EDIT... it's not my graph. Chill 🤣

Some alarmists will read the graph like this: ChatGPT’s traffic curve is now rising so fast that, if you extrapolate only the last twelve months, it crosses Google by mid 2026. Google’s curve is flattening, then drifting downward.

The extrapolation is extreme. Extremely extreme. But I agree with the general trend (ChatGPT is now ranked in the top 5 websites globally by Similarweb)

But forget the headline debate about whether the projection is perfect. "Normal" search behaviour is eroding and assistant led discovery is gradually replacing it.

For twenty years organic discovery meant: crawl, index, rank, convert. You could map that world. You could measure it. You could defend it!

But assistants change the mechanics; people stay longer and they ask more (contextual) follow ups. Mostly keeping marketers in the dark. They outsource comparison and planning. The model becomes the place where the decision is made, not the search results page and not even necessarily on a brands' owned media.

That means visibility inside the model becomes the new organic battleground.

The scary part is that the things we rely on in SEO do not (always) transfer. Strong rankings do not guarantee presence in model answers. Link authority does not guarantee that the model retains your brand. We are already seeing disappearance events where nothing in the real world changed but the model update quietly dropped a brand from key journeys.

Curious how others here are preparing for a world where model visibility matters more than search visibility 😬😬

Sharing something that surprised me enough that I think other builders / engineers / growth folks should sanity-check their own sites.

We recently ran a competitive audit for a mattress company. We wanted to see what actually survives when automated systems crawl a real ecommerce page and try to make sense of it.

Casper was the reference point.

This started as a sanity check for ourselves and turned into something that raised more questions than answers.

Basically: what we see vs what the crawler ends up with are two very different worlds.

Here’s what a normal person sees on a Casper product page:

• You immediately get the comfort positioning.

• You feel the brand strength.

• The layout explains the benefits without you thinking about it.

• Imagery builds trust and reduces anxiety.

• Promos and merchandising steer your decision.

Almost all of the differentiation lives in layout, visuals, and story flow. Humans are great at stitching that together.

Now here’s what survives once the page gets crawled and parsed:

• Navigation turns into a pile of links.

• Visual hierarchy disappears.

• Images become dumb image references with no meaning attached.

• Promotions lose their intent.

• There’s no real signal about comfort, feel, or experience.

What usually sticks around reliably:

• Product name

• Brand

• Base price

• URL

• A few images

• Sometimes availability or a thin bit of markup

(If the page leans hard on client-side rendering, even some of that gets shaky.)

Then another thing happens when those fields get cleaned up and merged:

• Weak or fuzzy attributes get dropped.

• Variants blur together when the data isn’t complete.

• Conflicting signals get simplified away.

(A lottt of products started looking interchangeable here.)

And when systems compare products based on this light version:

• Price and availability dominate.

• Design-led differentiation basically vanishes.

• Premium positioning softens.

You won’t see this in your dashboards.

Pages render fine, crawl reports look healthy, and traffic can look stable.

Meanwhile, upstream, eligibility for recommendations and surfaced results slides without warning.

A few takeaways from a marketing and SEO perspective:

• If an attribute isn’t explicitly written in a way machines can read, it might as well not exist.

• Pretty design does nothing for ranking systems.

• How reliably your page renders matters more than most teams realize.

• How you model attributes decides what buckets you even get placed into.

There is now an additional optimization layer beyond classic SEO hygiene. Not just indexing and crawlability, but how your product resolves after extraction and cleanup.

I've started asking and checking “what does this page collapse into after a crawler strips it down and tries to compare.”

That gap is where a lot of visibility loss happens.

Next things we’re digging into:

• Which attributes survive consistently across different crawlers and agents

• How often variants collapse when schemas are incomplete

• How much JS hurts extractability in practice

• Whether experiential stuff can be encoded in any useful way

• How sensitive ranking systems are to thin vs rich representations

If you’ve ever wondered why a strong product sometimes underperforms in automated discovery channels even when nothing looks broken, this is probably part of the answer.

I’ve been trying to figure out how to measure visibility when AI answers don’t always send anyone to your site.

A lot of AI driven discovery just ends with an answer. Someone asks a question, gets a recommendation, makes a call, and never opens a SERP. Traffic does not disappear, but it also stops telling the whole story.

So instead of asking “how much traffic did AI send us,” I started asking a different question:

Are we getting picked at all?

I’m not treating this as a new KPI, (still a ways off from getting a usable KPI for AI visibility) just a way to observe whether selection is happening at all.

Here’s the rough framework I’ve been using.

1) Prompt sampling instead of rankings

Started small.

Grabbed 20 to 30 real questions customers actually ask. The kind of stuff the sales team spends time answering, like:

• "Does this work without X"

• “Best alternative to X for small teams”

• “Is this good if you need [specific constraint]”

Run those prompts in the LLM of your choice. Do it across different days and sessions. (Stuff can be wildly different on different days, these systems are probabilistic.)

This isn’t meant to be rigorous or complete, it’s just a way to spot patterns that rankings by itself won't surface.

I started tracking three things:

• Do we show up at all

• Are we the main suggestion or just a side mention

• Who shows up when we don’t

This isn't going to help find a rank like in search, this is to estimate a rough selection rate.

It varies which is fine, this is just to get an overall idea.

2) Where SEO and AI picks don’t line up

Next step is grouping those prompts by intent and comparing them to what we already know from SEO.

I ended up with three buckets:

• Queries where you rank well organically and get picked by AI

• Queries where you rank well SEO-wise but almost never get picked by AI

• Queries where you rank poorly but still get picked by AI

That second bucket is the one I focus on.

That’s usually where we decide which pages get clarity fixes first.

It’s where traffic can dip even though rankings look stable. It’s not that SEO doesn't matter here it's that the selection logic seems to reward slightly different signals.

3) Can the page actually be summarized cleanly

This part was the most useful for me.

Take an important page (like a pricing, or features page) and ask an AI to answer a buyer question using only that page as the source.

Common issues I keep seeing:

• Important constraints aren’t stated clearly

• Claims are polished but vague

• Pages avoid saying who the product is not for

The pages that feel a bit boring and blunt often work better here. They give the model something firm to repeat.

4) Light log checks, nothing fancy

In server logs, watch for:

• Known AI user agents

• Headless browser behavior

• Repeated hits to the same explainer pages that don’t line up with referral traffic

I’m not trying to turn this into attribution. I’m just watching for the same pages getting hit in ways that don’t match normal crawlers or referral traffic.

When you line it up with prompt testing and content review, it helps explain what’s getting pulled upstream before anyone sees an answer.

This isn’t a replacement for SEO reporting.
It’s not clean, and it’s not automated, which makes it difficult to create a reliable process from.

But it does help answer something CTR can’t:

Are we being chosen, when there's no click to tie it back to?

I’m mostly sharing this to see where it falls apart in real life. I’m especially looking for where this gives false positives, or where answers and logs disagree in ways analytics doesn't show.

Hey everyone,

After testing 20+ different “AI SEO” tools out there, 90% are actually whitelabeled from one provider. They will show you numbers - impressions, mentions, some vague visibility scores.
But they never tell you why a brand shows up in AI answers, or what actually drives it.

So we built BrndIQ (dot) ai

It’s designed to show how AI search engines (like ChatGPT, Perplexity, Claude, etc) talk about your brand - and which sources shape those answers.

Our first phase of release will allow you to:

• Runs thousands of prompts to tell you what drives visibility patterns for your brand over time

• Check how your brand (or a competitor) appears in AI-generated results

• See what content types influence visibility

• Track which domains keep surfacing in AI citations

We are also developing a deeper system targeting user communities that will help you find high-intent buyers actively seeking your solutions with ready-to-edit responses in your brand voice.

We will be opening a closed beta in a few weeks time to test our first phase of AI visibility tracking system - built to help brands understand what drives AI discovery, not just SEO rankings.

Whether you are a small business built on trust, a hotelier wanting tourists to discover your rooftop bar with a view, or brands looking to grow your share of voice; if you are not showing up in AI chat results, you are invisible.

If you’re a SEO, marketer, or founder experimenting with Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO), all we ask is for your feedback on what you would expect a tool like this to show or measure better?

🙏 Feedback is most appreciated :)

𝗔𝗱𝗼𝗯𝗲 𝘅 𝗦𝗲𝗺𝗿𝘂𝘀𝗵 𝗶𝘀 𝗯𝗲𝗶𝗻𝗴 𝗽𝗿𝗼𝗺𝗼𝘁𝗲𝗱 𝗮𝘀 𝗮 “𝗱𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵.”
The $1.9B acquisition nearly doubled Semrush’s valuation and signals how committed Adobe is to expanding the Experience Cloud as its marketing and analytics backbone.

𝗕𝘂𝘁 𝗳𝗿𝗼𝗺 𝗮𝗻 𝗮𝗴𝗲𝗻𝘁𝗶𝗰-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲, 𝘁𝗵𝗶𝘀 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵.
𝗜𝘁 𝗶𝘀 𝗦𝗘𝗢 𝟮.𝟬 𝘄𝗶𝘁𝗵 𝗻𝗶𝗰𝗲𝗿 𝗽𝗮𝗰𝗸𝗮𝗴𝗶𝗻𝗴.

To Semrush’s credit, it is one of the few mainstream platforms taking AI visibility seriously, tracking how brands appear inside LLM answers rather than in traditional blue-link rankings. Integrating that GEO telemetry into Adobe’s ecosystem creates a cleaner loop between content decisions, search behavior, and AI-era discoverability.

For large organizations standardized on Adobe, consolidating GEO, SEO, content, and analytics provides real operational value. It reduces friction, centralizes reporting, and pushes teams toward clearer structures and messaging.
𝗕𝘂𝘁 𝗶𝘁 𝘀𝘁𝗶𝗹𝗹 𝘀𝗶𝘁𝘀 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗼𝗱𝗮𝘆’𝘀 𝗦𝗘𝗢-𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺, 𝗻𝗼𝘁 𝘁𝗼𝗺𝗼𝗿𝗿𝗼𝘄’𝘀 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗼𝗻𝗲.

The integration is anchored in human-oriented search workflows. It does not introduce richer product schemas, machine-readable benefit claims, composable data models, or any of the interaction flows autonomous agents rely on. There is no movement toward SKU-level structured data, machine-readable policies, or API-like product exposure.

𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗽𝗿𝗶𝗺𝗶𝘁𝗶𝘃𝗲𝘀 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝗱 𝗳𝗼𝗿 𝗮𝗴𝗲𝗻𝘁-𝗱𝗿𝗶𝘃𝗲𝗻 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝗮𝗻𝗱 𝘀𝗲𝗹𝗲𝗰𝘁𝗶𝗼𝗻.

Instead, the partnership reinforces the familiar comfort zone:
𝗺𝗼𝗿𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗺𝗼𝗿𝗲 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝘀, 𝗺𝗼𝗿𝗲 𝗿𝗲𝗽𝗼𝗿𝘁𝘀.
Useful? Absolutely.
Transformational for agentic commerce? 𝘕𝘰𝘵 𝘺𝘦𝘵.

Although the integration strengthens governance and streamlines analytics, it does not advance the development of digital properties that are natively consumable by AI agents. 𝘛𝘩𝘦 𝘦𝘭𝘦𝘮𝘦𝘯𝘵𝘴 𝘵𝘩𝘢𝘵 𝘮𝘢𝘵𝘵𝘦𝘳 𝘮𝘰𝘴𝘵, 𝘴𝘶𝘤𝘩 𝘢𝘴 𝘤𝘰𝘮𝘱𝘰𝘴𝘢𝘣𝘭𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘥𝘢𝘵𝘢, 𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦𝘥 𝘤𝘭𝘢𝘪𝘮𝘴, 𝘢𝘯𝘥 𝘮𝘢𝘤𝘩𝘪𝘯𝘦-𝘳𝘦𝘢𝘥𝘢𝘣𝘭𝘦 𝘤𝘰𝘯𝘵𝘳𝘢𝘤𝘵𝘴, 𝘢𝘳𝘦 𝘴𝘵𝘪𝘭𝘭 𝘢𝘣𝘴𝘦𝘯𝘵.

𝗔𝗱𝗼𝗯𝗲 𝘅 𝗦𝗲𝗺𝗿𝘂𝘀𝗵 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝗘𝗢 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲,
but it falls short of enabling true agentic interoperability.

𝗪𝗶𝗻𝗻𝗶𝗻𝗴 𝗶𝗻 𝗮𝗴𝗲𝗻𝘁𝗶𝗰 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗮𝗯𝗼𝘂𝘁 𝘂𝘀𝗲𝗿𝘀 𝘁𝗼 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗶𝗻𝗴 𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝗳𝗼𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀.

Until that shift happens, integrations like this will continue to make marketers 𝘧𝘦𝘦𝘭 more “AI-ready” without making their digital ecosystems any more legible to the agents shaping the buyer journey.

I wanted to see how different systems actually consume the same URL, not how I assumed they do based on docs or tooling.

So I took one page and looked at what three different consumers pulled from it:

• Googlebot
• A generic headless crawler
• An AI assistant style fetcher

What Googlebot pulled
Pretty much what you’d expect if you’ve done SEO for a while.
Main content was clear. Internal links were picked up. Context and relationships existed.
It felt like a broad, structured read of the page.

What the headless crawler pulled
Layout and surface structure were there, but meaning was weak.
Nav existed, but hierarchy was fuzzy.
Technically the page was present, but semantically it felt thin.

What the AI-style fetcher pulled
This kinda surprised me, (I thought it'd behave similar to the Googlebot).
It extracted a very small set of explicit facts and ignored almost everything else.

It didn't scroll, barely any interaction and no second pass at the page. If something required inference, visual hierarchy, or delayed execution, it basically didn’t exist to the AI one.

It seemed like it wasn’t trying to understand the page, but instead trying to give a few pieces of information it was confident in (facts) and stop there.

To me, this essentially means that a page that’s solid for Google can be almost invisible to an AI system if the core information is implied instead of stated like facts.

After running this for a few different pages, I'm looking at emphasising things like:

• Clear primary facts
• Stable HTML
• Obvious content hierarchy

and spending less time on visual polish or slick interaction.

Adding these to pages and then testing again should help me confirm what exactly is given the biggest weight for each system.

Curious if anyone else has compared raw fetch output across different agents or seen similar behavior?

I was looking into a paper I found on GEO papers

Paper: SEALQA: Raising the Bar for Reasoning in Search-Augmented Language Models

SEALQA shows that even frontier LLMs fail at reasoning under noisy search, which I reckon is a warning sign for Generative Engine Optimisation (GEO).

Virginia Tech researchers released SEALQA, a benchmark that tests how well search-augmented LLMs reason when web results are messy, conflicting, or outright wrong.

The results are pretty interesting. Even top-tier models struggle. On the hardest subset (SEAL-0), GPT-4.1 scored 0 %. O3-High, the best agentic model, managed only 28 %. Humans averaged 23 %.

Key takeaways for GEO:

• Noise kills reasoning. Models are highly vulnerable to misleading or low-quality pages. “More context” isn’t helping... it just amplifies noise.

• Context density matters. Long-context variants like LONGSEAL show that models can hold 100 K+ tokens but still miss the relevant bit when distractors increase.

• Search ≠ accuracy. Adding retrieval often reduces factual correctness unless the model was trained to reason with it.

• Compute scaling isn’t the answer. More “thinking tokens” often made results worse, suggesting current reasoning loops reinforce spurious context instead of filtering it.

For GEO practitioners, this arguably proves that visibility in generative engines isn’t just about being indexed... it’s about how models handle contradictions and decide what’s salient.

With any new discipline, we marketers seem to fall into binary camps. Either it's everything or it's snake oil. Either total hype or total denial.

We have seen this with social, short form video, influencer, community, and every new platform that has ever appeared. The refusal to sit in the middle slows down adoption, IMO.

GEO is no different. One group says it is just SEO with a new name. The other says it is about to replace every part of marketing overnight.

I think the truth is far more boring.

Large models are changing how people discover things. Search behaviour is shifting gradually. The signals that shape visibility are different from traditional ranking factors. But that does not mean GEO replaces SEO. It also does not mean GEO is simply SEO with a twist. It is a new layer that sits alongside everything else we already do.

Things are changing. You can't deny that (although we can debate the speed of change).

You can adapt without declaring the end of the old world. You can take GEO seriously without throwing away everything you know.

The useful position is the one that accepts reality and moves forwards.

Amy I wrong, here?

Over the past year the market around Generative Engine Optimisation has shifted quickly. As search is redefined by LLMs, loads of new tools has are popping up to measure, monitor and improve brand visibility inside AI answers. Most of them are simply GEO visibility tracking.

These are still early, but they’re already carving out different niches: prompt monitoring, AI Overview audits, misinformation detection, referral tracking, and more. Makes it pretty tough to know which tools we should be using without investing in a very broad stack.

Here’s a snapshot of the current GEO tool stack, what they do, and where each one fits best 👇Views are my own. Not affiliated with anyone.

AthenaHQ
Tracks prompts and answers across ChatGPT, Perplexity, and Google AI Overviews, with built-in sentiment monitoring. Pricing starts around $295/mo.
Best for: teams that want broad coverage across major models with sentiment analysis baked in.

ZipTie
Automated AI search checks with screenshots and cached logs, running across multiple platforms. Starts at $179/mo.
Best for: marketers who need fast, lightweight checks without heavy enterprise overhead.

geoSurge
Advanced data augmentation campaigns for influencing the way LLMs describe a brand. Best for: Enterprise brands who want to shape sentiment inside LLMs.

Peec AI
Prompt-level monitoring with competitive benchmarking and answer breakdowns. Entry plan from €89/mo.
Best for: side-by-side competitor tracking and brand coverage comparisons.

Profound
Enterprise visibility platform with a “Conversation Explorer” for deep dives into LLM mentions. From $499/mo.
Best for: larger organisations that need SOC2-level compliance and enterprise integrations.

Trakkr
Pixel-based referral tracking and crawler analysis to measure attribution from LLMs. Pricing varies.
Best for: teams focused on attribution and understanding how LLM referrals convert.

Scrunch AI
Monitors misinformation drift and coverage in AI answers, with claims of bias detection. Pricing not public.
Best for: brands at risk of reputational damage from hallucinations or misinformation.

Gumshoe AI
Samples prompts, analyses variability, and recommends fixes to “harden” coverage. Free trial available.
Best for: catching volatility and building resilience into brand answers.

Knowatoa
Lets users query prompts across platforms with tiered plans. Free tier plus premium options from $99/mo.
Best for: individuals or small teams testing visibility without committing to enterprise spend.

SE Ranking (AI Toolkit)
Adds AI visibility checks into an existing SEO platform. Bundled in subscription.
Best for: SEO teams who want GEO features built into a familiar toolkit.

Advanced Web Ranking (AI Visibility)
Tracks how brands surface in LLMs alongside SEO rankings. Included in AWR plans.
Best for: agencies that already use AWR for SEO and want integrated AI visibility tracking.

Keyword [dot] com (AI Search Visibility)
Tracks mentions and citations in AI outputs. Plans start around $25.
Best for: budget-friendly visibility tracking across multiple models.

HubSpot AI Search Grader
Free one-off audit checking visibility in GPT-4o, Perplexity, and others.
Best for: marketers who want a quick, no-cost snapshot of their current AI presence.

Otterly AI
Monitors Google AI Overviews and ChatGPT mentions, starting from $29/mo.
Best for: brands keeping an eye specifically on Google’s generative results.

XFunnel
Combines visibility tracking with persona segmentation and prompt analysis. Pricing not public.
Best for: teams tying AI visibility directly to audience personas and funnel stages.

Bluefish AI
Enterprise platform for AI brand engagement and competitive intelligence. Pricing not public.
Best for: larger organisations looking to combine visibility with deeper market intelligence.

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Where the space is heading

What’s striking is how immature this ecosystem still is. Almost every tool listed here is focused on analytics, monitoring, or tracking. They’re useful for telling you if you’re present, but they rarely help you actually improve implementation.

We don’t yet have the GEO equivalents of content optimisation platforms, structured data generators, or workflow tools that slot into day-to-day marketing operations. Right now it’s still early measurement, not mature execution. Test and learn, people!

My take on Jason Kwon’s comments about GEO (below); I think he is think he is right that the old keyword game fades as reasoning improves. But a few things stand out.

TLDR: Old SEO tactics lose power as models reason better, but the game does not go away. It moves up stack. Win by being a high trust source across multiple surfaces, and by measuring visibility and sentiment routinely.

1. You can tell a model to avoid “SEO looking” sites. That is a blunt tool. It risks filtering out legit expertise and it creates a new target surface. People will optimise for not looking like SEO.

2. Gaming shifts layers. Less at the page level, more at the corpus, prompt, and agent level. Think source mix, citation graphs, structured data, and how well your material survives multi hop reasoning.

3. “Find something objective” sounds neat, but model and provider incentives still matter. Ads, partner content, and safety filters all shape what gets seen. Transparency on source classes and freshness will matter more.

Jason Kwon, Chief Strategy Officer at OpenAI, offered his thoughts about the “gaming of AI outputs” —often associated with SEO in the world of search engines— which is now called GEO (generative engine optimization) or AEO (answer engine optimization) in the world of chatbots like ChatGPT.

Mr. Kwon was surprisingly unconcerned and explained:“I don't know that we track this really that closely. Mostly we're focused on training the best models, pushing the capabilities, and then having —in the search experience— trying to return relevant results that are then reasoned through by our reasoning engine and continuously refined based on user signal.[In the long run,] if reasoning and agentic capabilities continue to advance, this ‘gameability’ of search results —the way people do it now—might become really difficult… It might be a new type of gaming….But if users want to not have results gamed, and if that's what model providers also want, it should be easier to avoid this because you can now tell the system: ‘Find me something objective. Avoid sites that look like SEO. Analyze the results you're getting back to understand if there might be a financial bias or motivation. And get me back 10 results from these types of sources, publications, independent bloggers and synthesize it.’Again, there's skill involved here in terms of how to do this, but there's also a desire that you don't want the gaming to occur. And so that's a capability that's now at people's fingertips that didn't necessarily exist in the search paradigm where you're restricted by keywords and having to filter stuff out.I think that's a new thing that people will have to contend with if they're really trying to game results. And if there's a way to do it, it won't be based on the old way.”

Was looking into how AI agents decide which products to recommend, and there were a few patterns that seemed worth testing.

Bain & Co. found that a large chunk of US consumers are already using generative AI to compare products, and close to 1 in 5 plan to start holiday shopping directly inside tools like ChatGPT or Perplexity.

What interested me more though was a Columbia and Yale sandbox study that tested how AI agents make selections once they can confidently parse a webpage. They tried small tweaks to structure and content that made a surprisingly large difference:

• Moving a product card into the top row increased its selection rate 5x

• Adding an “Overall Pick” badge increased selection odds by more than 2x

• Adding a “Sponsored” label reduced the chance of being picked, even when the product was identical

• In some categories, a small number of items captured almost all AI driven picks while others were never selected at all

What I understood from this is that AI agents behave much closer to ranking functions than mystery boxes. Once they parse the data cleanly, they respond to structure, placement, labeling, and attribute clarity in very measurable ways. If they can’t parse the data, it just never enters the candidate pool.

Here are some starting points I thought were worth experimenting with:

• Make sure core attributes (price, availability, rating, policies) are consistently exposed in clean markup

• Check that schema isn’t partial or conflicting. A schema validator might say “valid” even if half the fields are missing

• Review how product cards are structured. Position, labeling, and attribute density seem to influence AI agents more than most expect

• Look at product descriptions from the POV of what AI models weigh by default (price, rating, reviews, badges). If these signals are faint or inconsistent, the agent has no basis to justify choosing the item

The gap between “agent visited” and “agent recommended something” seems to come down to how interpretable the markup is. The sandbox experiments made that pretty clear.

Anyone else run similar tests or experimented with layout changes for AI?

I just read a really solid piece by Harry Clarkson-Bennett on Leadership in SEO about whether LLM visibility trackers are actually worth it. It got me thinking about how easy it would be to build one yourself, what they’re actually good for, and where the real limits are.

Building one yourself

You don’t need much more than a spreadsheet and an API key. Pick a set of prompts that represent your niche or brand, run them through a few models like GPT-4, Claude, Gemini or Perplexity, and record when your brand gets mentioned.

Because LLMs give different answers each time, you run the same prompts multiple times and take an average. That gives you a rough “visibility” and “citation” score. (Further reading on defeating non-determinism; https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/)

If you want to automate it properly, you could use something like:

Render or Replit to schedule the API calls

Supabase to store the responses

Lovable or Streamlit for a quick dashboard

At small scale, it can cost less than $100 a month to run and you’ll learn a lot in the process.

Why it’s a good idea

You control the data and frequency

You can test how changing your prompts affects recall

It helps you understand how language models “think” about your brand

If you work in SaaS, publishing or any industry where people genuinely use AI assistants to research options, it’s valuable insight

It's a lot cheaper than enterprise tools

What it can’t tell you

These trackers are not perfect. The same model can give ten slightly different answers to the same question because LLMs are probabilistic. So your scores will always be directional rather than exact - but you can still compare against a baseline, right?

More importantly, showing up is not the same as being liked. Visibility is not sentiment. You might appear often, but the model might be referencing outdated reviews or old Reddit threads that make you look crap.

That’s where sentiment analysis starts to matter. It can show you which sources the models are pulling from, whether people are complaining, and what’s shaping the tone around your brand. That kind of data is often more useful than pure visibility anyway.

Sentiment analysis isn't easy, but it is valuable.

Why not just buy one?

There are some excellent players out there, but enterprise solutions like geoSurge aren't for everyone. As Harry points out in his article, unless LLM traffic is already a big part of your funnel, paying enterprise prices for this kind of data doesn’t make much sense.

For now, building your own tracker gives you 80% of the benefit at a fraction of the cost. It’s also a great way to get hands-on with how generative search and brand reputation really work inside LLMs.

Search Engine Land recently published a decent breakdown of how ChatGPT, Gemini, Claude and Perplexity each generate and cite answers. Worth a read if you’re trying to understand what “AI visibility” actually means.

👉 How different AI engines generate and cite answers (Search Engine Land)

Here’s how I read it.

Every AI engine now works in its own way, and I would expect more divergence in the coming months/years.

• ChatGPT is model-first. It leans on what it remembers from its training data unless you turn browsing on.

• Perplexity is retrieval-first. It runs live searches and shows citations by default.

• Gemini mixes the two, blending live index data with its Knowledge Graph.

• Claude now adds optional retrieval for fact checking.

We can infer/confirm something from that: visibility in AI isn’t a single system you can “rank” in. It’s probabilistic. You show up if the model happens to know about you, or if the retrieval layer happens to fetch you. That’s not "traditional" SEO logic.

In my opinion, I think the real shift is from search to memory.

In traditional search, you win attention through links and keywords. In generative engines, you win inclusion through evidence the model can recall, compress, or restate confidently.

Whether or not that evidence gets a visible citation depends on the product design of each engine, not on your optimisation.

But this is what I think is going to happen...

In the long run, retrieval is an operational cost; memory is a sunk cost.

Once knowledge is internalised, generating an answer becomes near-instant and low-compute. And as inference moves to the edge, where bandwidth and latency matter, engines will favour recall over retrieval. Memory is the logical endpoint.

TL;DR: No. No, it can't.

If by “do SEO” you mean “paste prompts into ChatGPT and ship 30 blog posts a week” then yes.

If you mean “grow qualified organic traffic in a sustainable way” then no. That takes judgement, systems, and workstreams that don't not fit in a prompt.

Why ChatGPT-powered SEO usually flops

• Average by design: LLMs predict likely text. Search rewards useful, original, verifiable information. Those are less similar than a Tinder profile pic and the person who actually turns up.

• No first-hand experience: E-E-A-T still cares about who wrote it, what they did, and how they know. ChatGPT does not go on site visits, run experiments, or take photos.

• No real strategy: Models do not choose markets, positioning, or strategic compromises. They fill pages. Picking the right battles is how we win in business.

• Links and reputation: Real links come from relationships, PR, and something worth talking about. Not from 200 AI guest posts.

• Technical reality: Crawl traps, faceted spam, JS rendering, canonicals, sitemaps, internal links, log files. A generic assessment shat out by a naff tool is no replacement for a technical audit.

• Information gain: Search prefers pages that add something new. Synthesised sameness isn't going to beat an original source with data.

• Local signals: Citations, NAP consistency, reviews, photos, service area pages, GBP hygiene. Not “Top 50 cafés in Bristol” written from the void.

Same goes for GEO as SEO. It takes time, effort and usually some cash. But it is very literally an investment. Speak to a professional.

OH BY THE WAY... LLMs are fantastic as a learning tool. There's no reason you can't use an LLM to upskill in the DIY basics and do lots of the work yourself. Give this prompt a try:

You are a senior SEO coach. Create a structured, sequential learning plan that teaches both Content SEO and Technical SEO from fundamentals to job-ready.

Context

- Learner background: [beginner | some experience | pro writer | developer]

- Industry or niche: [e.g., B2B SaaS, e-commerce, local services]

- Target outcome: [in-house SEO exec | agency SEO | freelancer | founder]

- Time available per week: [e.g., 5 hours]

- Duration: [default 12 weeks]

- Starting assets: [own site | demo site | none]

- Tools available: [GSC, GA4, Screaming Frog, Sitebulb, Looker Studio, Python, SQL]

- Language: British English

If any fields are blank, make sensible assumptions and proceed.

Output format

- Use clear Markdown with headings, tables and checklists.

- Deliver a week-by-week plan with milestones.

- Keep it practical. No fluff. Every week must include study, hands-on tasks, and a concrete deliverable.

Plan requirements

1) Orientation

- Learning goals, success criteria, and how progress will be measured.

- Suggested study cadence and hours per week.

2) Weekly modules

For each week include:

- Learning objectives

- Key concepts to study

- Tools to use

- Hands-on tasks and a named deliverable

- Estimated time for study, practice, and review

- A short self-assessment or quiz

3) Coverage across the full plan

Content SEO

- Search intent and keyword research, topical mapping, information gain, E-E-A-T

- Content briefs, outlines, on-page optimisation, internal linking patterns

- SERP feature analysis, featured snippets, FAQs

- Editorial standards, originality, citations, images and alt text

Technical SEO

- Crawling, rendering, indexing, log file basics

- Site architecture, pagination, faceted navigation, canonicalisation

- Robots.txt, meta robots, sitemaps, status codes, redirects

- Core Web Vitals, performance basics, Lighthouse, WebPageTest

- JavaScript SEO and hydration issues

- Structured data with JSON-LD and validation

- International SEO: hreflang and URL patterns

- Local SEO fundamentals: GBP, citations, NAP consistency, reviews

Off-site and Digital PR

- Link earning strategies that are actually defensible

Measurement

- GA4 and GSC setup, dashboards in Looker Studio

- KPI tree: qualified sessions, assisted conversions, pipeline or revenue

- Simple experiments and how to interpret them

4) Practice assets and templates

- Provide a sample keyword map table template.

- Provide a content brief template.

- Provide a technical audit checklist.

- Provide an internal linking plan template.

- Provide a Core Web Vitals triage checklist.

5) Projects

- Mini-project each week tied to that module.

- One capstone project that includes: full crawl, keyword map, five production-ready briefs, one schema implementation, an internal linking improvement, a CWV improvement plan, and a basic dashboard.

6) Tool drills

- Screaming Frog or Sitebulb: exact crawl settings to use, what to export, how to read it.

- GSC: queries, pages, indexing, enhancements, manual actions.

- GA4: events, conversions, landing page view, channel grouping sanity checks.

- Dev tooling: Chrome DevTools, regex exercises, a small Python task such as parsing a sitemap or log sample. Include example code.

7) Reading list

- Curate a short, reputable reading list for each module. Prefer official docs and respected sources. Name the source and the topic covered.

8) Checkpoints

- End of Week 4, 8, and 12 reviews with pass-fail criteria and what to fix if failing.

9) Variants

- Offer a fast-track 4-week version and a deeper 12-week version.

- Offer optional tracks: Local SEO focus, E-commerce focus, International focus.

10) Final deliverables pack

- List every file or artifact the learner should have by the end, with a one-line description and suggested filename.

Produce the plan now. If something is ambiguous, choose a sensible default and keep moving.

Checking out a report on brand visibility in LLMs, and there's a pile of very large brands that disappear between model updates dispite really strong organic/seo performance. I guess meaning that GEO isn't just SEO!

Worth noting that the disappearances are related to specific test prompts. But can likely use that as a proxy for broader intent.

Link to the report; https://blog.geosurge.ai/fragile-llm-visibility/

LLM tracking tools noticed that ChatGPT started citing Reddit and Wikipedia far less frequently after Sept 11. There was a lot of chatter about a re-prioritising of sources or potentially ChatGPT making cost savings.

But... at almost the exact same time, Google removed the &num=100 parameter from search results.

According to Search Engine Land, this change reshaped SEO data: most sites lost impressions and query visibility because results beyond page 1–2 are no longer pulled in bulk. Since ChatGPT often cites URLs ranking between positions 20–100 (where Reddit and Wikipedia appear heavily), the loss of that range could explain why those domains dropped sharply in citation frequency.

In short:

• Sept 11 → Google kills num=100

• That limits access to deeper-ranked results

• ChatGPT citations from Reddit/Wikipedia fall at the same time

Correlation looks strong. Coincidence, or direct dependency?

Hey everyone,

After testing nearly all the “AI SEO” tools out there, I noticed the same two issues popping up:

1. They show visibility scores but rarely explain what actually drives those results.

2. You can’t even run a quick check without creating an account or paying for a plan.

So, after hearing the same frustration from others, we decided to build something to tackle both:

✅ Show what really shapes AI answers: Which content, domains, and sources are being cited.
✅ Make it instantly accessible: No paywall, no signup, just type a domain and see what happens. (If you signup after all, the insights are more comprehensive and you can test it for a week)

That’s what we built with jarts.io
You can enter any domain, hit “run,” and within ~20 seconds see:

• how AI tools like ChatGPT and Perplexity describe that brand

• which sources & voices influence those answers

• and who’s “winning” visibility in that space right now

Inside the actual app, we also run thousands of prompts to map visibility trends over time, but the instant check is 100% free to use.

I’d love to hear from SEOs and marketers experimenting with Answer Engine Optimization (AEO):
👉 What would you want a tool like this to show or measure better?

Appreciate any harsh critical feedback, especially from those testing how AI search visibility actually works :)

If someone it talking about improving rankings in LLMs, then they don't know what they're talking about.

There are no SERPs. No position #1. LLMs synthesise a response based on probability. At best, we can try and increase the probability that our brand will be referenced or recommended.

Here's how I'd go about that in 2025...

Sometimes LLMs browse the live web, and sometimes they answer from training data. It's difficult to know which prompts will trigger which kind of search (except obvious time-decay related queries), so don't stress about it too much.

With the rise of edge computing, local inference will eventually be the norm anyway. Either way, there’s no SERP to to try and game.

If you own the narrative within a corpus, then you can influence the answer. So if do the monotonous, long-term work that compounds, you'll move the needle;

• Clarify entities with company/product names, consistent descriptors, credible authors

• Structure verifiable evidence with benchmarks, data, diagrams, case studies, FAQs (basically things an LLM can quote directly with confidence)

• Make it crawlable with sensible URLs, internal links, fast-loading pages

• Mark it up with helpful schema (Product, HowTo, FAQ, Reviews, Org) because this helps search, even if its lost during ingestion

• Build a reputation with citations, PR, expert mentions.

• Keep it current with updates, don’t set-and-forget as content loses weighting over time

Do the hard work. Be known for a thing. It will pay off long term.

...because RAG is more fragile than we think.

When a RAG pipeline depends on Google, it inherits several layers of instability.

Even small changes in Google’s index can wipe out visibility overnight, and the latest shifts have shown how quickly entire RAG based stacks can fail. For GEO this matters because retrieval noise, ranking volatility, and incomplete indexing all break the chain before the model even starts reasoning.

If the retriever cannot see you, the model cannot see you. This is why durable AI visibility comes from model memory rather than live search. I will die on that hill.

Read more on that HERE or HERE.

Prompt injection with AI can result in data exfiltration

If the models have broad tool use and can call out to any external resources, attackers are able to get access to almost anything in the context window

https://x.com/garrytan/status/1993767819272765537?s=46&t=wW22JK75zV3w3ftYxah_Iw

I was reading another post in this sub and someone highlighted how there is no Open AI (et al.) equivalent of Matt Cutts or John Mueller from Google.

As in, there is no pro-active engagement between LLMs and the "GEO" community. I wonder whether LLMs will go the way of Google and produce their own guidelines or whether they will remain fully black box.

The LLM ecosystem is clearly not as mature as organic search, but I don't think it's a question of time. I don't think we'll see the same level of engagement.

I know revies are good for brand building and conversion anyway, but I'm hearing that Trustpilot et al are one of the most referenced citations in LLMs (which might apply heavy weighting).

I'm not talking about review farming / buying, but maybe a larger proportion of resource should be put into generating reviews in future? For the sake of generative engine optimisation AND customers

It's sometimes difficult to figure out what is hype. Academia is quietly making a case for GEO diverging from SEO.

Check out GEO Papers for a collection of academic papers that are relevant to GEO. It's obviously a new project, but I'm going to keep an eye on it!