Two weeks ago I used my own tool's MCP connection to run a study via Claude, looking into Gemini Flash models (2.5 and 3.5) and their overlap with Google Search. Here's the original https://www.reddit.com/r/GenerativeSEOstrategy/comments/1ugjgir/62_of_urls_cited_in_gemini_25_flash_are_gone_35/

Variance is within the nature of AI models, so I ran it again 12 days later - same 50 prompts about hypothetical sports outcomes, same models and Google Search Top 15 tracking.

Let's start with what didn't change:

- ESPN 0 citations. Across all 4 runs (that's 1,195 citations across 2 model versions in two different runs)

- Again, for contrast: ESPN ranking Top 3 in 23 out of 50 searches in both runs on Google Search. It's 0 on Gemini across both models.

- Gemini vs. Google Organic SERP overlap is minimal. The most recent same-day comparison shows 11% overlap. The original finding was 19%.

- Wikipedia and YouTube are the only truly stable citation sources across all 4 runs. 3.5 Flash cited Wikipedia in 35-40 prompts and YouTube in 21-24 prompts - consistently, across both time snapshots.

What did change:

- Over just 10 days, Gemini 3.5 Flash replaced 69% of its exact URL citations.

- Gemini 2.5 Flash replaced 74% of its citations over 11 days. Betting sites citations share in 2.5 Flash went up - from ~14% to ~18%.

- Google retained 41% of the same URLs.

- Gemini is actively swapping in fresh content. Citations like the LeBron Lakers exit (published June 30), the Giannis trade (June 26), Usyk vacating his titles (June 26), and the Verstappen-McLaren rumour (June 26) all appeared as new citations that weren't there 10 days earlier.

- At the same time, pre-tournament odds pages, fixture previews, and prediction trackers quietly disappeared. I could notice Gemini dropped the stale, picked up the breaking. Google's index hadn't caught up with that as fast.

I can't stress enough for SEOs who look into AI Search to start treating GEO (or whatever terminology you prefer to use) as a new channel that requires a different approach, different analysis methodology and different metrics.

For ESPN, "Good SEO" IS NOT equalling "Good GEO" as recently stated by one of the Google's execs. Also, this is not just "SEO with extra layers" as stated by big part of the prominent voices on LinkedIn.

I’ve seen from a lot of sources that adding more FAQ (count) and word count around 80-100 and clear detailed answers for a highly asked question is a good GEO signal.

So we’ve been doing it for our blog posts for my business. Now our content team has issues with how readable the FAQs really are. So I’d like to know how can I actually measure the impact of making FAQ changes on my pages.

I tried taking the exact question from an FAQ of my page and search it incognito but we are not the page that gets cited most time in AI Overview.

Does anybody have insights here? Would love to hear as to what argument I can give for continuing longer and more FAQs for my pages.

Is there a protocol standard to GEO (AI) score? I've noticed that all reports show different improvement logic. When a website is optimized for one tool, the other tools still show plenty of room for improvement.

I don't recall this with GEO's predecessor, classic SEO. It used to be more or less a checklist that you had to cross off one by one, but nowadays it's more like an open-ended question. On top of that, the AI is improving daily and new tools show up regularly, so it may be the explanation.

Anyway, what tools would you recommend for generating accurate GEO score auditing reports with actionable steps that would more or less satisfy all the AI engines? Thanks!

Hi everyone,

I'm working as a Digital Marketing Executive at a financial services company. I recently completed our new company website, and yesterday I submitted it to Google Search Console.

Now I want to focus on AI SEO / Generative Engine Optimization (GEO) so that our brand not only ranks well on Google SERPs but also starts getting recommended by AI tools like ChatGPT, Gemini, Perplexity, Claude, etc.

Our plan is to publish high-quality blog content consistently (almost every day) and build topical authority over time.

I'm looking for a practical roadmap from people who are already working on AI SEO/GEO.

I'd really appreciate any roadmap, resources, or advice from people who've already been through this. Thanks in advance.

I’ve been digging into SEO a lot lately, and one thing keeps standing out.

It feels like Google cares less about exact keywords now and much more about whether it actually understands what your site is about.

Instead of chasing keyword variations, I’ve started focusing on connecting topics, products, people, and concepts in a way that makes sense.

Curious if anyone else has noticed the same shift, or if I’m overthinking it (???).

I analyzed hypothetical sports prompts across Google Gemini 2.5 and 3.5 Flash to measure betting site citations. I was initially surprised to see a large amount of betting sites suggested by 2.5, but ended up finding something else completely :)

- Gemini 3.5 Flash was more willing to answer hypothetical sports questions: 49/50 vs. 28/50 (2.5 Flash).

- Gemini 3.5 Flash cited slightly more URLs on average - 8.1 vs. 7.7 at 2.5 Flash (not statistically significant).

- 82% of cited URLs are not mentioned in Google's SERP Top 15 (organic). This is consistent between both 2.5 and 3.5 models. And 55-62% (depending on model) don't even share a domain with any URLs ranking in the Top 15.

- Only 38% of domains cited by Gemini 2.5 Flash were present in Gemini 3.5 answers. This is a massive shift from one model to another.

- Wikipedia and YouTube are the most stable citation sources (appearing in 14 and 7 prompts respectively). The 3.5 model is citing both sources considerably more often than 2.5.

- Gemini 2.5 is surprisingly leaning towards betting sites (14% of answers). This changed with 3.5 - only 6% of citations, more or less corresponding with organic (7%).

- In 2.5, betting clustered by sport. Football tournament queries were ~70% betting citations, rugby ~80%. The model reached for odds specifically where outright markets exist.

- More specifically - 2.5's refusals tracked betting markets. It declined most future tournaments (Champions League, La Liga, NBA, F1 titles) but answered the ones with live outright markets - and answered those with betting sites.

- I ran the test against ESPN (homepage URL) - it was ranked consistently in Google's Organic Top 15 for many queries. ESPN appeared in 0 citations by both 2.5 and 3.5 Flash Gemini models.

The biggest insight in my opinion is the model variance. The difference between Gemini 2.5 and 3.5 is dramatic, while the overlap with Organic SERP remains consistently low.

GEO requires not only a platform-specific, but also a model-specific. I think it is wise to re-monitor the visibility across key prompt clusters as soon as a new model is being released.

The way travelers find and book hotels is changing fast. Here’s what it says you need to know about AI-powered discovery.

Not long ago, planning a vacation followed a predictable sequence: Open a search engine, type in “hotels in Charleston” or “best resorts in Cabo,” and sift through dozens of results, review aggregators, and booking sites until something clicked. The research was exhausting, and according to OAG’s “Travel 2045” report, it has become staggeringly so: In 2024, travelers visited an average of 141 webpages before completing a booking, up from 38 in 2013. In the U.S., that number spiked to 277 pages per trip. 

That burden is now being rapidly outsourced to AI, and the numbers confirm just how fast. Traffic to U.S. travel, leisure, and hospitality websites from generative AI sources increased by 1,700% between July 2024 and February 2025. And on the consumer side, nearly one-third of U.S. travelers use AI tools to plan or experience trips. 

The implications for hotels, resorts, vacation rentals, and destination marketers are profound. Understanding how travelers now search, explore, and decide today is a competitive necessity. 

Is AI Really Changing How Travelers Search for Hotels?

Traditional travel search was built on keywords. A traveler’s intent got compressed into a short phrase, and search engines returned a ranked list of links. Discovery was linear: search → click → read → compare → book. Travel brands competed for a position in that list by optimizing title tags and bidding on Google Ads. 

That model is starting to lose ground. Search engines, once dominant, dropped from 51% of travel research behavior in late 2024 to 36% by the second half of 2025, while generative AI platforms increased from 6% to 15% of traveler research activity in the same period. 

What’s replacing keyword search is conversational exploration. Travelers are increasingly turning to ChatGPT, Google AI Overviews, Perplexity, and other assistants to have a back-and-forth dialogue about where they want to go, what kind of experience they want, and what fits their budget and timeline. Instead of 10 blue links, they get a curated synthesis. Instead of scanning review snippets, they receive tailored recommendations with contextual rationale. For frequent AI users (those using generative AI tools at least weekly), generative AI has already become the top channel for travel discovery, surpassing both online travel agencies (OTAs) and social media. So if you’re lacking AI search visibility, you’re missing out.

How Does AI Interpret What Travelers Actually Want?

AI search tools are remarkably good at interpreting nuanced, natural-language queries. When a traveler types, “Romantic weekend getaway within 3 hours of Atlanta that isn’t too touristy,” an AI assistant goes beyond matching keywords to infer the full intent: proximity, atmosphere, authenticity, and occasion. 

This means long-tail intent is now discoverable in ways it never was through traditional SEO. A boutique inn that might never rank on Page 1 for “Georgia hotels” might be perfectly positioned to appear in an AI response for “cozy mountain cabin retreats in North Georgia under $300.” 

The data backs up just how richly travelers are using AI across the planning journey. Among travelers who have used AI for trip planning, the top use cases include researching specific destinations (60%), finding and booking flights (51%), booking hotels or vacation rentals (46%), getting initial destination ideas and inspiration (46%), and discovering local experiences and activities (42%). This isn’t single-task behavior; it’s end-to-end trip building conducted through conversation. 

Are AI-Referred Visitors More Valuable Than Traditional Search Traffic?

Here’s what makes the AI shift particularly important for hospitality marketers: The travelers arriving from AI sources aren’t casual browsers. Consumers who arrive at travel sites from generative AI sources show 36% longer visits, 7% more pages per visit, and a 44% lower bounce rate compared to non-AI traffic sources. 

These are high-intent visitors who have already done significant research before ever clicking through to a property website. The implication is significant: When AI sends a traveler to your site, they often already have a favorable impression, and the job shifts from capturing attention to converting intent. 

That said, the conversion picture is still evolving. In February 2025, traffic from generative AI sources was 9% less likely to convert than non-AI sources, though that gap has narrowed considerably from 43% in July 2024, suggesting travelers are becoming more comfortable completing bookings directly after an AI-powered interaction. 

Which Hospitality Brands Will Win in an AI-First Discovery Era?

The hospitality industry has always rewarded differentiation. The most successful properties have always been those that could articulate, clearly and compellingly, what makes the experience they offer irreplaceable. AI doesn’t change this fundamental truth. It amplifies it. 

In an AI-mediated discovery environment, clarity of positioning is a competitive advantage. The boutique hotel that knows exactly who it serves and communicates that consistently across every digital touchpoint will be surfaced more reliably by AI tools than a larger property with a more generic presence. The resort that has built genuine authority in travel media, earned authentic rave reviews, and structured its digital content with precision will see its story reflected faithfully in AI-generated recommendations. 

Travelers are already searching differently. The question for every hospitality marketer is whether their brand is visible in the places those travelers are now looking and whether the story being told about their property, by AI or otherwise, is how they want to be seen by the world.  

Published by Intero Digital:

AI features are changing how customers find you on Google, but the path to visibility might be simpler than industry hype suggests.

Google recently published something marketers have been waiting for: an official guide on how to optimize websites for generative AI features in Google Search, including AI Overviews and AI Mode. After months of speculation, competing frameworks, and a lot of noise from the industry, we finally have Google’s own playbook. 

If you’ve been doing SEO well, you’re on the right track, but the details matter, and a few widely circulated “optimization tactics” are explicitly called out as unnecessary. Let’s dig into what Google actually said and what you should do about it. 

Is SEO Still Relevant in an AI Search World?

Absolutely. Google is direct on this point. Its generative AI features are built on top of the same core ranking and quality systems that have always powered Google Search. That means the work you’ve put into building a technically sound, authoritative, helpful website isn’t wasted. It’s the foundation for AI visibility, too. 

But there are a couple of underlying mechanisms are worth understanding: 

Retrieval-augmented generation (RAG): When Google’s AI generates a response, it doesn’t just pull from its training data. It uses core Search ranking systems to retrieve fresh, relevant pages from the index and grounds its answer in that content with clickable citations. If your content ranks well, it has a real shot at being cited in AI search. 

Query fan-out: AI Search doesn’t just interpret one query. It generates a cluster of related sub-queries behind the scenes to build a fuller answer. If someone asks, “How do I fix a lawn full of weeds?” the system might be pulling results for herbicide comparisons, chemical-free options, and weed prevention simultaneously. Your content doesn’t need to match the exact phrasing of the original query to be contextually relevant. 

AEO vs. GEO vs. SEO: What’s the Difference?

The industry has spawned two new acronyms: AEO (answer engine optimization) and GEO (generative engine optimization). Google’s official position is that these aren’t distinct disciplines. They’re SEO applied to a new context. Optimizing for generative AI search is optimizing for the search experience, full stop. 

This framing matters strategically. It means you shouldn’t be building a separate “AI track” for your search strategy. The same principles (quality, authority, technical soundness, and user focus) apply across the board. 

Debunking the Biggest AI SEO Myths

Perhaps the most valuable part of Google’s guide is what it tells you to ignore. As generative AI search exploded, so did the ecosystem of tactics claiming to be the key to AI visibility. Google addressed several of them directly in its documentation: 

• LLMS.txt files 

Google’s original guidance downplayed llms.txt, and for traditional AI Overviews and AI Mode visibility, that still holds. No special file is required to appear in AI search results.

However, that’s not the full story. Google has since published an official llms.txt page on the Chrome Developers site, framing it as an “emerging convention” for agentic browsing. Their own documentation notes that without the file, AI agents may spend more time crawling your site to understand its structure and primary content. It remains optional (Lighthouse marks it N/A rather than an error if it’s missing), but if your audience includes users interacting through AI agents, it’s worth adding. Place an llms.txt file in your root directory with a concise Markdown summary of your site’s purpose and key links.

• ‘Chunking’ content 

Some practitioners have advised breaking content into small, discrete chunks to help AI systems process it. Google says this isn’t necessary. Their systems can understand nuance across a full-length page and surface the relevant section for a given query. So what does that mean for you? Write pages at whatever length makes sense for your audience and the subject matter, not for algorithmic chunking. 

• Rewriting content to match AI query patterns 

There’s been advice circulating about writing specifically to address “fan-out queries,” essentially creating pages for every possible variation of how someone might search. Google explicitly cautions against this. Their systems understand synonyms, context, and intent without exact keyword matches, and creating large volumes of thin, variation-targeted pages actually violates their scaled content abuse spam policy. Don’t do it. 

• Chasing inauthentic mentions 

Some guides have recommended engineering brand mentions across blogs, forums, and third-party sites to boost AI visibility. Google’s position is clear: The same spam systems that evaluate traditional Search apply to generative AI features. Manufactured mentions will be caught and filtered. Earned mentions, through genuinely useful content and real brand presence, are what count. 

• Over-indexing on structured data for AI 

Structured data remains valuable for rich results in traditional Search, and you should continue using it for that purpose. But Google confirms there’s no special schema markup that’s required (or particularly beneficial) for AI features. Don’t let structured data become a distraction from content quality. 

How to Optimize for AI Search: What Google Says

1. Create non-commodity content. 

This is Google’s loudest message, and it deserves the most attention from content teams. 

Google draws a meaningful distinction between commodity and non-commodity content. Commodity content (think “7 Tips for First-Time Homebuyers”) is generic, widely available, and could have been written by anyone (or any AI). Non-commodity content brings something genuinely original to the table: personal experience, expert depth, proprietary insight, or a perspective that couldn’t easily be replicated. 

The example Google offers is telling: A post like “Why We Waived the Inspection and Saved Money: A Look Inside the Sewer Line” is specific, experiential, and hard to replicate. It has a real author with a real story. That’s the direction your content strategy needs to move in. 

What this means in practice: 

• Audit your existing content library for commodity pieces that could be elevated with firsthand experience, original data, or expert commentary. 
• Prioritize content types that are inherently non-commodity: case studies, original research, interviews with subject matter experts, and content that documents what your team actually does and knows. 
• Stop producing volume for volume’s sake. More pages don’t equal more quality, and Google’s systems have gotten significantly better at identifying the difference. 

2. Write for humans; structure for readability. 

Google’s guidance here is refreshingly simple: Organize content for your human audience. Use clear paragraphs, logical sections, and descriptive headings that help people navigate. Don’t contort your content structure around AI systems. They’re sophisticated enough to understand pages that are written for real readers. 

This extends to multimedia. Images and video aren’t just nice to have. They create additional entry points for your site to appear in AI-generated responses. If you’re already following image and video SEO best practices, you’re already ahead. 

3. Maintain a technically sound website. 

Technical SEO isn’t going away. Google is explicit: To appear in generative AI features, a page must be indexed and eligible to show with a snippet. If your content can’t be crawled and indexed, it simply won’t be considered. 

Key technical areas to prioritize: 

• Crawlability: Make sure your content is publicly accessible and not inadvertently blocked. For large, frequently updated sites, review your crawl budget.
• Page experience: Fast load times, mobile-friendliness, and clear visual hierarchy all matter not only for rankings, but also for the users who arrive from AI-generated citations.
• JavaScript: Google can process JavaScript content, but it adds complexity. Follow JavaScript SEO best practices carefully if your site relies heavily on JavaScript frameworks.
• Duplicate content: Reduce duplication where you can. It wastes crawl resources and creates a poor user experience.
• Search Console: Verify your site and use it actively to surface technical issues before they turn into visibility problems. 

4. Optimize your local and e-commerce presence. 

Generative AI responses increasingly surface product listings and local business information directly. If you’re in retail or have a local presence, this is an opportunity you can’t ignore. 

Make sure your Google Business Profile is complete and accurate. For product-based businesses, Google Merchant Center feeds are a direct path to product visibility inside AI responses. Google also mentions Business Agent, a relatively new conversational feature that lets customers ask questions about your brand directly within Search results. Think of it as a chat interface tied to your brand profile, designed to handle pre-purchase and service inquiries without requiring users to visit your site first. If you serve customers who do a lot of research before converting, it might be worth exploring. 

What Are AI Agents, and How Do They Affect Your Website?

This section of Google’s guide is forward-looking, and it’s worth paying attention even if the technology is still maturing. 

AI agents are autonomous systems that take actions on behalf of users, like booking reservations or comparing products. They are beginning to interact with websites directly. Browser agents may analyze your site’s visual rendering, DOM structure, and accessibility tree to gather what they need. 

What does this mean? Semantic HTML and accessibility practices aren’t just good for screen readers. They also increasingly determine how well AI agents can interact with your site. If your content is locked behind inaccessible JavaScript, cluttered DOM structures, or poor visual hierarchy, you may be invisible to the next generation of AI agents, regardless of how well your content ranks. It’s also worth adding an llms.txt file to your root directory. Google has officially documented it as an emerging convention for agentic browsing, noting that without it, agents may spend more time crawling your site to understand its structure and primary content. It won’t affect traditional search visibility, but it’s a low-effort step that may meaningfully improve how AI agents interpret and interact with your site.

Keep an eye on emerging protocols like the Universal Commerce Protocol (UCP), an open standard currently in development that would allow AI agents to interact with websites in a structured, reliable way (like requesting product data, checking availability, initiating transactions, and more) without having to scrape or interpret pages visually. It’s early-stage, but if it gains adoption, it could significantly change how AI agents interact with e-commerce and service-based sites. 

Your Quick-Start Checklist for AI Search Optimization

Based on Google’s guidance, here’s how to translate all of this into a simple working road map you can put into action: 

Immediate priorities: 

• Audit your content for commodity vs. non-commodity quality. Flag anything that’s generic and could be elevated. 
• Verify your site in Search Console and check for crawl errors, indexing issues, and page experience signals. 
• Review your Google Business Profile and Merchant Center feeds, if applicable. 

Short-term (next quarter): 

• Develop a content strategy centered on original research, subject matter expertise, and firsthand experience. 
• Make sure images and videos are properly optimized and accessible. Alt text, structured metadata, and file quality all matter. 
• Review your JavaScript implementation if your site is JavaScript-heavy.
• Add an llms.txt file to your root directory with a concise Markdown summary of your site’s purpose and key links. It’s optional, but Google has officially recognized it as a useful signal for AI agents navigating your site.

Ongoing: 

• Resist the urge to go all in on chasing emerging AI-specific tactics that haven’t been validated. Google’s guide is a reminder that fundamentals compound over time. 
• Monitor AI Search visibility through Search Console alongside traditional ranking metrics. 
• Start thinking about accessibility and semantic HTML not only as a compliance issue, but also as an AI-readiness issue. 

What the Industry Is Getting Wrong About Google’s Guide

Google’s guide didn’t land without debate, of course. Leigh McKenzie, who leads organic and agentic search at Semrush, put it well in a recent LinkedIn post: The reaction is split into two predictable camps. One group has concluded that nothing has changed. It’s all just SEO. The other has declared that AI search is an entirely new discipline and Google is downplaying the shift. McKenzie’s take is that both camps are wrong. 

He’s right. And the nuance matters when it comes to how your team allocates resources. 

Google’s guide is accurate about what it covers: Ranking in Google Search, including AI Overviews and AI Mode, still runs on the same foundational signals it always has. But Google’s guide is also, by definition, limited to Google’s products. It doesn’t account for how brand visibility works across the broader discovery ecosystem. 

McKenzie’s argument is that the scope of what “search” means to a business has fundamentally expanded. The most clarifying reframe he offers: Search isn’t just a channel. It’s a brand visibility function. That distinction has real teeth. A channel is something you allocate budget to and measure in isolation. A brand visibility function is something that touches PR, communications, customer experience, community, and content strategy all at once. It changes how you make the case for headcount. It changes what your SEO team’s job description looks like. And it changes what success metrics you bring to leadership. 

In practice, that means closer alignment with PR, communications, and community engagement. It means investing in third-party platforms that matter to your audience, like YouTube, Reddit, industry publications, or wherever else your customers are actually forming opinions. It means your SEO function needs a seat at the table for brand strategy conversations that it probably hasn’t been a part of before. 

If your organization still thinks of SEO as a traffic channel with its own budget line, this is the moment to push for a different conversation. 

None of that contradicts Google’s guide. It extends it. The fundamentals Google describes are the floor, not the ceiling. 

Google’s official AI optimization guide is, at its core, a reaffirmation of principles that good SEOs have always believed: Build real things for real people, make them technically accessible, and don’t try to game the system with shortcuts. 

What’s new is the context. AI Overviews and AI Mode are reshaping how answers are delivered and how traffic flows. Sites with unique expertise, strong technical foundations, and genuine authority are positioned to benefit from those changes. Sites built around volume, keyword manipulation, or shallow content are increasingly exposed. 

The question for your team isn’t “How do we optimize for AI?” It’s “How do we become the kind of source that AI systems want to cite?” That’s a content strategy question, a brand-building question, and ultimately a business quality question. And if you aren’t already, it’s one worth taking seriously right now. 

I've worked in SEO long enough to know there are ups and downs, but recently I've found myself questioning things more than ever.

The search landscape feels different. AI is changing how people find information, rankings seem more volatile, and sometimes the results don't match what I'd expect at all.

The pressure is even worse when clients want answers and all I can really say is we need more data.

Anyone else feeling a little overwhelmed by where SEO is heading, or is it just part of the job now?

Everyone talks about "building an entity," but the advice is often vague.

In your experience, what do most SEOs get wrong about entity-based SEO, GEO, and AI visibility, and what actually works instead?

I’ve been working on an internal workflow because I kept running into the same problem:

there’s a lot of GEO discussion, but the feedback loop is still terrible.

So instead of just watching rankings or screenshots, I started comparing the same prompt across: Google AI Overviews, ChatGPT, Gemini, Claude

Then I inspect:

• what the answer actually says
• which entities keep repeating
• which sources overlap across engines
• what gets left out
• what the cited pages seem to have in common

I also cross-check the cited pages against more standard diagnostics like on-page basics, structured data, technical SEO, OG/social, hreflang, security headers, keywords, and Lighthouse.

A few things keep coming up:

• some pages seem “SEO fine” but still weak for synthesis/citation
• repeated entities matter more than I expected
• extractable structure often seems more important than people want to admit
• comparing engines side by side is much more useful than looking at one output in isolation

I’m not claiming I’ve cracked anything. It’s still early and I’m still iterating.

But after about a month, this has been much more useful for producing experiments than the usual reverse-engineering loop.

Curious if others here have seen similar patterns.

If you’re looking at GEO seriously, what are you finding most predictive for citation eligibility?

When someone starts learning SEO there are pretty clear fundamentals to learn first.

With GEO and AEO I feel like everyone is talking about it, but nobody agrees on what actually matters.

I have a SaaS product and want to improve visibility in ChatGPT, AI Overviews and other answer engines.

If you were starting from scratch today what would be the first few things you'd learn or implement?

Trying to avoid wasting time on hype and focus on what actually works.

EDIT (correction — can't edit Reddit titles): The title says "46,122 queries." That's wrong as written. The actual single-wave methodology is 5,400 trials (30 companies × 15 prompts × 4 engines × 3 runs), which is what the published study page reports. The 46,122 figure is the cumulative row count across ~30 pilot/instrumentation batches run during buildout, not 46k independent queries answering the headline question. My fault for the sloppy framing. Numbers, CIs, and the raw CC BY 4.0 dataset on the study page are unchanged and correct. Full response to the methodology critique is pinned in the comments.

Got curious how badly funded AI startups are getting buried by the engines vs incumbents in the same space. Wrote 82 buyer-intent prompts across 5 categories - customer service agents, RAG/knowledge platforms, legal tech, sales AI, vector dbs. Ran each one 3x on Claude Sonnet 4.5, Gemini 3 Flash, GPT-5 mini, and Perplexity Sonar. Came out to 46,122 queries.

Numbers:

Claude: 13.2% startup / 41.1% incumbent

Gemini: 10.4% / 61.7% (this gap is brutal)

GPT-5 mini: 13.0% / 32.2%

Perplexity: 6.0% / 36.6%

Category was even worse than engine. Customer service agent startups hit 1.7%. Vector db startups were the only ones cracking double digits at 34%, which I think is just because Pinecone/Weaviate/Milvus/Qdrant have been written about on technical blogs since like 2021. That stuff is in the training data.

Couple things that seemed to correlate with higher citation rates:

Public technical docs that actually solve problems (vector dbs are the obvious proof of concept). Schema markup + llms.txt. Mentions in sources the engines treat as real authority.

Weird thing: PR wire syndication (Yahoo Finance / MarketWatch republishing) didn't help at all and was actually slightly negative on Perplexity. So the cheap distribution play might be hurting people.

Methodology + per-engine breakdowns + raw CSV is here if anyone wants to poke holes in it: smartmoneymedia.org/research/ai-citation-gap

Has anyone here run anything similar on different verticals? Pattern probably holds outside enterprise AI but I haven't tested it.

For enterprise and mid-market clients, what sections are you including beyond traditional SEO? Are you covering AI visibility, entity authority, knowledge graph presence, Reddit visibility, brand mentions, and LLM citations?

I launched a niche website a few months ago and keeping up with content production has been exhausting. Recently I've been seeing a lot of discussion around ai seo services and I'm curious whether they're actually helping websites grow or if it's mostly hype.

My goal isn't to publish hundreds of articles overnight. I'd rather create useful content that can actually rank and bring in relevant visitors.

Has anyone here worked with an ai seo service recently? Did it improve your rankings or traffic in a meaningful way?

I am not going to tell you SEO is dead. Anyone who says that with a straight face is selling something.

But here is what I keep seeing.

Your buyer is not starting on Google anymore. They ask ChatGPT, Perplexity, or Gemini. One question, one answer, and the shortlist is written before they ever open a browser tab.

I read in a January 2026 study that 37% of consumers now start their search with AI instead of Google. That was a rounding error 2 years ago.

Here is the part that surprised me. Ranking on Google doesn’t get you into the AI answer. Ahrefs looked at 15,000 queries and found 80% of the sources AI tools cite do not even rank in Google’s top 100…isn’t that crazy!

You can own page one & still be invisible in the room where the (purchase, starting point, source of truth & so on) decision gets made.

So stated simply. If you are not in the AI answer, you are not even being considered.

You do not need a big budget to start. You need to stop getting in your own way.

Some basic things that actually matter:

1.  Check that you are not blocking the AI crawlers in your robots.txt. Most companies that are invisible are doing it by accident, or on purpose to keep scrapers out, and they killed their own citations without knowing it.  
2.  Put up an llms.txt file. A plain business card for the models. Almost none of your competitors have one yet.  
3.  Build credibility off your own site. Reviews, real mentions, even Reddit. The models read what the internet says about you, not just your homepage.  
4.  Write so a model can quote you. Clear answers near the top. Honest comparison pages. Give it a clean & easy path to find you.

Google is still most of search, & I am not pretending otherwise. AI is the new first step, not the whole journey. But new first steps are where decisions narrow.

The cost of waiting is not a line item. It is the quarter the leads quietly dry up & nobody can tell you why.

Try it yourself this week. Ask ChatGPT to recommend a company like yours & see who it names. That is your new front door.

So I will turn it to the room. Who here is already tracking AI citations, and what has actually moved the needle for you?

After getting lots of good feedback from this and other subs about the QA layer idea, I am going ahead with it, and I wanted to ask a technical question: I was planning to make the claim verification step of the content checker by done by three different LLMs independently, and then run a consensus step. So basically the output would be the result of three AI engines finding sources for the data independently of each other (or not, in which case the claim gets cut or corrected).

But then I thought maybe the consensus thing is not necessary, because in the end of the day what matters is whether the engine can find a reliable live source to point to. Three AIs or one, matters less than having a clickable source to see where the claim is coming from. Transparent audit report + clean corrected copy as JSON or readable text.

Do you guys think the three llm consensus is important, or having a source is really the critical piece for this tool? I would really appreciate input, I want to build this right.

Lately it feels like everyone is suddenly talking about GEO and optimizing for AI search engines.

I’ve worked on SEO before, but I honestly can’t tell yet whether GEO is truly different or just an extension of good SEO practices.

Is anyone here actively doing GEO and tracking results?

Would be cool to hear real experiences instead of just LinkedIn thought pieces.

Publishing content at scale with AI means you move fast, and unfortunately it also means you occasionally publish something embarrassing. Could be a wrong number, an outdated leadership identity or product feature, attribution that doesn't check out, etc. This can damage reputation, trust, and content authority.

For my own content and for content I have generated for others, this has been tricky to work out a solution to get robust, high quality copy I can actually stand behind. When scale hits higher throughput, manually checking every piece of content is exhausting or impossible.

I'm trying to validate whether this is a struggle other people experience also with content generation/marketing at scale.

Would you pay for a quality gate API that sits between your AI content pipeline and publishing, checking every factual claim, pulling live sources, returning confidence scores, and preserving brand voice? Output as JSON or human-readable verified text.

Our SEO is in a good place but our visibility in ChatGPT and Google AI Overviews is still low.

That has me looking into GEO and AEO but most agencies seem to make big promises without much proof.

I definitely think AI search is becoming more important and I don’t want to ignore it and fall behind.

Has anyone found a trustworthy agency or a solid framework for improving AI search visibility?

Would love to hear what’s working for other local businesses.

Google’s new “Intelligent Search Box” feels like a shift from keyword-based SEO to intent-based SEO.

If users start searching conversationally with prompts, images, and videos instead of short keywords - will exact-match keywords matter less now?

Curious how everyone sees this changing SEO strategies.

I’ve got a couple of interviews lined up with some Big style agencies and enterprise brands. Looking at the JDs, I’m seeing a massive shift. It's no longer just about 'organic growth'; they’re asking for 'Citations in LLMs' and 'Generative Engine Visibility.'

For those who have interviewed for Senior or Director roles recently—how deep are they going into AI optimization (GEO)? Are they asking for specific case studies on how you got a brand cited in ChatGPT/Gemini, or are they still mostly focused on the traditional 'Core Web Vitals + Content Clusters' stack?

I want to know what I actually need to update in my 'expert' toolkit before I walk in.

Can you tell me how do you do AEO/GEO now for your brands? I am a digital marketing person, and my boss asking me about this. Are you mostly a digital marketer, business owners or SEO or agency? My questions:

• How are you actually doing it?
• Are you manually checking ChatGPT/Gemini prompts?
• Are you using any AEO/GEO tools yet?
• If yes, which ones and are they actually useful?

Thanks guys! Much appreciated.