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 I 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?

When GummySearch announced it was shutting down, I remember that a lot of people were asking similar questions. Mainly

“What’s the best alternative?”

It actually made me realise something recently. I think we’ve accidentally built Reddit research around tools instead of around a process.

The value isn’t so much in the tool but rather in the UNDERSTANDING of what customers were actually saying.

Hear me out here.

My current thinking looks something like this:

Layer 1: Collect the conversations
Native Reddit search
Reddinbox
Google
Perplexity
Manual research

Layer 2: Find the patterns
recurring complaints
buying questions
feature requests
competitor comparisons
language customers naturally use

Layer 3: Turn it into decisions
What content should we create?
What messaging should change?
What should Product prioritise?
Which objections keep appearing?
Which opportunities are competitors missing?

That’s the part I think most companies skip.

I think it’s clear that collecting Reddit conversations is becoming easier every year. But interpreting them is where the competitive advantage is.

Curious how everyone else has adapted since GummySearch announced its shutdown.

Have you replaced it with another tool, or has your research process changed entirely?

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.

https://preview.redd.it/voxart75itah1.png?width=1753&format=png&auto=webp&s=c0f8900184ad8dc84a901152e1de3defab644f54

I ran the same 50 best-tool prompts through all three and pulled out every brand they named. 150 answers later, they basically have different taste.

• ChatGPT is the over-sharer. Widest list every time, and it name-drops 59 obscure tools the other two never mention. It also recommended "ChatGPT" 16 times, very humble.
• Perplexity is the safe friend. Same well known names over and over, rarely takes a risk.
• Gemini is very on brand for Google. It pushed Canva more than twice as often as ChatGPT, leaned hard into the Google ecosystem, and quietly recommended Claude 13 times. Recommending a competitor more than itself is a choice.

The kicker: across everything, all three agreed on the same brand only 21% of the time. Same question, three different realities.

So "what does AI recommend" has no single answer. It depends entirely on which model you ask, and each one has a clear bias.

Which engine's taste do you trust most? And has anyone else caught Gemini recommending Claude in the wild?

This is my own view, not a company post.

Some numbers are going around about Generative Engine Optimization (GEO) and AI search:

• 93% are building a GEO practice in-house (Conductor, 2026 CMO Investment Report, 250+ executives)
• 48% have no GEO strategy in place at all (our own data at NeuroRank)
• 65% call AI search their single biggest challenge this year (GoodFirms, 2026, 20+ countries)

Here's what I can add. My team at NeuroRank has consulted with over 250 brands, every one of them enterprise. I have validated this personally through 1:1 meetings, not a faceless survey.

Yes, there is a knowledge gap. Yes, GEO is challenging as a new practice for brands to build. Yes, most brands are flying blind.

Now comes the challenge. Slow adoption of best practices. Taking shortcuts. Copying a competitor without decoding their own brand's position. An enthusiastic media team that fizzles out under decision paralysis from top management. (India-specific decision paralysis?)

One brand really took the cake: "we will build our own AI visibility platform." Six months later, they came back, in a crisis of falling leads and revenue.

A substantial number say "our corporate team in the US will manage it."

And the agencies. I run one, so I can say this plainly. Too many are selling GEO that is just their old SEO or PR retainer with a new label. They bill for reports and content volume, not for whether the brand gets into the answer. They optimize for the rankings and traffic they already know how to measure, because that is what they know how to invoice. The brand pays for the agency's learning curve and calls it a GEO practice. not surprizing that less than 11% of marketers feel that their agency can give them a reliable GEO service. Cringe in linkedin is not helping either

So what am I missing?

Here's my reading. This is a generation of marketers who have not witnessed a marketing impact at scale. The last one was social media, and it gave us almost four to five years to adopt before it became a crisis. AI search is not going to give us that long.

If you're inside a brand or an agency right now, tell me where this is wrong, or where it's landing for you.

One thing we've been noticing recently is that sitemap problems are rarely about not having a sitemap.

It's usually that the sitemap no longer reflects the site.

We've looked at a number of websites recently where the sitemap was technically present, but it contained old URLs, missed important pages, or hadn't been updated after major content changes.

Traditional search engines have become pretty good at discovering content through links.

But as more AI-powered search experiences rely on retrieving relevant pages efficiently, it feels like clean discovery signals are becoming more important, not less.

A few patterns we've been seeing:

• Key service pages missing from the sitemap
• Old redirected URLs still being submitted
• Blog content showing up months after publication
• Multiple sitemap files with conflicting information
• Sitemaps that haven't changed despite major site updates

None of these issues necessarily break a website.

But they add friction.

And one thing we've learned from technical SEO over the years is that small amounts of friction tend to compound.

Another thought we've been discussing internally:

Schema helps AI systems understand a page.

A sitemap helps them discover it.

Those solve two very different problems, and I think they're often lumped together under the broad umbrella of "AI SEO."

I'm curious whether others working on GEO or AI search optimization are seeing similar patterns.

Have sitemap quality or crawlability issues come up more often than you expected, or has content quality remained the biggest limiting factor?

We documented the patterns we've been seeing in more detail here for anyone interested:

Source: Zaillor Insights

(via u/SE_Ranking)

Our analysis of 300,000 domains shows that LLMs.txt doesn’t impact how AI systems see or cite your content today. ​​Even so, adding the file is a low-effort way to prepare for the next wave of AI indexing. Today it’s optional; tomorrow, it might be essential.

One exercise completely changed how I think about AI search visibility.

Most SEOs approach Perplexity the same way they approach Google.

They search:

• best CRM software
• email marketing platform
• project management tool

And then they check whether their client appears.

I think that's the wrong approach.

Your customers aren't opening Perplexity and typing keyword fragments.

They're having conversations.

They're asking:

"We're a 20-person SaaS company managing leads in spreadsheets. We need LinkedIn integration and can't spend more than $100/month. What CRM should we use?"

Or:

"We currently use WhatsApp and Excel to manage deliveries. Is there a better system that doesn't require hiring an IT team?"

Those queries produce completely different answers than traditional SEO keywords.

Over the last few weeks I've been manually testing industries and documenting citations, recommendations, and source patterns.

A few observations stood out:

1. The competition is bigger than websites

When I first started checking citations, I expected to find competing company websites.

Instead I found:

• Reddit threads
• YouTube transcripts
• Documentation pages
• Industry forums
• Review platforms
• News articles
• Product comparison sites

Sometimes the source influencing a recommendation wasn't a competitor's homepage at all.

It was a Reddit discussion from months ago.

Or a detailed comparison article.

Or a review page.

If you're only tracking SERP competitors, you're missing a large part of the ecosystem AI systems actually use.

2. Direct answers outperform beautiful introductions

This one surprised me.

Many websites still follow the traditional content formula:

Long introduction → background → context → answer.

AI systems seem to prefer:

Answer → explanation → supporting details.

For example:

"What is Perplexity SEO?"

Article A:

"Artificial intelligence has transformed information retrieval..."

Article B:

"Perplexity SEO is the practice of making content easier for AI systems to extract, verify, and cite."

Which answer is easier for an AI system to use?

The difference becomes obvious once you start reading citations closely.

3. Being recommended and being cited are different things

A lot of people only look for citations.

I think recommendations matter more.

I've seen cases where a company isn't directly cited but is repeatedly recommended.

I've also seen companies cited frequently but rarely recommended.

Those are different visibility layers.

One measures source usage.

The other measures commercial influence.

4. Trust signals appear everywhere

Many discussions focus exclusively on content.

But when you inspect sources, you keep finding:

• Reviews
• Third-party mentions
• Expert authors
• Industry publications
• Documentation
• Community discussions

It feels less like traditional ranking and more like building a web of evidence that your company is credible.

The experiment I'd recommend

Open Perplexity.

Forget keywords.

Write down 10 actual customer questions.

Not search terms.

Questions.

Run every query.

For each answer record:

• Which brands were recommended?
• Which domains were cited?
• Which sources appeared repeatedly?
• Did Reddit appear?
• Did review sites appear?
• Did documentation appear?

After doing this, you'll probably learn more about AI visibility in your niche than from reading 20 GEO blog posts.

Because you'll stop guessing and start seeing where the model is actually getting information.

Curious what everyone else is finding.

What has moved the needle most for you:

• Better content structure?
• Off-site mentions?
• Reviews?
• PR?
• Community discussions?

Or are we all still collectively reverse-engineering this thing?

For SEO professionals and agency owners, what's the biggest challenge you're dealing with in 2026?

• AI Overviews reducing clicks?
• Content saturation?
• Link building?
• Local SEO competition?
• Client expectations?

I'm interested in hearing what's actually happening in the industry and how people are adapting.

Hey everyone,

I've been doing SEO for a school based in a small city for the past 3 months, working completely solo from scratch. Here's where I'm at:

• GMB profile: optimized
• On-page SEO: done
• Off-page: currently working on it, but progress is slow

My main struggle is link building. Guest posting sites usually start at $20 and go up to $100+ per post, which isn't in my budget. So right now I'm relying on:

• Social bookmarking
• Classified submissions
• Article submissions

I also tried "Authority Flow" for backlinks, but that requires a link exchange (placing their link on my site too), and I'm not sure how good that is for SEO long-term. On top of that, the blogs I've reached out to for guest posts haven't approved my submissions yet, so I'm stuck waiting.

Traffic is still very low despite all this effort, and I need to show my client a progress report soon.

Has anyone dealt with a similar situation low/no budget, local school website, solo SEO ?

Any advice, especially from people who've grown local/education sites without a big budget, would be hugely appreciated. Thanks in advance!

We had a technical SEO on our podcast recently, and he mentioned seeing more and more sites built with AI coding tools (Lovable, Bolt, v0, that whole space), and while they look great, most of them are essentially invisible to search engines and LLMs.

The reason is that most of these tools generate single-page applications that rely on client-side rendering. When someone visits the site in a browser, JavaScript loads and builds the page content on the fly, so everything looks perfect. But when a search engine crawler or ChatGPT visits the same URL, they get a mostly empty HTML shell. The actual content isn't there.

The irony is that people are using AI to build their sites faster than ever, and those same sites end up invisible to AI search.

Something worth noting is that some of these tools have recently started addressing this. For example, Lovable added server-side rendering for new projects from mid-May 2026. But anything built before that, and most sites from other tools, probably still have the problem.

Many founders, freelancers, and small teams have shipped sites using these tools over the past year. And some of them are probably tweaking content, rewriting copy, or trying AI optimization tactics when the real issue is that nothing they publish is being seen in the first place. It's a build problem. 

Has anyone here dealt with this? If yes, did you notice a difference in visibility after going from a vibe-coded site to something server-rendered?

We're running containerized AI workloads, mostly LLM inference, across a hybrid cloud setup (on-prem + AWS). Great for flexibility, but it's surfaced some tough security and observability challenges.

Here's what we're wrestling with:

• Prompt injection filtering, especially via public API input
• Output sanitization before returning to users
• Auth and session control across on-prem and cloud zones
• Logging AI responses in a way that respects data sensitivity

We've started experimenting with a reverse proxy plus gateway approach to inspect, modify, and validate prompt and response traffic at the edge. Kubernetes network policies help enforce segmentation and control traffic flow between workloads.

For scanning model outputs, we're looking at tools like Presidio for PII detection and OpenAI's Moderation API for content filtering. But stitching all this together across hybrid environments is messy. The gateway layer seems like the right place to centralize this, but most open source proxies don't have built-in security policies beyond basic rate limiting.

Anyone else working on this? Curious how other teams are thinking about security at scale for containerized LLMs. Federated learning and secure enclaves like AWS Nitro are on our radar but feel complex to implement with containerized inference pipelines.

I am working on building a Generative Engine Optimization(GEO) strategy for an ecommerce firm and I want to test a few hypotheses on what works and what doesn't.
To test the hypotheses I wanted to know if I make a change on my website then how long do I have to wait for the LLM's(Gemini, Claude, ChatGPT, Perplexity) RAG system to start showing the impact of my changes in their citations/rankings?

Any help/reference will be great.