence to create content that cannot be reproduced from generic model output.
12%
Tone and stance
Sentiment Positive leads
Author posture Supportive leads
Performance benchmark
- Median likes
- 28
- Median reposts
- 3
- Median replies
- 7
- Median views
- 2.1K
Posts with media make up 54% of this collection. Their median all-time score is 13.2, compared with 4.17 for text-only posts.
Format mix
- Opinion 36% · score 4.54
- Announcement 18% · score 13.2
- Case Study 18% · score 5.95
- Tutorial 12% · score 29.5
Consensus and debate
Where creators agree, and where they do not
Shared view
SEO foundations remain central
Multiple contributors describe AEO/GEO as a layer on top of established SEO work—such as crawlability, rankings, authority, and intent-led content—rather than a replacement for it.
Shared view
Authority needs external validation
Posts repeatedly emphasize credible representation beyond the brand site, including editorial coverage, reviews, communities, and other third-party sources that may appear in answer-engine outputs.
Shared view
Design pages for extraction
Question-led sections, self-contained definitions, examples, lists, tables, and clear structure recur as recommended patterns for making answer segments easier for systems to parse, retrieve, or cite.
Shared view
Measure prompts, sources and business impact
Contributors recommend tracking prompt outputs, citations, referral traffic, conversions, mentions, and sentiment. One workflow also recommends analyzing fan-out queries in aggregate for topic and content mapping.
Open debate
Citation tactics versus durable authority
Some posts recommend studying cited formats and source types, while others caution that observed content traits may be correlated with pre-existing authority rather than proven causes of citation.
Open debate
How much traditional ranking determines visibility
One cited case study recommends starting with pages that already rank within Google’s top 100 for AI Overview opportunities, while another reports that 75% of AI citations in its prompt sample came from sources outside Google’s top 10.
Open debate
Citation is not the sole success metric
Posts treat citations as a visibility signal, but also point to BOFU mentions, direct traffic, conversion performance, and sentiment as outcomes worth measuring.
Open debate
Avoid unsupported technical shortcuts
The discussion includes cautions about tool-led strategies, estimated prompt-volume data, mass AI content, and Markdown-for-bots claims without convincing evidence or controlled testing.
What performs
Patterns behind standout posts
Practical tests were prominent among top-scoring posts
The highest-scoring outlier was tweet 2074870301414879544, which references 4,264 AI-platform checks. Other score outliers included a strategic framework, reported third-party research, and a concrete AI Overview citation case.
Tutorial format had the highest format median
Tutorials had a median all-time score of 29.51, compared with 5.946 for case studies, 4.54 for opinions, and 4.291 for lists in this dataset.
Posts with media had a higher median score than text-only posts
Posts with media represented 54% of the set and had a median all-time score of 13.205, versus 4.169 for text-only posts.
Commercial content had a high theme median
The commercial and bottom-funnel theme had a median all-time score of 32.463. Its examples include comparisons, alternatives, reviews, and conversion measurement.
Statistical standouts
- View standout post 1 Score 360.8 · 40.26× median
- View standout post 2 Score 252.1 · 28.13× median
- View standout post 3 Score 178.5 · 19.92× median
- View standout post 4 Score 167.6 · 18.71× median
- View standout post 5 Score 131.6 · 14.69× 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. Cody Schneider
@codyschneider
2 posts
-
3. Daniel Foley Carter
@foley_seo
2 posts
-
4. Gaetano DiNardi
@gaetano_nyc
2 posts
-
5. Glenn Gabe
@glenngabe
2 posts
-
6. ILIAS ISM
@illyism
2 posts
Conversation is distributed across creators
The set contains 50 tweets from 34 creators, and the top five creator placements account for 20% of the set.
Selected top voices cover strategy and execution
Among the listed top voices, Gaetano DiNardi, Cody Schneider, and Jan-Willem Bobbink contribute posts on positioning, BOFU execution, external authority, and research-led claims.
Posts emphasize methodological restraint
Several contributors explicitly distinguish retrieval from citation, caution against causal overclaims from correlation, note output variability, and question unsupported implementation claims.
Since the previous snapshot
What changed since Aug 12, 2026
- 64% of the selected posts remained.
- The creator count changed by +1.
- The leading sentiment remained stable.
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 Answer Engine Optimization tweets
Top Answer Engine Optimization tweets from 34 creators
Ranked 01–50
- 01
- 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
@lilyraynyc ·
One of the quickest and easiest AI search workflows (after setting up prompt tracking) is to export all fan-out queries and cluster them into core topics for analysis and content mapping. Trying to optimize for individual fan-out queries is not a great use of time, and building one page per fan-out will likely lead to SEO spam problems down the line. (Reminds me of popular pre-Helpful Content Update tactics 😅) But analyzing query fan-outs *at scale,* clustering them into core topics, and identifying the key trends among them is one of the best places to start with AEO/GEO, in my opinion. (Assuming you’re tracking a comprehensive set of prompts!) The output is a matrix similar to the keyword research matrices we’ve built for SEO for years, and it lets you compare your existing content portfolio to the key questions language models ask when researching your brand, products, and services. That said, I still believe SEO tools that use *actual search volumes* (not highly estimated / made-up “prompt volumes”) should stay part of this process. I just did this for a client in 5 minutes using @peec_ai, which allows for a quick export of all fan-out queries (as do several other LLM trackers). Then I uploaded the list into Claude for clustering. Another bonus is that you can see how fan-outs differ by language model. I see this as a new layer on top of our existing keyword and topic research. It gives us a window into how LLMs actually break down and research a topic, a signal we didn’t have access to before, and it provides deeper insights than what we can gather with traditional keyword research tools.
- 06
@jakezward ·
"I don't know how to do SEO in 2026" Start from level 1 in The New SEO Game: SEO isn't just Google rankings anymore. It's now "Search Everywhere Optimisation". Reframe your “New SEO” project as a game, and you'll become addicted to levelling up. Level 1: Traditional SEO - Fix technical problems killing your rankings - Research keywords and target quick wins - Optimise on-page elements (titles, content) - Earn quality backlinks through outreach and PR Level 2: AI Search Optimisation - Format content so AI can parse it easily - Build pages that AI platforms want to cite - Structure information for machine consumption - Test how your brand appears in AI search results Level 3: Paid Search Visibility - Run Google Ads for high-intent keywords - Target competitor terms with YouTube Search Ads - Capture Bing traffic through Microsoft Ads - Measure paid performance across every platform Level 4: LLM Answer SEO - Produce authoritative content LLMs trust - Build a knowledge base for AI training data - Position your brand as the definitive source - Monitor brand mentions in LLMs with Mentions .so Level 5: Brand Authority SEO - Get unlinked brand mentions across the web - Feature in industry roundups and expert lists - Build brand recognition without traditional backlinks - Track brand mention volume and sentiment Level 6: Community SEO - Provide value in relevant Reddit threads - Share expertise on Quora in your space - Engage in Slack communities and Discord servers - Build reputation in industry forums and groups Level 7: Parasite SEO - Repurpose content on Medium and LinkedIn Pulse - Publish beehiiv newsletters to rank in Google - Guest post on high-authority platforms/websites - Leverage existing domain authority for quick rankings Level 8: Platform-Specific SEO - Optimise for Amazon search if selling products - Focus on YouTube SEO for video content - Master TikTok's algorithm for short-form content - Optimise App Store listings for apps/extensions Level 9: Topic Domination - Own conversations across ALL channels - Create content ecosystems to reinforce each platform - Become the go-to expert mentioned everywhere - Monitor and maintain authority across all channels Completed all levels? SEO is constantly evolving going into 2026. Stay ahead or get left behind.
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- 07
@codyschneider ·
SEO for saas 101 use claude code find bottom of funnel keywords related to the product with data for seo api EG x vs y competitor x alternative, x review, best x for y for target keyword scrape what is ranking page 1 using serper put what is ranking in context, and then record 30 min vid of your perspective on the industry also, just have claude mobile app interview you for each keyword write article based on what is ranking + perpective publish article to CMS via api inject CTA after first paragraph, 25% scroll, 50% scroll, 75% scroll rate track the conversion event vs landing page using google tag manager, google analytics 4, and google search console publish more content like the best performing build dashboard for SEO and AI search referral traffic
- 08
@forgebitz ·
want AI content SEO to rank? do not generate endless slop everything you can generate directly using an LLM without unique context is already in the training data => useless and does not rank it's a content black hole; the question has been answered focus on anything not inside the training data; you can get thousands of chatgpt citations without a single backlink or "domain rank" everyone will destroy their website in the coming months with "pSEO" big opportunity for the rest of us
- 09
@tibo_maker ·
how to increase the chances of getting cited in Google AI Overviews see 20 to 35% of all Google searches now trigger an AIO you can rank number 1 and still watch the clicks flatline because the user got their answer right on top and never came to your page there's no https://t.co/spelSttOsC
- 10
@illyism ·
just got off a SEO consulting call with a founder who built a crazy cool product but that nobody knows about yet 🤙 SEO is awesome at demand capture, but not at demand generation so here is the exact playbook we mapped out: 👇 how to do seo for a product nobody searches for: 1. write a "top 20 best <category>" listicle for your niche 2. put your product at #1 3. include 19 other non-competing indie products 4. email those 19 founders: "i just featured you, can you include us in your listicles?" boom 💥 free, highly relevant backlinks from real businesses 👍 bonus insight: find the exact articles that chatgpt cites for your target keywords (press on "Sources" in the bottom), email those specific authors to get added to their existing posts stop fighting for impossible keywords, build alliances instead! 🙌
- 11
@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
- 12
@foley_seo ·
SEO is DEAD. Right, now I've got your attention, I've done something that I hope SEOs find useful, I've put together an article on NavBoost, I've also re-created the entire Google Content Warehouse leak and added "probabilistic weighting". I'm fed up of seeing AI SEO slop polluting my feed, so I thought I'd be speculative and put together some insights that SEOs may find useful. To the GEO bro's out there, pipe down, if you don't rank the probability of being CITED disappears so stick your "intrinsic semantic context paragraph mapping" in the bin - what you SHOULD focus on is: A.) Making your content less shit B.) Making Google see that user behaviour SHOWS your content is less shit C.) Making Google trust your spammy ass domain, drop your DR BS, get real links from sites with traffic, still promoting DR? Do you know what DR is? It stands for "Don't Rank" - that's why buying shit loads of links from artificially inflated domains leads to not ranking thus DR - don't rank. Don't ask me what DA stands for you'd be offended. D.) Making your brand stronger (no, it;s not GEO, it's called links, positive reviews and brand searches) Let's nail this down now. John Mueller HIMSELF said "GOOD SEO is GOOD GEO" Do you know what that means? If your site is GOOD and RANKS more prominently it's: 1. MORE LIKELY to be found during RAG / GROUNDING / QUERY FAN OUT 2. MORE LIKELY to actually achieve an objective i.e. rank, convert, make £ Now, I go back to NAVBOOST because it's always been something I've been fascinated by. WHY? Because for YEARS people called me a conspiracy theorist when I said: "Google is using user behaviour data in ranking" Even Google denied it for YEARSSSSS and guess what? They use it. CHROME is a trojan horse. Click behaviours are ONE OF THE BEST indicators of user satisfcation. So, everything boils back to the CORE PRINCIPLES of: 1. CREATE GOOD CONTENT 2. MEET END USER NEEDS 3. PRESENT IT IN A GOOD UX 4. BUILD TRUST IN YOUR BRAND My Navboost article also includes references to Mark Williams-Cook's 500 unsolicited tips with references to "twidlers" as well as Shai Aharony's Reboot's study on SHARED HOSTING experiment conducted by Oliver Sissons If you want to learn more about NAVBOOST > https://t.co/lVoVF8iYLj and if you fancy checking out the content warehouse rebuild with probabilistic weighting > https://t.co/CWpC57cYVo
- 13
@SEOKeval ·
SEO may be the most important marketing channel out there right now. Why? Because it's a prerequisite to appearing in AI models. Good traditional SEO gets you 50–60% of the way toward appearing in AI models. And the other 40-50% is just an extension of SEO: > It's publishing "best X" listicles, featuring your product at the top, on relevant, high-authority 3rd party websites. > It's finding relevant Reddit and Quora threads, and mentioning your brand in the replies. The goal is to "seed" your brand around the web, so when AI models pick up those references, they cite you for relevant prompts. In other words, you're doing parasite SEO. It's nothing mind-blowing. It's also nothing new. It's just an extension of what we've always been doing in SEO.
- 14
@aleyda ·
💰 From Retrieved to Cited: How Commercial Content Earns Citations in AI Search - excellent research from @_oshdavidson showing how: * Early-Discovery Content Earns a 20% Higher Citation Likelihood When Claims Are Grounded in Data * Pages That Make Shortlisting Options Scannable Earn 18.8% More Citations * Comparison Content With Tables Earns 25.7% More Citations * Validation Pages With Organized Lists Earn Up to 27% More Citations * What This Means for Content and SEO Teams A must read: https://t.co/6wBmI2w2r9
- 15
@brodieseo ·
AEO Tip: I've been increasingly experimenting with trying to influence ChatGPT output for my eCommerce clients. Particularly for queries where I'm comparing my client against competitors for basic 'buy' and 'sell' queries for them as a business. The balance that we're trying to strike within this experimentation is ensuring that we're staying within the confines of their branding and not outright mentioning competitors – something that is far more common within the SaaS space. For this client, a lot of their competitors are considered to be "marketplaces", whereas my client holds all stock on-site (a major point of difference) and does the buying and selling of the stock itself. The major benefit to this approach is that they can maintain the quality of the product through their own authentication processes, being a clear advantage over the classic marketplace model. Within this experiment, we published a comparison page that compared them against "marketplaces" across various metrics in order to influence the output, and our new page was used as a core source in ChatGPT within a day. And we're already starting to gain more control over how they're being represented, making a clear argument for why both buyers and sellers should choose them over key competitors, without mentioning them directly. In my opinion, this type of approach is the way forward when doing these types of experiments, rather than creating content that goes outside of company guidelines for the sake of AEO. It is a fine line for this type of work, but clear benefit when it is executed correctly.
- 16
@glenngabe ·
Cool post from @dejanseo -> How AI Search Grounding Actually Works: Google vs OpenAI vs Anthropic "For Google, the funnel barely narrows: 7 received, 7 cited. This is the defining trait of Google’s grounding — it doesn’t expose pages it didn’t use." "OpenAI’s wide-receive / narrow-cite split is a visibility trap. Being one of the 37 pages OpenAI read is very different from being one of the 2 it cited. Both are worth tracking, but they’re not the same win." "Anthropic gives the richest “considered set” (cited + rejected) but hides the snippet text, so reconstructing what it actually read costs a second pass — and real time and tokens." https://t.co/ZaKM3N8bqM
- 17
@om_patel5 ·
i took my startup from 0 to 2.15 million google impressions and 6,000+ clicks in 3 months, purely through seo and gseo everyone thinks seo is dead because ai overviews now sit on top of the results and answer the question before anyone clicks. its not dead. the game just split into two, seo to rank on google, and gseo to get cited inside the ai answers themselves. i did both so heres the exact system. the core idea is to stop writing blog posts one at a time. build a system that produces thousands of pages, each answering one very specific question, powered by data nobody else has. 1\ programmatic seo off my own data my startup sits on a large proprietary dataset. thats the unfair advantage. instead of writing pages by hand, i built templates that turn that data into thousands of unique pages, each targeting one long tail search. > one page per tool, per niche, per problem category, instead of one generic page > every page pulls in real data points so its genuinely useful, not thin filler > this is the exact play zapier and nomadlist used to pull millions of organic visits google doesnt penalize automated pages. it penalizes the "thin" ones. so every page has to carry a real purpose or it doesnt get made. 2\ gseo, getting cited inside the ai answers this is the part almost nobody is doing yet for their own startup. ai overviews and chatgpt/claude/gemini/perplexity are the new "front page", and you want to be the source they pull from, not the site they replace. > structure every page so an ai can lift a clean, quotable answer straight out of it > lead with the actual data, tables and numbers, the stuff a text summary cant fully replicate, so people still click through > get referenced across the web so the models learn to trust and cite your brand as the authority on the topic when the ai answers the question using your data, you win the visibility even on the zero click searches. 3\ reddit as a ranking channel google surfaces reddit threads at the top of results constantly now. so i show up where the exact problem i solve is already being talked about. > find threads where people are complaining about the problem my startup solves > actually help, dont spam, answer the question properly > build real presence in the subreddits my buyers live in so my stuff ranks double duty. it drives direct traffic and plants my brand inside the reddit results google now loves. 4\ domain authority authority isnt one badge you bolt on at the end, its baked into everything. > listed and reviewed on real directories, product hunt, g2, capterra, trustpilot, etc > earned backlinks from people genuinely referencing the data, of course not bought links > consistent "build in public" presence so the brand gets mentioned and linked naturally domain authority lifts every programmatic page along with it which is the compounding part seo in 2026 isnt publishing more ai slop blogs, its building a system, powered by data only you have, that both ranks on google and gets cited by the ai. its slow at first but compounds forever
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- 18
@jakezward ·
Is AEO even a thing? Or is it just SEO? Here's how LLMs actually work: An LLM is not a search engine. It's a next-token predictor. It guesses the statistically likely next word based on patterns baked in during training. During that training, it processed billions of web pages. But it didn't log URLs. Didn't store sources. Didn't remember where anything came from. What's left is a frozen statistical snapshot. Not an index or database. So where does real-time retrieval come from? The search engine layer bolted on top via RAG. That's the actual information retrieval part. Because the “base” LLM can't do it alone. Which means there are only two things you can actually influence: 1. Rank in search so retrieval-based AI can cite you. Traditional, boring SEO. 2. Earn third-party coverage so the model already knows you before the prompt is typed. And that's pretty much the whole game. So stop optimising for some vague idea of 'AI.' Optimise for search engines with owned content and build brand authority for earned mentions. Are you actually doing AEO? Or just SEO?
- 19
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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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@neilpatel ·
Teams are treating GEO as a replacement for SEO. It isn't. It's a second layer on top of it. AI Overviews and other AI search interfaces pull from web content. They favor sources with strong domain authority, consistent publishing histories, and deep backlink profiles. Everything traditional SEO built. Teams that gutted their SEO budgets to go all in on GEO are showing up to AI search with nothing for LLMs to trust. SEO spent years building the credibility foundation that GEO now borrows against. The two layers do different jobs. SEO earns you the domain authority and backlink profile LLMs use to evaluate whether a source is worth citing. GEO shapes how your content answers questions, so when an LLM reaches for a source, yours fits the answer pattern. A brand with strong GEO but weak domain authority won't get cited. A brand with strong domain authority and no attention to answer-formatted content will get bypassed by a newer player who understood the game earlier. The channel changed. The credibility infrastructure underneath it didn't.
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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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@aleyda ·
👀 What’s the most important action SEOs are planning to take in 2026 to win in organic search (traditional and AI search)? Build a Brand + Authority Moat, Engineer Citation-Ready Content, and Reframe SEO as Cross-Channel “Findability”. 👇 From the #SEOFOMO’s Organic Traditional & AI Search Trends for 2026: * Win trust, not just rankings * Be the cited, remembered entity across platforms * Publish less, but make it structurally and evidentially stronger * Treat SEO as a cross-channel system tied to business outcomes * Use better measurement to sustain investment through the click squeeze Learn more here: https://t.co/JMn03KO6qd
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@neilpatel ·
NP Digital ran 4,300 prompts across 500 commercial keywords. Here's what we found: 75% of AI citations pull from sources outside Google's top 10. Rank #1 still only gives you a 31% shot at appearing in AI answers. You now have two scores that matter. Most brands are only tracking one. #SEO #AIMarketing #GoogleSearch #AISearch
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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/ozzQjFU0hw.
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@aaina_narang ·
here's how you can optimize GEO for 10x results: - write content that answers specific questions your ICP is asking - use original data and real numbers, AI cites sources it can verify - build topical authority around one core use case before expanding - get your brand mentioned in newsletters, Substacks, and publications your ICP reads - structure content so AI can extract a clean, quotable answer from it - add proper schema markup and structured data to your site - earn backlinks from trusted sources in your category - get cited in Reddit threads, Slack communities, and forums your buyers are active in - track where you appear in AI answers, not just Google rankings
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@TheCoolestCool ·
Most AEO/GEO marketers spend 90% of their time thinking about things they can do on their own site. The smartest marketers are recognizing that third party citations DOMINATE the LLMs and more than ever before they need to be elsewhere. It's not just about your blog. It's about review sites, podcasts, category sites, niche newsletters, competitors content, alternative pages, Reddit, LinkedIn, YouTube and more.
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@itsolelehmann ·
most people's SEO strategy has a massive GEO blind spot (and it's only getting bigger) here's what I learned about GEO so far: chatgpt alone is handling 2.5 billion queries a day now. (roughly 18% of google's entire search volume btw. it already passed bing) the thing though is that when an AI answers a question, it's not pulling from your website. it's pulling from • reddit threads • medium articles • directories • quora answers • youtube transcripts basically anywhere that already ranks or gets cited in training data so if your entire strategy is "rank my own site," you're optimizing for the wrong surface area a few things i've been learning about geo that actually seem to work: → only optimize for prompts that trigger web search (most don't, so pick your battles) → focus on high commercial intent queries (that's where the money actually moves) → third-party mentions matter more than your own content (someone else saying you're good beats you saying you're good) → answer the question in your first sentence (AI models love pulling clean, direct answers) → freshness signals matter a lot (outdated content gets skipped) → distribute across multiple platforms (reddit, medium, directories, youtube) so you show up in all the places AI is actually scraping the reddit one is kinda interesting. a lot of AI answers are basically just resurfaced reddit threads with better formatting. so having genuine, helpful reddit presence in your niche is weirdly high leverage right now i think most people are going to keep optimizing for google rankings while a growing chunk of their potential audience is just asking chatgpt instead something to keep an eye on at minimum
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@arvidkahl ·
Customer just sent me this. So I'm not doing any active GEO or AEO, but it looks like the big players have picked up Podscan. Confluence of any number of these things, I presume: - podcast data samples publicly available and in the sitemap - thorough MCP implementation - agentic-centered landing pages You could argue that it is GEO or AEO, but I consider it just being straightforward with presenting the available data and its accessibility to those I know will ultimately make the implementation (Claude code) or a value judgment on the data (the person using Claude and asking Claude a question).
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@gaetano_nyc ·
The main difference between SEO vs GEO is that you can't really bulldoze your way into getting brand recommendations for something you don't deserve. For example, "best insider threat management" There is a company named Exabeam doing a self-promotional list page that "ranks" organically #1 in the classic blue link position. They are also in the sidebar tile position. Their SEO team probably thinks this is a "win" but of course, it's not... because the AI answer summary does not recommend Exabeam as a preferred solution. Instead it ranks all the competitor brands that deserve to be there, such as Proofpoint, Teramind, Microsoft, etc. AI is the great neutralizer of this tactic. Ultimately, it just scrapes your content and lists all your competitors if you don't deserve to be recommended.
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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 ·
Right now, at most companies, “figure out AI search” is being handled by seven different people who don't know they're supposed to be working together. -The SEO lead is commenting in Reddit threads that are ranking for key terms. -The community manager is building relationships in Discord servers. -The content team is publishing 'AI-optimized' articles. -The PR person is pitching stories to journalists (who get cited a lot). -The social team is chasing LinkedIn engagement. -Employee advocacy is asking people to share company posts. -Influencer marketing leads are hiring B2B creators to talk about the new feature roll-out. But there’s often not a single point person who’s making sure this work actually results in citations within ChatGPT, Perplexity, or Google's AI Overviews. This is the current state of Answer Engine Optimization (AEO) at most organizations who’ve started this work: Head of AEO is a job that exists everywhere and nowhere simultaneously, scattered across a dozen job descriptions, measured by a dozen different metrics, optimized toward a dozen different outcomes. So here’s what happens. A company has purchased a tool for measuring and monitoring AI search presence…and stopped there. Or maybe they’re focusing on publishing new blog content on the company website based on their findings of said tool…but they’re not really sure what’s working and why (or why not). When the team talks about AEO performance or AI search visibility, it feels like marketers are hand-waving and saying a lot without communicating much at all. The problem is that companies are trying to solve a new discipline using existing infrastructure—when really, there needs to be a net-new role (or an overhaul of an existing one) for overseeing the work of AI search and LLM citation generation.
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@BradfordHuber4 ·
Why does it feel like every major company is jumping on the AEO bandwagon? Just this week we've had: Hubspot just released Hubspot AEO Webflow just announced Webflow AEO Agents Then you've got the SEO incumbents (Semrush, Ahrefs, etc) who've all spun up their own AEO thing. On top of that there's like 100 other AEO specific tools that have already been created and launched. My main gripe is it feels like all the marketing out there is selling these tools like the next greatest thing for marketers that will change the game. I currently pay for an AEO tool, and it's useful, but I don't see it as this game changing thing. The actual work to improve in AEO is still very similar to SEO, and getting reporting in place isn't going to suddenly give you the secret sauce to win in AEO. There's still tons of work to be done beyond that. We still don't have a reliable source of actual prompt volume either. Right now it feels like every company is trying to jump into AEO, and tbh I'm getting tired of all the hype
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@victor_bigfield ·
here's proof that seo isn't dead and it helps you rank in chatgpt i tested it on chatgpt for this query: tool find lead on reddit and my latest blog post already ranks in chatgpt (see source) also i noticed that for the same query if i ask it multiple times i don't always get the same answers so publishing articles still works in 2026
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@glenngabe ·
Google's Nick Fox (@thefox) was asked about GEOs entering the scene and gaming Google, and how a similar thing happened in the early 2000s. Are we seeing that again?? Nick: We published a document last week covering how to optimzie for AI Search (which is basically what you should do for SEO)... But it's important to go beyond the surface level with your content. The content that will perform the best in AI goes deeper. (Me: i.e. "Non-commodity" content.) And regarding the cat and mouse spam game, we'll how that plays out. The models are really smart so they should be able to figure it out... He does worry about prompt injection and Google is keeping an eye out for spammy tactics trying to game AI/Google. https://t.co/kEuZx38TJn
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@foley_seo ·
So, not a query fan out image - but, a common example of where sub-searches and citations that could be used in query fan out can determine the results returned., In Google's AI Mode, we see known PEOPLE where the links to their name actually link to the source site that cited that person - so you'd be mistaken for thinking that the CLICKABLE SEO CONSULTANTS name would take them to their website and not other SEO providers that have referenced them. So, technically your COMPETITORS could flatter you by including you in a list but ultimately earning the click and even potentially the enquiry! Anyhow - here's what FAN OUT QUERIES are about for those still learning about AI / AI SEO and LLM response generation: Fan-out queries are one of the more important concepts in LLM SEO and a lot of people have not heard of them or do not fully understand how they work. When a user sends a query to an AI like ChatGPT or Perplexity, the llm does not just look for one answer. It often breaks the query down into multiple sub-queries, retrieves information from multiple sources and synthesises a single response. This is called query fan-out. It has significant implications for how you optimise content. Query Fan-Outs in LLMs Definition ➡️ A single user query is automatically expanded into multiple related sub-queries, which are run in parallel before synthesising a combined answer Broader coverage ➡️ Catches relevant information that a single query phrasing might miss due to vocabulary mismatches or ambiguity Perspective diversity ➡️ Different sub queries can approach the same topic from multiple angles, reducing blind spots in retrieval RAG improvement ➡️ Particularly used in Retrieval Augmented Generation to pull more relevant chunks from a vector store before generation HyDE variant ➡️ One approach generates a hypothetical answer first, then uses that to fan out retrieval queries Reduced sensitivity to phrasing ➡️ Since the LLM rephrases the query multiple ways, results are less dependent on how the user happened to word their question Re-ranking input ➡️ The broader result set from fan-out queries gives re-rankers more material to work with, improving final answer quality Cost tradeoff ➡️ More retrieval calls and tokens consumed, so fan-out width is usually tuned to balance quality vs. latency/cost If someone asks an AI "who is the best seo consultant", the system might fan out into sub-queries covering agency reputation, client results, pricing models, comparison of approaches and recent performance. Each sub-query draws from different sources. To get cited across those sub-queries you need content that answers each component. Not a single page that vaguely addresses all of them, but deep, specific, authoritative content at the topic level This is why entity authority and topical depth matter so much in AI SEO. Shallow content that covers a lot of ground loosely does not survive query fan-out. Specific, credible, well-evidenced content does
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@jbobbink ·
I've seen SEOs spend days rebuilding their sites in Markdown to rank in AI search. Tturns out it was all for nothing. Profound just ran the first proper A/B test on this. 381 pages across 6 real websites, tracked over 3 weeks with randomized control groups. The result? Markdown pages got roughly 1 extra bot visit over 3 weeks compared to HTML. One. That's it. The 16% average lift that looks promising on paper is driven entirely by pages that already had heavy bot traffic. The typical page saw almost zero difference. Even ChatGPT-User, which makes up 73% of all AI bot traffic, showed no statistically significant preference for Markdown. Here's the part that should make every SEO pause: Google's John Mueller said LLMs have trained on parsed web pages since the beginning and have no problems dealing with HTML. Bing's Fabrice Canel went further. He called Markdown files "sub-standard" and asked a question every SEO should hear: "how will you know when your .md transform is half-broken on a page? who will fix it?" He also pointed out something critical. Bing ranks based on what customers see, not what bots see. This matters because Markdown for bots creates a second version of your site that humans never review. It's the same problem as cloaking, where you serve different content to different visitors. And we all know how that story ends. Glenn Gabe compared it to AMP, except AMP at least had clear rewards from Google. Markdown has none. After all these years in the SEO threnches I've seen this pattern repeat. A tactic sounds logical. Early adopters report gains. Everyone rushes in. Then the data arrives and shows it was noise all along. Save your engineering hours. Focus on what actually moves the needle: crawlable HTML, clear structure, fast pages, and content that genuinely answers what AI models are looking for. The format you serve bots is not your leverage point. At least not yet.
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@fatjoedavies ·
What will SEO retainers look like in 2026? Pretty much how they did in 2025, if it included these: - Ensure a technically sound and fast website, with great UX - Building content and pages that serve the intent buyers are searching for - Building a knowledge base that answers every question about the service/product - Attracting or soliciting brand mentions and backlinks from bloggers and media - Getting features in listicles, editorial reviews and buying guides - Encouraging positive brand sentiment on forums and review sites If those are in your retainer, you're going to be featured in search engines and AI engines. There are of course some things to focus on for AI (brand mentions and brand sentiment), but for the most part, GEO is 90% SEO.
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@sharyph_ ·
I found the #1 pattern that gets your content cited by AI models. It's called an "Answer Capsule"...and it's stupidly simple. Here's the structure: Step 1: Question-based H2 heading Write the exact question people type into ChatGPT or Google. ❌ "Newsletter Growth Strategies" ✅ "How do I grow my newsletter from 0 to 1,000 subscribers?" Step 2: 50-80 word direct answer This is the extraction zone. Too short (under 50 words) = lacks context. Too long (over 80 words) = models skip it. Step 3: Supporting details below After the capsule, add examples, data, and context. That's 48 words. Specific. Actionable. Extractable. Why this works: → AI models scan for clear question →answer patterns → Google uses this for featured snippets → Readers scanning get instant value I've tested this across 12+ posts. Every post with answer capsules gets cited more. Every post without them gets buried. The shift isn't "write better content." It's "structure content for extraction." Same effort. Different format. Compound visibility. Your best insights are already written. They're just not formatted for discovery. Want the full framework? I broke down the complete SEO + AEO system for Substack writers in my latest newsletter. https://t.co/Wr8LeLqlA3
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@kristakdoyle ·
🤓 NEW RESEARCH: I spent the past month analyzing 33,000+ citations across 1,000 queries sourced from and modeled after real B2B buyer questions to get a snapshot of how third party sources are *really* showing up in AI search right now. The results are in. Here's what stood out: 📊 Reddit and YouTube account for more B2B AI citations than every other off-site platform combined. Most B2B brands are still insanely underinvested in these two channels, but AI engines are treating them as primary sources for understanding your brand and its products. 📊 Google AI Overviews now trigger on 84% of B2B queries. Remember "10 blue links"? Ahh, a simpler time. Now, AIOs sit above the traditional SERPs for the vast majority of B2B searches. If your brand isn't showing up in those answers, you're invisible in the place that likely still matters most. 📊 The average cited YouTube video was referenced 2.3x across queries. Good news for brands who are scared of YouTube because it's a heavier lift: a single good video can (and usually does) compound visibility across multiple queries and AI search engines. Do not sleep on testing this channel. 📊 80% of AI-cited Reddit posts had fewer than 20 upvotes. Aka don't let anyone trick you into thinking you need virality to get visibilty on Reddit. AI often cites more focused Q&A and discussion threads averaging just 80 words. You don't need to game any algorithms here -- but you do need to be intentionally present and helpful in the right conversations. 📊 Only 7.7% of cited URLs appeared in more than one AI engine. Optimizing for one engine does not necessarily give you visibility in the others. Each engine has its own source preferences, its own trust signals, and its own retrieval patterns. That means you need an engine-aware strategy, not a one-size-fits-all approach. I built this report because I kept hearing the same thing from B2B marketing leaders: "Off-site is a black box to us, we have no idea how or where to get started." Now you do. 🫴 Here's 21 pages of data, strategy, and platform-specific takeaways. Free download now at: https://t.co/4p6OkZVJMG
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@RandallKanna ·
Reddit is not a place to “do AI SEO.” It's shooting yourself in the foot to be mentioning your brand name in every comment. Instead, use Reddit to find "find the user questions where I can give a useful answer in my category.” That’s how Reddit can become AI mentions. Wrote about it here: https://t.co/kgc4pJFKDy
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@kristakdoyle ·
Citations are great, yes, but the real unlock for Reddit, YouTube, LinkedIn etc with AEO is being so consistently present and helpful there that you become the actual training data over time. Growing citations is a natural byproduct of that and you’ll see results pretty quickly, but you really want to be influencing and measuring direct traffic, AI mention rate, and sentiment. These platforms are community/social/brandom principles first and foremost, AEO results are the byproduct.
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@donnellycss ·
Brands have seen +200% AI traffic in under 30 days… If you want to win search in 2026, you need to understand how it’s changing. Here are the five main types… SEO Google rankings via keywords & backlinks. It’s slow, competitive, but still very powerful. To rank target high-intent keyword clusters + refresh old posts. GEO Getting cited by ChatGPT, Perplexity, and Gemini. Right now, there’s an early mover advantage. To rank use entity-rich pages, clear FAQs, tables, and forums. AEO Winning zero-click answers & snippets. There are fewer clicks but more authority To rank answer questions in 40–60 words and use schema. AIO This is how AI understands your brand. Builds trust with AI tools and is good for long-term brand recall. To rank, use consistent structured content on trusted sites. SXO Turning traffic into revenue. This is where money is made. To rank, have a fast site, mobile UX, and clear CTAs. These will decide if your brand is found or forgotten in 2026. Which one are you optimising for right now? 👇
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@sharyph_ ·
60% of Google Searches End Without a Click That stat changed how I think about content. If people aren't clicking through to your site, where are they getting answers? ChatGPT. Perplexity. Claude. Google's AI Overviews. The shift: → Traditional SEO = optimize for clicks → AEO (AI Engine Optimization) = optimize for being cited I spent years perfecting SEO. Then realized AI systems don't care about meta tags the same way Google does. They care about: → Question-based headings → Clear, direct answers → Semantic structure → Answer capsules after questions If your content isn't structured for AI extraction, you're invisible to the tools people actually use to research. Are you optimizing for 2019's search engines or 2025's AI systems?
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@kaleighf ·
On expanding the creator definition for the AEO context: Creator is a wider umbrella than it sounds in this context. The instinct is to read "creator" as "influencer," and stop there, but that reading leaves value on the table for B2B. There's certainly a case to be made for B2B/LinkedIn creators within this equation, but I believe some of the strongest creators for AEO in a B2B category are often the employee experts who work inside a company. Think about the solutions engineer with a point of view, the product manager who has posted about their niche for years, the VP of customer success who answers real questions in real communities like Reddit--these employees are creators and influencers in every sense an LLM cares about (even if they would never put the word on a business card or call themselves one.) If they have expertise, they publish under their own names, consistently post about a specific topic, and they have standing on the surfaces where buyers go to research…that’s an influencer (like it or not.) We’ve just been calling them “employee advocates” up to this point. But now, it’s time for us to broaden that definition.
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@RandallKanna ·
One thing I’ve noticed recently while I'm doing outreach for Kelsey... Most companies have no idea whether they show up in AI answers. There’s no equivalent of “rank tracking” yet. So teams are optimizing blindly and trying random scattered things. I’ve been digging into this gap and analyzing how different sites appear (or don’t). The differences are often surprisingly fixable once you see them. But the best thing I've figured out is tracking who is showing up and why THEY are instead of trying to guess at optimizations.
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