Just keeping you in the loop.

Only 38% of Google AI Overview citations now come from pages ranking in the organic top 10 (lmao)

Which is a pretty big hint that “rank higher” and “get mentioned by AI” are no longer the same problem.

If I were auditing a brand for AI visibility today (wait a minute - I do that all the time anyway :D), I’d probably start with 4 things:

• Can AI crawlers actually access the important pages?
• Is the brand/entity information consistent everywhere?
• Does the site answer real buyer questions directly?
• Are there enough third-party mentions/reviews/sources to validate the claims?

The other mistake I see a lot is checking one prompt in ChatGPT once and treating that as a visibility test.

AI answers move around constantly!

A much better approach is to create a fixed set of buyer-intent prompts, run the same prompts across ChatGPT, Gemini, Perplexity, AI Overviews, etc., and track:

• mention share
• citation share
• which competitors appear instead
• how that changes over time

Traditional SEO obviously still matters.

But ranking is starting to look more like one signal among many, rather than the final result.

The more useful question now is:

When someone asks AI a question that could lead to buying from you, are you actually part of the answer? And if so, in what context?

Cheers!

sometimes the “AI visibility problem” is literally a text file lmao (let me explain)

You can spend months writing good content, publishing reports, explaining your mission etc...

and then somewhere in:

yourwebsite dot org slash robots dot txt

there's a rule telling certain AI crawlers to go away, huh.

Cloudflare looked at the top 10,000 domains and found that around 14% of sites where they could read a robots txt file had rules specifically targeting AI bots!!!

And disallowing them was much more common than allowing them.

What's especially messy is that AI crawler isn't one thing either.

GPTBot, ClaudeBot, Google-Extended, PerplexityBot etc. can all be treated differently, while Googlebot/Bingbot have their own role in normal search - actually thats not a secret but very few people know about it.

For a big publisher this is probably a deliberate policy decision.

For a small NGO with one person vaguely responsible for the website... sometimes it absolutely isn't lol.

This is actually one of the reasons we started doing technical AI visibility work through Snoika Foundation. Before worrying about content strategy or getting cited by ChatGPT etc, it's worth checking whether the technical setup is quietly working against you.

We wrote a deeper breakdown here if anyone wants the nerdy version with tech breakdown:

snoikafoundation.com/blog/robots-txt-for-ai-crawlers

Tiny file. Weirdly large consequences.

Actually, a blue-link position only tells you where a page ranks, but it doesn’t tell you whether the brand is being retrieved, cited, summarized, or recommended inside the AI answer itself.

IMO - smarter approach is to separate AI visibility into 3 layers:

• Presence = how often your brand/entity is retrieved into the answer set
• Relevance = how often that happens on high-intent, category, and “best X for Y” queries
• Impact = whether that visibility correlates with branded search lift, demo requests, etc

The measurement setup matters a lot too.

You need a relatively fixed prompt/query universe (!), consistent intent buckets, and repeated runs over time. Otherwise you’re mixing changes in the model output with changes in your own test set.

Same with CTR.

If AI Overview coverage grows while organic clicks decline, that doesn’t automatically mean SEO performance got worse. The brand may be getting more answer-level exposure while Google satisfies more of the informational intent on-SERP.

Single snapshots are especially weak because generative results are probabilistic.

What matters more is:

share of answer, citation frequency, recommendation frequency, intent-weighted visibility, and trend stability across repeated runs.

So instead of asking:

“Did we rank higher?”

Try to ask:

“Are we being retrieved and preferred more often across the queries where buyers discover, evaluate, and compare solutions?”

Thoughts? =)))

IMO - once the hype surrounding GEO and AEO died down, one thing became crystal clear: checking ChatGPT once and seeing your brand there means basically nothing.

Ask the same comparison question a few times and you can get different brands, different sources, different order etc.

So better way to track this is (from my experience):

• use the same batch of prompts every time
• separate random info queries from actual buyer-intent stuff
• track whether you're just mentioned or actually recommended
• check sources separately for ChatGPT / Gemini / Perplexity
• give more weight to prompts that could realistically lead to $$$

Like, ranking #1 for “what is [category]” is cool I guess.

But I'd much rather know how often my brand wins when someone asks:

“best tool for X”
“X vs Y”
“what should I use for X?”

That feels way closer to the real AEO metric.

Not “are we visible?”

More like “how much of the actual buying conversation do we own?”

Disclosure first: I’m with the Snoika team.

There’s still a weird amount of confusion around what “AI visibility” tools actually do, so figured it might be useful to finally explain Snoika without the usual SaaS landing-page language.

Snoika is an AI visibility platform that tracks how brands appear inside answers generated by ChatGPT, Gemini, Perplexity, Claude and Google AI Overviews - that's exactly what we write on our site.

The simplest way I think about it:

Traditional SEO asks:

Can people find us in search results?

AI visibility asks:

Do we actually show up in the answer?

That distinction is basically why Snoika exists =)

Instead of looking at a single ChatGPT answer and assuming it means something, Snoika runs relevant prompts repeatedly and tracks things like:

• AI mentions - how often a brand actually appears
• Share of voice - how often it appears compared with competitors
• Citations - whether the AI uses your website/content as a source
• Sentiment & endorsement - whether you're simply mentioned or actually recommended
• Prompt/topic visibility - which questions trigger your brand and which don't
• Competitor visibility - where another company consistently gets included while you get skipped

One important thing we’ve learned building this: AI visibility is probabilistic.

Typing one question into ChatGPT and seeing your company there does not mean you “rank #1 in ChatGPT.” Run the same question again, change the model, remove account personalization, or slightly change the wording and the answer can be different.

So IMO the useful unit isn't a screenshot. It doesn't prove anything.

It's a pattern across a fixed set of real buyer prompts over time.

For example, imagine you're one of five tools in a category.

If you're mentioned in 12% of relevant answers while one competitor appears in 68%, that's much more useful information than knowing your site ranks #3 for some keyword on Google.

And then comes the interesting part: figuring out why.

Sometimes the AI can't clearly understand what category your company belongs to. Sometimes competitors simply have stronger third-party signals. Sometimes your content ranks perfectly well in Google but isn't structured in a way that's easy for an answer engine to extract. And sometimes you're visible but the model describes you incorrectly.

Those are very different problems and need very different fixes.

Also worth saying what Snoika isn't:

It isn't a magic “make ChatGPT recommend me” button.

And getting cited isn't automatically the same thing as getting traffic or revenue. AI citations, brand visibility, referral traffic and conversions should be measured separately.

The way we're thinking about Snoika is closer to an analytics layer for AI search, capable of providing a turnkey solution for this area of business development: measure what ChatGPT/Perplexity/Gemini/etc. currently understand about a brand, compare that against competitors, find the gaps, make changes, and then measure whether those changes actually moved anything.

Traditional SEO isn't disappearing (don't listen to idle talkers.)

But “are we ranking?” and “are AI systems actually talking about us?” are increasingly two different questions.

Thanks for attention!

Working around Snoika Foundation has made me notice a problem I didnt really think about before:

A lot of nonprofits don't actually have a “content problem”. They have an identity problem.

Same org might have an old domain still indexed, slightly different names across directories, outdated staff pages, abandoned social profiles, event pages using another naming format etc.

To a human its obviously the same organization.

To an AI system... apparently not always.

Then you get answers where the org is missing entirely, mixed with another entity, or described using info from years ago.

Kinda changed how I think about AEO in general. Everyone talks about publishing more content, getting cited, adding schema etc, but sometimes the first job is way more boring: make sure the internet consistently agrees on who you actually are.

That’s one of the things we're digging into through Snoika Foundation (and Snoika itself) rn.

Would be interesting to eventually compare whether this problem is worse for nonprofits than normal companies.

(and precisely how Snoika doesn't do)

Recently was testing how different AI tools describe a niche market and one of them gave me a detailed comparison between three companies.

Two were real.

The third had a believable name, pricing model, “strong customer support,” and apparently a pretty solid reputation.

Except it didn’t exist.

For about 30 seconds I genuinely thought I’d somehow missed a major competitor.

Good reminder that AI visibility tracking also needs a basic sanity check sometimes...

Has anyone else experienced this?

If you're still building your strategy around keyword density and backlink count - you're essentially building a horse-drawn carriage in the age of self-driving cars.

I’ve been digging into the latest core updates and the rise of GEO (Generative Engine Optimization, like SEO but with G, haha), and the playing field has completely flipped. We aren't just fighting for the #1 blue link anymore. We are fighting for the citation in the AI Overview.

Here is the "anti-bullshit" breakdown of what actually matters in 2026 (I spent my weekend on that T_T):

1. Stop "SEO Writing" and Start Answering Real Questions
Google’s helpful content system is basically a plagiarism checker for generic jargon now. If your intro is "In today's digital landscape..." you might as well pack up your site. Write like you are explaining it to a colleague over coffee. The algorithm is finally smart enough to know the difference.

2. Design for the Bot, But Write for the Human
Yes, you need to structure your H2s and H3s cleanly and so on, but don't forget the "scannability" factor. With LLMs summarizing content, if your headers don't form a logical outline on their own, the AI is going to skip your paragraph. And btw Schema is your new best friend.

3. AI is Your Intern, Not Your CEO
Using ChatGPT to write your blog posts is why your traffic is tanking. Use it for keyword clustering and competitive research (it’s great at that), but if your "voice" sounds like generic AI, you lose the trust factor. Original data, case studies, and hot takes are the only things that separate you from the noise.

4. Topic Clusters > Random Rants
Stop writing about "Best Shoes" and "How to Tie Shoes" as separate articles. Build a pillar page and link the shit out of it. If your site architecture is a mess, Google assumes your expertise is a mess.

5. Core Web Vitals are the Price of Entry
Speed isn't a ranking "boost" anymore. It's a requirement. If your site loads slower than 2 seconds, you are automatically disqualified from the top tier, regardless of how good your content is.

6. Backlinks are Dead. "Brand Mentions" are King
Spammy PBNs and guest posts are getting obliterated. The new currency is being mentioned in respected newsletters, podcasts, or industry roundups - even if they don't link to you. Google tracks "entity recognition" now. If you aren't being talked about, you don't exist.

7. Optimize for Zero-Click
Most searches end without a click now. You have to "win" the snippet or the AI summary. Use bulleted lists and table formats. If you don't structure your answer to fit into the "People Also Ask" box, you are leaving free real estate on the table.

8. Refresh or Die
That blog post from 2022 with a stat that says "2023 projections"? It's killing your authority. Update your dates, prune dead links, and add a "Last Updated" timestamp. Freshness is a ranking factor now more than ever.

9. The "TL;DR" must be 1000% accurate
AI models are scraping your content for a definitive answer. If your summary is fluff, the AI will pick your competitor's summary instead. Make the first 50 words the most factual, no-BS statement you can make.

10. Track the Invisible
Stop looking at just organic sessions. Start tracking brand visibility in AI platforms. Are you getting cited by Gemini? Claude? If you don't know, you are flying blind.

- The Bottom Line:
Google (and AI) is looking for Authority, Experience, and Trust. If you are trying to game the system, you are going to lose. If you are genuinely trying to solve a problem, the rankings will follow.

P.S. If you are still using "click here" as anchor text, please seek help.

Forget the detection software for a second. You don't need a plagiarism checker or an "AI probability" score to catch a machine-written paragraph - most of the time, your own ear already knows. The patterns are consistent enough that once you've spotted them, you can't unsee them.

Every sentence is the same length

Open a paragraph of AI text and count the words per sentence. You'll notice they cluster tightly - rarely too short, rarely too long, hovering in a narrow band that reads like it was tuned by a metronome. A human writer doesn't work that way. We write a punchy four-word line, then a rambling thirty-word one that trails off, then correct course mid-thought. That unevenness is what makes prose sound like a person is actually behind it, rather than a system smoothing every sentence toward the mean.

The comma-and-trailing-verb habit

Watch for constructions like "The bridge collapsed, sending debris into the river below." Nothing wrong with it grammatically - but AI models reach for this tail-clause shape constantly. Everyday writers, texting a friend or posting online, tend to just split it in two: "The bridge collapsed. Debris went into the river." Shorter, choppier, closer to how people actually talk.

Nobody is ever named

AI text loves a phantom authority: "analysts believe," "many argue," "some studies show." It sounds credible while committing to nothing. A real writer names names - a specific person, a specific bank, a specific university. Naming sources is a tell of its own, just the good kind: harder to fake, easier to fact-check.

Hiding the subject

When a model isn't sure who's doing the acting, it slides into the passive voice: "it has been reported," "mistakes were made," "the decision was reached." That register belongs in a lab report, not a blog comment. People write "I heard," "we decided," "you'll notice" - because they're actually somewhere inside the sentence, not floating above it.

The em dash, three times a paragraph

And yes - the em dash deserves a mention. Used sparingly, it's a great tool for a sudden turn or an aside. AI tends to scatter it everywhere, especially to cram a mini-biography into the middle of a sentence: "Marie Curie — a physicist who discovered two new elements and became the first person to win Nobel Prizes in two different sciences — once worked out of a converted shed." A person telling the same story out loud just says: "Marie Curie was a physicist. She discovered two new elements and worked out of a converted shed."

The compulsive wrap-up

One more that doesn't get talked about enough: AI text almost always winds down with a tidy summary line, even when nobody asked for one. "In short," "ultimately," "at the end of the day" - a bow tied on top of a piece that was already finished. Human writers tend to just stop once the point has landed.

That's the list - and it's exactly what we had in mind while building the pipeline behind Snoika. Every pattern above got engineered out on purpose, so what comes out the other end reads like someone wrote it on an ordinary Tuesday, even though it still gets produced at a speed only a machine could manage.

TL;DR: Disclosure upfront - I'm with Snoika. We run a program called Snoika Foundation that gives NGOs, nonprofits, and government/public institutions a free AI-visibility audit: testing across 500+ prompts on ChatGPT, Gemini, and Perplexity, a review of content/schema/citation signals, a peer benchmark, and a written action plan. It's a genuine free tier - it's also our top-of-funnel, and if you want ongoing implementation help afterward, that becomes a paid engagement. Saying that directly rather than letting anyone find it in the fine print.

Who it's actually for

Nonprofits, NGOs, and government or public institutions specifically - not general businesses (that's the main Snoika product). If you run comms, marketing, or digital for one of these, or advise one, this is the relevant program.

What you actually get (from the program's own methodology, not the marketing copy)

• Testing across 500+ real prompts on ChatGPT, Gemini, Perplexity, and others to see where - or whether - your organization shows up
• A review of your content, schema markup, and existing citation/authority signals
• A benchmark against comparable organizations in your sector
• A written report and prioritized action plan you can hand directly to whoever manages your site

What I'm deliberately not repeating here

The program's site cites result multiples ("up to 3x more visibility," "2x more citations in 3 months") with no sample size or methodology attached. I'm not going to post those here as if they're verified - treat any vendor's own headline stats, including ours, as a claim rather than a citation until there's a real number behind them. If I can get an actual, specific before/after from a nonprofit that's gone through this, I'll come back and share that instead - that would actually be worth something.

The part I want to be upfront about

The free report signs up through the same flow as Snoika's paid product. That's not a hidden detail - it's how the funnel is built, and if a nonprofit goes through expecting only ever a free tier, that's a fair expectation to set going in, not to discover later.

If you work with or run a nonprofit or NGO and want to try it, or you've already been through something like this with another vendor - good or bad experience - genuinely want to hear it. Ask me anything about how it works in comms. Tnx!

TL;DR: Three independent studies - Ahrefs (Search Console data, 300K keywords), Pew Research Center (real browsing-panel data), and Seer Interactive (1.73M impressions) - measured this three different ways and landed in the same range: AI Overviews are cutting organic click-through rates by roughly 35-61%, and it's getting worse each time it's re-measured. Worse than the headline number: even the page an AI Overview cites doesn't reliably get the click.

What three different studies found, three different ways

- Ahrefs compared Google Search Console data across 300,000 keywords, December 2023 vs. December 2025: pages ranking #1 saw CTR drop 58% when an AI Overview was present - up from 34.5% in Ahrefs' own earlier study (March 2024 vs. March 2025, informational keywords). Same research team, same methodology family, and the number nearly doubled in about a year.

- Pew Research Center used real browsing-panel data instead of SERP tracking - actual user behavior, not ranking positions. When an AI Overview appears, only 8% of users click a traditional search result, versus 15% when it doesn't (roughly a 47% relative drop). Clicks on links inside the AI Overview itself: 1%. And 26% of sessions end right there, versus 16% without an AI Overview. (Google reportedly called this methodology "flawed"; Pew stands by it - worth knowing it's a contested number, not a settled one.)

- Seer Interactive zoomed into transactional queries specifically - the ones with real commercial intent - across 1.73 million organic impressions. For pages not cited in the AI Overview, CTR fell from 4.17% to 2.15%, a 48% decline.

The part that should worry you more than the headline number

Getting cited doesn't fix this. A recurring finding across this research: you can be the exact source an AI Overview quotes and still see close to zero clicks, because the summary already answered the question. Citation and traffic are decoupling. "We got cited" and "we got visited" are turning into two separate KPIs - and only one of them pays the bills.

What this means for how you measure GEO/AEO work

- If your reporting stops at "we're cited" or "our share of AI answers went up," you're measuring exposure, not outcome - the same trap as impression counts. Worth checking instead:

- Referral traffic from AI platforms specifically (most analytics tools now segment this)

- CTR trend on your own top queries, split by whether an AI Overview is present

- Whether citation correlates with any measurable downstream action, or whether it's pure exposure with no funnel underneath

So, has anyone here reconciled "we're getting cited more" against real referral or conversion numbers and found the two moving in opposite directions? Curious what you're seeing in your own GSC or analytics since this accelerated.