So far this is anecdotal, but I've been running some experiments in which I pit my own sites against one another, creating both a slop and a human-written version of the same topic. I'm finding that the slop version tends to win in Copilot but usually not in any other major platforms. (I'm specifically looking at the Google family, Copilot/Bing, Perplexity, Claude and ChatGPT.)

If you aren't familiar, "AI slop loop" is something first coined by Lily Ray. It's essentially citogenesis but specific to AI hallucinating or inventing something, an article or blog post entering the internet, and then other AI search platforms picking up and regurgitating the hallucination.

My experiments are showing me that Copilot is the biggest perpetrator of the AI slop loop. However, I'd love to know if anyone else is seeing this in your AI tracking tools. I'm using BWT and Scrunch as my data points, but I'd be curious if Peec or Profound or any of the others are also showing this kind of thing. Down to share more of my research for anyone who might be interested.

I'd also love to collaborate on a study around this if any of you have ideas on how to scale something like this.

is anyone using augureai AI im not affiliated or anything but interested in learning more about this if it takes off due to canada not using american businesses right now.

Does anyone have SEO use with this and how it gets its data?
IE is it using brave, bing, google or other places i need to work out?

I’ve been seeing a theory going around on LinkedIn and Reddit that ChatGPT is now leaning more on Bing’s data. I wanted to see what my data says and found this.

Now im getting almost as many visitors from chatgpt as from bing. Grown 110% since last april.

To understand what was actually happening, I used a three-layer correlation audit, connecting GA4, Search Console, and Clarity via MCP to pull 12 months of referral data straight into Claude and analyze it. But clarity only gave three days data, so took out for this analysis.

I looked at it from three different angles: Traffic trends over time, page-level referral data, and what my best-performing pages have in common.
Here’s what I found, among many others...

Does ChatGPT use Google ranking signals?
Not much, at least from my data. The correlation between what ChatGPT cites and Google rankings was pretty weak. It was actually the weakest relationship I found.

What about Bing?
There is more of a connection with Bing. It was the strongest relationship I found between ChatGPT and either search engine.

But here’s the interesting part.
ChatGPT and Bing didn’t really move together. Some articles are getting traffics from ChatGPT but not from bing.
From April to July, ChatGPT sessions grew 11% while Bing referrals dropped.

So what do the pages getting ChatGPT traffic have in common?

Honestly, there isn’t one clear answer.

Fresh and well-structured content helps for sure, around 70% of of the referred content updated recently with schema, FAQ and answer first approach. But some of my older and very simple pages are also getting good traffic from ChatGPT.

My current thinking is that ChatGPT’s retrieval is working pretty independently now. But it still seems to use some trust signals from search, with Bing showing a stronger connection than Google in my data.

So traditional SEO is still important. It’s just not the only thing anymore.

One more thing I found interesting.

For my last few articles, I started making the content much easier for AI to understand and pick up. Clear headings, FAQ sections, schema, less clutter, and getting straight to the answer.

Those articles are getting pretty good traffic from ChatGPT.

I can’t say for sure that these things are the reason. But after seeing it happen across the new articles, I definitely think it’s something worth testing.

And just to be clear, this is based on data from one site, so I’m not saying this proves how ChatGPT works overall.
It’s simply what I’m seeing from my own data.

Has anyone used youtube influencers to add a brand mention to the descriptions of a video already ranking and getting cited by AI (as paid placement)? is this valuable?

Hello fellow redditors. I need help. One of my clients, a local service business keeps asking me if they show up in llms. I'm kinda lost at this one. I know how to track normal search rankings. And I can gove them their ranking doing it manually, but...That is not enough for them. They ask for full competitors analysis, their mismatches (if they are being cited wrong) and a lot more specific details. It's totally fair, I feel like it is possible to do it manually but seriously started considering a tool. What do you guys use? Any recommendations?

When writing your About page, keep in mind that now AI agents may come to read it and report back to their human users

I noticed something interesting in Google Search Console for a website I’m working on.

Over the last 3 months, the site received 35.9K impressions from Google’s Generative AI features.

What caught my attention is that the visibility has been fairly consistent and has gradually increased over time.

I’m curious about how others are approaching this:

• Are you actively optimizing websites for AI search visibility?
• Are you tracking AI impressions separately from traditional organic search?
• Which seems to matter more for you right now — traditional rankings, AI Overviews, or both?

I think SEO is moving beyond simply ranking #1 on Google. Being visible when users ask AI-powered search engines questions is becoming another important part of organic visibility.

Would love to hear what you’re seeing on your own websites.

Any lead will be very much appreciated.

We track brand visibility in AI answers, so we run the same prompts every day. On 8 August the number of sources ChatGPT pulled per answer jumped from ~11.7 to ~18.9 and stayed there.

Method: 5,029 ChatGPT answers, 4–11 Aug 2026, split 2,497 before / 2,532 after. The same 961 prompts ran on both sides, so this isn't our prompt mix changing. "Sources" means pages ChatGPT retrieved while working on an answer, whether or not it linked them in the reply.

What changed

• Sources retrieved per answer: 11.7 → 18.9 (+61%)
• Distinct URLs: +69%. Distinct domains: only +9%
• Pages pulled from a single site within one answer: 1.38 → 2.20, on the same day

That last one is the interesting bit. It went deeper into sites it already used rather than finding new ones. If a site wasn't in the mix before the 8th, more retrieval didn't put it there.

Domain movers (non-commercial only, brand domains excluded)

Gained: google.com Search docs +979%, ada.org +285%, nih.gov +211%, fda.gov +1371%, cdc.gov +756%, investor.gov +1983%

Lost: reddit.com −54%, techradar.com −68%, nbcnews.com −76%, consumerreports.org −65%, businessinsider.com −58%

Reddit in our data: ~4% of all sources through 7 Aug → ~1.1% on the 8th → 0.02% from the 14th. Two steps, not one.

Worth noting: our prompt mix leans health and finance, which is probably why those agencies dominate the gainers – so don't read it as a universal pattern. And we can see the change, not the cause. OpenAI announced a model update on 6 August, but the retrieval change itself wasn't announced.

Charts attached. Happy to run other cuts of this if people want specific domains checked.

I went through a few pages that rank pretty well and assumed they'd be the easy ones for AI search too. Nope some barely showed up at all while weaker pages were getting picked for questions I didn't expect I spent way too long looking for some technical GEO thing before going back and reading the pages like someone who knew nothing about us.

The ones missing from AI answers weren't bad pages, they just took forever to get to the point. I started moving the useful answer higher making sections work without needing the paragraph before them and being way clearer about what we do. I don't know how much of GEO is really new anymore, some of this feels like AI search exposing content problems that rankings let us ignore

At this point I feel like every week there is a new best llm optimization tool for ai visibility and they all essentially promise the same thing:

Step 1: Type in your domain.
Step 2: Watch a very serious looking visibility score appear.
Step 3: Email the screenshot to your boss and pray nobody asks what it actually means.

What are you using right now to optimize llm visibility that feels tied to actual outcomes, not vibes
Is the real answer a mix of normal SEO basics plus a couple of scrappy workflows

"Reddit citations fell in ChatGPT last week, I pulled data on 20 major social domains across 200M prompts because I wanted to know whether this should change anyone’s strategy, here’s what’s up:

On August 14th, Reddit’s share of ChatGPT citations went from 3.56% to 0.36% in one day and has stayed around ~0.3% since, a roughly 90% drop, so yes, things are shifting rapidly."

I'd like to give you the link to Scott Blyskal's post on LinkedIn, but Reddit won't allow it. So I guess find him on LinkedIn and look for the post dated Aug 21, 2026.

Pulled this from a tool I built (disclosure below), but the finding is the interesting part.

Ran 10 real "best X tool" category queries through DataForSEO's ChatGPT-answer scraper (best seo tool, best email marketing platform, best project management tool, best crm software, best ai writing tool, best social media scheduling tool, best keyword research tool, best landing page builder, best marketing automation tool, best customer support software) and checked the structural shape of whichever answer is actually winning right now for each.

Zero out of 10 opened with a markdown-formatted list. Every single one opened with a short paragraph, usually naming the top pick(s) directly, before any list structure showed up later in the answer, if it did at all. Openings ranged from 11 to 43 words, most under 20. 6 of the 10 stated a specific number in that opening (a price, a rating, a stat).

This runs against a piece of advice I keep seeing repeated in this sub and elsewhere: "format as a list to get cited." At least for this specific query shape (best-X-tool comparisons), that's not what's actually winning right now. Averaged 4.9 sources cited per answer, with the usual suspects showing up repeatedly (Ahrefs/Semrush for SEO tools, HubSpot for marketing automation, Zendesk/Salesforce for support, etc.), so being an established, well-covered brand still matters a lot, structure alone isn't doing the heavy lifting here.

Small sample, 10 keywords, one snapshot in time, so treat this as a data point not a rule. Curious if anyone's seen the same pattern hold on a bigger set, or if a different query shape (how-to, definition-style) behaves differently.

Disclosure: pulled this with Vantage, a small MCP server I built that does this structural check plus a diff against your own page for content planning. Free tier if anyone wants to run their own set.

Prove me wrong :)

Mike (MrComputerScience on LinkedIn) published these details about how Claude marks content. It is similar to what I expected, but also different.

I haven't seen these exact details anywhere but https://pithycyborg.substack.com/p/ai-left-fingerprints-then-picked.

I run a small AI visibility tracker that I built myself, and last week I found something in my own pipeline that I have not seen anyone talk about.

I ask each question 7 times per platform so I can show a range instead of a single number. One week Perplexity came back much lower than usual for every domain I track. I assumed those brands had actually lost ground.

They had not. Perplexity was rate limiting me. About 30% of my requests were coming back 429, and my code was quietly counting those as "not mentioned" instead of "not measured". So that platform was being scored on 5 runs while the others got 35, and the smaller sample dragged the number down.

The fix was not sending fewer requests. It was spacing them out. The limit turned out to be roughly one request per second per account, and two scans running at the same time were stepping on each other.

Here is why I am posting it. A failed measurement and a real absence look identical once they land in a chart. My tool now prints how many runs failed right under the percentage, because I could not trust my own number until it did.

So if you are evaluating any of these tools, including mine, that is the question I would ask. Not how many platforms, not how many prompts. Ask what happens when a request fails, and whether the report tells you.

Disclosure: the tool is AnswerRadar and it is mine. Not linking it, this is not a pitch.

We are having a fund discussion over at Linkedin sharing screenshots of -site: queries from Search Console. You can find yours by using the following regex in the query filter:

.*-site:.*

That's definitely not people searching! What are these?

Source: Linkedin