AI FactScan
A new 2026 Pew Research Center report puts a number on something many people already feel: AI is moving from a side tool into the normal path of finding information.
Pew found that 60% of U.S. adults say they read AI summaries at the top of search results. That is higher than the 49% who say they use AI chatbots like ChatGPT, Gemini, or Copilot, suggesting that AI-generated answers are reaching people even outside dedicated chatbot apps. For many readers, they are becoming the first layer of the web.
This is useful, but it also means readers may trust a polished answer before checking what it cites.
Search used to begin with sources
Traditional search results asked you to choose. You saw a list of links, skimmed the source names, and decided whether to open a government page, a university report, a news article, a company blog, or a forum thread.
AI summaries change that order. The answer comes first. The source layer becomes smaller, quieter, and easier to skip.
That matters because the source layer is where trust is built. A claim based on a public health agency is not the same as a claim based on a marketing page. A peer-reviewed study is not the same as a Reddit comment. A current regulatory notice is not the same as an outdated article that has been copied across the web.
The interface makes trust feel automatic
AI search summaries are designed to feel helpful: top placement, clean prose, and links near the answer. The experience can make source evaluation feel finished before the reader has inspected any source.
The problem is not only that an AI summary can be wrong. The deeper problem is that different kinds of sources can get flattened into the same confident answer. A source may be official, academic, commercial, community-based, outdated, self-referential, or only loosely related to the claim. The summary can hide those differences.
Research summaries raise the stakes
The risk gets sharper when AI summaries are used to understand research. A chatbot can blend an abstract, a press release, a review article, and a news story into one smooth answer, even though they carry very different evidentiary weight.
A narrow finding can become a broad conclusion, or mixed evidence can be presented as consensus. This is why source checking is not just about catching fake links; it is about seeing what kind of evidence the answer is standing on before it shapes your judgment.
A quick way to check the source layer
You do not need to manually audit every sentence in every AI summary. But when a summary affects a report, a paper, a purchase, a health question, a policy decision, or a client email, the source layer deserves a quick inspection.
Start with a few simple questions:
- Is at least one source primary, official, or clearly authoritative for the topic?
- Are the sources current enough for the claim being made?
- Are the sources independent, or are they repeating the same claim from one origin?
- Is the AI relying on general overview pages for specific factual claims?
- Would you cite these sources if the AI summary did not exist?
If the answer is no, the summary may still be useful as a starting point. It should not be the endpoint.
Where AI FactScan fits
AI FactScan is built for this first layer of source checking. It does not replace reading the underlying source, and it does not decide the truth of a claim for you. It helps bring the source layer back into view.
When you scan an AI answer, AI FactScan surfaces source-quality signals: whether a cited domain looks official, academic, media-based, community-based, self-referential, invalid, or otherwise worth closer inspection, helping you decide which sources deserve trust, skepticism, or a deeper read.
As AI summaries become a front door to information, the habit changes from "Does this answer sound right?" to "What is this answer standing on?"
The takeaway
Pew's 60% figure is a sign of where information behavior is going. People are not just using AI to draft text. They are using it to read the web for them.
That makes source quality a basic literacy skill. A summary can be convenient and still stand on weak sources.
Use AI summaries as a starting point, not a stopping point. The source layer is where trust begins.