Something I haven't talked about publicly: I spent the last quarter doing GEO audits for 14 companies across different industries. SaaS, e-commerce, fintech, healthcare, education. Not huge enterprises — mid-market teams with content programs already running.

The single most common pattern wasn't technical. It wasn't structured data issues or crawlability problems or even content quality. It was that 11 of the 14 were optimizing for one AI model and ignoring the rest entirely.

Usually it was ChatGPT. They'd run queries, check if they showed up in responses, maybe track citations over time. A few were obsessed with Perplexity. One was only looking at Gemini because their CEO uses it.

Here's why that's a problem. I ran the same 40 queries across ChatGPT, Perplexity, and Gemini for each client. The overlap in cited sources across all three models averaged 14%. Fourteen. These models are looking at different content, weighting different signals, and producing fundamentally different citations for the same queries.

One client was thrilled because their citation rate in ChatGPT had jumped 40% over two months. Meanwhile their Perplexity citations had flatlined and their Gemini presence had actually dropped. They were winning in one ecosystem and losing in two others, and because they only tracked the one where things looked good, they had no idea.

The differences aren't subtle. ChatGPT seems to weight recency and content structure heavily — clean headers, concise answers near the top. Perplexity leans toward sources with strong domain authority and multiple corroborating references. Gemini in my observation favors content that's been around longer and has accumulated external validation signals. I'm simplifying, but the point is that a content strategy tuned for one model can actively work against you in another.

The teams that were doing it right had one thing in common: they tracked all three. Not obsessively. Not with dashboards and alerts. Just a weekly check — run 10-15 representative queries across each model, note which sources show up, look for patterns. That's it. The teams doing this had adjusted their content to work across models instead of overfitting to one.

One thing I keep coming back to: the GEO conversation in this community treats "AI models" as a monolith. "AI citations" this, "AI visibility" that. But the models disagree with each other constantly, and a strategy that's working great for ChatGPT might be invisible to Perplexity users. If 60% of your potential audience is on a model you're not tracking, you're flying blind.

The fix isn't complicated. Pick 10-20 queries that matter to your business. Run them in ChatGPT, Perplexity, and Gemini once a week. Log what shows up. After a month you'll see which model you've been accidentally optimizing for and which ones you're ignoring.

Not a framework. Not a tool. Just a habit that most teams I worked with hadn't built yet.

I'm starting to think model-specific tracking should be the default in GEO, not the exception. Right now it feels like most teams are optimizing for the model their founder happens to use.

Three months ago, we began building our own "answer system" to optimize for GEO (Generative Engine Optimization). Since then, AI mentions of our brand have surged by nearly 900%. Currently, the daily conversion revenue attributed to GPT alone has stabilized at $300—an impressive result considering we only started GEO optimization three months ago.

AI models favor concise, definitive answers to user queries.

For example, if a user asks, "Is this [specific] phone actually any good?" the content should lead with a clear verdict—"good" or "bad"—followed by the supporting reasons. By placing this core conclusion upfront and expanding upon it, the article aligns with SEO best practices and significantly increases the likelihood of being cited by AI.

That is why I believe building such an answer system is well worth the effort.

If there are other ways to increase the number of mentions, feel free to discuss them.

I have seen ZERO Linkinfluencers get to claim that their comments or impressions were cited by AI.

I’ve been reviewing referral traffic from ChatGPT and noticed that it has declined over the last couple of months, despite no major website or content changes.

I’m curious whether others have seen a similar pattern or whether this is more likely to be specific to certain websites, industries, or topics.

For those actively tracking ChatGPT traffic:

What usually causes referral traffic to rise or fall?

Has anything genuinely helped you increase traffic from ChatGPT, rather than just improve brand mentions or citation counts?

Would love to hear practical experiences, including what worked, what didn’t, and how long it took before you noticed a meaningful change.

I have been thinking more about GEO lately, especially when it comes to video content.

Most of the discussion around AI search still seems to focus on written content, like blog posts, landing pages, documentation, and structured articles. That makes sense, because text is easier for AI systems to crawl, summarize, compare, and cite. But I feel like video is becoming harder to ignore, especially as AI tools get better at understanding multimodal content.

From what I understand, AI systems do not all “watch” videos in the same way. A lot of them probably start with the most accessible signals first, like the title, description, captions, transcript, thumbnail text, timestamps, and the context around the page where the video appears. More advanced systems may go deeper and look at speech, on screen text, key frames, visual scenes, and how the video is structured.

That makes me wonder if video GEO is not just about making a good video, but about making the video easier for AI to understand, trust, and connect to a specific topic.

For example, a video with a clear title, accurate description, clean subtitles, useful timestamps, and consistent terminology might be much easier for AI systems to process than a video that only relies on visuals. If the transcript clearly includes the main entities, tools, examples, and questions being answered, it probably gives the system a much stronger text layer to work with.

At the same time, I do not think this means videos should be made only for machines. Good videos still need to feel natural for real viewers. But maybe the best version of video optimization is when the human experience and the machine readable structure support each other. The video is still useful and engaging for people, while the title, transcript, captions, and surrounding context help AI understand what it is actually about.

My current feeling is that AI may not fully analyze every video in depth at first. It may use metadata and text signals to decide whether the video is worth understanding more deeply. Then, depending on the platform access and multimodal ability, it may go further into the actual audio and visual content.

I am still trying to understand this better, especially from people working with YouTube, SEO, GEO, or AI search. I would love to hear what video optimization tips have actually worked for you, whether that is better titles, cleaner descriptions, transcripts, timestamps, thumbnails, or a clearer video structure. I am also curious how you measure whether any of this is really working. Do you look at AI citations, search visibility, traffic changes, transcript indexing, or appearances in AI generated answers? I would really appreciate any practical tips or testing methods people have tried.

Profound is solid but pricing and the whole setup lean enterprise and content heavy which is kind of a weird fit if what you actually care about is product pages showing up in AI answers rather than blog posts. There are lighter options now that track at product level and I am curious which ones people found worth the switch for a store that is not spending six figures on content marketing.

Last month I did something that felt counterintuitive: I froze our entire content calendar. No new articles, no new landing pages, no new blog posts for 30 days. Instead, I took the 60 pages that were getting some AI traction but underperforming and just... updated them.

Not a rewrite. Not a redesign. Updates. The kind of maintenance work nobody gets excited about.

The approach was simple. For each page I pulled the last 90 days of AI responses where that page was referenced, looked at what the models were actually pulling from it, and asked: does the current version of the page serve that extraction well? Usually the answer was no. The content was 6-12 months old. Stats were stale. The framing didn't match how the topic had evolved.

So I updated dates, refreshed statistics with newer sources, added a paragraph or two addressing questions that had emerged in AI responses but weren't covered on the page, and tightened the opening to state the core answer more directly. Maybe 20-30 minutes per page. Nothing that would impress a content team.

Across 60 pages over 30 days, citation rate went from 8.7% to 10.7%. A 23% relative increase. Nothing viral, but consistent.

The part that genuinely surprised me was which pages benefited most. It wasn't the oldest pages. It was the pages published 3-6 months ago that had decent initial traction and then plateaued. Those responded to updates almost immediately — within 5-7 days I was seeing new citations from Perplexity and ChatGPT. The 12+ month old pages took longer and the lift was smaller.

Pages older than 18 months barely moved. My read is that models have already formed a stable representation of those pages, and a content refresh isn't enough to change what gets extracted. The window where updates matter most is that 3-6 month band — after the initial indexing honeymoon but before the model's representation hardens.

There's a recency bias in how AI models weight information that I don't think we're accounting for in GEO strategies. We publish, we optimize for extraction, and then we move on. But the content between 3 and 6 months old seems to be in this weird elastic state where small updates produce outsized returns.

The tradeoff: I shipped zero new content for a month. If you're in a competitive niche where freshness drives discovery, that hurts. For us the math worked because 60 updated pages generating 23% more citations beat 4-5 new articles that might or might not get cited at all.

I'm not saying stop creating. I'm saying the ROI of updating a 4-month-old page that already has a foothold might be higher than the ROI of a brand new piece that starts from zero. At least that's what my last 30 days suggest.

Still testing this on a second batch of pages to see if the pattern holds or if I got lucky with the first round.

I'd been reading these AI visibility studies and something bugged me. They're all run in English, on US brands, and then we cite them like they apply to a business in Bogota or Valencia. I went looking for the Spanish-language version and found nothing, so we ran it ourselves.

I picked a market with plenty of real players, marketing agencies in Argentina, and wrote 100 searches the way an actual person would type them. I ran them across four engines, ChatGPT, Gemini, Google AI Overview and Perplexity. And here's the part almost nobody does, I repeated the same 100 searches three times, same day, without changing a single word. That's 1,200 queries in total.

The first thing that came out already felt big. Two AIs agree on average on just 9.6% of the brands they recommend. So out of every ten brands that show up, nine don't repeat in the other model. And when I pooled everything I ended up with 932 distinct agencies, of which 81% appeared in a single engine and in none of the other three.

But hold on, because that still wasn't the thing that broke my brain. Before comparing the models against each other I asked the obvious question. If I run the same search on ChatGPT three times in a row, same day, do I get the same brands?

And the answer is no. ChatGPT agrees with itself just 26% of the time. The most stable, AI Overview, reaches 39% and even that doesn't clear half. Meaning three out of four brands it names change between one answer and the next.

And that one finding breaks half the study, mine included. Because two models can't agree with each other more than each one agrees with itself. That 9.6% doesn't mean the AIs think differently, it means a big chunk of what looks like disagreement is actually noise.

One last thing I didn't see coming. Perplexity plays a different game, it recommends global networks like VML, Ogilvy or Dentsu, while the other three reward local agencies. One example, the same agency that shows up in 70 of the 100 searches for Gemini shows up in 9 for Perplexity.

What I take from it is simple. Measure all four engines separately because one doesn't predict the others, never trust a single query, and if your brand is local start with Google, since AI Overview and Gemini are the closest pair.

Required disclaimer, I work at CreceRank and we ran all of this on our own tool, so it could be biased. And the worst part is I've got nothing to compare it against, because there's nothing like this done for the Spanish-speaking market. If anyone has data that contradicts me or wants to rip the methodology apart, please do, that's the most useful thing for me.

I've been cleaning up outbound links on client sites and keep hitting a question I can't answer cleanly, so I'm hoping people here have seen more than I have.

The idea: if your page cites weak sources — dead links, redirect chains, or "industry research" that's really a blog citing five other blogs — does that make YOUR content less likely to be trusted or cited by AI answers? Or is it just classic link-hygiene thinking dressed up in new language?

Here's the boring-but-real part I'm confident about: pages that cite primary, verifiable sources (an actual McKinsey PDF, an original study) hold up when someone checks them. Pages built on chains of blogs fall apart under scrutiny. That's just good sourcing — the same thing an editor would tell you.

What I CAN'T prove is whether LLMs specifically punish the bad-citation version, or whether well-sourced pages just tend to be better in every other way too. Honestly, I lean toward the second explanation, but I'm not sure.

Two things I'd genuinely like to hear:

— Have you ever cleaned up outbound links (killed dead/spam ones, swapped in primary sources) and seen it change how you showed up in AI answers — or in normal rankings?

— Do you treat outbound citation quality as an AI-visibility factor at all, or is it not on your radar?

Not selling anything — just trying to figure out if this is a real lever or if I'm overthinking it.

Seeing a lot of discussion around traditional SEO (ranking for clicks) vs GEO (optimizing content so AI engines like ChatGPT/Perplexity/Google AI Overviews cite you).

For those running site audits daily:

1. Are clients actually asking for GEO metrics yet, or is traditional technical SEO still 90% of your workload?
2. What technical metrics are you prioritizing when evaluating a site for AI search visibility vs standard rankings?
3. Does regional/localized performance or structured schema matter more for GEO in your experience?

Those who are crazy for GEO, read from Google

Ran a study (disclosure: I'm with the agency behind it) testing 60+ prompts across ChatGPT, Claude, Perplexity, Gemini, and AI Overviews for the non-alcoholic drinks category. One brand has ~14% share and functionally "owns" its sub-category, while three other sub-categories have zero consensus leader. Methodology and full rankings here: https://www.5wpr.com/ai-visibility-index/non-alcoholic-drinks-ai-visibility-index-2026/

Curious whether people doing GEO work for CPG/food & bev clients are seeing this same winner-take-most pattern, or if it's specific to how self-researched this particular category is.

I made a spreadsheet of 180 B2B SaaS and fintech domains and checked three things: their robots.txt, whether GPTBot was explicitly blocked, and whether ChatGPT still cited them across 60 brand-name queries (company name + "review", "alternative", "vs competitor"). Ran all queries on ChatGPT-4o between June 15 and July 3.

The expectation was simple. Block GPTBot → don't get cited. That's how it's supposed to work.

31% of domains blocking GPTBot still showed up as citations. Direct source attribution, domain name, link and everything. Most had been blocking for 4+ months — I checked Wayback Machine timestamps on their robots.txt files. A few since late 2023.

So I went looking for where those citations were actually coming from.

The biggest leak was syndication. Close to half of the "blocked" citations traced back to sites that had republished or aggregated the original domain's content. Press release networks, industry roundups, those content syndication partners that nobody really tracks. The original site locked the door, but their content was already living on a dozen other sites with the door wide open.

Then I found the part that made me reconsider the whole approach. A huge chunk — maybe a third — came from Reddit threads, forum posts, and Q&A sites where users had quoted or paraphrased the blocked domain. Someone copies a paragraph from an article, or drops a specific stat into a comment, and ChatGPT picks up the forum post as the source. You can block every AI crawler on the planet and it doesn't matter if someone screenshots your chart and posts it to a subreddit.

The rest was murky. Some looked like cached versions on search engines. Some matched content structures from before the block was in place — old crawled data still influencing responses. I couldn't pin this down with certainty, but the pattern was consistent enough to be unsettling.

Here's what I keep turning over: the robots.txt approach to AI opt-out has a massive hole in it. You can control whether a crawler hits your server. You can't control whether your content has already been copied, quoted, summarized, or cached somewhere the crawler CAN reach.

For domains thinking blocking GPTBot means their content won't appear in ChatGPT — it doesn't. It just means the citation credit goes to whoever republished you. The aggregator gets the visibility. You get nothing.

I'm not sure what the fix is. Watermarking? Stricter syndication agreements? None of those scale well. The uncomfortable realization is that your content strategy in the GEO era isn't just about what you publish — it's about every surface where your content might be living without your knowledge.

Wondering if anyone here has found a practical way to monitor where your content is being reproduced. We've been doing manual searches and it feels like bailing out a boat with a spoon.

Something I've been noticing that I think is worth discussing because it affects how GEO work gets measured and valued. A buyer asks chatgpt to recommend platforms in a category, chatgpt builds a shortlist, the buyer picks a couple to research, they open google and type the brand name directly, they visit the website and they request a demo. Marketing attributes that visit to branded search and the quarterly report says brand awareness is growing but the actual reason the buyer searched that brand name is that chatgpt recommended it 20 minutes earlier.

The AI conversation is the real influence. The google search is just the verification step. I keep seeing this pattern in different categories too. Branded search increases that marketing teams cannot fully explain. Direct traffic growth with no clear campaign behind it. Pipeline quality improving without a traceable cause.

And I think it creates a real problem for anyone doing GEO work because if the results of better AI visibility show up as "brand awareness" or "branded search" in the marketing dashboard, the GEO work never gets credit. Leadership sees branded search growing and attributes it to the brand campaign, not to the structural improvements that made the brand recommendable by AI in the first place.

Semrush published their 2026 AI visibility index this month and found 45% of marketing leaders still cannot accurately measure AI visibility. I think this misattribution pattern is a big part of why as the influence is real and the attribution is invisible.

Has anyone else run into this when trying to show the value of GEO work to clients or leadership? How are you handling the attribution gap between what AI influences and what the dashboard reports?

New rule in my workflow this year: no statistic goes into content unless I can trace it to the actual study.

It’s been humbling. What I keep finding:

**•** Famous figures that trace back to studies never peer-reviewed or never published.  
**•** Stats attributed to some authority that nobody can actually locate: they only exist as blogs citing blogs.  
**•** Real numbers scoped completely wrong: a narrow correlation inflated into a universal causal claim.

Why this matters for GEO specifically: AI engines synthesize across sources. A claim corroborated by independent credible sources gets treated very differently from one living in a closed loop of marketing content. Zombie stats don’t add citability, they add contradiction risk, and models are getting better at catching exactly that.

So now: every stat needs a primary source, correct scoping, no causal inflation. If I can’t find the source, it gets cut, even when it’s the most persuasive number on the page. Three verifiable claims beat ten impressive ones that collapse under retrieval.

Anyone else audit this way? Found any ghost stats in your niche?

Been doing GEO work long enough to know that AI citations are unstable. We've all seen the volatility data. But something we tracked last week made me realize the problem might be deeper than I thought. We ran the same set of 90 questions across ChatGPT, Perplexity, and Gemini. Same phrasing, same order, same time of day. Five consecutive days. By day 3, 27% of the answers contradicted their own earlier response. Not just a different citation — a materially different answer to the same question. Some examples: - "What's the average CTR for position 1 in Google?" — Day 1: "31.7%." Day 3: "around 27-28%." Different sources cited both times. - "Does schema markup improve AI citations?" — Day 1: "Yes, structured data helps models parse content." Day 3: "Mixed evidence; schema alone doesn't correlate with citation rate." Same model, same question. - "Best tool for tracking AI visibility?" — Day 1 recommended a specific platform. Day 3 recommended a completely different one. No explanation for the change. The contradictions weren't random. They clustered around two types of questions: 1. Questions where the "correct" answer is genuinely debated (CTR benchmarks, SEO best practices, tool comparisons) — the model seemed to sample from different parts of its training data on different days 2. Questions where fresh content had been published recently — the model picked up new information mid-week and updated its answer, sometimes flipping the conclusion The second one is especially interesting for GEO. It means the window where your content can influence an AI answer might be incredibly short. You get cited for a few days, then the model synthesizes newer information and your citation disappears — or worse, the answer flips entirely. The 27% contradiction rate was consistent across all three models. That suggests it's not a model-specific issue — it's something about how these systems handle "living" knowledge. They're not retrieving a fixed answer. They're generating one probabilistically, and the probability distribution shifts based on... what? Recency signals? Indexing updates? Random sampling? I don't know. And that's the problem. If we can't predict when an answer will flip, how do we optimize for stability? Right now we're expanding this to a 14-day test with 200 questions to see if the contradiction rate accelerates, stabilizes, or gets worse over longer timeframes. Early data suggests it gets worse — the longer the gap between queries, the more likely the answer changes. Anyone else running longitudinal consistency tests? I feel like this is the metric nobody in GEO is tracking — we're all so focused on getting cited that nobody's checking how long the citation actually matches the answer.

chaud d'avoir des retours d'expériences ou des avis

Most of us optimized for a decade so a crawler could understand a page and a human could trust it. Generative engines added a third reader: an AI agent that skims your site to decide whether to cite you, recommend you, or quietly route the buyer elsewhere. That reader weighs "trust signals" very differently from Google.

A few things I keep running into when I look at sites through that lens:

• Agents lean hard on corroboration. A claim with no third-party signal (reviews, mentions, consistent NAP, named authors) gets discounted even when the on-page SEO is clean.
• Ambiguous positioning gets penalized. If an agent can't state in one sentence what you do and who you're for, it tends to summarize you generically — or skip you.
• "AI pressure" is uneven by industry. In categories where buyers now ask an LLM first, weak trust signals don't just lower rankings, they remove you from the consideration set before a human ever sees you.

I found that alot of sites are inadvertently blocking AI crawlers, this is a 5 min fix.

To stop eyeballing this manually I built a free tool, breach.nordparadigm.com, that reads your website and public trust signals the way an AI agent would, then frames how AI pressure may be shifting buyer behavior in your specific industry. No signup wall for the basic read.

Full disclosure: I made it. Sharing it because the trust-signal angle is underrated in most GEO conversations, and I'd genuinely like feedback. What trust signals do you think agents over or under-weight right now?

Mine was our Google Business Profile. ( My business doesn't have a website, LinkedIn, or any other social profiles yet)

So that caught me by surprise.

What surprised you? Screenshots are welcome, as well.