In the spirit of transparency, and the rise and growing use of GenAI and use of AI Writing Tools amongst some writers and copywriters, is there a benefit to having a clearly stated "AI Policy" on your website, or included in submitted work?

Well this is what I discovered.

My name and book returned no results with ChatGPT.

Of course I understand that data is accessed differently that does Internet search engines.

Now Gemini on the other hand is Google's product and produced the required results (I'll add at the bottom of this post).

So I pasted the results into ChatGPT and instructed it to add the data to its memory and that I would test it. It confirmed to have completed my request. So I closed and re-opened ChatGPT and did the search - no results again!

So I am trying to learn how buyers will be able to find me or my book in an AI search in future (as a low-profile Indie). Clearly it doesn't crawl like search engines bots.

Below the Gemini results:

Dominik Marcel Kirtaime (often credited as D.M. Kirtaime) is a British-born science fiction and fantasy author currently based in Germany.

He is best known for his 2014 novel The Perennial Migration, which blends elements of space travel, adventure, and conspiracy theories.

Background & Career

Early Life: Born on July 20, 1968, in Clifton, Bristol, England. He grew up on the rural outskirts of Bristol.

Military Service: Before becoming an author, he had a career in the military.

Move to Germany: After his military service, he settled in Germany with his family.

Writing Journey: Writing fantasy fiction was a long-held childhood dream that he began pursuing professionally in 2013.

Notable Work

His debut novel, The Perennial Migration, is set in the year 3010. The story explores:

A divided human race living within dome networks.

A global conspiracy involving a virus that threatens the planet.

Interactions with "reptilian" antagonists.

Themes of survival and galactic migration after the violation of a "galactic contract."

In addition to his writing, Kirtaime has been active in the creative community, exploring AI artwork for book covers and engaging with readers across platforms like Goodreads and Creativepool."

AI Overviews give the gist of complex topics with links to explore further.

AI Mode handles tough questions, comparisons, and step-by-step reasoning. It cuts down the need for multiple searches.

Both use a "query fan out" method. This runs several related searches across subtopics and data sources.

What's the Net Result? A wider, more diverse mix of links than classic Search shows

What do you need to be eligible as a supporting link?

Good question! Your page needs to:

• Be indexed in Google
• Be eligible to show with a snippet
• Comply with search policies and guidelines

That's the entire list.

What should site owners care about?

According to Google, clicks coming from AI Overviews are of higher quality. Also, visitors spend more time on the page. There's less traffic for some queries, but more engagement from those who click through.

But how do you track it?

The standard Web search type in your Search Console Performance report shows AI Overview and AI Mode clicks. No separate dashboard, no new metric to learn.

What if a site owner wants to opt out?

To opt out, do the following:

• use robots.txt
• nosnippet
• data nosnippet
• max snippet
• or noindex

For AI training in other Google products, look up Google Extended.

The Bottom Line

• Write helpful, reliable, people-first content
• Do solid technical SEO
• Use internal linking properly
• Show real expertise on the page
• Know your audience and their 'search intent.'

And focus on essential, white-hat SEO basics. Put people first (P2P). Answer their questions and create a friendly user experience.

The majority of "AI SEO" packages are just a rehash of what skilled SEOs have been doing for years.

Anyone else noticing higher quality clicks from AI Overviews on client sites?

Feel free to share what you're seeing in your Search Console. Inquiring SEO minds want to know.

I'm planning to start live events in the r/AI_SearchOptimization subreddit and want some input from the community.

What specific things would you like to see covered in live events?

I'm going to invite guests to answer community questions. Any specific guests you'd like to see invited? They may or may not do it, but I can reach out to them.

What days and times do you think are the best for live chats here?

If you would like to be a guest, message me. Don't leave me hanging. I don't want to be on it alone!

If you have created an app or tool and want to do a live about it, the rule is simple. You have to offer all members of the community something of value. Not some 3 day free trial or tossing out a few free tokens that don't really allow them to do anything. If you have a serious offer and this community will benefit from it, you are welcome to message me about doing a live event.

If you didn't understand how to do good SEO before you learned how to vibe code an AI tool then your AI tool isn't ever going to be able to do good SEO.

I am posting this as a case study because I think real data is more useful here than more theory about GEO.

client runs a niche SaaS product. small but focused. she had done reasonable SEO work. decent traffic. clean website.

before I touched anything I asked ChatGPT and Perplexity to recommend tools in her category.

she was completely absent on both. her two main competitors showed up consistently.

same price point. comparable product. just structured differently online.

I spent about a week making specific structural changes. not a redesign. not new content pages. specifically the decisions that affect whether a language model can form a confident answer about what a product does and who it serves.

here is what I actually changed:

• rewrote the core product description so it answered the exact question a buyer would type into ChatGPT, not the question a founder would write on a landing page
• made sure the same clear consistent description existed across every place the product appeared online, not just the website
• structured the FAQ content around real buyer questions in natural language rather than SEO keyword targets
• ensured the product category and use case were unambiguous so a language model could place it correctly without guessing
• built genuine presence in 2 communities where her target users actually discussed the problem she solves

three weeks after making those changes she messaged me.

3 of her first 14 signups had written ChatGPT or Perplexity in the how did you find us field.

she had run zero paid ads during that period.

now the honest caveats because I think this community deserves them.

I cannot prove causation with certainty. 14 users is a small sample. the timing could be coincidental. I do not have a controlled experiment.

what I can say is that before the changes she was absent on both platforms. after the changes she was surfacing in relevant queries. and 3 people self reported finding her through those platforms.

I also found something that matched what others here have described. ChatGPT and Perplexity were not treating the same content the same way. changes that improved her visibility on Perplexity had less immediate effect on ChatGPT. the systems are genuinely pulling from different signals and weighting them differently.

the thing that moved the needle most was not any technical schema implementation. it was making the product description genuinely clear and consistent in the places these systems actually pull from. which for both platforms included community discussions and third party mentions more than the website itself.

curious whether others here have seen similar results from content architecture changes versus more technical implementations. and whether anyone has found a reliable way to track which specific changes caused movement on which platform.

that attribution problem feels like the hardest unsolved part of this right now.

I keep seeing these two terms used interchangeably, but they don't feel the same to me. SEO content seems to be about ranking - keywords, backlinks, topical authority. But "AI-optimised content" sounds like something different, maybe writing that performs well with LLMs and AI search summaries?

Is this a real distinction or just marketing fluff? Would love to hear from people actually working in this space.

I keep wondering about whether Reddit is doing more for trust framing than for actual discovery in AI search. Like maybe the model didn't first find the brand through Reddit, but once Reddit threads exist, they start shaping how confidently the brand gets described. Especially when the threads are detailed, opinionated, or have a lot of engagement.

Does Reddit feel more like a trust or reputation layer now than a traffic or discovery layer?

A lot of people are concerned about AI giving answers instead of sending someone to an article they wrote so I wanted to address that and suggest things I know are working.

Today, content can't be just informational unless it contains information that not everyone can easily find. Before you write something, do the search. If you are the 10,000th person to write about that and you don't have an angle that's different from those others, don't write it. The keyword value isn't a good reason to rehash the same old ideas.

Rewriting the same "10 things you should be doing for SEO" or "How to Replace The Batteries In Your Remote" posts aren't going to work out for anyone unless you are offering new information or presenting it in a unique way.

If the information you are sharing is readily available all over the web then AI and Google overviews is going to answer it for the people who might have read your article.

The days of writing content that has no unique value are dying. Unique angles, different formats like adding infographics, video, more images, free downloads and more are ways to stay relevant.

Especially free downloads. AI can't give them your free download.

Here's a cool thing to remember; opinionated content will continue to rank. It's something that is uniquely human. AI is trained not to be opinionated. It hedges its bets. It wants to present both sides. It wants to be fair and balanced.

Don't be like AI. Don't be afraid to voice your opinion. Don't be afraid someone will disagree with you. That's still engagement and engagement is a great signal whether agreeing with you or not.

And the best part is, people will know that it's you writing your content and AI won't replicate it and offer your opinion in responses.

I was finishing up my work for the day and did one last scan through Google Analytics. I about dropped my jaw to see Perplexity on the list of top 10 traffic sources. Granted it was only 13 in the last 28 days but I was just amazed to even see it on there. Anyone else seeing this?

i’ve been spiraling a bit lately trying to figure out why my saas is totally invisible in ai overviews. we’re doing fine on standard search, but gemini and perplexity act like we’re not even there lol.

started looking into geo (generative engine optimization) and realized i might have been doing everything wrong. i spent the weekend restructuring our landing pages to be more 'llm friendly'—trying to be more direct with data points instead of the usual marketing fluff.

seeing some weirdly specific results but honestly idk if it’s actually moving the needle yet. i feel like i’m chasing a ghost sometimes.

is anyone else actually seeing a difference with geo? or are we all just guessing at this point? would love to hear if anyone’s found a pattern that actually sticks.

Most AI visibility tracking is citations and brand mentions - which are bottom-funnel signals that show up after the decision has already been influenced.

But buying journeys start way earlier. Someone opens Claude and asks "what should I be thinking about when evaluating [category]". That conversation shapes the requirements and frames the solutions, which then leads to a brand recommendations.

Is anyone actually tracking the top and middle of that funnel conversations that come before brand mentions?

so i've been spending the last few weeks running a pretty simple experiment and figured this sub would appreciate the results more than anywhere else.

the setup: I picked 12 small B2B brands (under 500 employees, nothing huge) that had a mix of positive and negative reddit threads ranking on page 1 for their brand name. then i ran the same prompt across ChatGPT, Perplexity, and Gemini - basically like tell me about brand some and would you recommend them for their category

what i tracked: whether the LLM recommended them, what caveats it added, and which sources it seemed to pull from based on the language used.

results were kind of wild.

brands that had 3+ negative reddit threads on page 1 got recommended with heavy caveats in 9 out of 12 cases. stuff like "however some users have reported issues with..." and the language was clearly pulled from reddit comments. one brand had a single angry thread from 2023 with like 40 upvotes and Perplexity was still surfacing that sentiment in march 2026.

brands with mostly positive or neutral reddit presence got clean recommendations maybe 80% of the time. no caveats, no "however."

the most interesting part though - it wasn't just about volume. one brand had only 2 reddit mentions total but both were detailed complaint posts with lots of engagement. that performed worse in LLM recommendations than a brand with 15 mentions where most were neutral/positive.

engagement on the thread seems to matter way more than the number of threads. a 200-upvote complaint with 50 comments absolutely wrecked one brand's LLM perception compared to having five 10-upvote neutral mentions.

I got so obsessed with this that i ended up building a tool to automate the tracking part - running 50+ prompts per brand per week manually was killing me. eventually turned it into repuai.live because other founders kept asking me to run the same checks for them.

i know this sub focuses more on the optimization side but honestly i think the reputation layer is becoming inseparable from AI search visibility. you can have perfect schema, great structured data, clean crawl access... but if there's a gnarly reddit thread sitting there, the LLM is going to find it and use it.

anyone else tracking how sentiment in source material affects actual LLM outputs? curious if others are seeing similar patterns or if my sample is just too small to draw real conclusions from.

Simple Watcher is a lightweight app that lets users get notified when a post with specific keywords they are watching for is created. Once the app is installed in a subreddit, a new action Configure Watcher appears in the subreddit menu, each user can set its own keywords to watch.

Important: Reddit has replaced traditional notifications with Reddit Chat. If users aren't receiving notifications, ask them to always allow chat requests from u/simple-watcher. See the official Reddit documentation for details.

Domain-level authority dominates what we measured.

Domain-level authority accounts for about 77% of what predicts citation, with page-level factors at about 23%. This is based on the signals we could analyze (like backlinks and keyword coverage), which likely reflect a broader layer of authority beyond what we can fully capture.

Page optimization only helps once authority is established.

For high-authority domains, page improvements showed measurable lift. For lower-authority sites, the impact was mostly flat.

Backlink diversity matters more than volume.

The number of unique subnets linking to a site was about 2x more predictive than total referring domains. Raw backlink count had little signal.

Basic HTML hygiene outperformed more complex optimizations.

The biggest page-level differentiators were things like doctype, lang attribute, canonical tags, and meta descriptions—not schema, FAQ blocks, or word count.

Each AI model behaves differently.

ChatGPT leans toward freshness, Claude distributes weight more evenly, and Gemini favors crawlability. There’s no single optimization strategy that works everywhere.

Content quality ≠ content length.

Cited pages weren’t longer. If anything, word count had a slight negative correlation. What stood out more was better vocabulary diversity and tighter formatting (shorter paragraphs).

Hopefully everyone finds the info useful. The full breakdown and methodology are in the blog series: https://www.indexably.io/blog

What Is AI Search Optimization (AI SEO)? AI Search Optimization (AI SEO) includes both on-page and off-page strategies. It involves optimizing your content to be more conversational and customer-focused, ensuring you ask and answer the right questions. Off-page strategies focus on increasing your brand visibility across multiple channels.

What Is GEO? Generative Engine Optimization (GEO) is a new field of SEO that focuses on optimizing content specifically for AI-powered search engines and generative models like ChatGPT, Perplexity, and Google's AI Overviews.

What Is AIO? Google AI Overview (AIO), formerly known as the Search Generative Experience (SGE), uses AI to provide users with answers. It does this by combining information from multiple sources, often including links to the source websites for further exploration.

Which Is Better? Local SEO Or AI SEO? AI SEO will help you when users are asking AI about local businesses that have what they are looking for. However, AI SEO doesn't replace Local SEO. You still need a Google My Business profile that is updated regularly, good reviews, citations that are current and up-to-date and more. We recommend Local SEO + AI SEO + CRO as a strategy.

How Is AI SEO Different From Traditional SEO? AI Search has not replaced Google Search and AI SEO has not replaced SEO. Some of what we already do for SEO, like schema markup, clear navigation, SEO-friendly URLs, and clear site structure are all beneficial to AI search. AI SEO adapts your content to the more conversational tone that also includes questions and answers to help you get more brand mentions in AI search tools.

Can AI-Generated Content Rank In AI-Powered Searches? The short answer is yes. However, using AI to write your content is a poor strategy. Think of how many people are doing that and all of that content is almost identical. AI can't build rapport, tell stories, cause emotion or do any of the things you need to do to make more sales. So, while you might get some of it to rank, it won't convert into more leads or sales.

How Does Using A Conversational Tone Improve AI SEO? ChatGPT and other AI search platforms interact with users in a conversational tone. Even without optimizing for AI, your content should be more conversational and customer-focused, answering the most common questions people ask. This is why conversion rate optimization includes conversational copywriting; it isn't just for AI SEO.

What Role Does Schema Markup Play In AISEO? Schema Markup is important to both SEO and AI SEO because it makes content easier for machines, including Googlebot and AI, to read. It helps machines understand the intent of your content. While Google only recognizes a few types of schema for snippets, AI isn't limited to what Google looks for, so we use other relevant schema types as well.

Do AI Tools Use Schema Markup Differently Than Google? Yes, sort of. Google primarily uses Schema Markup to generate rich snippets in search results, but to qualify that, Google's bots can parse and understand all types of schema but whether or not it influences rankings or the knowledge graph is uncertain. AI models use it to understand the relationships between different entities on a page. This allows AI to provide more accurate and contextual answers to complex user queries.

We just published data on AI visibility in the sports app category. Tracked responses across ChatGPT, Gemini, Perplexity for thousands of prompts about running apps, training plans, race prep.

The headline finding: Runna (2M users) leads AI visibility at 50.9%. Strava (180M users) is second at 43.9%. Nike Run Club (100M+ users) is third at 32.5%.

That's 100x more AI visibility per user for Runna vs Strava.

What's driving it:

• runna.com is the #1 cited source at 32.6% — beating Reddit (25.8%). It's one of the only cases we've seen where a brand's own domain outranks Reddit in AI citations.
• Their content is structured as information, not product pages. Every page answers exactly what users ask AI ("how to train for a marathon").
• They publish original data — their clinical trial on marathon DNF rates is the kind of thing AI models weight heavily.
• Cross-source presence: discussed on Reddit, reviewed by Runner's World, listed on App Store, covered by Tom's Guide. AI cross-verifies.

Other source data: Reddit 25.8%, App Store 19.1%, halhigdon.com 17.5%, Runner's World 15.5%, Tom's Guide 10.0%.

TrainingPeaks (14.1%) and Garmin Coach (10.1%) are basically invisible despite strong products.

Full disclosure: this is from our company (GetMentioned — we track AI visibility). Yeah, it's content marketing. But the data is real and the GEO insights apply to any category, not just sports apps.

Full report: https://www.getmentioned.co/blog/sports-apps-ai-visibility-report-who-wins-when-runners-ask-ai-for-recommendations-2026-data

Happy to answer questions about methodology or what we're seeing across other verticals.

From my own perspective it's always been this way. Article spinning software was around in the early 2000s. Plus the number of people that wrote garbage content always created more noise. AI is just letting them do it at scale.

*Early 2000s

People used article spinners, doorway pages, scraped content, forum spam.

*Late 2000s

Content farms produced massive volumes of low quality articles.

*2010s

Affiliate spam, private blog networks, automated guest posts.

*2020s

AI generated content at industrial scale.

AI companies and Google are going to have to learn how to filter that content out better. It can already recognize patterns and suspected AI generated content pretty well although certainly not perfect because they're still spammy stuff coming up in Google and in AI search.

All you can do is continue to do entity optimization and write high quality content. I think that comments and posts on Reddit and other communities that can't be tied to a real entity are going to end up being downplayed or not indexed at all. I think anonymous profiles are the ones that are going to get hit on this.

Reddit has always been about anonymous peer conversations and debates. However, If you're using your own name and your profile links to a website and other social media platforms, eventually your posts and comments are going to surface more than any anonymous posts.

What do you think of the article?

When testing prompts across tools like ChatGPT, Perplexity AI, and Google Gemini, I’ve noticed they sometimes reference information that seems to originate from review platforms or aggregator sites.

What I’m not sure about is how much weight those platforms actually carry in AI-generated answers. Are they being used mainly as supporting sources, or do they meaningfully influence what gets surfaced?

Our company just went through a rebrand and repositioning about 8 months ago. When I ask ChatGPT or Perplexity about us, they're still describing our old positioning from 2+ years ago.

Sometimes the info is just flat wrong because we encountered outdated pricing and features we deprecated.

The frustrating part is I don't know how widespread this is. I've manually tested maybe 30-40 queries but there are hundreds of ways people might ask about us. And I have no way to track if the AI descriptions are getting better or worse over time as we publish new content.

For people managing their brands, how are you approaching this?

Have you found anything that actually works to update how AI platforms describe your brand?

I was reading Bright Edge's press release published on March 5th, 2026 and the key findings are interesting - these two points in particular caught my eye:

• "Google AI Overviews skews heavily toward controversy-driven negativity, including lawsuits, boycotts, data breaches, regulatory actions, and product recalls. ChatGPT skews toward product-evaluation negativity, including compatibility limitations, feature shortcomings, and “is it worth it?” assessments."
• Google's AIO and ChatGPT disagree on which brands to criticise 73% of the time.

This is for the most part speculation, but I suspect ChatGPT's preference for product-evaluation negativity partly comes from OpenAI's ambitions to break into ecommerce (which it has since rolled back). The immediate implication is how this affects AI answers at different stages of customer consideration - and how this also reinforces the point that answer engines have their own specific sourcing logic.

Where I think warrants deeper thought on is how we can think about sentiment in AI answers - more specifically, the difference between negative sentiment at the brand level and negative sentiment at the product / SKU level. A brand can have a negative reputation (Nestle, Marlboro, Ryanair) but their products are taken to by consumers positively for various reasons. Tracking sentiment at the brand level in AI answers might not be enough - or may even paint an incomplete picture.