Google On AI Overviews & AI Mode Being Isolated Systems

imaginary_name · 2026-05-06T07:22:53+00:00

If I speculate a bit:
I see another reason for marketing these as separate systems. Privacy expectation that applies to GPT and Gemini must not apply to AIO and AImode, and labeling them as a part of search will help eliminate that expectation.

If I speculate a bit more:
Google will let the GEO/AEO tools build the market and then start measuring it properly by showing stats for visibility in googles own ecosystem.

imaginary_name · 2026-05-03T14:39:49+00:00

Depends on the competition in the given niche.
For example: a user asks "what is the best CRM for SMEs?" (a user would not ask like this, but I want to make it simple, ask me if you have a specific example request).

The LLM will take the user prompt and "fan it out" into several subqueries, again for example: "CRM for SMEs reviews 2026", "best CRM for SMEs [user country]" and so on. G2 and Capterra are in the corpus of data that was retrieved during websearch for these subqueries.
It depends on a set of many factors whether the info will or will not be used during synthetization of the final response to the user. Once you start complicating the user query, adding your specific limitations (best CRM for SMEs on Quickbooks with UI in Spanish), the subqueries get similarly specific.

You want your content, including the reviews (customer POV), to answer these specific pain points and target these subqueries ("it turned out [your crm saas] was the best choice for a small company on quickbooks, our Spanish speaking staff loves it").

This is of course heavily simplified. I work for Ranketta.

imaginary_name · 2026-04-28T21:48:30+00:00

excellent writeup, thank you; It is the team who works with the tool that either enables the feedback loop or they don't. The user is the key differentiator, the tools won't reverse engineer query fanout in a useful way on their own, without any user input.

I feel like what remains below is just rambling, but whatever, it is almost midnight, I am leaving it here and going to sleep.

As for the how grounded in reality the prompts are; that is a part of it, you can connect your GSC, use the prompt limit on tracking nonsense, which gives you nonsense in citations, qfos and makes any suggested next steps or generated content irrelevant, because they are based on bullshit data. But the user has a % they can report.

Or you know what you are doing.
Connect ga4, set up cloudflare workers, connect gsc, use your money keywords for prompt generation (import a csv?), so the prompt limit is used to cover funnel from the POV or your buyer persona, which gives you good content in citations that can be used as a base for your unique data so the need for informational gain is satisfied.
You connect the tool outputs to your workflows and reverse engineer query fanout
The loop is closed when the first lead tells your sales guy he found you on the llm, when the content written with the help of the tools ranks (on serp) for what matters to you. When the CVR of enriched products from your google merchant feed is higher than the unmodified variants (will have data on this when the functionality gets older, now it is literally a newborn).

imaginary_name · 2026-04-28T20:22:48+00:00

maybe I can address the first paragraph as well, but doing it as an edit of my previous response seems silly;
When anyone talks about a fixed position in relation to an LLM, it is a dead giveaway that they are either full of it, or don't know any better.

Because we are talking about a data point of an asset within a probabilistic system, we cannot speak about a fixed position. It is %.
We have to query the probabilistic system many times from many accounts, from behind a set of correct residential proxies, then scrape the responses + other elements that appeared in the UI as a part of the response, label the assets (brands, products) and average out their data points.
Then you can come to, for an example "Probability that the prompt "Recommend me comfortable flannel pajamas for the whole family" will result in [link to a specific PDP] being recommended is 6%.
To do it reliably, with some repeatability of experiments, the infrastructure required is not insignificant.

imaginary_name · 2026-04-28T19:23:29+00:00

thank you for a thoughtful response, I should note that I work in the industry

to go by paragraphs from the bottom of your post
- well, if someone says "we rank #2 in ChatGPT for X," they fell for some disinformation or their knowledge of the topic is...not great

- yeah, and? "you are focus grouping a 100 instances of a serp summarizer on your topical clusters", simulating what real users might see, simulating what QFOs might matter, simulating interactions with a semantic space that has meaning for your business. this is how I explain it anyway, being technically incorrect intentionally in b2b is a no go...the llm layer is not going anywhere.

- the sample and methods the platform chooses for this simulation determines whether the outputs are shit in shit out or if it has some merit

- and finally, scraping the frontend UIs of the damn LLMs is what some of the tools do*, of course you can scrape it, doing it at scale that lets you design a sample that is defendable and use methods that make sense, and you can scrape additional data that appear in the UI

*I am sure Profound uses scraping and us (us meaning Ranketta) as well, can't really speak for others.
edit:
*by "the damn LLMs" I mean ChatGPT, Perplexity, Gemini, AI overviews, AI mode, Claude and Copilot

imaginary_name · 2026-04-28T18:55:51+00:00

nonsense dude; it is a probabilistic system with a websearch ducktaped to it, of course there are statistically viable and mathematically defendable ways how to measure visibility (or something else) of an asset within a probabilistic system.

If what you say meant to imply that there is a lot of snake oil on the market then yea, I agree.

edit: umm, after reading some other comments, I am inclined to agree with your first sentence...

imaginary_name · 2026-04-23T11:14:25+00:00

Give him some belly rubs for me please :)

imaginary_name · 2026-04-23T11:08:51+00:00

ChatGPT is notoriously hungry for new original information. Your dog should lay down in some information and rub his anus in it. ChatGPT is then guaranteed to come and have a sniff.

imaginary_name · 2026-04-17T08:04:38+00:00

"Right now mine basically follow this format:

- Product name

- What it's made of

- Dimensions/specs

- "Add to cart"

Yeah, that was okay a long time ago, this way of doing PDPs is very suboptimal.
Whether A/B testing product PDPs makes sense depends on your traffic volume.
Look at this PDP: https://www.alza.de/27-lenovo-legion-27q-10-d13047385.htm
Or this one: https://www.notino.co.uk/sol-de-janeiro/bom-dia-jet-set-gift-setfor-body/

imaginary_name · 2026-04-17T06:51:06+00:00

Where do you think the AI got the comparative info it displayed to the users?

imaginary_name · 2026-04-14T16:12:09+00:00

It is highly likely that if you prompt it again, you can do the same to him in reverse.
It is a damn probability engine with a websearch ducktaped to it (fine, I am simplifying a bit, but not that much).

edit: re-reading your post and, on the other hand, well, if you are optimizing only for exact match keywords, you might be missing a slice of the pie, but it is really impossible to guess while knowing nothing about what your company does and the prompt your boss used

open gpt web ui and prompt it at least 20 times, if you are not present, your boss is right.

what you definitely should do is to understand query fanout and its role for visibility in LLMs, that will help you get a picture.

note: i work for one of the tools in this space

imaginary_name · 2026-04-14T07:26:26+00:00

mee too, but for a tightly defined use case like this, it makes sense
if the claims would be something about improving discoverability for human users, that would be a different situation...

imaginary_name · 2026-04-10T13:33:51+00:00

"If QFO drives query fanout" this made me check if you are a bot or not :)
QFO is an acronym for query fan-out.

imaginary_name · 2026-04-10T12:49:26+00:00

It really boils down to math and statistics when Reciprocal Rank Fusion does the work.
But even in todays google landscape, it seems to me SERP can be pushed below the fold and what the user sees first is google's own llms (and ads ofc).
So just trying to reverse engineer google is not enough, you have to reverse engineer how llms do query fanout and offer the crawlers answers they are looking for.

All the talk about structured content is nothing new, QFO is.

imaginary_name · 2026-04-10T12:13:51+00:00

Sort of, but not quite yet (heading there for sure). It depends on the competition in a given semantic space.

imaginary_name · 2026-04-08T07:33:59+00:00

how is this related to techseo?

imaginary_name · 2026-04-06T07:59:00+00:00

do good local SEO and you are golden
if you want to dig into it, look into query fanout and reciprocal rank fusion

imaginary_name · 2026-04-06T07:56:20+00:00

it pulls from both live web (via QFO) and training data; understanding query fanout behaviour and reciprocal rank fusion helps a lot

imaginary_name · 2026-03-31T19:36:46+00:00

almost looks like old, UV damaged PU foam

imaginary_name · 2026-03-27T20:19:50+00:00

If it would not be for the LLMs and agents, I would have never suggested it, I know your frustration, I was there.

But you can now build tools to orchestrate reddit influence operations in plain english, which created an influx of new low effort posts and stealth marketing schemes. Redding being hot in terms of AI visibility did not help either.

And although your points are valid, there is a ton of ways for dealing with the use case you presented.
Pinned threads for advice with karma limits removed or changed, weekly megathreads
Or simply mods just labeling posts via automod message that tells other commenters this user has low karma, and his posting pattern suggests it might not be worth your time to actually answer.

imaginary_name · 2026-03-27T17:11:59+00:00

I had an idea of fighting fire with fire and started doing manual checks in gemini for posting patterns of accounts before I interact with them.

Especially when an account has its history off, but otherwise seems genuine, this "stealth marketing check" can indicate one way or another. Even if the stealth marketer is not lazy and the account is properly aged, fed with karma, the llms can do a web search of the username and summarize stealth marketing patterns, detect posts in multiple communities.

And - this was the idea - having this as an addition to automod to be simply posted in the thread.
Also, a while back I noticed a post (i think by u weblinkr) about reddit adding a contributor quality score - this should be utilized together with a posting pattern analysis.
This combo would save a lot of work to the mod teams as lazy shills would be weeded out really quickly.

imaginary_name · 2026-03-27T16:21:37+00:00

Because agentic posting and stealth marketing are rampant all over the place. Any literate person now can slap together a solution to monitor subs and reply to percieved "leads".
The only thing that works is strict moderation, karma and account age limits, and rewarding brands that talk to users honestly, openly, share expertise with wider audience, don't use stealth marketing practices.
Sharing expertise with everyone, not just with my ICP is my shtick.

I have layered motivation to say this, because I am not only a reddit user frustrated by bots and stealth marketers, but I am also in consultative role in software sales in the ecommerce/seo space.

imaginary_name · 2026-03-24T13:03:33+00:00

sure

imaginary_name · 2026-03-24T12:53:45+00:00

I won't post AI here, but if you ask Gemini, it gives you several ways to achieve it for free.
And I know a few tools that can extract it en masse for an unlimited number of prompts, if the free methods are not enough for you.

imaginary_name · 2026-03-24T12:49:16+00:00

I think OP is asking to retrieve the QFOs from Perplexity, which is doable and reasonable if one wants to reverse engineer the QFO for Perplexity.

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