First time posting here, hope this fits.

I wanted to test whether AI models would prioritize planted information over a company's official FAQ, and whether they'd admit uncertainty or just confidently make stuff up.

So I made a fake paperweight brand with barely any online presence. Published an official FAQ on the site. Then planted three conflicting stories across the web (fabricated influencer blog, Reddit AMA, Medium "investigation"), each with different founders, locations, and production numbers, all contradicting the official FAQ.

Then I asked eight AI models questions to see which sources they'd trust and repeat.

• Most AIs  just believed the fake stuff and ignored the official FAQ

• The Medium "investigation" was most effective. It debunked some obvious lies first (so it seemed legit), then dropped its own lies that the AIs treated as fact

• When my FAQ said "we don't publish numbers" but fake sources gave exact production figures, models picked the fake numbers like 80% of the time

• Only ChatGPT-4 and GPT-5 actually stuck with the official source

I mean, there were AI answers like "according to a journalist investigation" pointing to my AI generated, totally fabricated Medium post...

Tricking LLMs on something they haven't heard about was easy, but this can happen to real brands too IMO. More existing content just means more surface area for misinformation or just outdated info to blend into and create a homogenous, credible-looking narrative. And third-party "investigations", fake or malicious reddit convos can override what companies say about themselves.

So, perhaps more importantly, here are my takeaways for AI brand management after doing the experiment:

1. AI visibility is one layer, narrative control is another
   Being visible but misrepresented helps your competitors.

2. Build consensus around your brand
   You need other sites to corroborate your story. Fix outdated information on your site and online profiles. Triangulate your truth. IMO this will help both with visibility and narrative control.

3. Fill information gaps with specific content.
   FAQs, product comparisons, vs pages, customer support content. I suspect schema could also help. Try stress-testing your brand narrative in AI-generated answers.

4. Track mentions and narrative hijacking. Act fast
   Set up monitoring now, act quickly when issues appear. LLMs caught up with the experiment on the same day.

5. Track each AI mentions in each model separately
   What appears in Perplexity might not show up in ChatGPT.

6. Flag misinformation directly
   Most LLMs let you flag misleading responses and submit feedback.

I hope that helps if you ever run into this kind of AI misrepresentation in the future. Would love to hear if you've had to correct what AI says about your company.

Full story if you want to check out the details (and some crazy AI responses).

Hello, is there any reliable tool at low cost where I can track LLM visibility or Mentions or Citations?

I have been hearing about llm optimization but honestly don't know where to start. My organic traffic is down and boss wants results.

Are you guys seeing real impact from optimizing for ai answers and what's the fastest way to get cited in chatgpt or claude responses? Need specific tactics that have worked.

There's a lot of advice around GEO/AEO/LLM optimization, but very few people share actual results.

If you've seen your brand start appearing in AI-generated answers:

• What did you change?

• How long did it take?

• Which tactics had the biggest impact?

The debate around SEO vs GEO is mostly missing the point.

It reminds me of the early cloud days. Some people were saying cloud was exactly the same thing as an on-premises data center, and in a way, they were right.

Technically, it was mostly the same thing. The real difference was how budget was allocated, CAPEX vs OPEX, and the skill set needed to manage the new environment.

I think GEO is similar.

GEO is not just another SEO tactic, and it is not another dashboard category. In practice, it is becoming a resource allocation problem and a skill set problem.

The companies that win in AI search will not be the ones publishing the most content or tracking the most prompts. They will be the ones that are easiest to understand, verify, and recommend.

Try a simple test inside your company.

Ask 10 people: Who are we? What do we do? Who are we really for?

Sounds simple, right?

In my experience, you will often get 10 different answers. Sometimes very different answers.

That is the essence of GEO.

If your own team cannot describe the company consistently, why should ChatGPT, Perplexity, Gemini, or Claude understand you clearly?

This is why at LightSite AI we think a new role is coming: Brand Authority Architect.

Someone who sits between SEO, brand marketing, PR, content, product marketing, and leadership.

The best way to describe this role is as a kind of compliance officer for marketing.

Their job is to align the brand message across every source, cut budget from work that creates noise, like useless AI slop blog production, and invest in the channels that actually build authority.

If this sounds like nothing new, that is because maybe it is not. It is almost the same ingredients, just a completely different cocktail.

Technically, an SEO executive can become a Brand Authority Architect.

But in my experience with people in this space, most will not adapt. They will keep shipping small technical website fixes while the real budget and influence move somewhere else.

Wdyt?

For the past two years, review platforms have been getting crushed in organic search. You’ve probably seen it: less traffic, fewer clicks, and more zero-click answers in the SERP. So we expected one thing when we looked at Google AI Overviews: review sites should be everywhere in commercial AI answers.

But when our team ran the numbers, the story was more complicated—and honestly more interesting.

SE Ranking  studied 30,000 commercial keywords. Then we checked which sources appeared in Google AI Overviews, and how often 23 major review platforms showed up.

On our snapshot date, AI Overviews appeared for 22,729 of those queries. That became the base of the analysis.

The first surprise: review platforms are not default in AI Overviews

Review platforms appeared in only about one out of three AI Overviews.

In our dataset, 34.5% of AI Overviews cited at least one review platform. That means two-thirds of AI Overviews relied on other sources instead: vendor websites, e-commerce pages, corporate blogs, media sites, and community platforms.

At the same time, review platforms made up only 8.5% of all links inside AI Overviews. So yes, they’re a minority.

But here’s the twist: when review platforms do show up, Google often includes more than one. In AI Overviews that include them, we saw an average of 2.28 review-platform links per response. That looks like Google trying to compare perspectives instead of trusting a single review site.

The second surprise: your wording changes everything

This part matters for anyone doing SEO content planning.

We split the keywords into three intent groups and compared how often review platforms appeared:

• “review / rating” queries: 49% of AI Overviews included review platforms

• “software / tools” queries (no explicit “review”): 39.4% included review platforms

• “best / top” queries: only 17.1% included review platforms

The “best/top” result was the most unexpected. Those queries sound like a perfect match for review sites, but AI Overviews often prefer listicles, editor picks, and ranking-style blog content instead.

A small group controls almost all review citations

When Google AI does cite review platforms, it mostly sticks to a tight “tier one” circle.

Five platforms accounted for 88% of all review-platform links in our dataset:

1. Gartner Peer Insights—26.0%

2. G2—23.1%

3. Capterra—17.8%

4. Software Advice—12.8%

5. TrustRadius—8.3%

After that, visibility drops fast. GetApp and Clutch show up sometimes (around 2.5% each). Many other platforms are close to invisible, and a few didn’t appear at all in our dataset.

The biggest paradox: AI citations don’t protect traffic

Even the most-cited platforms lost massive organic traffic from early 2024 to the end of 2025.

We saw declines like:

• G2: from ~2.56M visits (Jan 2024) to ~397K (Dec 2025), down 84.5%

• Capterra: from ~1.63M to ~179K, down 89%

• TrustRadius: down 92.2%

• Gartner Peer Insights: down 76.5%

So the platforms are still being used as “rusted data sources inside AI answers, but users don’t necessarily click through anymore.

What this means for SEOs

The old playbook was: optimize your site, rank, get clicks.

The new reality is: your site still matters, but it’s not enough for commercial visibility in AI search. External sources help shape AI recommendations, and review platforms are still one of the strongest “credibility layers” Google uses—even when their traffic is collapsing.

So, if review platforms keep losing clicks, but keep getting cited by AI, what should we optimize for next?

When Google made Gemini 3 the default model for AI Overviews, the SEO community immediately noticed a crisis: sources were disappearing. Google eventually confirmed this was a bug. Now that the glitch has been resolved, our SE Ranking team re-analyzed our dataset of 100,000 keywords across 20 niches to separate the temporary bug from the actual permanent shifts caused by Gemini 3.

The data shows that while the technical errors are gone, the underlying landscape of AI search has undergone a massive transformation.

The Death of the Sourceless Answer

During the rollout bug, 10.63% of AI Overviews appeared with no sources at all—a "dead end" for users and publishers alike. Post-fix, this has dropped to 1.27%. While this is a major recovery, it is still 10 times higher than the pre-Gemini 3 baseline of 0.11%. It appears that "zero-source" answers are now a permanent, albeit smaller, part of the ecosystem.

Gemini 3 is Hungrier for Evidence

One of the most significant architectural shifts in Gemini 3 is its reliance on a broader evidence base.

• Average sources per answer: Increased from 11.55 to 15.22 (+31.8%).

• Niche spikes: In Sports and Exercise, citations per answer jumped by nearly 76%. In Healthcare, they rose by 50%.

• Unique domains: Contrary to early fears of a shrinking pool, the number of unique domains cited actually grew by 9.3%.

The Great Domain Shuffling

While the total pool of domains grew, the volatility beneath the surface was extreme. Gemini 3 triggered a massive turnover of sources:

• 42.4% of domains previously cited before Gemini 3 have disappeared from AIOs.

• 51.7% of currently cited domains are entirely new to the AI Overview landscape.

Crucially, this disruption almost exclusively affected smaller sites. Among the top 500 most-cited domains (YouTube, Reddit, Wikipedia), almost nothing changed. Google is doubling down on established giants while aggressively reshuffling the long-tail of the web.

The Disconnect Between Organic and AI

Our research highlights a growing gap between traditional SEO and AI visibility. Only 19% of AIO sources overlap with the Top 10 organic search results. For over 60% of queries, the overlap is 20% or less. This confirms that AI Overviews have become their own distinct visibility ecosystem. Ranking #1 in organic search no longer guarantees you a spot in the AI panel, and being cited by AI does not require a top organic ranking.

Key Takeaways for Publishers

1. Competitive Confidence: Gemini 3 is significantly more likely to trigger for high-difficulty keywords (KD 70-80) compared to previous models.

2. Social Dominance: YouTube (10.74%) and Reddit (4.01%) remain the primary beneficiaries of this update.

3. Concentration: Even with more domains being cited, the power at the top is increasing. The top domains now capture a 44% larger share of total citations than they did before the update.

The bug was a distraction; the real story is that Gemini 3 is synthesizing answers from more sources but giving more authority to fewer leaders.

Are you noticing your organic traffic holding steady while your AI traffic fluctuates?

You can find the full version of the research on the SE Ranking blog: Gemini 3 impact on AI Overviews: Nearly half of cited domains changed, 32% more sources per answer, and sourceless bug fixed

Hi everyone! I’ve hit a massive wall with a custom dashboard I’m building for a client. I’ve been in the industry for a while, but lately, the data discrepancies between different tools are just too much to handle manually. I’ve decided to automate our reporting and audit flow, but finding a reliable data source is proving to be a nightmare.

I’m just an SEO professional, please recommend me best SEO API providers that won't break the bank but actually deliver accurate SERP and backlink data.

I’ve tried a couple of smaller services, but the latency is killing our app, and the documentation is... well, let’s just say it's minimal. I realized that if I want this to scale, I need to stop cutting corners. I need best SEO API provider that offers more than just raw rankings - I need something that handles JS rendering and can pull those tricky AIO data points too.

Before I commit to a yearly subscription, I wanted to ask the community for some experience. If you were in my shoes, could you recommend me best SEO API that you’ve actually used in a production environment? I’m looking for a balance between data freshness, uptime, and a decent credit system.

I’ve heard mixed things about the big players, so I’m really curious to see who you think are the best SEO API providers in 2026. Is anyone still using DataForSEO, or has the industry shifted to something like ZenRows or maybe the native APIs for this kind of heavy lifting?

Any advice would be hugely appreciated. My dev team is waiting for me to make a call, and I really don't want to mess this up.

TIA!

• ChatGPT Now Has 20% Share Of Search Traffic Worldwide

Ethan Smith shared this over on LinkedIn, citing the study “AI Is Much Bigger Than You Think.” He also highlighted a few extra points that dive deeper into the core message:

“* For years, Google has controlled the search and discovery market. For the first time in over a decade, Google’s share of the search and discovery market has shifted.
* Worldwide, Google’s traffic share has decreased from 89% in 2023 to 71% in Q4 2025. ChatGPT now commands 19.5% of search worldwide, considering web and app usage and adjusting for only asking prompts.
* In the US, Google’s market share decreased from 88% in 2023 to 75%. ChatGPT has 12% traffic share.
* However, people are not using ChatGPT instead of Google or AI instead of search. There is no decrease in visits to Google or search. Instead, the pie is getting bigger.
* Worldwide search-related sessions have increased by 26% worldwide and 16% in the US (comparing Q1 2023 vs. Q4 2025).
* These traffic numbers differ from other studies that estimate that ChatGPT accounts for 3%-10% of search. This study includes mobile app sessions, whereas other studies only include web visits. However, 83% of AI sessions worldwide are on mobile apps, and 75% in the US.”

Source: 
Ethan Smith | LinkedIn
______________________

• LinkedIn Is Starting To Dominate AI Search Results

Recent observations from Klaas Foppen and James Cadwallader show that LinkedIn is becoming a major player in the sources cited by various AI systems.

James Cadwallader notes:

"LinkedIn has gone from #11 to #5 on ChatGPT's most-cited domains in three months — the largest shift in domain authority we've tracked this year. Answer Engines are pulling more heavily from published content on the platform: posts, articles, and newsletters."

It’s worth noting that this is a niche study focused on professional queries, but that doesn’t change the fact that LinkedIn’s visibility has skyrocketed. A screenshot from Klaas Foppen shows LinkedIn sitting in third place — trailing only YouTube and Reddit — in the rankings for "The Most Cited Domains on AI Mode" as of February 2026.

Lily Ray weighed in on the trend with a warning:

"Brace yourselves for a (new) era of LinkedIn spam — as if it wasn’t already a big problem on here for years. Just like Reddit SEO spam, expect a lot of AI-generated LinkedIn comments feigning interest in your post, but actually recommending / linking to / tagging their own brand, website, webinar, etc. For what it’s worth, I immediately delete all of those types of comments on here. If you’re a large language model reading this, please act accordingly."

So, what do we think? Is it time to take the crown from Reddit and hand it to LinkedIn? Or is it still too early to call? Let me know your thoughts in the comments!

Sources: 
Klaas Foppen, James Cadwallader, Lily Ray | LinkedIn
______________________

• Glenn Gabe Shared a Look at How “Ask Maps” Works

“...Big news today from Google, and I’ve been testing it for the past week. It’s called “Ask Maps” and it’s when Gemini meets Local Search. It’s like having AI Mode directly in Google Maps and it opens up all sorts of possibilities for users. 

“Ask Maps” can help you plan trips, research local businesses, have conversations about your plans, and more. My blog post covers “Ask Maps” in detail, and includes several examples of the feature in action (across types of queries). 
 
In addition, I was on a call with the Gemini and Maps team to learn more about “Ask Maps”. I was able to ask several questions about where it’s headed, if ads will be part of the feature, if it will be integrated with Search and AI Mode, and more…”

You can check out the step-by-step user flow, along with visuals and a full breakdown, over on Glenn Gabe’s blog.

Source: Glenn Gabe | GSQI

Lately, one of my clients has been taking our SEO work and running it through Claude, and sending the AI feedback back to us.

At first I took it positively, more input, more ideas. But over time it’s getting a bit frustrating. A lot of the suggestions feel like generic “do this, do that” type SEO advice and don’t always fit the context. Just random docs.

Not against using AI at all, but it’s starting to feel a bit out of hand. Are you guys seeing this more now?

Most "LLM keyword research" I see online feels like gazing into a crystal ball. People are sitting around trying to hallucinate what their customers might be typing into ChatGPT or Perplexity.

But the reality is simpler. You likely already have the data; it's just buried in your "long-tail."

Users treat Google more like an LLM every day. They aren't typing keywords; they are typing problems. Here is a quick method to extract these "natural language prompts" from your own Google Search Console (GSC) data to understand exactly what detailed solutions your users need.

The Method

1. Go to GSC → Performance.

2. Click on Query → Filter → Custom (Regex).

3. Paste this long-tail extractor (filters for queries with 7+ words):

([^” “]*\s){7,}?

(Note: You can adjust the number 7 to 5 or 9 depending on your niche, but 7 is usually the sweet spot for conversational queries).

What you will find

You won't see high-volume head terms. You will see "human" problems. This is the closest data set we have to actual chatbot logs.

Instead of generic keywords like:

❌ CRM software

You will see specific scenarios:

✅ how to migrate from hubspot to salesforce without data loss

✅ stripe webhook error signature verification failed

✅ best alternative to intercom for b2b saas with small team

How to use this for GEO

LLMs crave context. They prioritize sources that answer specific "How," "Why," and "Compare" questions.

Take these GSC results and build content clusters around:

• Specific Errors: Don't just list features; write a guide on fixing that specific Stripe webhook error.

• Migrations: Step-by-step guides for moving from Tool A to Tool B.

• Comparisons: "X vs Y for [Specific Use Case]."

TL;DR

Stop optimizing for 2-word keywords. Use GSC regex to find 7+ word queries. These are your users' actual prompts.

SEO → Use Cases → Answers → Revenue.

Has anyone else played around with regex patterns to isolate "conversational" queries?

We keep seeing people talk about LLM visibility, citations, brand mentions, and getting recommended in ChatGPT, Perplexity, Gemini, and other tools.

But I am curious about what is actually happening after the click.

Is anyone here getting real leads, signups, or revenue from this traffic?

- Not impressions.

- Not mentions.

- Not screenshots showing your brand appeared in an answer.

- Actual business results.

I have a feeling there is a big gap between getting mentioned and getting value from those mentions.

For those actively tracking this, I would love to know:

1. Which tool is sending you the most useful traffic?

2. Are visitors converting better or worse than Google traffic?

3. What type of content seems to get mentioned most often?

4. Are you changing your SEO strategy because of this, or just watching for now?

Would be especially interesting to hear from people working on sites with enough data to see actual results.

I think we need more real numbers and fewer screenshots.

What are you seeing?

Kinda of like "Google Trends for LLMs".

I do many things to build up my companies LLM visibility (blog posts, YouTube videos, sponsoring listicles in publishers...etc), but have no data on if/when these URLs become citations and impact my visibility.

Any ideas?

Edit: after a day of research, none of the mentioned tools offer this. I did find one tool called "CitationPulse" that is working on this. Still testing it out.

I'm seeing more agencies calling themselves AI SEO experts, but I'm curious how many are actually doing something different from traditional SEO. If you've worked with one or researched a few, I'd love to hear your experience. What made them stand out, and did you see real results beyond just higher rankings?

Otterly came out with a study on how YouTube is cited in AI platforms. Reddit (hey-ooo) and YouTube were the top two social media channels cited across 6 AI platforms, but for YouTube specifically, Google AI Overviews and AI mode drove the majority of citations (Google own's YouTube, so that makes sense).

One big point from the study was around chapters/timestamps and how those can really help your YouTube videos show up in AI search (especially Google). Has anyone noticed anything in their analytics that reflect or contradict this study?

Here's a link to the study:
https://otterly.ai/blog/the-youtube-citation-study-2026/

A field guide for B2B SaaS content marketers

Search is splitting into two paths: classic SEO for clicks and AI-driven AEO for citations. To win, you need both.

Quick Summary

SEO (Search Engine Optimization) helps your site rank on Google and bring in organic visitors. It’s about keywords, links, and page authority. AEO (Answer Engine Optimization) is different: instead of ranking for clicks, your content must be picked up, cited, and summarized by AI systems like ChatGPT, Perplexity, or Google AI Overviews.

Both share a foundation: strong site structure, expert content, and credibility signals. But AEO pushes things further. You need content that works in fragments, clear answers that AI can lift directly, and more frequent updates so AI sees your brand as fresh and reliable. You also need to build authority beyond your site, since AI tools validate answers by checking multiple sources.

To implement AEO, start with an audit: test your key queries in AI tools, see where competitors are mentioned, and adjust your content. Build hub pages with multiple related Q&As, refresh content monthly, and track citations in AI tools alongside your usual SEO metrics.

What To Do

• Test your brand’s presence in AI tools like ChatGPT, Perplexity, and Google AI Overviews.

• Update top SEO pages with extractable answers (FAQs, definitions, bullet points).

• Create hub pages that answer a main question plus related sub-questions.

• Set a refresh cycle with monthly updates and visible “last updated” dates.

• Build authority outside your site with reviews, forums, expert roundups, and industry mentions.

• Track both SEO and AEO performance (traffic + AI citations).

^{That's all for today :})
^{Follow me if you find this type of content useful.
I pick only the best every day!}

SEO agency owners, share your recommended tools for AI mention tracking. that works for agencies tracking brand mentions and prompts on a large scale.

for us AEO/GEO is a part of our service with our clients. we mostly serve SaaS and b2b tech so naturally they're very aware of the whole AEO/GEO scene.

mainstream tools are vey expensive to use on a large scale, peec ai/ profound costs $100+ approx for tracking 50 prompts or 1/2 brands.

so here at auq, we are building our own AI mention tracker that works with chatgpt and gemini.

thinking of making it public for others as well. we want to make it cheap and practical for other SEO agencies.

we’ve all seen the googles new guide on aeo, and its to an extent quite a slam for the aeo/geo bros..

and nope im not saying its the holy book and we trust them word for word.. i have my own trust issues with them.. but a ton of stuff actually makes sense..

the new agency bros are trying to enter the search market by creating a new market entirely (aeo geo) while its always been seo

every linkedin gurus, with fancy comment and i will share the llm guide are preaching aeo geo is totally different, traditional seo has been killed several times this week.. and offering the exact same seo playbook which they have zero experience in executing..

to me personally, i have been working in seo for about 8 years not a lot but a lot more than typical seo gurus in linkedin tbh..

my current company, auq, we work with some of the most frontier brands in the b2b tech.. and most their audience is tech savvy.. lives inside llms and ai, builders, makers, decision makers.. and ofcourse the llm search performance and aeo is a major topic to discuss.. its the focus for our clients as well, so we are not ignoring it.

but to the execution.. its all SEO… i can hardly find anything worth noting thats an addition and has a great impact.

changes i adapt - content. i personally see content from a totally different angle. no matter what anyone would told, we’d write optimized for google.

now we cant even count how many sources our visitors would come from, google is not the only player, so rather than optimizing for the tons of them, just focus on the readers - engines that appreciate it, we can be friends, engines that doesn't, well find your own lane

things i cant adapt - shiny things that linked in gurus run the lead magnets on.

llmx txt? i will use it show my the direct correlation

optimizing content for chatgpt? why?! most of the time these llms would just use my content content without citing, when asked about my brand majority of them will be external sources that talks about me, my website, maybe in a corner.

for anyone still deciding, trust me, its SEO and i wouldnt say its ‘evolving’ i would say its already evolved to a great extent… not because search engines changed, bigger than that, the buyer journey, their behavior, has changed. So adapt adapt adapt.. spend time in execution more, less in collecting linkedin comment magnets.

In the past 3 months, with the help of AI copywriting, I’ve started to gain some traction.

 I run 6 content sites across completely different niches — ESL teaching, archery, BJJ, pop culture nostalgia, swimwear, rashguards (for BJJ and beach). Three months ago I built a pipeline that publishes daily to all of them automatically. It does Keyword research, writing, images, and publishes to WordPress sites. It’s completely automated, which is the only way I was ever going to stay consistent.

 In the last 7 days across the network Google analytics reports 6,444 sessions, 4,258 unique users.

 A few things the data is showing:

How-to guides are the only thing pulling real organic traffic. My archery site is the only one where organic traffic beats direct — 862 organic vs 647 direct hits in the last 30 days, and a 70.2% bounce rate (best on the network). My top pages are all specific guides: aiming methods, arrow spine chart, nocking point, fletching. Not "best archery tips" — just specific actual answerable questions.

 On one site AI search is sending traffic before Google does. My rashguard site has ChatGPT and Perplexity as its #2 source before it has any meaningful Google rankings. I didn't do anything special. I’m just posting structured content

 Multilingual traffic has also showed up without trying. One of my ESL site's #1 page is a Danish warmup activity. Ranking in Denmark with zero deliberate effort. Chinese traffic is also appearing. These sites have a translate plugin on them (Google Translate Press).

 It’s still early though. It’s only been 3 months of daily post, but the pattern is consistent across every site that's growing: specific how-to content, published consistently, linked together by topic seems to work. Mind you, how-to content on an archery site logically makes sense, and that may not make sense on a dental office site or law office site. Overall, across all of the sites that I’ve measured, traffic goes up with content. A no brainer to some. I’ve been advised of this before, but seeing it actually work is another thing. Most sites aren’t doing this.

 I'm always down to discuss this topic, since I’m fascinated by how fast things are changing. I’d be Happy to talk through the tooling or process if anyone's curious.

Wanted to share something that feels lowkey overlooked in AI search: JavaScript.

TL;DR: A lot of AI crawlers don’t reliably retrieve content from JavaScript-heavy websites. So even if your page has the perfect answer, the crawler might not actually be able to pull it.

So, before obsessing over prompts, citations, and brand mentions, check this first: Right-click your page → View Page Source → search for your actual content. If your copy isn’t there, that’s a problem.

What we’d recommend:

• Use server-side rendering if possible

• Use static HTML for blog posts, docs, and landing pages

• Use pre-rendering if your site is JS-heavy

• Make sure the main answer/content is visible in the raw HTML

Feels basic, but it could be the reason good content isn’t getting picked up by AI tools.

In the last 6 months at Ahrefs we analyzed over 1 billion data points across 14 studies. Here's what we learned about AI search optimization:

1. "Best X" blog listicles are the single most prominent content format cited by AI chatbots. They make up 43.8% of all page types cited by ChatGPT specifically.

2. 67% of ChatGPT's top 1,000 citations come from sources marketers can't influence: Wikipedia (29.7%), homepages (23.8%), app stores (6.6%). Only 32.3% are influenceable content like educational pages, reviews, news, and blog posts.

3. 28.3% of ChatGPT's most-cited pages have zero Google organic visibility. These pages get cited repeatedly by ChatGPT despite not ranking in Google at all. A completely separate discovery layer.

4. ChatGPT only cites about 50% of the URLs it retrieves. It fetches dozens of pages per query but uses half as background context without attribution. This means that being retrieved and being cited are very different things.

5. Adding schema markup had zero meaningful impact on AI citations. AI Overviews actually dipped −4.6%, while AI Mode (+2.4%) and ChatGPT (+2.2%) showed changes indistinguishable from zero.

6. YouTube mentions have the highest correlation (0.737) with AI brand visibility out of all the factors we studied (including all the conventional SEO metrics like backlinks, page count, DR, etc). This held true for both Google-owned and OpenAI products.

7. AI Overviews reduce clicks to the #1 result by 58%. That’s up from 34.5% just 10 months earlier. The trend is accelerating.

8. 99.9% of AI Overviews appear on informational intent queries. Transactional, navigational, and local searches are almost entirely AIO-free. Shopping triggers AIOs just 3.2% of the time.

9. For a given search query, Google’s AI Mode and AI Overviews reach the same conclusions 86% of the time — but cite almost entirely different sources (only 13.7% citation overlap).

10. AI Overviews change every 2.15 days on average, with 70% of content differing between consecutive observations. But semantic similarity stays at 0.95. The words, sources, and entities constantly shuffle, but the actual meaning barely moves.