u/mjain_entrepreneur

A recent report found a publisher placing sponsored FAQ-style messages in the AI-readable version of its pages, even though human visitors could not see them.

Perplexity blocked the content from influencing its search index and warned that similar practices could affect a publisher’s trust score.

That is an extreme example, but it points to a broader content challenge.

Content now has to be easy for AI systems to understand without becoming mechanical or difficult for people to read.

For those creating content now, what does your actual workflow look like for making the same piece easy for AI systems to understand while keeping it useful and natural for human readers?

What practices have genuinely helped you balance both?

Google’s new Generative AI Performance report finally gives us a separate view of visibility across AI Overviews and AI Mode.

For properties that have access, you can see AI impressions, which pages are appearing, how visibility changes over time, and break it down by country or device.

What’s harder to tell is what those impressions actually mean for performance, since the report doesn’t connect them to specific queries or clicks.

If a page suddenly goes from 2,000 to 10,000 AI impressions, that looks positive. But without the queries or separate click data, how much does that actually change what you do with the page?

For anyone who has access to the new report already, have you started using it in your SEO or GEO workflow?

How useful are you finding the AI impression data so far? Is it actually influencing what you optimize, or are you mostly treating it as another visibility signal for now?

I’ve been trying to work out what a genuinely useful LLM visibility audit looks like beyond testing a few prompts and saving screenshots.

Testing buyer-intent prompts across platforms sounds simple. But results can shift by model, location, search mode, and run.

So how are you auditing this in practice? Do you repeat prompts, weight high-intent queries, or separate genuine visibility gaps from normal volatility?

Which signals have actually proved useful for you over time?

This is becoming a much more interesting SEO problem to me.

I found a recent study that shows how only 37.9% of cited URLs appeared in the first 10 SERP blocks for the exact query. Around 31% were beyond the top 100. 

Say your page ranks #3 for a query that triggers an AI Overview, but Google cites three competitors and leaves you out.

What do you check first?

Is it whether the cited pages are covering related fan-out queries better or  whether they have stronger supporting content or off-site mentions?

Do you usually find there’s a clear reason a ranking page isn’t being cited, or is AI Overview citation volatility still too high to confidently attribute changes?

Would love to hear the actual workflow you use to investigate the gap.

Google keeps saying there are no special optimizations required for AI Overviews. The same SEO fundamentals still apply.

llms.txt, extra schema, aggressive content chunking, pages for every fan-out query, rewriting everything into answer blocks, etc.

I’m more interested in what didn’t work. Has anyone tested one of these tactics, tracked citations before and after, and eventually decided it wasn’t worth doing?

And on the flip side, was there something that actually improved AI overview citations for you?

Would be useful to hear actual before/after observations rather than another list of GEO best practices.

I’ve been looking at referral traffic from Reddit and trying to understand what actually makes it grow.

Even when a brand is active in relevant communities and contributes helpful comments, the traffic can remain low or fluctuate significantly from one month to another.

For those who have worked on Reddit as an organic marketing channel:

What usually causes Reddit referral traffic to increase or decline?

Is most of the traffic driven by a few successful posts, direct links, older Reddit threads appearing in Google, or the size of the subreddit?

How do you encourage more website visits without making your contributions feel promotional?

Would love to hear practical examples of what worked, what didn’t, and what you would avoid.

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’ve been rethinking a few SEO habits that still look productive in reports, but don’t seem to drive results that much anymore.

For me, it’s things like creating separate pages for every small keyword variation, refreshing old content by just adding more terms or sections, and publishing posts that are mostly cleaner versions of what already ranks.

None of these are always wrong. But I’m questioning them more when they don’t improve intent coverage, add proof, bring real examples, or help the reader make a better decision.

At this point, I’m trying to spend less time on “content activity” and more time on pages that make the site genuinely clearer, more useful, or more trustworthy.

Curious what others are seeing. What SEO habits have you stopped or reduced because they no longer feel worth the effort?

I’ve been rethinking a few SEO habits that still look productive in reports, but don’t seem to drive results that much anymore.

For me, it’s things like creating separate pages for every small keyword variation, refreshing old content by just adding more terms or sections, and publishing posts that are mostly cleaner versions of what already ranks.

None of these are always wrong. But I’m questioning them more when they don’t improve intent coverage, add proof, bring real examples, or help the reader make a better decision.

At this point, I’m trying to spend less time on “content activity” and more time on pages that make the site genuinely clearer, more useful, or more trustworthy.

Curious what others are seeing. What SEO habits have you stopped or reduced because they no longer feel worth the effort?

With AEO, the problem may not always be on the website.

Many teams still treat AEO like on-page SEO. Better blogs, better product pages, better structure, better FAQs, better schema. All of that matters, but I don’t think it just about that anymore.

AI answers seem to be shaped by a much wider set of sources now. Reviews, Reddit threads, YouTube videos, LinkedIn posts, comparison articles, G2 pages, old listicles, and community discussions can all influence how a brand is understood.

A recent analysis of 30 million AI citations found Reddit, YouTube, and LinkedIn among the most-cited domains in AI-generated answers. That stood out to me because it means buyers asking “is this tool worth it?” or “how does it compare to X?” may get answers shaped by sources the brand does not fully control.

That makes me wonder if AEO is slowly becoming less about just optimizing owned content and more about understanding the off-site sources AI systems trust.

Are you checking where AI tools are pulling information from for your brand?

And if you are, how much priority are you giving to places like Reddit, YouTube, LinkedIn, reviews, comparison pages, and community discussions?

Curious if anyone here has actually worked on improving their off-site brand footprint for AEO and seen it change how AI tools describe or recommend them.

One AEO shift I keep coming back to is topic ownership.

Not in the old “publish more topic cluster pages” way, but in the sense of whether your brand has enough useful, connected, and trustworthy content around the full topic.

With query fan-out, AI search may not stop at one keyword or one page. It can look across related subtopics, use cases, comparisons, buyer questions, limitations, and supporting evidence before deciding which sources to include.

That makes me wonder if keyword ownership is becoming only one part of the picture.

A page can rank well for a keyword, but the brand may still be weak across the broader topic if the surrounding content is thin, outdated, or missing.

Curious how others are approaching this.

How much are you prioritizing topic ownership in your AEO or GEO strategy right now?

Are you mapping buyer questions, comparison pages, use cases, pricing, limitations, and competitor gaps around your main topics?

And for those already doing this, have you seen any impact on AI citations, brand mentions, AI referrals, or overall visibility?

At the beginning of the year, a lot of the AEO strategy seemed to revolve around one question: how do we get cited more often?

Six months later, that feels like only one part of the picture.

Google has started bringing AI search data into Search Console.

AEO shortcuts are also losing relevance. Strong SEO, original insights, and trustworthy content still matter most.

Query fan-out means planning around one keyword and one page may no longer be enough. AI search is also moving beyond answers to comparisons, recommendations, and actions.

This makes citation counts alone a limited measure of success. Brands also need to know how consistently they appear, whether they are represented accurately, and whether that visibility produces business results.

Which of these shifts is having the biggest impact on your AEO strategy right now?

As AI visibility becomes a bigger part of search and content strategy, teams now have several parameters to compare. Platform coverage, prompt relevance, access to the actual responses and citations, competitor tracking, historical trends, recommendations, reporting, and pricing can all influence the decision.

But feature lists may not always tell you what will prove most valuable once the tool becomes part of your workflow. For those who have evaluated or used an AI visibility tool, what did you prioritize when choosing one, and what has actually helped you the most in practice?

Google has published one of its clearest responses yet to the growing list of GEO “best practices.”

For AI Overviews and AI Mode, Google says you do not need to:

• Create an llms.txt file
• Break every page into tiny chunks
• Rewrite content specifically for AI systems
• Chase inauthentic brand mentions
• Add special AI-only schema

That is a fairly direct pushback against the idea that content needs to be reformatted for machines before it can appear in AI Search.

Google’s recommended approach sounds much more familiar: create original, expert-led content, make claims easy to verify, keep important information crawlable, structure pages for readers, and get the SEO fundamentals right.

But I do not think this makes GEO irrelevant.

Google can explain what works within Google Search. It does not tell us exactly how ChatGPT, Perplexity, Claude, and other platforms will select, combine, or describe sources.

That is where I see the distinction:

SEO strengthens the source.

GEO helps you understand where the brand appears, which sources are shaping the answer, how competitors are positioned, and whether the information being presented is accurate.

Has Google’s guidance changed anything in your GEO strategy?

And which GEO tactic do you think has received the most attention without enough evidence behind it?

Over the past several weeks, our referral traffic from ChatGPT has grown nearly 4X.

What’s interesting is that there wasn’t one big tactic behind it.

We refreshed outdated pages, created stronger comparison and use-case content, answered buyer questions more directly, and made important product claims easier to find and verify.

We also strengthened internal links and topical clusters, improved pages already attracting traffic from AI platforms, and filled content gaps where competitors were appearing instead of us.

None of this was particularly flashy. It was mostly a series of small improvements that made the overall content base clearer, fresher, more useful, and better connected.

My takeaway so far is that ChatGPT referral traffic probably doesn’t grow because you optimize a few isolated pages. It grows when the wider source layer becomes easier for AI to understand, retrieve, trust, and reference.

That’s only based on what we’ve seen, though.

For those seeing measurable traffic from ChatGPT, what has actually worked for you?

Apart from content refreshes, comparison pages, internal linking, and filling competitor gaps, what other strategies have helped you increase referral traffic?

Most AEO reports focus on citations, brand mentions, visibility scores, and share of voice. Those metrics are useful. But I’m not sure they are enough to tell us whether AEO is actually working.

A brand could be gaining visibility while AI is still showing outdated pricing, missing important features, associating it with the wrong use cases, or presenting a weak comparison against competitors.

In that situation, the visibility report looks positive, but the buyer may still be getting an inaccurate picture of the product. I feel this matters because buyers are asking AI increasingly specific questions:

Which product fits their use case?
What are its limitations?
How does it compare with other options?
Is the pricing worth it?

AI may be shaping that perception before the buyer reaches the website, books a demo, or speaks to sales. That makes AEO part of the pre-sales journey.

That raises an important question: are we only measuring whether AI mentions the brand, or also whether it represents the brand accurately?

Is anyone actively tracking answer accuracy across prompts and platforms? And when you find an inaccurate or outdated representation, what changes do you make first?

One hard part with AI search is that visibility can change even when nothing obvious has changed on your site.

A brand may show up in AI answers consistently for one month, then disappear the next. I have noticed that sometimes the cited URL changes, but the brand mention stays. Other times, a competitor starts appearing for prompts they were missing from earlier. Sometimes, the answer changes across AI search platforms.

With traditional SEO, movement was easier to explain through rankings, impressions, clicks, CTR, or technical changes. With AI search, it is harder to know the reason for a sudden drop.

These are a few things our team at Scalenut has been closely watching to understand whether the drop is just normal AI search volatility or something that actually needs fixing:

• Where AI referral traffic is coming from.
• Which competitors keep showing up.
• Which external sources seem to influence visibility.
• Which trusted sources we are still missing from.
• Whether the brand mention stays even when the cited source changes.

How are you diagnosing sudden drops in AI visibility, and what signals are you watching before deciding something needs to be fixed?

One hard part with AI search is that visibility can change even when nothing obvious has changed on your site.

A brand may show up in AI answers consistently for one month, then disappear the next. I have noticed that sometimes the cited URL changes, but the brand mention stays. Other times, a competitor starts appearing for prompts they were missing from earlier. Sometimes, the answer changes across AI search platforms.

With traditional SEO, movement was easier to explain through rankings, impressions, clicks, CTR, or technical changes. With AI search, it is harder to know the reason for a sudden drop.

These are a few things our team at Scalenut has been closely watching to understand whether the drop is just normal AI search volatility or something that actually needs fixing:

• Where AI referral traffic is coming from.
• Which competitors keep showing up.
• Which external sources seem to influence visibility.
• Which trusted sources we are still missing from.
• Whether the brand mention stays even when the cited source changes.

How are you diagnosing sudden drops in AI visibility, and what signals are you watching before deciding something needs to be fixed?

One thing that seems to be happening more often now is that organic rankings and AI visibility don’t always move together.

A page can rank in the top 3 and still not be used in the AI answer. Another page may not rank as strongly for the exact query, but still gets cited or mentioned because it answers a related angle better.

That makes prioritization harder. Earlier, if a page ranked well, the next move was usually clearer: protect the ranking, improve CTR, refresh the content, or push conversions.

Now the question is less obvious. When rankings and AI citations don’t line up, what do you change first? Do you start by improving the existing page, building supporting content around related questions, or strengthening external proof and mentions?

Would like to know how others are handling this. Are you prioritizing AI visibility only for commercial queries, or are you trying to close the gap across informational content too?

Google’s latest AI Search updates made me think about a question content teams are probably already debating. For years, a lot of SEO programs were built around covering every basic question in a category like “what is X,” or “how does X work,” or “why is X important.” Those pages made sense when they brought traffic, educated buyers, and gave brands more surface area in search. But AI Search has changed the equation a bit.

If AI Mode can answer the basic question, keep the context, handle follow-ups, and help users explore a topic more deeply without restarting the search, then the value of basic informational content becomes harder to judge.

At the same time, cutting that content completely feels risky too. Because it still helps define the category, supports internal linking, builds topical depth, and gives search engines and AI systems more context about your brand and expertise.

So where do you draw the line? Does basic educational content still deserve the same time, budget, and publishing priority it used to?

Would like to know how others are thinking about this. Are you still creating informational content at the same pace, or are you becoming more selective? And when AI Search can do more of the research upfront, how are you deciding what content is actually worth publishing?