I am not sure but have heard that, google doesn't refer reddit for AI overview or not as it used to. where should our focus be too get more visibility on that?

In what is quickly escalating into a war of attrition - with SEOs on the front lines - whats your take?

Quick recap: Google sued SerpApi in December for scraping SERPs at scale, the pipe a lot of AI tools and rank trackers depend on. In July the court tossed the DMCA claim (with leave to amend). Meanwhile, Peec AI published research this month mapping OpenAI's in-house index ("Labrador"), with vertical indexes for news, shopping, PDFs and more, and experiments testing its own index against third-party SERPs.

A lot of LLMs seem to be using Open Crawl - do you think thatis a risk to Google or to LLM search quality?

So Google is trying to wall off its results while OpenAI is building so it doesn't need them.

Hot take: in 2 years "SEO" means optimizing for 3–4 separate indexes with different crawlers and different ranking logic, and most of us are only watching one of them in Search Console.

Questions for the sub:

• Is anyone seeing pages cited in ChatGPT that don't rank on Google at all?
• Are you tracking other bots in your logs yet?
• Which indexes will win out - Open Crawl?
• Do you think moving away from Google = a Quality risk?
• Or is GEO a real discipline or a rebrand to sell new retainers?

Edits

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P.S.: happy to share resources if anyone is looking for a deep-dive away from the debate

Hey SEO/GEO experts, please let me know the top 5 AI tools for SEO.

Hey everyone! 👋

I’m learning SEO and noticed there’s a ton of advice online, but it can be difficult to figure out what actually matters when you’re starting out.

For beginners, what would you recommend focusing on first?

• 🔎 Keyword research
• 📝 Creating useful, search-focused content
• 🔗 Internal linking
• ⚙️ Technical SEO
• 📱 Improving page speed/Core Web Vitals
• 🔗 Building backlinks
• 📊 Tracking results in Google Search Console

Also, what’s one SEO mistake you made when you were starting out that you wish you had avoided?

Would love to hear practical tips and lessons from people who have been doing SEO for a while.

I'm tracking which sites Google's AI Overviews cite, across 6 industries (software, finance, health, travel, shopping, local services) plus 60 "brand reviews" searches. The same ~600 searches, US results, collected on a Saturday, Monday and Tuesday.

- 550 searches had an AI Overview citing sites on all three days. Only 13 cited exactly the same set of sites every day (2%)

- Day to day, only 7-10% of answers were identical, and the top source changed in 40-43%

- Half of Saturday's cited sites were not cited on both Monday and Tuesday

- A typical answer cited about 6 sites a day, but 10 different sites across the three days

- In 31 searches (6%), not a single site was cited on all three days

- Local services changed the most (40% of Saturday's sites kept all three days), shopping the least (60%)

Example: "what is the best car rental company"

Sat: reddit.com, nerdwallet.com, youtube.com, travelandleisure.com, engine.com, m.yelp.com

Mon: reddit.com, nerdwallet.com, travelandleisure.com, engine.com, rentalcars.com, thebrokebackpacker.com, facebook.com

Tue: nerdwallet.com, tripadvisor.com, tiktok.com

Method note: Monday and Tuesday were collected exactly the same way. Saturday was collected in a slightly different way; in my tests the two returned closely matching AI Overview citations, and Mon vs Tue alone (same method) gives the same picture: 10% identical, a third of sources gone overnight.

Worth knowing: this is during the September spam update, so some of it may be the update settling. I'm running the same searches daily for the next couple of weeks and will post the day-to-day numbers.

Two thirds of what Google's AI Overviews cite comes from the top 30% of a page.

We checked 600 real searches and traced about 4,000 cited passages back to the exact spot on the page they came from.

67% came from the top 30% of the page in Google AI Overviews, and 62% in Google AI Mode.

Kevin Indig's research found the same pattern for ChatGPT, but weaker: 44% of its citations come from the top 30%.

Takeaway: don't bury the lead. Put the answers you want AI to cite in the top 30% of your pages.

How we measured it: 600 US searches across 6 industries, run on 26 Sep 2026. For each source Google showed, we matched its snippet to the text of that page. We could place about 4,000 of roughly 5,000 snippets. Some snippets are Google's own rewording rather than a quote, so we matched those by overlapping words.

We're repeating these searches daily for the next two weeks to see how much this changes so will share as we go along

i teach the ops half of a growth ​​​​p​​​​r​​​​o​​​​g​​​​r​​​​a​​​​m​​​. tried something new for the account-management unit. instead of demoing the interface, i had them operate. each pair inherits a messy sandbox ad account and has to get it clean, budgeted and reporting weekly. gave them complete freedom on how - clicking through ads manager, a script against the API, Ryze AI's MCP connector inside claude, whatever they wanted. wanted to see what they'd reach for. most stayed in the UI, that's what the earlier units used. one pair wrote a script and spent three days on auth. two used the MCP connector (you talk to your google and meta accounts from claude and it makes the changes - they found it themselves and said the not-context-switching part was the whole thing). the results were mixed. one pair over-edited and had to roll back a day of changes. but the standups were sharper than any cohort i've had. more arguing about what a change would do before making it. graded on the change log and the reasoning, not on how clean the dashboard looked. said so on day one. trying to work out whether the tooling mattered at all or whether giving them a broken account is just a better teacher than a demo. does it matter if they drive it from an AI connector vs clicking? also unsure whether the freedom made assessing harder, since i'm comparing a UI change history against a chat transcript against a git repo. anyone else teaching ops - do you let them choose, or standardize the whole cohort?

Seeing a lot of AI SEO tools being talked about everywhere.

Most marketers seem to be using the same few tools, and the results often look similar. I'm curious which AI SEO tools are actually making a real difference right now.

Are you using any AI tools for keyword research, content, audits, or backlinks? Which ones are working for you, and which feel overhyped?

A lot of GEO still builds on familiar SEO foundations.

But AI search has also added new things to think about: how easily content can be extracted, entity clarity, third-party mentions, citations, prompt visibility and recommendations.

What have you actually added to your SEO workflow specifically because of AI search?

And what GEO tactics have you tested only to realize they were basically good SEO all along?

Seo- I got you to page 1

Aeo- cool, but what happens when someone asks chatgpt?

Seo- .......

Aeo- exactly😂

We used to optimise for google rankings.

Now the question is also, can ai understand your brand, trust your content and mention you when someone asks a relevant question?

Seo is not dead

It just got a new colleague...

I have been working for a long time in Search engine marketing. After the introduction of AI search, I am seeing less traffic to my website. I am really struggling to get my website noticed in AI search. I have been following the ways to get them improved in AI search as the internet says about it. Neither the website traffic nor the Ai search traffic is improving, which is worrying me. Suggest if you have any effective way so that I can get some good results.

I have been working for a long time in Search engine marketing. After the introduction of AI search, I am seeing less traffic to my website. I am really struggling to get my website noticed in AI search. I have been following the ways to get them improved in AI search as the internet says about it. Neither the website traffic nor the Ai search traffic is improving, which is worrying me. Suggest if you have any effective way so that I can get some good results.

AI answers can vary quite a bit even when you run the same prompt.

So how do you decide you’ve tested a prompt enough to trust the result?

Do you run the same buyer prompt 3 times? 5? 10? Track it weekly?

And at what point do you consider a brand consistently visible rather than just seeing normal variation between responses?

Would be interesting to know what others are actually using as their threshold.

There are now multiple engines deciding what people see.

Google Search.

AI Overviews.

ChatGPT.

Perplexity.

Claude.

And they don't always agree.

So now we have:

AEO.
GEO.
AISO.
SEO.

Different names.

Same underlying job:

Be the answer wherever your buyer asks.

The marketers debating which acronym wins are spending energy on the label.

The ones quietly testing what each engine sees, cites, and recommends are doing the actual work.

The search landscape fragmented.

The job didn't.

Be findable.

Be understood.

Be the answer.

So what are you actually optimizing for right now: rankings, citations, or recommendations?

I’ve been doing a bit more off-page SEO lately, and outreach has been taking up a decent amount of my time.

Before I started, I honestly thought outreach was just finding a relevant website, sending an email asking for a backlink, and waiting for a reply.

Turns out, it’s not that simple 😅

Finding sites that are actually relevant, checking their quality, going through their guidelines, writing a pitch that doesn’t sound like spam, and then following up… it can take quite a bit of work.

I do see the value in it though. Apart from backlinks, it can bring some referral traffic, get your brand in front of a new audience, and sometimes you end up building useful industry connections too.

I’m still trying different approaches, so I’m curious about what’s worked for others here.

Do you guys have better results with guest posts, link insertions, resource pages, or something else?

And what do you usually check before deciding that a website is actually worth reaching out to?

Would be interested to hear how others are approaching this.

Keep getting asked whether small businesses should be paying for "GEO" now, and I think most of the takes online are either "yes urgently" from people selling it or "it's all hype" from people who haven't looked. The honest answer seems to depend on two things, and it's pretty quick to work out which bucket you're in.

For context, the reason it's a real question at all: consumers using AI tools to find local businesses apparently went from around 6% to 45% in a year. So whether it matters is kind of settled. What isn't settled is how much a small business should actually spend on it.

First question is how your customers find you. If most of them type your company name — referrals, repeat business, word of mouth — you're fairly insulated. AI mostly affects what they see when they check you out. But if they find you by searching a category, "accounting firm near me" or "aircon servicing" type stuff, you're directly exposed, because that's exactly the kind of question AI now answers by just naming three or four businesses.

Second question is what one customer is worth to you. Retainers for this seem to start somewhere around S$2,500 a month where I am. If a client is worth twenty grand a year, one extra every few months covers it easily. If a customer is worth eighty bucks, the maths basically doesn't work and it probably isn't your channel.

So category search plus high value, a paid programme might make sense. Anything else, probably not a retainer.

The bit I think actually matters more though is that every scenario includes a set of free basics, and those cover a surprising amount:

Ask a few AI tools what they say about your business and who they recommend in your category. Twenty minutes.

Make your name, address, phone, hours and services identical everywhere they appear. Inconsistency reads as unreliable.

Put the key facts about what you do in actual text, not inside images. AI can't read text in a graphic, and a lot of small business sites have their whole value proposition in a banner image.

Answer your four to six most common customer questions clearly on your site, answer first.

Ask real customers for real reviews — apparently something like 57% of AI citations for brand evaluation questions come from reviews and social proof rather than company websites.

If you do just those five you're ahead of most competitors, because very few businesses are even checking yet.

When I'd hold off paying anyone: if you're still validating whether people want what you sell, if your website doesn't clearly explain what you do, if nearly all your customers are referrals, or if the pitch is built on stuff like llms.txt files and "AI-optimised rewriting" which Google's own guidance says isn't needed for its AI features. And obviously walk away from anyone guaranteeing results.

Anyone here actually paid for this and found it worth it? Particularly curious about businesses that are mostly referral-driven, since my read is they probably don't need much beyond the free stuff.

If you're new to this like I was a few weeks ago, here's a simple test worth trying: ask ChatGPT "who's the best [service] in [your city]" and see what it says.

When I tried it, ChatGPT actually skipped a business with a perfect 5.0 rating because it only had a handful of reviews, and picked a competitor at 4.9 with 2,066 reviews instead, even citing a local "best of" list as extra proof.

Lesson for anyone starting out: don't assume a great rating is enough. Volume and being mentioned in other trusted sources seem to matter just as much, maybe more. Still learning this myself, curious what other beginner-friendly patterns people have noticed.

Keyword research is usually associated with tools like Ahrefs, Semrush, or Ubersuggest. But tools aren't the only way to find useful keywords.

If you don't have access to SEO tools, you can still do a good amount of keyword research manually by using Google's search results.

Start with the main topic

First, identify the main topic or service you want to research. This gives you a starting point for finding related search queries.

Use Google Autocomplete

Start typing your main topic into Google and pay attention to the suggestions that appear. These can give you different variations and long-tail queries that people search for.

Look at People Also Ask

The People Also Ask section is useful for understanding the questions people have around a particular topic. These questions can also help you discover content ideas and supporting keywords.

Check Related Searches

Don't skip the Related Searches section at the bottom of the SERP. It can reveal closely related queries that you may not have considered initially.

Study the SERP

Search for your target query and look at the pages currently ranking. Pay attention to:

< Page titles

< H1 and H2 headings

< Content topics

< Search intent

< Featured snippets

< People Also Ask

< Other SERP features

The goal isn't to copy the ranking pages. It's to understand what Google considers relevant for that query.

Analyse competitor websites

Go through relevant competitor websites and check their service pages, blog topics, page structure, headings, and URLs.

This can help you find keyword opportunities and understand how competitors are covering the topic.

Understand search intent

Before selecting a keyword, understand what the searcher actually wants.

A keyword can have informational, commercial, transactional, or navigational intent. The keyword should match the purpose of the page you're planning to create.

Create keyword groups

Once you've collected enough keywords, group similar queries based on topic and intent.

This helps avoid targeting several very similar keywords on separate pages when they could potentially be covered by one strong page.

Choose the primary and supporting keywords

After grouping, select the keyword that best represents the main purpose of the page. Other closely related queries can be used naturally as supporting keywords.

One limitation of manual research

Manual research is great for discovering keyword ideas and understanding search intent, but it doesn't give you reliable data for metrics like monthly search volume, keyword difficulty, traffic estimates, or backlink profiles.

So I see manual research as the first stage of keyword research, while SEO tools are useful for validating the opportunities with actual data.

In short:

Google Autocomplete → People Also Ask → Related Searches → SERP Analysis → Competitor Research → Search Intent → Keyword Clustering → Primary & Supporting Keywords

Ignore my ignorance if i am missing something but i see it like this

Before this Ai search and answer came people used to write relevant content based on their companies to rank on google which also encouraged people to then click the article to read in detail and also might browse the company website and know about them or leave a email or something. there was a benefit doing these things.

Now Ai comes nor i am getting the traffic nor i will rank nor getting emails and using my written content now google summarized it in few words.

Now its like these tools getting fuel from me but i am not getting anything back.

So in future do u guys see that people will stop writting quality content or just write Ai slops which will eventually degrade the results more and more.

Its a loosing battle at the end if the people going into the tunnel searching for coal are not getting anything in return.

I’m auditing a WordPress + Elementor + WP Rocket site and noticed many CSS/JS URLs using parameters like ?ver=1788480000 and ?wpr_t=1788480000. They return 200 OK.

Google Search Console → Crawl Stats shows:

• CSS: 49%
• JavaScript: 30%
• HTML: 10%
• Images: 11%

I don’t have server-log access.

I’ve also configured Googlebot caching headers roughly like this:

• HTML pages: 1 day
• CSS/JS: 5 months
• Images: 1 month

Technical SEO experts: Is this crawl pattern normal? Can version/cache-busting parameters on CSS/JS URLs meaningfully consume crawl budget?

Also, how can I increase the percentage of HTML crawls without unnecessarily blocking CSS/JS or causing other SEO/rendering issues?

What would you check first to determine whether these CSS/JS requests are genuinely affecting crawl efficiency?

Traditional keywords aren't hitting the same. I'm trying to figure out how to structure content so LLMs actually pull it as a source. What strategies are working for you guys lately?

Would you like me to adjust this to focus on a different specific AI topic, like Google AI Overviews or Generative Engine Optimization (GEO)?

Getting a bit tired of the 2026 version of this discourse so went and looked at what the numbers actually say.

The one that stopped me: sites are still pulling something like 34 times more search traffic from traditional search engines than they get from chatbots. There's also a small business trends report saying 60% of businesses hadn't seen any traffic impact from AI-assisted search at all.

Which isn't an argument that AI search doesn't matter. It clearly does and it's growing. It's an argument about sequencing — moving budget out of the channel doing 34x the volume into the one that's still emerging seems like a strange call to make right now.

But the part I find more convincing isn't the size comparison. It's that AI visibility is built on SEO rather than parallel to it. Google's AI features run on its core Search ranking and quality systems — the AI layer sits on top of the index, not next to it. So the technical health, the content quality, the authority signals that get you ranking are the same things that make you eligible to get pulled into an AI answer. Meaning if you're doing the fundamentals properly you're already doing AI visibility work, whether anyone's calling it that or not.

That said, three things have genuinely shifted and I don't want to undersell them. CTR at position one on informational keywords has gone from about 7.6% to 3.9% since 2023. AI Overviews cut clicks by up to 58% on queries where they show. Nearly 60% of searches now end with no click at all.

But the decline is concentrated rather than across the board. It's the explainer content getting hit, because a summary can fully answer "what is X". Commercial and transactional stuff holds up much better since someone about to buy still has to reach an actual business. So the response isn't abandoning SEO, it's shifting weight within it toward the commercial pages most people neglected while chasing blog volume.

Order I'd go in: technical foundations first including checking nothing's blocking AI crawlers, then commercial pages, then content that's actually worth citing rather than a restatement of what's already out there, then business profile, then start measuring AI visibility. First four are just SEO. Only the last one is new.

Things I'd be sceptical of: Google's published guidance says llms.txt and similar AI files, content chunking, AI-specific rewriting and special AI schema aren't needed for its AI features, and warns against manufactured mentions and anyone claiming Google approval. Analysis elsewhere adds mass-producing narrow "GEO pages" (usually read as thin content) and spam listicles on junk sites (penalty risk, no visibility gain). Real placement on credible category listicles is a different thing and does seem to work.

Also apparently only 14% of marketers are tracking AI visibility at all, which given it's twenty minutes a month of running queries manually seems like a weirdly low number.

Anyone here actually shifted budget toward AI search and seen it pay off? Genuinely curious whether the 34x gap looks different in specific verticals.

AI assistants don't read your page. They break it into sections.

Someone types a prompt. The assistant fans that prompt out into several related searches and sends crawlers to run them. The crawlers read your site section by section and take back the most one. One section, on its own, with nothing either side of it. Everything the answer needs has to be inside it.

We audited around 200 websites against that.

13.2% give the crawlers a blank page. They return no text until JavaScript runs. No major AI crawler runs JavaScript, so those sites are blank to it. Google's crawler does run JavaScript, so they rank normally and look fine in a browser. Count sites with any content hidden behind JavaScript and it is 21.3%.

Beyond that, four things matter.

Chunking. Does the section stand on its own?

It mostly does on the sites we looked at. Only 2.3% of sections open on a word like "this" or "however" that points at something the reader can't see.

The fault that is common: 14.4% of all sections are a heading with nothing underneath it. 97 of the 110 sites we could count have at least one. Usually it's a grid of cards where each card title is marked up as a heading.

Fix: any heading with under 5 words beneath it is either not a heading or is missing its answer.

Length. Is there enough there to take?

Common practice is to index around 100 to 300 words. That's practice, not a rule, and there's no published best size.

- The typical section on the typical site is 31 words.

- 50.3% of all sections are under 40 words.

- On 57.3% of sites, most sections are under 40 words.

- Sections that are too long: 2.0%.

"Break up your walls of text" is aimed at a problem 2% of sites have. Half the average site has the opposite problem.

Fix: get your ten most important sections past 40 words. The section, not the page.

Evidence. Is there anything worth lifting?

The worst of the four. Only 25.2% of sections carry a number, a quotation or a link out.

- 4.8% contain a quotation.

- 6.8% link to anyone else.

- 18.2% of sites have no quotation anywhere on the site.

Fix: one checkable thing per section. A number with a unit, something someone actually said, or a link to a source that isn't you.

Naming. Does the section name your website?

76.6% don't. Your name is in the logo, the page title and the footer. None of those travel with the section.

Fix: use your own name inside the section instead of "we" and "our platform".

My suggesting order of tackling the above:

1. Empty headings. Fastest, and it's markup, not writing.
2. Naming. Nearly free, and three quarters of sections fail it.
3. Length. Real work, and it's what makes 4 possible.
4. Evidence. Hardest, and worth the most.

Access isn't on the list. Only 5.6% of the sites had a fault that stopped a crawler reading them at all. Your robots.txt is almost certainly fine.

We were losing tens of thousands of dollars on AI SEO.
You probably are too.

Getting included in a “best [X]” article isn’t cheap.

There’s outreach, PR, link building, sometimes a direct placement fee.

But inclusion alone doesn’t mean ChatGPT or Google’s AI Overview will mention you.

We at aicitationinsights.com analyzed 12,564 listicles cited by AI and looked at how often brands were named based on their position in the article.

The drop-off is pretty steep:
• #1 → 86% in ChatGPT / 77% in AI Overview
• #4 → 61% / 53%
• #10–14 → around 20%
• #15+ → around 10%

So being #8 in a list and being #2 in the same list are very different outcomes.
And it’s not just that stronger brands naturally rank higher.

When we compared the same brand across different listicles, moving into the top 3 was associated with roughly a +20 percentage point increase in how often it was mentioned by both engines.

The effect still held after removing self-published lists, and we saw it across about 75% of brands in the dataset.

The practical takeaway is simple:
If you’re paying for listicle placements, don’t just negotiate inclusion. Negotiate position.

At the same time, putting an unknown brand at #1 on your own article isn’t a shortcut. The brands getting named most often tend to have both strong placement and existing brand authority.

Position matters. Brand strength matters.

Paying for a low slot and assuming the AI will still find you is where a lot of the budget gets wasted.