A Working Framework for AI Search Myths, Debunked by 15 Million Data Points

We've worked on over 50 AI research studies at Ahrefs. Nearly every one was built to test a claim someone in the industry (or Google) was repeating, with little evidence to back it up.

Below is a culmination of the most interesting things we've learned, and the myths we've debunked, from recent AI search studies, covering 15 million data points.

AI Search Truths We've Discovered

1. AI Visibility: "Best Of" Listicles Boost Your Competitor's AI Visibility More Than Your Own 2. llms.txt: Nothing Reads Your llms.txt File. 97% Get Zero Fetches. 3. Schema: Adding Schema Markup Isn't A Shortcut To Citation In AI Search. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals. The same pattern also shows up in Working Framework, where the practical question is how the signal becomes visible.

The useful check is whether this improves the system behind search performance, not only the words on the page. Internal links, crawlable content, clear entities, current evidence, and a sensible page structure all help the recommendation become easier to trust.

1. AI Visibility: "Best Of" Listicles Boost Your Competitor's AI Visibility More Than Your Own

Myth busted: "Publish 'best' list featuring your brand and AI will recommend you." There's nothing ChatGPT loves more than a "Best" recommendation list. Across 750 ChatGPT prompts, Glen Allsopp found "best X" blogs (e.g. "The 10 best SEO. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

The Data

My colleague Mateusz Makosiewicz ran a controlled experiment, publishing 34 self promotional lists on five domains and tracking 9,886 answers across ChatGPT, Gemini, Perplexity, and Copilot. Mateusz confirmed what SEOs like Lily Ray had. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

The risk is usually hidden in the execution layer. A page can look fine to a human and still fail for an automated visitor if the form, call to action, rendering path, or confirmation step is not accessible enough for the agent to complete the task.

What To Do Instead

Outreach, review building, and word of mouth engineering (e.g. influencer campaigns, or "touring" a topic you want your brand to become synonymous with) can get your brand named across dozens of independent and, hopefully, authoritative. The practical read is that brand signals need to be consistent enough for both people and AI systems to form a stable view of the company, its expertise, and its trust signals.

The practical value is in connecting the idea to an observable signal. That means deciding what should be checked, what would prove the issue is real, and where the team should make the smallest useful improvement first.

2. llms.txt: Nothing Reads Your llms.txt File. 97% Get Zero Fetches.

Myth busted: "You need an llms.txt file for AI visibility." In May 2026 Google gave us contradicting advice: we should be auditing our llms.txt files, even though they're not required for AI visibility. We decided to check the data. The practical question is what this changes in the system: the page structure, the evidence presented, the measurement habit, or the way the topic is connected to related work.

The Data in practice

We studied the server logs and bot traffic of 137,000 sites using Ahrefs Web Analytics and Bot Analytics. 28% had published an llms.txt file, but a staggering 97% of those files were never read. Not by bots, and not by humans. Of. The measurement question is whether this signal changes a decision, not whether it adds another number to a dashboard. Useful reporting connects visibility, engagement, and business outcomes without pretending every AI influenced journey will produce a clean click path.

The reporting question is whether this signal changes a decision. If it only creates another number in a dashboard, it adds noise. If it helps separate profile activity, website visits, calls, bookings, and direction requests, it can make local performance easier to understand.

What To Do Instead in practice

Put that effort into making your existing content easy to crawl, parse, and cite. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

3. Schema: Adding Schema Markup Isn't A Shortcut To Citation In AI Search

Myth busted: "Adding schema markup to a page will instantly boosts your AI citations." There's been a trend of "GEO experts" attributing quick AI visibility wins to schema optimization. The search implication is whether the section improves the evidence around the page, not simply whether it adds more wording. Clear entities, crawlable structure, internal links, and useful context are what make the topic easier to evaluate.

How The Data changes the decision

We put that to the test, tracking 1,885 pages that added JSON LD schema, matched against 4,000 control pages, measuring citation change before and after. After 30 days, we saw no meaningful uplift on Google AI Mode or ChatGPT. AI Overviews. The practical question is what this changes in the system: the page structure, the evidence presented, the measurement habit, or the way the topic is connected to related work.

How What To Do Instead changes the decision

Use schema markup to build long term entity associations, marking up Organization and Person data with sameAs links to Wikipedia, Wikidata, and Crunchbase, since a well defined entity is what feeds Google's Knowledge Graph, and a clearer. The practical question is what this changes in the system: the page structure, the evidence presented, the measurement habit, or the way the topic is connected to related work.

What the visibility signal actually changes

What the visibility signal actually changes: a Working Framework for AI Search Myths, Debunked by 15 Million Data Points should be treated as a visibility signal, not a standalone headline. Introduction We've worked on over 50 AI research studies at Ahrefs. Nearly every one was built to test a claim someone in the industry (or Google) was repeating, with little evidence to back it up. Below is a culmination of the most interesting things we've. A useful companion note is Working Framework, because it looks at a nearby part of the same system.

What the visibility signal actually changes: the practical question is whether the page, brand evidence, and surrounding content make the answer easier to trust. If that support is weak, search systems can still understand the topic but fail to connect it confidently to the brand.

What the visibility signal actually changes: that is why the response should begin with an audit of the evidence already on the site before creating a new asset. The fastest improvement is often a clearer page, a better internal link, or a stronger explanation of why the brand belongs in the answer.

Where the evidence needs to be tested

Where the evidence needs to be tested: a single study or ranking observation should not become a strategy by itself. It should become a diagnostic prompt: which source is being trusted, which query pattern is affected, and which part of the site would make that trust easier to earn?

Where the evidence needs to be tested: that keeps the response grounded. The goal is to improve the evidence chain around the topic rather than publish another summary that repeats what every other page already says.

Where the evidence needs to be tested: the important distinction is between a useful signal and a fashionable talking point. A useful signal changes the brief, the page structure, the linking plan, or the measurement view.