AI visibility: what is proven and what is speculation
Berger+Team · Oct 16, 2026
Topic: AI discoverability (GEO)
This query recently showed up in our Google search data: "many geo tips online contradict each other. what is scientifically or officially proven about ai visibility and what is speculation?"
Whether a person or a tool typed it, we cannot see. The question is still better than most guides on the subject. So here is an attempt at an honest answer — sorted by what actually stands behind each claim.
What is officially documented
Here there is vendor documentation. That is not an opinion and not a study, but a statement from the people who run the systems.
Structured data following schema.org is processed by search engines. Google documents in Search Central which types are read for which presentation format. It is readable and maintained by Google itself. What it does not say: that more markup automatically means better placement.
Crawlers can be controlled via robots.txt, including those of AI providers. OpenAI, Anthropic, Google and Perplexity publish their identifiers. That an exclusion is respected is a promise by the providers — technically it cannot be enforced.
Excluding from training is not the same as excluding from reading. Several providers now separate the two into distinct identifiers. You can therefore permit content to be read for an answer while forbidding its use in training.
What has been studied scientifically
Here there are publications with a method. That does not make them the final word — it means someone measured and disclosed how.
That content can be deliberately prepared for generative answers has been studied. The Princeton work on Generative Engine Optimization tested different preparations against each other and measured differences in citation frequency. The strongest effect came from verifiable elements: attributed quotations, figures with a source, concrete details instead of general phrasing.
That is a solid direction. What it is not: a guarantee, nor a statement about your business in your niche.
That a growing share of searches ends without a click has been measured repeatedly — by different parties, with different numbers and different methods. The order of magnitude holds; the exact percentage does not. Anyone giving you a precise figure without naming the survey is passing off an estimate as a measurement.
What is plausible but unproven
Most of what circulates as advice right now belongs here. That does not make it wrong — only unproven.
The llms.txt file is a proposal: provide a content index for language models. The idea is sensible. Whether large providers read and weight it is confirmed by none of them. We have one because it costs nothing — not because we know it works.
Optimal text lengths, paragraph lengths, list formats. Very precise recommendations circulate. We have found no solid source covering more than one model.
That mentions carry more weight than links. This is a widespread thesis and it is plausible — language models do not reason like a link graph. We have not measured it soundly, and we know of no survey that does so cleanly.
What is simply false
"We register your website with ChatGPT." There is no registration. No provider runs a directory you sign up to.
"We guarantee first place in AI answers." There are no places. Two models answer the same question differently, and the same model answers differently on two days.
"AI visibility shows up in visitor numbers." If a model reads your page and builds an answer from it, the person asking never sees your address. The benefit arises, but it appears in no statistic.
How to work out which drawer a piece of advice belongs in
Three questions that carry surprisingly far:
- Who is saying this, and what do they gain? A provider selling a service is not a neutral source. That does not disqualify them — it just means a second source is needed.
- Is there a measurement behind it, and is it described? "We found that" is not a method. How many cases, over what period, compared to what?
- What would have to happen for the claim to be false? If no answer comes, it is not a statement about the world but an opinion.
Our own rule
In our articles we state where a figure comes from, and when we cannot prove something we say so. That occasionally produces unsatisfying sentences like "we have not measured this". We prefer them to a number that sounds good and holds up to nothing.
If you want to know how to actually measure your own position rather than estimate it, that is here. And why a machine reads literally rather than by meaning is here.
Frequently asked questions.
How do you measure success when AI answers no longer bring traffic to my site?
The classic click-through rate loses meaning once a user gets their answer directly from the AI, without ever visiting a site. The new metric can be called Share of Model Voice: how often, and in what context, is your business mentioned when someone asks ChatGPT, Perplexity or Claude a typical question from your industry — and is it described correctly and positively?
You measure this by regularly putting typical customer questions to the major AI systems yourself and logging whether and how your business appears. Success is then no longer shown by visitor numbers, but by being present and cited correctly, even when no direct click results from it.
How these figures come together into a picture you can read in two minutes each morning is something we described at length: the visibility cockpit.
Can an AI confuse my business with another one?
Yes, and it happens more often than people assume. AI systems map a name onto an entity — a single unambiguous thing in their model of the world. If the name is shared, or the data about it contradicts itself, statements land on the wrong company: someone else’s opening hours, someone else’s services, someone else’s reviews.
We know this from our own experience. Our name is held by several organisations worldwide, among them an AI company of the same name. Searching for the short form, we rank only sixth; for the full name, first — a snapshot, not a fixed value.
There is no trick for this, only unambiguity: the same spelling of name, address and phone number everywhere, machine-readable company details on your own site, and cross-references between the profiles that genuinely belong to you. The clearer the signals, the harder the confusion. A single entry is not enough — it is the sum that counts.
What is the difference between SEO, GEO and LLMO?
The three terms build on each other rather than replacing one another. SEO (Search Engine Optimisation) makes sure a page ranks well in classic search results. GEO (Generative Engine Optimisation) extends that goal to being cited as a source in AI-generated answers — in a ChatGPT or Perplexity answer, for example.
LLMO (Large Language Model Optimisation) goes a step further and gets more technical: it means specifically understanding and influencing how individual language models process, weigh and reproduce content — essentially reverse-engineering the model in question. In practice: SEO remains the entry ticket, GEO secures broad citability, and LLMO fine-tunes the details for the systems that matter most to a given business.
Whether you need this distinction day to day is a separate question — we answered it at length: Do you really need five new acronyms?
